system

Abstract

The system according to the embodiment aims to predict the path and strength of typhoons over a long period of time and provide users with action support. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a notification unit, and a suggestion unit. The collection unit collects observation data. The analysis unit analyzes the data collected by the collection unit and predicts the typhoon's path and strength. The notification unit notifies the user based on the prediction results obtained by the analysis unit. The suggestion unit suggests alternative measures based on the information notified by the notification unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology is limited to predicting the path and strength of typhoons over a short period of time, which can affect users' plans.

[0005] The system according to the embodiment aims to predict the path and strength of typhoons over a long period of time and provide users with action support. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a notification unit, and a suggestion unit. The collection unit collects observation data. The analysis unit analyzes the data collected by the collection unit and predicts the typhoon's path and strength. The notification unit notifies the user based on the prediction results obtained by the analysis unit. The suggestion unit suggests alternative measures based on the information notified by the notification unit. [Effects of the Invention]

[0007] The system according to the embodiment can predict the path and strength of typhoons over a long period of time and provide users with action support. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A typhoon prediction system according to an embodiment of the present invention uses a generation AI to predict the path and strength of a typhoon, providing users with behavioral support and safety. The typhoon prediction system collects observation data, analyzes it using a generation AI, and predicts the path and strength of the typhoon up to one month in advance. Based on the prediction results, the system then provides users with the latest typhoon information and warnings. The system also suggests alternative means of transportation and accommodation for users facing transportation or accommodation problems due to the typhoon. For example, the typhoon prediction system collects data such as temperature, humidity, wind speed, air pressure, and precipitation, and analyzes it using a generation AI to predict the path and strength of the typhoon. The generation AI then analyzes the collected data to predict the path and strength of the typhoon up to one month in advance. The generation AI accurately predicts the path and strength of the typhoon based on past typhoon data and meteorological models. For example, the system can predict the typhoon's origin, direction of travel, wind speed, and changes in air pressure. Based on the prediction results, the system then provides users with the latest typhoon information and warnings. For example, if a typhoon's path affects a user's travel destination, the generation AI will notify the user of that information. It will also suggest alternative means for users who are facing transportation or accommodation problems due to the typhoon. For example, it will provide information on the status of transportation services and the availability of accommodations, helping users to take appropriate action. This allows the typhoon prediction system to help users understand the typhoon's impact in advance and act safely. This allows users to understand the typhoon's impact in advance and act safely. For example, if a typhoon occurs during the summer vacation travel season, users can change their travel plans and avoid having to worry about transportation or accommodation arrangements. Receiving the latest typhoon information and warnings also helps users act safely.

[0029] A typhoon forecasting system according to an embodiment includes a collection unit, an analysis unit, a notification unit, and a proposal unit. The collection unit collects observation data. The observation data includes, but is not limited to, temperature, humidity, wind speed, atmospheric pressure, and precipitation. The collection unit collects data from, for example, meteorological satellites and ground observation stations. The collection unit can also acquire data from meteorological satellites in real time. The collection unit can also periodically collect data from ground observation stations. For example, the collection unit automatically acquires data from meteorological satellites and periodically collects data from ground observation stations. The analysis unit uses a generation AI to analyze the data collected by the collection unit and predict the course and strength of a typhoon. The analysis is performed, for example, based on past typhoon data and a meteorological model, but is not limited to, such examples. For example, the analysis unit predicts the course and strength of a typhoon based on past typhoon data. The analysis unit can also predict the course and strength of a typhoon using a meteorological model. The analysis unit can also predict the course and strength of a typhoon using machine learning or deep learning. For example, the analysis unit learns past typhoon data and predicts the typhoon's path and strength based on a meteorological model. The notification unit notifies the user based on the prediction results obtained by the analysis unit. The notification is performed, for example, through a smartphone app, but is not limited to this example. For example, the notification unit notifies the user of the latest typhoon information through the smartphone app. The notification unit can also notify the user of the latest typhoon information via email. Furthermore, the notification unit can also notify the user of the latest typhoon information through a website. For example, the notification unit notifies the user of the typhoon's path and strength through the smartphone app. The suggestion unit suggests alternative means based on the information notified by the notification unit. The suggestion may, for example, provide information on the operation status of public transportation or the availability of accommodation facilities, but is not limited to this example. For example, the suggestion unit provides information on the operation status of public transportation facilities to help the user take appropriate action. The suggestion unit can also provide information on the availability of accommodation facilities to help the user take appropriate action. Furthermore, the suggestion unit can identify affected areas and users based on the user's location information and suggest appropriate alternative measures.For example, the suggestion unit identifies affected areas and users based on the user's location information, and provides information on the operation status of public transportation and the availability of accommodation facilities. As a result, the typhoon prediction system according to the embodiment can predict the path and strength of a typhoon and provide users with action support and safety.

[0030] The collection unit can collect data on temperature, humidity, wind speed, air pressure, and precipitation. For example, the collection unit uses data from a ground observation station to collect temperature data. The collection unit can also use data from a weather satellite to collect humidity data. The collection unit can also use data from a weather satellite to collect wind speed data. For example, the collection unit automatically acquires temperature data from a ground observation station and acquires humidity data from a weather satellite in real time. The collection unit can also periodically acquire wind speed data from a weather satellite. This allows for the collection of various observation data, thereby improving the accuracy of predicting the path and strength of typhoons. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input data acquired from a weather satellite into a generation AI and have the generation AI analyze the data.

[0031] The analysis unit can predict the path and strength of a typhoon based on past typhoon data and a meteorological model. The analysis unit can predict the path and strength of a typhoon based on, for example, past typhoon data. The analysis unit can also predict the path and strength of a typhoon using a meteorological model. The analysis unit can also predict the path and strength of a typhoon using machine learning or deep learning. For example, the analysis unit can learn from past typhoon data and predict the path and strength of a typhoon based on a meteorological model. The analysis unit can also use a generation AI to analyze past typhoon data and a meteorological model and predict the path and strength of a typhoon. This improves the accuracy of typhoon predictions by utilizing past data and a meteorological model. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input past typhoon data and a meteorological model into the generation AI and cause the generation AI to predict the path and strength of a typhoon.

[0032] The notification unit can notify the user through a smartphone app. For example, the notification unit can notify the user of the latest typhoon information through the smartphone app. The notification unit can also notify the user of the latest typhoon information via email. Furthermore, the notification unit can notify the user of the latest typhoon information through a website. For example, the notification unit can notify the user of the typhoon's path and strength through the smartphone app. The notification unit can also notify the user of the typhoon's path and strength via email. Furthermore, the notification unit can notify the user of the typhoon's path and strength through a website. This allows the user to quickly provide information through the smartphone app. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can generate information on the typhoon's path and strength using a generation AI and notify the user through the smartphone app.

[0033] The suggestion unit can provide information on the operation status of public transportation and the availability of accommodation facilities. For example, the suggestion unit can provide information on the operation status of public transportation facilities to support the user in taking appropriate action. The suggestion unit can also provide information on the availability of accommodation facilities to support the user in taking appropriate action. Furthermore, the suggestion unit can identify affected areas and users based on the user's location information and propose appropriate alternative means. For example, the suggestion unit can identify affected areas and users based on the user's location information and provide information on the operation status of public transportation facilities and the availability of accommodation facilities. This makes it possible to provide appropriate alternative means to users who are suffering from the effects of a typhoon. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can use a generation AI to analyze the operation status of public transportation facilities and the availability of accommodation facilities and propose them to the user.

[0034] The collection unit can collect data from weather satellites or ground observation stations. For example, the collection unit acquires data from weather satellites in real time. The collection unit can also periodically collect data from ground observation stations. Furthermore, the collection unit can automatically acquire data from weather satellites and periodically collect data from ground observation stations. For example, the collection unit automatically acquires data from weather satellites and periodically collects data from ground observation stations. This improves prediction accuracy by collecting information from various observation data sources. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired from weather satellites into a generation AI and have the generation AI analyze the data.

[0035] The analysis unit can predict the path and strength of a typhoon using machine learning or deep learning. The analysis unit can predict the path and strength of a typhoon using, for example, machine learning. The analysis unit can also predict the path and strength of a typhoon using deep learning. Furthermore, the analysis unit can perform analysis using machine learning or deep learning using a generation AI. For example, the analysis unit can use a machine learning algorithm to learn past typhoon data and predict the path and strength of a typhoon. The analysis unit can also use a deep learning algorithm to predict the path and strength of a typhoon based on a weather model. This further improves prediction accuracy by utilizing machine learning or deep learning. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input a machine learning algorithm into the generation AI and cause the generation AI to predict the path and strength of a typhoon.

[0036] The suggestion unit can identify affected areas and users based on the user's location information. The suggestion unit can identify affected areas and users based on, for example, the user's location information. The suggestion unit can also provide information on the operation status of public transportation and the availability of accommodation facilities based on the user's location information. The suggestion unit can also analyze the user's location information using a generation AI to identify affected areas and users. For example, the suggestion unit can identify affected areas and users based on the user's location information and propose appropriate alternative means. The suggestion unit can also provide information on the operation status of public transportation and the availability of accommodation facilities based on the user's location information. In this way, by utilizing the user's location information, more appropriate alternative means can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's location information into the generation AI and cause the generation AI to identify affected areas and users.

[0037] The collection unit can analyze the collection history of past observation data and select the optimal collection method. For example, the collection unit can discover from the past collection history that collection efficiency is high during a specific time period and concentrate collection during that time period. The collection unit can also confirm that data from a specific observation station is highly reliable based on the past collection history and prioritize the use of that observation station. Furthermore, the collection unit can analyze the past collection history and optimize the data collection method under specific weather conditions. For example, the collection unit can concentrate collection during a specific time period based on the past collection history. The collection unit can also prioritize the use of data from a specific observation station based on the past collection history. In this way, collection efficiency is improved by analyzing the past collection history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past collection history data into the generation AI and have the generation AI select the optimal collection method.

[0038] When collecting observation data, the collection unit can filter the data based on the user's current location information and areas of interest. For example, the collection unit prioritizes collecting weather data for the area where the user is currently located. The collection unit can also prioritize collecting data related to specific weather phenomena in which the user is interested. Furthermore, the collection unit can collect data for areas that may be affected based on the user's location information. For example, the collection unit prioritizes collecting weather data based on the user's current location information. The collection unit can also prioritize collecting related weather data based on the user's areas of interest. This makes it possible to provide highly relevant information by filtering data based on the user's location information and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's location information and area of ​​interest data to a generation AI and have the generation AI perform data filtering.

[0039] When collecting observation data, the collection unit can select the optimal collection means depending on the type of data. For example, when collecting temperature data, the collection unit can prioritize data from ground observation stations. Furthermore, when collecting wind speed data, the collection unit can also prioritize data from meteorological satellites. Furthermore, when collecting precipitation data, the collection unit can also prioritize radar observation data. For example, the collection unit automatically acquires temperature data from ground observation stations and acquires wind speed data from meteorological satellites in real time. Furthermore, the collection unit can periodically acquire precipitation data from radar observation data. In this way, by selecting the optimal collection means depending on the type of data, collection accuracy is improved. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input collection means depending on the type of data to the generation AI and cause the generation AI to select the optimal collection means.

[0040] When collecting observation data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting weather data for the area where the user is currently located. The collection unit can also prioritize collecting weather data for destinations where the user is traveling. Furthermore, the collection unit can collect data for areas that may be affected based on the user's location information. For example, the collection unit prioritizes collecting weather data based on the user's current location information. The collection unit can also prioritize collecting weather data for destinations where the user is traveling. This makes it possible to provide highly relevant data by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0041] The collection unit can analyze the user's social media activities and collect related data when collecting observation data. For example, the collection unit can collect weather data for locations where the user has checked in on social media. The collection unit can also analyze the user's social media posts and collect related weather data. Furthermore, the collection unit can collect related weather data by referring to the activities of the user's friends on social media. For example, the collection unit collects weather data based on the user's social media check-in information. The collection unit can also analyze the user's social media posts and collect related weather data. This makes it possible to provide highly relevant data by analyzing the user's social media activities. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related weather data.

[0042] When collecting observation data, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit adjusts the type of data to collect based on feedback provided by the user in the past. The collection unit can also adjust the collection frequency by referring to the user's past feedback. Furthermore, the collection unit can optimize the collection means based on the user's past feedback. For example, the collection unit adjusts the type of data to collect based on the user's past feedback. The collection unit can also adjust the collection frequency based on the user's past feedback. In this way, the collection method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0043] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the typhoon. For example, the analysis unit performs a detailed analysis for a typhoon with high importance. The analysis unit can also perform a simplified analysis for a typhoon with low importance. Furthermore, the analysis unit can perform a detailed analysis if the typhoon's path will affect the user. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the typhoon. This makes it possible to provide more important information by adjusting the level of detail of the analysis according to the typhoon's importance. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input typhoon importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0044] During analysis, the analysis unit can apply different analysis algorithms depending on the typhoon category. The analysis unit can apply different analysis algorithms depending on, for example, the strength of the typhoon. The analysis unit can also apply different analysis algorithms depending on the typhoon's path. Furthermore, the analysis unit can select an optimal analysis algorithm depending on the typhoon category. For example, the analysis unit can apply different analysis algorithms depending on the strength of the typhoon. The analysis unit can also apply different analysis algorithms depending on the typhoon's path. This improves analysis accuracy by applying the optimal analysis algorithm depending on the typhoon category. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input typhoon category data to the generation AI and cause the generation AI to apply the optimal analysis algorithm.

[0045] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. Furthermore, the analysis unit can analyze the user's past analysis results and select the optimal analysis method. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. In this way, by referring to the user's past analysis results, the analysis accuracy is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0046] During analysis, the analysis unit can determine the priority of analysis based on the timing of typhoon occurrence. For example, if the typhoon is about to occur, the analysis unit can increase the priority of analysis. Also, if the typhoon is far away from occurring, the analysis unit can decrease the priority of analysis. Furthermore, the analysis unit can adjust the analysis schedule based on the timing of typhoon occurrence. For example, if the typhoon is about to occur, the analysis unit can increase the priority of analysis. Also, if the typhoon is far away from occurring, the analysis unit can decrease the priority of analysis. In this way, by determining the priority of analysis based on the timing of typhoon occurrence, more important information can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input typhoon occurrence time data to the generation AI and have the generation AI determine the analysis priority.

[0047] During analysis, the analysis unit can adjust the order of analysis based on the relevance of typhoons. For example, if the path of a typhoon will affect the user, the analysis unit prioritizes the order of analysis. Also, if the relevance of a typhoon is low, the analysis unit can postpone the order of analysis. Furthermore, the analysis unit can adjust the schedule of analysis based on the relevance of typhoons. For example, if the path of a typhoon will affect the user, the analysis unit prioritizes the order of analysis. Also, if the relevance of a typhoon is low, the analysis unit can postpone the order of analysis. In this way, by adjusting the order of analysis based on the relevance of typhoons, more important information can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input typhoon relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0048] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can use detailed technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can provide the analysis results in simple language. Furthermore, the analysis unit can adjust the way the analysis results are presented according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can use detailed technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can provide the analysis results in simple language. By adjusting the technical terminology in the analysis according to the user's level of expertise, more appropriate information can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology in the analysis.

[0049] The notification unit can adjust the level of detail of the notification based on the importance of the typhoon when providing a notification. For example, the notification unit provides a detailed notification for a typhoon with high importance. The notification unit can also provide a simplified notification for a typhoon with low importance. Furthermore, the notification unit can provide a detailed notification if the typhoon's path will affect the user. For example, the notification unit adjusts the level of detail of the notification based on the importance of the typhoon. This makes it possible to provide more important information by adjusting the level of detail of the notification according to the importance of the typhoon. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input typhoon importance data to a generation AI and cause the generation AI to adjust the level of detail of the notification.

[0050] The notification unit can apply different notification algorithms depending on the typhoon category when making a notification. The notification unit can apply different notification algorithms depending on, for example, the strength of the typhoon. The notification unit can also apply different notification algorithms depending on the typhoon's path. Furthermore, the notification unit can select an optimal notification algorithm depending on the typhoon's category. For example, the notification unit can apply different notification algorithms depending on the typhoon's strength. The notification unit can also apply different notification algorithms depending on the typhoon's path. This improves notification accuracy by applying the optimal notification algorithm depending on the typhoon category. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input typhoon category data to the generation AI and cause the generation AI to apply the optimal notification algorithm.

[0051] The notification unit can improve the accuracy of notifications by referring to the user's past notification results when sending notifications. For example, the notification unit adjusts the notification algorithm based on the user's past notification results. The notification unit can also improve the accuracy of notifications by referring to the user's past notification results. Furthermore, the notification unit can analyze the user's past notification results and select the optimal notification method. For example, the notification unit adjusts the notification algorithm based on the user's past notification results. The notification unit can also improve the accuracy of notifications by referring to the user's past notification results. In this way, the notification accuracy is improved by referring to the user's past notification results. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's past notification result data into the generation AI and cause the generation AI to improve the accuracy of notifications.

[0052] The notification unit can determine the priority of the notification based on the timing of the typhoon's occurrence when making a notification. For example, if the typhoon is about to occur, the notification unit can increase the priority of the notification. Also, if the typhoon is far away from occurring, the notification unit can decrease the priority of the notification. Furthermore, the notification unit can adjust the notification schedule based on the timing of the typhoon's occurrence. For example, if the typhoon is about to occur, the notification unit can increase the priority of the notification. Also, if the typhoon is far away from occurring, the notification unit can decrease the priority of the notification. In this way, by determining the priority of the notification based on the timing of the typhoon's occurrence, more important information can be provided. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input typhoon occurrence time data to the generation AI and have the generation AI determine the priority of the notifications.

[0053] The notification unit can adjust the order of notifications based on the relevance of the typhoon when making a notification. For example, if the typhoon's path will affect the user, the notification unit prioritizes the order of notifications. Also, if the typhoon's relevance is low, the notification unit can postpone the order of notifications. Furthermore, the notification unit can adjust the schedule of notifications based on the relevance of the typhoon. For example, if the typhoon's path will affect the user, the notification unit prioritizes the order of notifications. Also, if the typhoon's relevance is low, the notification unit can postpone the order of notifications. In this way, by adjusting the order of notifications based on the relevance of the typhoon, more important information can be provided. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input typhoon relevance data to a generation AI and cause the generation AI to adjust the order of notifications.

[0054] The notification unit can adjust the use of technical terminology in the notification depending on the user's level of expertise. For example, if the user has technical expertise, the notification unit can use detailed technical terminology. Furthermore, if the user does not have technical expertise, the notification unit can provide the notification in simple language. Furthermore, the notification unit can adjust the way the notification is expressed depending on the user's level of expertise. For example, if the user has technical expertise, the notification unit can use detailed technical terminology. Furthermore, if the user does not have technical expertise, the notification unit can provide the notification in simple language. By adjusting the technical terminology in the notification depending on the user's level of expertise, more appropriate information can be provided. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input the user's level of expertise data into a generation AI and cause the generation AI to use technical terminology in the notification.

[0055] The suggestion unit can adjust the level of detail of the proposal based on the importance of the alternative means when making the proposal. For example, the suggestion unit provides a detailed proposal for an alternative means with high importance. The suggestion unit can also provide a simplified proposal for an alternative means with low importance. Furthermore, the suggestion unit can adjust the schedule of the proposal based on the importance of the alternative means. For example, the suggestion unit provides a detailed proposal for an alternative means with high importance. The suggestion unit can also provide a simplified proposal for an alternative means with low importance. In this way, by adjusting the level of detail of the proposal according to the importance of the alternative means, more important information can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input importance data of the alternative means to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0056] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the alternative means. For example, the proposal unit can apply different proposal algorithms for alternative means of transportation. Also, the proposal unit can apply different proposal algorithms for alternative means of accommodation. Furthermore, the proposal unit can select an optimal proposal algorithm depending on the category of the alternative means. For example, the proposal unit can apply different proposal algorithms for alternative means of transportation. Also, the proposal unit can apply different proposal algorithms for alternative means of accommodation. In this way, by applying the optimal proposal algorithm depending on the category of the alternative means, the proposal accuracy is improved. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input category data of the alternative means to the generation AI and cause the generation AI to apply the optimal proposal algorithm.

[0057] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit, for example, adjusts the proposal algorithm based on the user's past proposal results. The suggestion unit can also improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit can also analyze the user's past proposal results and select an optimal proposal method. For example, the suggestion unit adjusts the proposal algorithm based on the user's past proposal results. The suggestion unit can also improve the accuracy of the proposal by referring to the user's past proposal results. In this way, the suggestion accuracy is improved by referring to the user's past proposal results. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0058] When making a proposal, the suggestion unit can determine the priority of the proposal based on the timing of providing the alternative means. For example, if the timing of providing the alternative means is approaching, the suggestion unit can increase the priority of the proposal. Also, if the timing of providing the alternative means is distant, the suggestion unit can decrease the priority of the proposal. Furthermore, the suggestion unit can adjust the schedule of the proposal based on the timing of providing the alternative means. For example, if the timing of providing the alternative means is approaching, the suggestion unit can increase the priority of the proposal. Also, if the timing of providing the alternative means is distant, the suggestion unit can decrease the priority of the proposal. In this way, by determining the priority of the proposal based on the timing of providing the alternative means, more important information can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the timing of providing the alternative means to the generation AI and cause the generation AI to determine the priority of the proposals.

[0059] The suggestion unit can adjust the order of suggestions based on the relevance of the alternative means when making suggestions. For example, if the relevance of the alternative means is high, the suggestion unit prioritizes the order of suggestions. Also, if the relevance of the alternative means is low, the suggestion unit can postpone the order of suggestions. Furthermore, the suggestion unit can adjust the schedule of suggestions based on the relevance of the alternative means. For example, if the relevance of the alternative means is high, the suggestion unit prioritizes the order of suggestions. Also, if the relevance of the alternative means is low, the suggestion unit can postpone the order of suggestions. In this way, by adjusting the order of suggestions based on the relevance of the alternative means, more important information can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input relevance data of the alternative means to a generation AI and cause the generation AI to adjust the order of suggestions.

[0060] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal depending on the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can use detailed technical terminology. Furthermore, if the user does not have technical expertise, the suggestion unit can provide a proposal in simple language. Furthermore, the suggestion unit can adjust the way the proposal is expressed depending on the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can use detailed technical terminology. Furthermore, if the user does not have technical expertise, the suggestion unit can provide a proposal in simple language. By adjusting the technical terminology in the proposal depending on the user's level of expertise, more appropriate information can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's level of expertise data into a generation AI and cause the generation AI to use technical terminology in the proposal.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The typhoon prediction system can also analyze a user's past behavioral history and suggest individual actions based on the prediction results. For example, it can analyze a user's behavioral patterns when affected by a typhoon in the past and suggest optimal actions to take if a similar situation occurs. It can also suggest more appropriate alternative methods based on information about the transportation and accommodations the user has used in the past. Furthermore, it can optimize action plans in areas that may be affected by a typhoon based on the user's past behavioral history. This allows users to take advantage of past experience and act more safely and efficiently.

[0063] The typhoon prediction system can also provide detailed forecast information for each region based on the user's location information. For example, it can provide a detailed typhoon path forecast for the user's current location and suggest specific evacuation routes and evacuation locations. It can also provide detailed forecast information for the region the user is traveling to and assist with changes to travel plans. Furthermore, it can provide detailed forecast information for areas that may be affected based on the user's location information. This allows the user to obtain detailed information based on their location and take more appropriate actions.

[0064] The typhoon prediction system can also adjust its prediction algorithm based on past user feedback. For example, it can analyze feedback provided by users in the past to improve the accuracy of predictions. It can also adjust the way prediction results are presented based on past user feedback. It can also optimize the timing of predictions based on past user feedback. By incorporating user feedback, it is possible to provide more accurate predictions and appropriate information.

[0065] The typhoon prediction system can also use the user's past behavioral history to suggest individual actions based on the prediction results. For example, it can analyze the user's behavioral patterns when affected by past typhoons and suggest optimal actions to take if a similar situation occurs. It can also suggest more appropriate alternative methods based on information about the transportation and accommodations the user has used in the past. Furthermore, it can optimize action plans in areas that may be affected by a typhoon based on the user's past behavioral history. This allows users to take advantage of past experience and act more safely and efficiently.

[0066] The typhoon prediction system can also provide detailed forecast information for each region based on the user's location information. For example, it can provide a detailed typhoon path forecast for the user's current location and suggest specific evacuation routes and evacuation locations. It can also provide detailed forecast information for the region the user is traveling to and assist with changes to travel plans. Furthermore, it can provide detailed forecast information for areas that may be affected based on the user's location information. This allows the user to obtain detailed information based on their location and take more appropriate actions.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The collection unit collects observation data. The observation data includes temperature, humidity, wind speed, air pressure, precipitation, etc. The collection unit collects data from meteorological satellites and ground observation stations, and can obtain data from meteorological satellites in real time. It can also collect data from ground observation stations periodically. Step 2: The analysis unit uses the generation AI to analyze the data collected by the collection unit and predict the typhoon's path and strength. The analysis is based on past typhoon data and meteorological models, and can also use machine learning and deep learning. Step 3: The notification unit notifies the user based on the prediction results obtained by the analysis unit. Notifications are sent via a smartphone app, email, or website. Step 4: The suggestion unit proposes alternative means based on the information notified by the notification unit. The proposal provides information on the operation status of transportation services and the availability of accommodations, helping the user to take appropriate action.

[0069] (Example 2) A typhoon prediction system according to an embodiment of the present invention uses a generation AI to predict the path and strength of a typhoon, providing users with behavioral support and safety. The typhoon prediction system collects observation data, analyzes it using a generation AI, and predicts the path and strength of the typhoon up to one month in advance. Based on the prediction results, the system then provides users with the latest typhoon information and warnings. The system also suggests alternative means of transportation and accommodation for users facing transportation or accommodation problems due to the typhoon. For example, the typhoon prediction system collects data such as temperature, humidity, wind speed, air pressure, and precipitation, and analyzes it using a generation AI to predict the path and strength of the typhoon. The generation AI then analyzes the collected data to predict the path and strength of the typhoon up to one month in advance. The generation AI accurately predicts the path and strength of the typhoon based on past typhoon data and meteorological models. For example, the system can predict the typhoon's origin, direction of travel, wind speed, and changes in air pressure. Based on the prediction results, the system then provides users with the latest typhoon information and warnings. For example, if a typhoon's path affects a user's travel destination, the generation AI will notify the user of that information. It will also suggest alternative means for users who are facing transportation or accommodation problems due to the typhoon. For example, it will provide information on the status of transportation services and the availability of accommodations, helping users to take appropriate action. This allows the typhoon prediction system to help users understand the typhoon's impact in advance and act safely. This allows users to understand the typhoon's impact in advance and act safely. For example, if a typhoon occurs during the summer vacation travel season, users can change their travel plans and avoid having to worry about transportation or accommodation arrangements. Receiving the latest typhoon information and warnings also helps users act safely.

[0070] A typhoon forecasting system according to an embodiment includes a collection unit, an analysis unit, a notification unit, and a proposal unit. The collection unit collects observation data. The observation data includes, but is not limited to, temperature, humidity, wind speed, atmospheric pressure, and precipitation. The collection unit collects data from, for example, meteorological satellites and ground observation stations. The collection unit can also acquire data from meteorological satellites in real time. The collection unit can also periodically collect data from ground observation stations. For example, the collection unit automatically acquires data from meteorological satellites and periodically collects data from ground observation stations. The analysis unit uses a generation AI to analyze the data collected by the collection unit and predict the course and strength of a typhoon. The analysis is performed, for example, based on past typhoon data and a meteorological model, but is not limited to, such examples. For example, the analysis unit predicts the course and strength of a typhoon based on past typhoon data. The analysis unit can also predict the course and strength of a typhoon using a meteorological model. The analysis unit can also predict the course and strength of a typhoon using machine learning or deep learning. For example, the analysis unit learns past typhoon data and predicts the typhoon's path and strength based on a meteorological model. The notification unit notifies the user based on the prediction results obtained by the analysis unit. The notification is performed, for example, through a smartphone app, but is not limited to this example. For example, the notification unit notifies the user of the latest typhoon information through the smartphone app. The notification unit can also notify the user of the latest typhoon information via email. Furthermore, the notification unit can also notify the user of the latest typhoon information through a website. For example, the notification unit notifies the user of the typhoon's path and strength through the smartphone app. The suggestion unit suggests alternative means based on the information notified by the notification unit. The suggestion may, for example, provide information on the operation status of public transportation or the availability of accommodation facilities, but is not limited to this example. For example, the suggestion unit provides information on the operation status of public transportation facilities to help the user take appropriate action. The suggestion unit can also provide information on the availability of accommodation facilities to help the user take appropriate action. Furthermore, the suggestion unit can identify affected areas and users based on the user's location information and suggest appropriate alternative measures.For example, the suggestion unit identifies affected areas and users based on the user's location information, and provides information on the operation status of public transportation and the availability of accommodation facilities. As a result, the typhoon prediction system according to the embodiment can predict the path and strength of a typhoon and provide users with action support and safety.

[0071] The collection unit can collect data on temperature, humidity, wind speed, air pressure, and precipitation. For example, the collection unit uses data from a ground observation station to collect temperature data. The collection unit can also use data from a weather satellite to collect humidity data. The collection unit can also use data from a weather satellite to collect wind speed data. For example, the collection unit automatically acquires temperature data from a ground observation station and acquires humidity data from a weather satellite in real time. The collection unit can also periodically acquire wind speed data from a weather satellite. This allows for the collection of various observation data, thereby improving the accuracy of predicting the path and strength of typhoons. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input data acquired from a weather satellite into a generation AI and have the generation AI analyze the data.

[0072] The analysis unit can predict the path and strength of a typhoon based on past typhoon data and a meteorological model. The analysis unit can predict the path and strength of a typhoon based on, for example, past typhoon data. The analysis unit can also predict the path and strength of a typhoon using a meteorological model. The analysis unit can also predict the path and strength of a typhoon using machine learning or deep learning. For example, the analysis unit can learn from past typhoon data and predict the path and strength of a typhoon based on a meteorological model. The analysis unit can also use a generation AI to analyze past typhoon data and a meteorological model and predict the path and strength of a typhoon. This improves the accuracy of typhoon predictions by utilizing past data and a meteorological model. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input past typhoon data and a meteorological model into the generation AI and cause the generation AI to predict the path and strength of a typhoon.

[0073] The notification unit can notify the user through a smartphone app. For example, the notification unit can notify the user of the latest typhoon information through the smartphone app. The notification unit can also notify the user of the latest typhoon information via email. Furthermore, the notification unit can notify the user of the latest typhoon information through a website. For example, the notification unit can notify the user of the typhoon's path and strength through the smartphone app. The notification unit can also notify the user of the typhoon's path and strength via email. Furthermore, the notification unit can notify the user of the typhoon's path and strength through a website. This allows the user to quickly provide information through the smartphone app. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can generate information on the typhoon's path and strength using a generation AI and notify the user through the smartphone app.

[0074] The suggestion unit can provide information on the operation status of public transportation and the availability of accommodation facilities. For example, the suggestion unit can provide information on the operation status of public transportation facilities to support the user in taking appropriate action. The suggestion unit can also provide information on the availability of accommodation facilities to support the user in taking appropriate action. Furthermore, the suggestion unit can identify affected areas and users based on the user's location information and propose appropriate alternative means. For example, the suggestion unit can identify affected areas and users based on the user's location information and provide information on the operation status of public transportation facilities and the availability of accommodation facilities. This makes it possible to provide appropriate alternative means to users who are suffering from the effects of a typhoon. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can use a generation AI to analyze the operation status of public transportation facilities and the availability of accommodation facilities and propose them to the user.

[0075] The collection unit can collect data from weather satellites or ground observation stations. For example, the collection unit acquires data from weather satellites in real time. The collection unit can also periodically collect data from ground observation stations. Furthermore, the collection unit can automatically acquire data from weather satellites and periodically collect data from ground observation stations. For example, the collection unit automatically acquires data from weather satellites and periodically collects data from ground observation stations. This improves prediction accuracy by collecting information from various observation data sources. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired from weather satellites into a generation AI and have the generation AI analyze the data.

[0076] The analysis unit can predict the path and strength of a typhoon using machine learning or deep learning. The analysis unit can predict the path and strength of a typhoon using, for example, machine learning. The analysis unit can also predict the path and strength of a typhoon using deep learning. Furthermore, the analysis unit can perform analysis using machine learning or deep learning using a generation AI. For example, the analysis unit can use a machine learning algorithm to learn past typhoon data and predict the path and strength of a typhoon. The analysis unit can also use a deep learning algorithm to predict the path and strength of a typhoon based on a weather model. This further improves prediction accuracy by utilizing machine learning or deep learning. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input a machine learning algorithm into the generation AI and cause the generation AI to predict the path and strength of a typhoon.

[0077] The suggestion unit can identify affected areas and users based on the user's location information. The suggestion unit can identify affected areas and users based on, for example, the user's location information. The suggestion unit can also provide information on the operation status of public transportation and the availability of accommodation facilities based on the user's location information. The suggestion unit can also analyze the user's location information using a generation AI to identify affected areas and users. For example, the suggestion unit can identify affected areas and users based on the user's location information and propose appropriate alternative means. The suggestion unit can also provide information on the operation status of public transportation and the availability of accommodation facilities based on the user's location information. In this way, by utilizing the user's location information, more appropriate alternative means can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's location information into the generation AI and cause the generation AI to identify affected areas and users.

[0078] The collection unit can estimate the user's emotions and adjust the timing of collecting observation data based on the estimated user emotions. For example, if the user is feeling anxious, the collection unit can increase the collection frequency and provide the latest observation data. Furthermore, if the user is relaxed, the collection unit can return the collection frequency to normal and collect only necessary data. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting important data and perform analysis quickly. For example, the collection unit can estimate the user's emotions and adjust the timing of collecting observation data based on the estimated emotions. This allows for adjusting the collection timing according to the user's emotions, thereby providing more appropriate data. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of collecting observation data.

[0079] The collection unit can analyze the collection history of past observation data and select the optimal collection method. For example, the collection unit can discover from the past collection history that collection efficiency is high during a specific time period and concentrate collection during that time period. The collection unit can also confirm that data from a specific observation station is highly reliable based on the past collection history and prioritize the use of that observation station. Furthermore, the collection unit can analyze the past collection history and optimize the data collection method under specific weather conditions. For example, the collection unit can concentrate collection during a specific time period based on the past collection history. The collection unit can also prioritize the use of data from a specific observation station based on the past collection history. In this way, collection efficiency is improved by analyzing the past collection history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past collection history data into the generation AI and have the generation AI select the optimal collection method.

[0080] When collecting observation data, the collection unit can filter the data based on the user's current location information and areas of interest. For example, the collection unit prioritizes collecting weather data for the area where the user is currently located. The collection unit can also prioritize collecting data related to specific weather phenomena in which the user is interested. Furthermore, the collection unit can collect data for areas that may be affected based on the user's location information. For example, the collection unit prioritizes collecting weather data based on the user's current location information. The collection unit can also prioritize collecting related weather data based on the user's areas of interest. This makes it possible to provide highly relevant information by filtering data based on the user's location information and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's location information and area of ​​interest data to a generation AI and have the generation AI perform data filtering.

[0081] When collecting observation data, the collection unit can select the optimal collection means depending on the type of data. For example, when collecting temperature data, the collection unit can prioritize data from ground observation stations. Furthermore, when collecting wind speed data, the collection unit can also prioritize data from meteorological satellites. Furthermore, when collecting precipitation data, the collection unit can also prioritize radar observation data. For example, the collection unit automatically acquires temperature data from ground observation stations and acquires wind speed data from meteorological satellites in real time. Furthermore, the collection unit can periodically acquire precipitation data from radar observation data. In this way, by selecting the optimal collection means depending on the type of data, collection accuracy is improved. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input collection means depending on the type of data to the generation AI and cause the generation AI to select the optimal collection means.

[0082] The collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated user emotions. For example, if the user is feeling anxious, the collection unit can prioritize collecting data related to the path of a typhoon. Furthermore, if the user is relaxed, the collection unit can prioritize collecting regular weather data. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting important weather data. For example, the collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated emotions. This allows for providing more important information by prioritizing data according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the data to be collected.

[0083] When collecting observation data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting weather data for the area where the user is currently located. The collection unit can also prioritize collecting weather data for destinations where the user is traveling. Furthermore, the collection unit can collect data for areas that may be affected based on the user's location information. For example, the collection unit prioritizes collecting weather data based on the user's current location information. The collection unit can also prioritize collecting weather data for destinations where the user is traveling. This makes it possible to provide highly relevant data by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0084] The collection unit can analyze the user's social media activities and collect related data when collecting observation data. For example, the collection unit can collect weather data for locations where the user has checked in on social media. The collection unit can also analyze the user's social media posts and collect related weather data. Furthermore, the collection unit can collect related weather data by referring to the activities of the user's friends on social media. For example, the collection unit collects weather data based on the user's social media check-in information. The collection unit can also analyze the user's social media posts and collect related weather data. This makes it possible to provide highly relevant data by analyzing the user's social media activities. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related weather data.

[0085] When collecting observation data, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit adjusts the type of data to collect based on feedback provided by the user in the past. The collection unit can also adjust the collection frequency by referring to the user's past feedback. Furthermore, the collection unit can optimize the collection means based on the user's past feedback. For example, the collection unit adjusts the type of data to collect based on the user's past feedback. The collection unit can also adjust the collection frequency based on the user's past feedback. In this way, the collection method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0086] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can provide simple, highly visible analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that focus on the main points. For example, the analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated emotions. This allows for more appropriate information to be provided by adjusting the presentation method of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0087] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the typhoon. For example, the analysis unit performs a detailed analysis for a typhoon with high importance. The analysis unit can also perform a simplified analysis for a typhoon with low importance. Furthermore, the analysis unit can perform a detailed analysis if the typhoon's path will affect the user. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the typhoon. This makes it possible to provide more important information by adjusting the level of detail of the analysis according to the typhoon's importance. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input typhoon importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0088] During analysis, the analysis unit can apply different analysis algorithms depending on the typhoon category. The analysis unit can apply different analysis algorithms depending on, for example, the strength of the typhoon. The analysis unit can also apply different analysis algorithms depending on the typhoon's path. Furthermore, the analysis unit can select an optimal analysis algorithm depending on the typhoon category. For example, the analysis unit can apply different analysis algorithms depending on the strength of the typhoon. The analysis unit can also apply different analysis algorithms depending on the typhoon's path. This improves analysis accuracy by applying the optimal analysis algorithm depending on the typhoon category. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input typhoon category data to the generation AI and cause the generation AI to apply the optimal analysis algorithm.

[0089] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. Furthermore, the analysis unit can analyze the user's past analysis results and select the optimal analysis method. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. In this way, by referring to the user's past analysis results, the analysis accuracy is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0090] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can provide a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result. For example, the analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. This allows for more appropriate information to be provided by adjusting the length of the analysis according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0091] During analysis, the analysis unit can determine the priority of analysis based on the timing of typhoon occurrence. For example, if the typhoon is about to occur, the analysis unit can increase the priority of analysis. Also, if the typhoon is far away from occurring, the analysis unit can decrease the priority of analysis. Furthermore, the analysis unit can adjust the analysis schedule based on the timing of typhoon occurrence. For example, if the typhoon is about to occur, the analysis unit can increase the priority of analysis. Also, if the typhoon is far away from occurring, the analysis unit can decrease the priority of analysis. In this way, by determining the priority of analysis based on the timing of typhoon occurrence, more important information can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input typhoon occurrence time data to the generation AI and have the generation AI determine the analysis priority.

[0092] During analysis, the analysis unit can adjust the order of analysis based on the relevance of typhoons. For example, if the path of a typhoon will affect the user, the analysis unit prioritizes the order of analysis. Also, if the relevance of a typhoon is low, the analysis unit can postpone the order of analysis. Furthermore, the analysis unit can adjust the schedule of analysis based on the relevance of typhoons. For example, if the path of a typhoon will affect the user, the analysis unit prioritizes the order of analysis. Also, if the relevance of a typhoon is low, the analysis unit can postpone the order of analysis. In this way, by adjusting the order of analysis based on the relevance of typhoons, more important information can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input typhoon relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0093] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can use detailed technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can provide the analysis results in simple language. Furthermore, the analysis unit can adjust the way the analysis results are presented according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can use detailed technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can provide the analysis results in simple language. By adjusting the technical terminology in the analysis according to the user's level of expertise, more appropriate information can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology in the analysis.

[0094] The notification unit can estimate the user's emotions and adjust the notification presentation style based on the estimated user emotions. For example, if the user is feeling anxious, the notification unit can provide a simple, highly visible notification. The notification unit can also provide a detailed notification if the user is relaxed. Furthermore, if the user is in a hurry, the notification unit can provide a notification that focuses on the main points. For example, the notification unit can estimate the user's emotions and adjust the notification presentation style based on the estimated emotions. This allows for more appropriate information to be provided by adjusting the notification presentation style according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the notification unit can be performed using, for example, an AI, or without an AI. For example, the notification unit can input the user's emotion data into the generation AI and have the generation AI adjust the notification presentation style.

[0095] The notification unit can adjust the level of detail of the notification based on the importance of the typhoon when providing a notification. For example, the notification unit provides a detailed notification for a typhoon with high importance. The notification unit can also provide a simplified notification for a typhoon with low importance. Furthermore, the notification unit can provide a detailed notification if the typhoon's path will affect the user. For example, the notification unit adjusts the level of detail of the notification based on the importance of the typhoon. This makes it possible to provide more important information by adjusting the level of detail of the notification according to the importance of the typhoon. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input typhoon importance data to a generation AI and cause the generation AI to adjust the level of detail of the notification.

[0096] The notification unit can apply different notification algorithms depending on the typhoon category when making a notification. The notification unit can apply different notification algorithms depending on, for example, the strength of the typhoon. The notification unit can also apply different notification algorithms depending on the typhoon's path. Furthermore, the notification unit can select an optimal notification algorithm depending on the typhoon's category. For example, the notification unit can apply different notification algorithms depending on the typhoon's strength. The notification unit can also apply different notification algorithms depending on the typhoon's path. This improves notification accuracy by applying the optimal notification algorithm depending on the typhoon category. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input typhoon category data to the generation AI and cause the generation AI to apply the optimal notification algorithm.

[0097] The notification unit can improve the accuracy of notifications by referring to the user's past notification results when sending notifications. For example, the notification unit adjusts the notification algorithm based on the user's past notification results. The notification unit can also improve the accuracy of notifications by referring to the user's past notification results. Furthermore, the notification unit can analyze the user's past notification results and select the optimal notification method. For example, the notification unit adjusts the notification algorithm based on the user's past notification results. The notification unit can also improve the accuracy of notifications by referring to the user's past notification results. In this way, the notification accuracy is improved by referring to the user's past notification results. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's past notification result data into the generation AI and cause the generation AI to improve the accuracy of notifications.

[0098] The notification unit can estimate the user's emotion and adjust the length of the notification based on the estimated user's emotion. For example, if the user is feeling anxious, the notification unit can provide a short and to-the-point notification. The notification unit can also provide a detailed notification if the user is relaxed. Furthermore, the notification unit can provide a concise notification if the user is in a hurry. For example, the notification unit can estimate the user's emotion and adjust the length of the notification based on the estimated emotion. This allows for more appropriate information to be provided by adjusting the length of the notification according to the user's emotion. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the notification unit can be performed using, for example, an AI, or without an AI. For example, the notification unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the notification.

[0099] The notification unit can determine the priority of the notification based on the timing of the typhoon's occurrence when making a notification. For example, if the typhoon is about to occur, the notification unit can increase the priority of the notification. Also, if the typhoon is far away from occurring, the notification unit can decrease the priority of the notification. Furthermore, the notification unit can adjust the notification schedule based on the timing of the typhoon's occurrence. For example, if the typhoon is about to occur, the notification unit can increase the priority of the notification. Also, if the typhoon is far away from occurring, the notification unit can decrease the priority of the notification. In this way, by determining the priority of the notification based on the timing of the typhoon's occurrence, more important information can be provided. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input typhoon occurrence time data to the generation AI and have the generation AI determine the priority of the notifications.

[0100] The notification unit can adjust the order of notifications based on the relevance of the typhoon when making a notification. For example, if the typhoon's path will affect the user, the notification unit prioritizes the order of notifications. Also, if the typhoon's relevance is low, the notification unit can postpone the order of notifications. Furthermore, the notification unit can adjust the schedule of notifications based on the relevance of the typhoon. For example, if the typhoon's path will affect the user, the notification unit prioritizes the order of notifications. Also, if the typhoon's relevance is low, the notification unit can postpone the order of notifications. In this way, by adjusting the order of notifications based on the relevance of the typhoon, more important information can be provided. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input typhoon relevance data to a generation AI and cause the generation AI to adjust the order of notifications.

[0101] The notification unit can adjust the use of technical terminology in the notification depending on the user's level of expertise. For example, if the user has technical expertise, the notification unit can use detailed technical terminology. Furthermore, if the user does not have technical expertise, the notification unit can provide the notification in simple language. Furthermore, the notification unit can adjust the way the notification is expressed depending on the user's level of expertise. For example, if the user has technical expertise, the notification unit can use detailed technical terminology. Furthermore, if the user does not have technical expertise, the notification unit can provide the notification in simple language. By adjusting the technical terminology in the notification depending on the user's level of expertise, more appropriate information can be provided. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input the user's level of expertise data into a generation AI and cause the generation AI to use technical terminology in the notification.

[0102] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is feeling anxious, the suggestion unit can provide simple, highly visible suggestions. Furthermore, if the user is relaxed, the suggestion unit can provide detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can provide suggestions that focus on the main points. For example, the suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated emotions. This allows for more appropriate information to be provided by adjusting the way suggestions are expressed according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way suggestions are expressed.

[0103] The suggestion unit can adjust the level of detail of the proposal based on the importance of the alternative means when making the proposal. For example, the suggestion unit provides a detailed proposal for an alternative means with high importance. The suggestion unit can also provide a simplified proposal for an alternative means with low importance. Furthermore, the suggestion unit can adjust the schedule of the proposal based on the importance of the alternative means. For example, the suggestion unit provides a detailed proposal for an alternative means with high importance. The suggestion unit can also provide a simplified proposal for an alternative means with low importance. In this way, by adjusting the level of detail of the proposal according to the importance of the alternative means, more important information can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input importance data of the alternative means to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0104] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the alternative means. For example, the proposal unit can apply different proposal algorithms for alternative means of transportation. Also, the proposal unit can apply different proposal algorithms for alternative means of accommodation. Furthermore, the proposal unit can select an optimal proposal algorithm depending on the category of the alternative means. For example, the proposal unit can apply different proposal algorithms for alternative means of transportation. Also, the proposal unit can apply different proposal algorithms for alternative means of accommodation. In this way, by applying the optimal proposal algorithm depending on the category of the alternative means, the proposal accuracy is improved. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input category data of the alternative means to the generation AI and cause the generation AI to apply the optimal proposal algorithm.

[0105] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit, for example, adjusts the proposal algorithm based on the user's past proposal results. The suggestion unit can also improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit can also analyze the user's past proposal results and select an optimal proposal method. For example, the suggestion unit adjusts the proposal algorithm based on the user's past proposal results. The suggestion unit can also improve the accuracy of the proposal by referring to the user's past proposal results. In this way, the suggestion accuracy is improved by referring to the user's past proposal results. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0106] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is feeling anxious, the suggestion unit can provide short and to-the-point suggestions. Furthermore, if the user is relaxed, the suggestion unit can provide detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can provide concise suggestions. For example, the suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. This allows for more appropriate information to be provided by adjusting the length of the suggestions according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the suggestions.

[0107] When making a proposal, the suggestion unit can determine the priority of the proposal based on the timing of providing the alternative means. For example, if the timing of providing the alternative means is approaching, the suggestion unit can increase the priority of the proposal. Also, if the timing of providing the alternative means is distant, the suggestion unit can decrease the priority of the proposal. Furthermore, the suggestion unit can adjust the schedule of the proposal based on the timing of providing the alternative means. For example, if the timing of providing the alternative means is approaching, the suggestion unit can increase the priority of the proposal. Also, if the timing of providing the alternative means is distant, the suggestion unit can decrease the priority of the proposal. In this way, by determining the priority of the proposal based on the timing of providing the alternative means, more important information can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the timing of providing the alternative means to the generation AI and cause the generation AI to determine the priority of the proposals.

[0108] The suggestion unit can adjust the order of suggestions based on the relevance of the alternative means when making suggestions. For example, if the relevance of the alternative means is high, the suggestion unit prioritizes the order of suggestions. Also, if the relevance of the alternative means is low, the suggestion unit can postpone the order of suggestions. Furthermore, the suggestion unit can adjust the schedule of suggestions based on the relevance of the alternative means. For example, if the relevance of the alternative means is high, the suggestion unit prioritizes the order of suggestions. Also, if the relevance of the alternative means is low, the suggestion unit can postpone the order of suggestions. In this way, by adjusting the order of suggestions based on the relevance of the alternative means, more important information can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input relevance data of the alternative means to a generation AI and cause the generation AI to adjust the order of suggestions.

[0109] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal depending on the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can use detailed technical terminology. Furthermore, if the user does not have technical expertise, the suggestion unit can provide a proposal in simple language. Furthermore, the suggestion unit can adjust the way the proposal is expressed depending on the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can use detailed technical terminology. Furthermore, if the user does not have technical expertise, the suggestion unit can provide a proposal in simple language. By adjusting the technical terminology in the proposal depending on the user's level of expertise, more appropriate information can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's level of expertise data into a generation AI and cause the generation AI to use technical terminology in the proposal. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, notification unit, and suggestion unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects observation data using the camera 42 or sensor of the smart device 14, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts the path and strength of a typhoon using a generative AI. The notification unit is realized, for example, by the control unit 46A of the smart device 14, and notifies the user of the latest typhoon information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests alternative measures to the user. The collection unit can, for example, estimate the user's emotions and adjust the timing of collecting observation data based on the estimated emotions. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, notification unit, and suggestion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects observation data using the camera 42 or sensor of the smart glasses 214, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts the path and strength of a typhoon using a generative AI. The notification unit is realized, for example, by the control unit 46A of the smart glasses 214, and notifies the user of the latest typhoon information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests alternative measures to the user. The collection unit can, for example, estimate the user's emotions and adjust the timing of collecting observation data based on the estimated emotions. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, notification unit, and suggestion unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects observation data using the camera 42 or sensors of the headset-type terminal 314, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts the path and strength of a typhoon using a generative AI. The notification unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and notifies the user of the latest typhoon information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests alternative measures to the user. The collection unit can, for example, estimate the user's emotions and adjust the timing of collecting observation data based on the estimated emotions. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, notification unit, and suggestion unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects observation data using the camera 42 and sensors of the robot 414, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts the path and strength of a typhoon using a generative AI. The notification unit is realized, for example, by the control unit 46A of the robot 414, and notifies the user of the latest typhoon information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests alternative measures to the user. The collection unit can, for example, estimate the user's emotions and adjust the timing of collecting observation data based on the estimated emotions.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] The typhoon prediction system can also analyze a user's past behavioral history and suggest individual actions based on the prediction results. For example, it can analyze a user's behavioral patterns when affected by a typhoon in the past and suggest optimal actions to take if a similar situation occurs. It can also suggest more appropriate alternative methods based on information about the transportation and accommodations the user has used in the past. Furthermore, it can optimize action plans in areas that may be affected by a typhoon based on the user's past behavioral history. This allows users to take advantage of past experience and act more safely and efficiently.

[0112] The typhoon prediction system can also estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is feeling anxious, the system can send an early notification to provide a sense of security. If the user is feeling relaxed, the system can delay the notification to reduce stress. Furthermore, if the user is in a hurry, the system can prioritize the notification of important information. This allows for more effective information provision by sending notifications at the optimal timing according to the user's emotions.

[0113] The typhoon prediction system can also provide detailed forecast information for each region based on the user's location information. For example, it can provide a detailed typhoon path forecast for the user's current location and suggest specific evacuation routes and evacuation locations. It can also provide detailed forecast information for the region the user is traveling to and assist with changes to travel plans. Furthermore, it can provide detailed forecast information for areas that may be affected based on the user's location information. This allows the user to obtain detailed information based on their location and take more appropriate actions.

[0114] The typhoon prediction system can also estimate the user's emotions and customize the content of suggestions based on the estimated emotions. For example, if the user is feeling anxious, it can make suggestions that provide a sense of security. If the user is relaxed, it can provide detailed information. Furthermore, if the user is in a hurry, it can make simple, to-the-point suggestions. This makes it possible to provide more effective action support by making optimal suggestions based on the user's emotions.

[0115] The typhoon prediction system can also adjust its prediction algorithm based on past user feedback. For example, it can analyze feedback provided by users in the past to improve the accuracy of predictions. It can also adjust the way prediction results are presented based on past user feedback. It can also optimize the timing of predictions based on past user feedback. By incorporating user feedback, it is possible to provide more accurate predictions and appropriate information.

[0116] The typhoon prediction system can also estimate the user's emotions and adjust the way the prediction results are presented based on the estimated emotions. For example, if the user is feeling anxious, a simple, highly visible prediction result can be provided. If the user is relaxed, a detailed prediction result can be provided. Furthermore, if the user is in a hurry, a prediction result that focuses on the main points can be provided. In this way, by adjusting the way the prediction results are presented according to the user's emotions, more appropriate information can be provided.

[0117] The typhoon prediction system can also use the user's past behavioral history to suggest individual actions based on the prediction results. For example, it can analyze the user's behavioral patterns when affected by past typhoons and suggest optimal actions to take if a similar situation occurs. It can also suggest more appropriate alternative methods based on information about the transportation and accommodations the user has used in the past. Furthermore, it can optimize action plans in areas that may be affected by a typhoon based on the user's past behavioral history. This allows users to take advantage of past experience and act more safely and efficiently.

[0118] The typhoon prediction system can also estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is feeling anxious, the system can send an early notification to provide a sense of security. If the user is feeling relaxed, the system can delay the notification to reduce stress. Furthermore, if the user is in a hurry, the system can prioritize the notification of important information. This allows for more effective information provision by sending notifications at the optimal timing according to the user's emotions.

[0119] The typhoon prediction system can also provide detailed forecast information for each region based on the user's location information. For example, it can provide a detailed typhoon path forecast for the user's current location and suggest specific evacuation routes and evacuation locations. It can also provide detailed forecast information for the region the user is traveling to and assist with changes to travel plans. Furthermore, it can provide detailed forecast information for areas that may be affected based on the user's location information. This allows the user to obtain detailed information based on their location and take more appropriate actions.

[0120] The typhoon prediction system can also estimate the user's emotions and customize the content of suggestions based on the estimated emotions. For example, if the user is feeling anxious, it can make suggestions that provide a sense of security. If the user is relaxed, it can provide detailed information. Furthermore, if the user is in a hurry, it can make simple, to-the-point suggestions. This makes it possible to provide more effective action support by making optimal suggestions based on the user's emotions.

[0121] The processing flow of the second embodiment will be briefly explained below.

[0122] Step 1: The collection unit collects observation data. The observation data includes temperature, humidity, wind speed, air pressure, precipitation, etc. The collection unit collects data from meteorological satellites and ground observation stations, and can obtain data from meteorological satellites in real time. It can also collect data from ground observation stations periodically. Step 2: The analysis unit uses the generation AI to analyze the data collected by the collection unit and predict the typhoon's path and strength. The analysis is based on past typhoon data and meteorological models, and can also use machine learning and deep learning. Step 3: The notification unit notifies the user based on the prediction results obtained by the analysis unit. Notifications are sent via a smartphone app, email, or website. Step 4: The suggestion unit proposes alternative means based on the information notified by the notification unit. The proposal provides information on the operation status of transportation services and the availability of accommodations, helping the user to take appropriate action.

[0123] 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.

[0124] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] 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.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0170] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects observation data; an analysis unit that analyzes the data collected by the collection unit and predicts the course and strength of the typhoon; a notification unit that notifies a user based on the prediction result obtained by the analysis unit; a suggestion unit that suggests an alternative means based on the information notified by the notification unit. A system characterized by:

2. The collecting unit Collect temperature, humidity, wind speed, pressure, and precipitation data 2. The system of claim 1.

3. The analysis unit Predicting the path and strength of typhoons based on past typhoon data and weather models 2. The system of claim 1.

4. The notification unit Notify the user via a smartphone app 2. The system of claim 1.

5. The proposal unit Providing information on transportation and accommodation availability 2. The system of claim 1.

6. The collecting unit Collect data from weather satellites or ground stations 2. The system of claim 1.

7. The analysis unit Predicting the path and strength of typhoons using machine learning and deep learning 2. The system of claim 1.

8. The proposal unit Identify affected areas and users based on user location information 2. The system of claim 1.

Images