system

Abstract

The system according to the embodiment aims to improve the accuracy of typhoon forecasts and provide them to users promptly. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects typhoon forecast data provided by the Japan Meteorological Agency. The analysis unit analyzes the data collected by the collection unit and predicts the typhoon's path, strength, and area of ​​impact. The provision unit provides the user with the prediction results obtained by the analysis 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] With conventional technology, there is room for improvement in the accuracy of typhoon forecasts and the speed at which information is provided.

[0005] The system according to the embodiment aims to improve the accuracy of typhoon forecasts and provide them to users promptly. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects typhoon forecast data provided by the Japan Meteorological Agency. The analysis unit analyzes the data collected by the collection unit and predicts the typhoon's path, strength, and area of ​​impact. The provision unit provides the user with the prediction results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the accuracy of typhoon forecasts and provide them to users promptly. [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 forecast system according to an embodiment of the present invention uses AI to analyze typhoon forecast data provided by the Japan Meteorological Agency and provide more accurate typhoon forecasts. The typhoon forecast system collects typhoon forecast data provided by the Japan Meteorological Agency, analyzes it using AI, and predicts the typhoon's path, strength, and impact area. The forecast results are provided to users in real time. For example, the typhoon forecast system collects typhoon forecast data provided by the Japan Meteorological Agency. Then, the typhoon forecast system uses AI to analyze the collected data and predict the typhoon's path, strength, and impact area. The AI ​​learns past typhoon data and weather patterns and makes predictions based on this. The forecast results are provided to users in real time. Users can check the typhoon's path, strength, and impact area through the app. Furthermore, the typhoon forecast system forecasts the typhoon's progress until it passes, allowing users to evacuate and prepare with peace of mind. For example, users can identify evacuation sites and prepare necessary supplies before the typhoon approaches. The typhoon forecast system also suggests alternative transportation and accommodation options. For example, if public transportation is suspended due to a typhoon, the system can suggest alternative means of transportation and provide information on accommodations, allowing users to take prompt and appropriate action. In this way, the typhoon forecast system can ensure the safety of users and minimize damage by accurately predicting the typhoon's path, strength, and area of ​​impact. AI analysis enables more accurate predictions than conventional forecasts, providing users with extremely useful information.

[0029] A typhoon forecast system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects typhoon forecast data provided by the Japan Meteorological Agency. The typhoon forecast data includes, but is not limited to, wind speed, precipitation, and predicted path. The collection unit acquires data, for example, through the Japan Meteorological Agency's API. The collection unit can also collect data periodically. For example, the collection unit acquires the latest typhoon forecast data every hour. The analysis unit analyzes the data collected by the collection unit and predicts the typhoon's path, strength, and impact area. The analysis unit uses AI to learn past typhoon data and weather patterns and makes predictions based on this. For example, the analysis unit predicts the typhoon's path using a machine learning algorithm. The analysis unit can also predict the typhoon's strength using deep learning. The analysis unit can also predict the typhoon's impact area using a statistical model. The provision unit provides the prediction results obtained by the analysis unit to a user. The provision unit displays the prediction results to the user, for example, through an app. The providing unit can also provide the forecast results via a website. The providing unit can also notify the forecast results via email or SMS. For example, the providing unit notifies the user of the typhoon's path, strength, and impact area in real time based on conditions set by the user. As a result, the typhoon forecast system according to the embodiment can analyze typhoon forecast data from the Japan Meteorological Agency using AI and provide more accurate typhoon forecasts.

[0030] The analysis unit can study past typhoon data or weather patterns and make predictions based on them. Past typhoon data includes, but is not limited to, past typhoon tracks, wind speeds, and precipitation amounts. For example, the analysis unit can train a machine learning model using past typhoon data to predict the track of a typhoon. The analysis unit can also study weather patterns and predict the strength of a typhoon based on the learned data. For example, the analysis unit can study seasonal weather variations and regional weather characteristics to predict the area of ​​impact of a typhoon. Furthermore, the analysis unit can use deep learning to combine past typhoon data and weather patterns to make predictions. For example, the analysis unit uses a deep learning model that inputs past typhoon data and weather patterns and outputs the track, strength, and area of ​​impact of a typhoon. This improves the accuracy of predictions by learning past data and weather patterns. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI.

[0031] The providing unit can predict the duration of the typhoon and provide the user with information on evacuation or preparation. Examples of information on evacuation or preparation include, but are not limited to, evacuation sites, evacuation routes, and lists of emergency supplies. For example, the providing unit can suggest the user an optimal evacuation site based on the predicted path of the typhoon. The providing unit can also provide the user with evacuation routes based on the typhoon's strength and impact area. For example, the providing unit can display the safest evacuation route from the user's current location. The providing unit can also provide the user with a list of necessary emergency supplies based on the prediction of the duration of the typhoon. For example, the providing unit can list emergency supplies such as food, water, and medicine in case the effects of the typhoon linger. This allows the user to evacuate and prepare with peace of mind. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0032] The providing unit can suggest alternative means of transportation or accommodation. Examples of alternative means of transportation or accommodation include, but are not limited to, alternative transportation routes and lists of accommodations. For example, if public transportation is suspended due to a typhoon, the providing unit can suggest alternative means of transportation to the user. For example, the providing unit can provide information on buses, taxis, rental cars, etc. Furthermore, if there is a shortage of accommodations due to the typhoon, the providing unit can also provide information on accommodations to the user. For example, the providing unit can display a list of accommodations with available rooms. Furthermore, the providing unit can suggest the optimal alternative means based on the user's current location and destination. For example, the providing unit can suggest the accommodation closest to the user's current location. This allows the user to take prompt and appropriate action. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or without AI.

[0033] The providing unit can suggest alternative means of transportation when public transportation is suspended. Examples of alternative means of transportation include, but are not limited to, buses, taxis, and rental cars. For example, when trains or planes are suspended due to a typhoon, the providing unit suggests alternative means of transportation to the user. For example, the providing unit provides bus operation information. The providing unit can also provide taxi reservation information. For example, the providing unit introduces an app that allows the user to reserve a taxi. The providing unit can also provide the availability of rental cars. For example, the providing unit provides information that allows the user to reserve the nearest rental car. This allows the user to find an appropriate alternative means of transportation even when public transportation is suspended. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0034] The providing unit can provide information about accommodation facilities. The information about accommodation facilities includes, but is not limited to, for example, the location, price, and availability of the accommodation facilities. For example, when there is a shortage of accommodation facilities due to a typhoon, the providing unit provides the user with information about accommodation facilities. For example, the providing unit displays a list of accommodation facilities with available rooms. The providing unit can also provide price information for accommodation facilities. For example, the providing unit provides price comparison information to help the user find accommodation facilities that fit their budget. The providing unit can also provide location information for accommodation facilities. For example, the providing unit displays the accommodation facilities closest to the user's current location. By providing the information about accommodation facilities, the user can find an appropriate place to stay. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0035] The collection unit can analyze the past collection history of typhoon data and select the optimal collection method. The collection history includes, for example, past collection dates and times, types of collected data, etc., but is not limited to these examples. The collection unit, for example, selects the most reliable data source from the past collection history. The collection unit can also determine the optimal collection frequency based on the past collection history. For example, the collection unit analyzes the past collection history and selects a collection method that minimizes data loss. Furthermore, the collection unit can evaluate the reliability of data based on the past collection history and select the optimal collection method. For example, the collection unit analyzes the past collection history and preferentially selects a highly reliable data source. In this way, the optimal collection method can be selected by analyzing the past collection history. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without using AI.

[0036] When collecting typhoon forecast data, the collection unit can filter the data based on the user's current location information and area of ​​interest. Examples of filtering include, but are not limited to, the range of location information and the method for setting the area of ​​interest. For example, the collection unit prioritizes the collection of typhoon forecast data that will have the greatest impact on the user's current location. The collection unit can also collect related typhoon forecast data based on the area of ​​interest set by the user. For example, the collection unit filters and collects the most relevant typhoon forecast data based on the user's location information. Furthermore, the collection unit can determine the priority of the data to be collected based on the user's location information and area of ​​interest. For example, the collection unit prioritizes the collection of typhoon forecast data closest to the user's current location. This allows for the collection of highly relevant data based on the user's location information and area of ​​interest. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0037] When collecting typhoon forecast data, the collection unit can evaluate the reliability of the data and prioritize collection of highly reliable data. Data reliability includes, but is not limited to, the data source and the frequency of data updates. For example, the collection unit can evaluate the reliability of each data source and prioritize collection of highly reliable data. The collection unit can also evaluate the reliability of data in real time and select data to collect. For example, the collection unit collects the most reliable data based on the reliability evaluation results of past data. Furthermore, the collection unit can develop an algorithm for evaluating data reliability and select the optimal data source. For example, the collection unit selects highly reliable data based on the data source and update frequency. By prioritizing collection of highly reliable data, the accuracy of predictions is improved. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0038] When collecting typhoon forecast data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. Highly relevant data includes, but is not limited to, data based on the user's location information and data based on a region of interest. For example, the collection unit prioritizes collecting typhoon forecast data that has the greatest impact on the user's current location. The collection unit can also collect related typhoon forecast data based on a region of interest set by the user. For example, the collection unit filters and collects the most relevant typhoon forecast data based on the user's location information. Furthermore, the collection unit can also determine the priority of data to be collected by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting typhoon forecast data closest to the user's current location. This allows highly relevant data to be collected by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0039] When collecting typhoon forecast data, the collection unit can analyze the user's social media activity and collect related data. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. The collection unit can collect related typhoon forecast data, for example, based on location information shared by the user on social media. The collection unit can also analyze the content of the user's social media posts to collect related typhoon forecast data. For example, the collection unit can collect related typhoon forecast data based on the activities of the user's friends on social media. Furthermore, the collection unit can determine the priority of data to be collected based on the user's social media activity. For example, the collection unit can prioritize collection of highly relevant data based on the frequency of the user's social media activity. This allows highly relevant data to be collected by analyzing the user's social media activity. Some or all of the above-described processing by the collection unit can be performed, for example, using AI or without AI.

[0040] When collecting typhoon forecast data, the collection unit can adjust the collection method by reflecting the user's past feedback. Past feedback includes, but is not limited to, user ratings, comments, and usage history. For example, the collection unit customizes the type of data to be collected based on the user's past feedback. The collection unit can also adjust the collection frequency by reflecting the user's past feedback. For example, the collection unit selects an optimal data source based on the user's past feedback. Furthermore, the collection unit can develop an algorithm for optimizing the collection method based on the user's past feedback. For example, the collection unit analyzes the user's ratings and comments and adjusts the collection method. In this way, the collection method can be optimized by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the typhoon. Examples of the importance of a typhoon include, but are not limited to, the typhoon's strength, area of ​​impact, and path. For example, the analysis unit performs a detailed analysis for a typhoon with a high level of importance. Furthermore, the analysis unit can also perform a simplified analysis for a typhoon with a low level of importance. For example, the analysis unit dynamically adjusts the level of detail of the analysis based on the typhoon's importance. Furthermore, the analysis unit can develop an algorithm for evaluating the typhoon's importance and select an optimal analysis method. For example, the analysis unit evaluates the importance based on the typhoon's strength and area of ​​impact, and adjusts the level of detail of the analysis. By adjusting the level of detail of the analysis based on the typhoon's importance, appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the typhoon category. Examples of typhoon categories include, but are not limited to, Category 1 and Category 2. The analysis unit selects an optimal analysis algorithm depending on, for example, the typhoon category (strength, path, etc.). The analysis unit can also apply different analysis methods based on the typhoon category. For example, the analysis unit dynamically switches analysis algorithms depending on the typhoon category. Furthermore, the analysis unit can develop an algorithm for evaluating the typhoon category and select the optimal analysis method. For example, the analysis unit evaluates the category based on the typhoon's strength and path, and applies the optimal analysis algorithm. This improves the accuracy of the analysis by applying the optimal analysis algorithm depending on the typhoon category. 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.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. Past analysis results include, but are not limited to, past prediction accuracy and successful analysis examples. 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. For example, the analysis unit analyzes the user's past analysis results and selects an optimal analysis method. Furthermore, the analysis unit can develop an algorithm for improving the accuracy of the analysis based on the past analysis results. For example, the analysis unit evaluates the accuracy of past predictions and applies an optimal analysis algorithm. This improves the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI or without AI.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the timing of typhoon occurrence. Examples of typhoon occurrence timing include, but are not limited to, seasons, months, and weeks. For example, the analysis unit prioritizes analysis of typhoons with the greatest impact based on the timing of typhoon occurrence. The analysis unit can also dynamically adjust the priority of analysis according to the timing of typhoon occurrence. For example, the analysis unit optimally allocates analysis resources taking into account the timing of typhoon occurrence. Furthermore, the analysis unit can develop an algorithm for evaluating the timing of typhoon occurrence and select an optimal analysis method. For example, the analysis unit applies an optimal analysis algorithm based on the timing of typhoon occurrence. This allows for determining the priority of analysis based on the timing of typhoon occurrence, thereby providing appropriate analysis results. 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.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of typhoons. Examples of the relevance of typhoons include, but are not limited to, the typhoon's path, area of ​​impact, and strength. For example, the analysis unit prioritizes analysis of the most important typhoons based on the relevance of typhoons. The analysis unit can also dynamically adjust the order of analysis according to the relevance of typhoons. For example, the analysis unit optimally allocates analysis resources taking into account the relevance of typhoons. Furthermore, the analysis unit can develop an algorithm for evaluating the relevance of typhoons and select an optimal analysis method. For example, the analysis unit evaluates the relevance based on the typhoon's path and area of ​​impact and adjusts the order of analysis. In this way, by adjusting the order of analysis based on the relevance of typhoons, appropriate analysis results 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 using AI.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Technical terms include, but are not limited to, meteorological terms and analytical terms. For example, if the user has specialized knowledge, the analysis unit uses detailed technical terms. Furthermore, if the user does not have specialized knowledge, the analysis unit can provide analysis results in simple language. For example, the analysis unit can adjust the way the analysis results are expressed according to the user's level of expertise. Furthermore, the analysis unit can develop an algorithm for adjusting the use of technical terms and select the optimal analysis method. For example, the analysis unit can evaluate the user's level of expertise and apply the optimal expression method. By adjusting the use of technical terms in the analysis according to the user's level of expertise, more understandable analysis results can be provided. Some or all of the above-described processing in the analysis unit can be performed, for example, using AI or without AI.

[0047] The providing unit can adjust the level of detail of the information to be provided based on the impact of the typhoon when providing the information. The level of detail of the information includes, but is not limited to, the impact of the typhoon, a user request, and the like. For example, the providing unit provides detailed information for a typhoon with high impact. The providing unit can also provide simplified information for a typhoon with low impact. For example, the providing unit dynamically adjusts the level of detail of the information according to the impact of the typhoon. Furthermore, the providing unit can develop an algorithm for evaluating the impact of the typhoon and select an optimal information provision method. For example, the providing unit applies an optimal information provision algorithm based on the impact of the typhoon. This allows appropriate information to be provided by adjusting the level of detail of the information based on the impact of the typhoon. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without AI.

[0048] The providing unit can apply different information provision algorithms depending on the typhoon category when providing information. Examples of information provision algorithms include, but are not limited to, recommendation algorithms and filtering algorithms. The providing unit selects an optimal information provision algorithm depending on, for example, the typhoon category (strength, path, etc.). The providing unit can also apply different information provision methods based on the typhoon category. For example, the providing unit dynamically switches the information provision algorithm depending on the typhoon category. Furthermore, the providing unit can develop an algorithm for evaluating the typhoon category and select the optimal information provision method. For example, the providing unit evaluates the category based on the typhoon's strength and path and applies the optimal information provision algorithm. This improves the accuracy of information provision by applying the optimal information provision algorithm depending on the typhoon category. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0049] The providing unit can improve the accuracy of information provision by referring to the user's past provision results when providing information. Past provision results include, but are not limited to, the user's ratings, feedback, and usage history. For example, the providing unit adjusts an information provision algorithm based on the user's past provision results. The providing unit can also improve the accuracy of information provision by referring to the user's past provision results. For example, the providing unit analyzes the user's past provision results and selects an optimal information provision method. Furthermore, the providing unit can develop an algorithm for improving the accuracy of information provision based on the past provision results. For example, the providing unit applies an optimal information provision algorithm based on the user's past ratings and feedback. As a result, the accuracy of information provision is improved by referring to the user's past provision results. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0050] The providing unit can determine the priority of information provision based on the timing of the typhoon's occurrence when providing information. The priority of information provision includes, but is not limited to, the timing of the typhoon's occurrence and the degree of impact, for example. The providing unit can, for example, prioritize providing information about typhoons with the greatest impact based on the timing of the typhoon's occurrence. The providing unit can also dynamically adjust the priority of information provision according to the timing of the typhoon's occurrence. For example, the providing unit optimally allocates information provision resources taking into account the timing of the typhoon's occurrence. Furthermore, the providing unit can develop an algorithm for evaluating the timing of the typhoon's occurrence and select an optimal information provision method. For example, the providing unit applies an optimal information provision algorithm based on the timing of the typhoon's occurrence. This allows appropriate information to be provided by determining the priority of information provision based on the timing of the typhoon's occurrence. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0051] The providing unit can adjust the order of information provision based on the relevance of the typhoon when providing the information. The order of information provision includes, but is not limited to, the relevance and impact of the typhoon. For example, the providing unit prioritizes providing the most important information based on the relevance of the typhoon. The providing unit can also dynamically adjust the order of information provision according to the relevance of the typhoon. For example, the providing unit optimally allocates information provision resources taking into account the relevance of the typhoon. Furthermore, the providing unit can develop an algorithm for evaluating the relevance of the typhoon and select the optimal information provision method. For example, the providing unit evaluates the relevance based on the typhoon's path and impact area and adjusts the order of information provision. As a result, appropriate information can be provided by adjusting the order of information provision based on the relevance of the typhoon. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or without AI.

[0052] The providing unit can adjust the use of technical terms in providing information according to the user's level of expertise when providing the information. Examples of the use of technical terms include, but are not limited to, the user's level of expertise and the importance of the information. For example, the providing unit uses detailed technical terms when the user has specialized knowledge. Furthermore, the providing unit can provide information in simple language when the user does not have specialized knowledge. For example, the providing unit can adjust the way the information is presented according to the user's level of expertise. Furthermore, the providing unit can develop an algorithm for adjusting the use of technical terms and select the optimal way to present the information. For example, the providing unit can evaluate the user's level of expertise and apply the optimal way of presentation. By adjusting the use of technical terms in providing information according to the user's level of expertise, more understandable information can be provided. Some or all of the above-described processing by the providing unit can be performed, for example, using AI or without AI.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The typhoon forecast system further collects data on the user's past evacuation behavior, and the analysis unit uses the data to predict the user's evacuation behavior pattern. For example, the past evacuation behavior data includes evacuation locations, evacuation routes, evacuation times, etc. The analysis unit uses this data to predict what actions the user will take under what circumstances. This allows the provision unit to provide more appropriate evacuation information to the user. For example, if the user previously selected a specific evacuation location, the provision unit can preferentially suggest that evacuation location. Also, if the user previously used a specific evacuation route, the provision unit can preferentially suggest that route. Furthermore, the provision unit can suggest an appropriate evacuation start time by taking into account the time periods when the user previously began evacuating. This allows the provision of more personalized evacuation information by utilizing the user's past evacuation behavior data.

[0055] In addition to predicting the path of a typhoon, the analysis unit can evaluate the vulnerability of infrastructure (e.g., power grids, communication networks, transportation networks, etc.) that may be affected by the typhoon. For example, the analysis unit can predict the vulnerability of infrastructure in a specific area using past typhoon data and infrastructure damage data. This allows the provision unit to provide users with information about infrastructure vulnerability. For example, if a power outage is expected due to the typhoon, the provision unit can suggest countermeasures to the user to prevent the power outage. Furthermore, if there is a possibility that the communication network will be affected, the provision unit can suggest alternative means of communication to the user. Furthermore, if there is a possibility that the transportation network will be affected, the provision unit can also suggest alternative means of transportation to the user. This makes it possible to provide information that takes into account the vulnerability of infrastructure.

[0056] The typhoon forecast system also collects users' social media activity, and the analysis unit uses the data to predict the user's interests and behavioral patterns. For example, a user's social media activity includes the content of posts, the number of likes, the number of followers, etc. The analysis unit uses this data to predict what information the user is interested in. This allows the provision unit to provide more appropriate information to the user. For example, if a user is interested in a specific region, the provision unit can prioritize providing typhoon information related to that region. Also, if a user is interested in a specific topic, the provision unit can provide information related to that topic. Furthermore, based on the user's social media activity, the provision unit can predict the user's behavioral patterns and provide information at the appropriate time. This makes it possible to provide more personalized information by utilizing the user's social media activity.

[0057] The typhoon forecast system can further collect users' past feedback and use the data in the analysis unit to improve the prediction algorithm. For example, users' past feedback includes ratings, comments, usage history, etc. The analysis unit can use this data to improve the accuracy of the prediction algorithm. This allows the provision unit to provide more accurate typhoon forecasts to users. For example, based on the users' past feedback, the analysis unit can adjust the prediction algorithm and improve the prediction accuracy. Furthermore, by analyzing the users' past feedback, the analysis unit can identify and improve weaknesses in the prediction algorithm. Furthermore, based on the users' past feedback, the analysis unit can develop a new prediction algorithm. This allows the accuracy of the prediction algorithm to be improved by utilizing the users' past feedback.

[0058] The typhoon forecast system can further collect the user's geographical location information, and the analysis unit can use that data to predict the user's evacuation behavior. For example, the user's geographical location information includes the user's current location, past movement history, areas of interest, etc. The analysis unit can use this data to predict what actions the user will take under what circumstances. This allows the provision unit to provide more appropriate evacuation information to the user. For example, if the user evacuates from their current location, the provision unit can suggest the optimal evacuation route. Also, if the user has evacuated to a specific area in the past, the provision unit can preferentially suggest that area. Furthermore, the provision unit can also suggest appropriate evacuation locations taking into account the user's areas of interest. This makes it possible to provide more personalized evacuation information by utilizing the user's geographical location information.

[0059] The typhoon forecast system further collects data on the user's past evacuation behavior, and the analysis unit uses the data to predict the user's evacuation behavior pattern. For example, the past evacuation behavior data includes evacuation locations, evacuation routes, evacuation times, etc. The analysis unit uses this data to predict what actions the user will take under what circumstances. This allows the provision unit to provide more appropriate evacuation information to the user. For example, if the user previously selected a specific evacuation location, the provision unit can preferentially suggest that evacuation location. Also, if the user previously used a specific evacuation route, the provision unit can preferentially suggest that route. Furthermore, the provision unit can suggest an appropriate evacuation start time by taking into account the time periods when the user previously began evacuating. This allows the provision of more personalized evacuation information by utilizing the user's past evacuation behavior data.

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

[0061] Step 1: The collection unit collects typhoon forecast data provided by the Japan Meteorological Agency. Typhoon forecast data includes wind speed, precipitation, and predicted path. The collection unit obtains data through the Japan Meteorological Agency's API and can collect data periodically. For example, it can obtain the latest typhoon forecast data every hour. Step 2: The analysis unit analyzes the data collected by the collection unit and predicts the typhoon's path, strength, and area of ​​impact. The analysis unit uses AI to study past typhoon data and weather patterns, and makes predictions using machine learning algorithms, deep learning, and statistical models. Step 3: The provider provides the prediction results obtained by the analysis unit to the user. The provider displays or notifies the user of the prediction results via an app, website, email, SMS, etc. Based on the conditions set by the user, the provider can notify the user in real time of the typhoon's path, strength, and area of ​​impact.

[0062] (Example 2) A typhoon forecast system according to an embodiment of the present invention uses AI to analyze typhoon forecast data provided by the Japan Meteorological Agency and provide more accurate typhoon forecasts. The typhoon forecast system collects typhoon forecast data provided by the Japan Meteorological Agency, analyzes it using AI, and predicts the typhoon's path, strength, and impact area. The forecast results are provided to users in real time. For example, the typhoon forecast system collects typhoon forecast data provided by the Japan Meteorological Agency. Then, the typhoon forecast system uses AI to analyze the collected data and predict the typhoon's path, strength, and impact area. The AI ​​learns past typhoon data and weather patterns and makes predictions based on this. The forecast results are provided to users in real time. Users can check the typhoon's path, strength, and impact area through the app. Furthermore, the typhoon forecast system forecasts the typhoon's progress until it passes, allowing users to evacuate and prepare with peace of mind. For example, users can identify evacuation sites and prepare necessary supplies before the typhoon approaches. The typhoon forecast system also suggests alternative transportation and accommodation options. For example, if public transportation is suspended due to a typhoon, the system can suggest alternative means of transportation and provide information on accommodations, allowing users to take prompt and appropriate action. In this way, the typhoon forecast system can ensure the safety of users and minimize damage by accurately predicting the typhoon's path, strength, and area of ​​impact. AI analysis enables more accurate predictions than conventional forecasts, providing users with extremely useful information.

[0063] A typhoon forecast system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects typhoon forecast data provided by the Japan Meteorological Agency. The typhoon forecast data includes, but is not limited to, wind speed, precipitation, and predicted path. The collection unit acquires data, for example, through the Japan Meteorological Agency's API. The collection unit can also collect data periodically. For example, the collection unit acquires the latest typhoon forecast data every hour. The analysis unit analyzes the data collected by the collection unit and predicts the typhoon's path, strength, and impact area. The analysis unit uses AI to learn past typhoon data and weather patterns and makes predictions based on this. For example, the analysis unit predicts the typhoon's path using a machine learning algorithm. The analysis unit can also predict the typhoon's strength using deep learning. The analysis unit can also predict the typhoon's impact area using a statistical model. The provision unit provides the prediction results obtained by the analysis unit to a user. The provision unit displays the prediction results to the user, for example, through an app. The providing unit can also provide the forecast results via a website. The providing unit can also notify the forecast results via email or SMS. For example, the providing unit notifies the user of the typhoon's path, strength, and impact area in real time based on conditions set by the user. As a result, the typhoon forecast system according to the embodiment can analyze typhoon forecast data from the Japan Meteorological Agency using AI and provide more accurate typhoon forecasts.

[0064] The analysis unit can study past typhoon data or weather patterns and make predictions based on them. Past typhoon data includes, but is not limited to, past typhoon tracks, wind speeds, and precipitation amounts. For example, the analysis unit can train a machine learning model using past typhoon data to predict the track of a typhoon. The analysis unit can also study weather patterns and predict the strength of a typhoon based on the learned data. For example, the analysis unit can study seasonal weather variations and regional weather characteristics to predict the area of ​​impact of a typhoon. Furthermore, the analysis unit can use deep learning to combine past typhoon data and weather patterns to make predictions. For example, the analysis unit uses a deep learning model that inputs past typhoon data and weather patterns and outputs the track, strength, and area of ​​impact of a typhoon. This improves the accuracy of predictions by learning past data and weather patterns. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI.

[0065] The providing unit can predict the duration of the typhoon and provide the user with information on evacuation or preparation. Examples of information on evacuation or preparation include, but are not limited to, evacuation sites, evacuation routes, and lists of emergency supplies. For example, the providing unit can suggest the user an optimal evacuation site based on the predicted path of the typhoon. The providing unit can also provide the user with evacuation routes based on the typhoon's strength and impact area. For example, the providing unit can display the safest evacuation route from the user's current location. The providing unit can also provide the user with a list of necessary emergency supplies based on the prediction of the duration of the typhoon. For example, the providing unit can list emergency supplies such as food, water, and medicine in case the effects of the typhoon linger. This allows the user to evacuate and prepare with peace of mind. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0066] The providing unit can suggest alternative means of transportation or accommodation. Examples of alternative means of transportation or accommodation include, but are not limited to, alternative transportation routes and lists of accommodations. For example, if public transportation is suspended due to a typhoon, the providing unit can suggest alternative means of transportation to the user. For example, the providing unit can provide information on buses, taxis, rental cars, etc. Furthermore, if there is a shortage of accommodations due to the typhoon, the providing unit can also provide information on accommodations to the user. For example, the providing unit can display a list of accommodations with available rooms. Furthermore, the providing unit can suggest the optimal alternative means based on the user's current location and destination. For example, the providing unit can suggest the accommodation closest to the user's current location. This allows the user to take prompt and appropriate action. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or without AI.

[0067] The providing unit can suggest alternative means of transportation when public transportation is suspended. Examples of alternative means of transportation include, but are not limited to, buses, taxis, and rental cars. For example, when trains or planes are suspended due to a typhoon, the providing unit suggests alternative means of transportation to the user. For example, the providing unit provides bus operation information. The providing unit can also provide taxi reservation information. For example, the providing unit introduces an app that allows the user to reserve a taxi. The providing unit can also provide the availability of rental cars. For example, the providing unit provides information that allows the user to reserve the nearest rental car. This allows the user to find an appropriate alternative means of transportation even when public transportation is suspended. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0068] The providing unit can provide information about accommodation facilities. The information about accommodation facilities includes, but is not limited to, for example, the location, price, and availability of the accommodation facilities. For example, when there is a shortage of accommodation facilities due to a typhoon, the providing unit provides the user with information about accommodation facilities. For example, the providing unit displays a list of accommodation facilities with available rooms. The providing unit can also provide price information for accommodation facilities. For example, the providing unit provides price comparison information to help the user find accommodation facilities that fit their budget. The providing unit can also provide location information for accommodation facilities. For example, the providing unit displays the accommodation facilities closest to the user's current location. By providing the information about accommodation facilities, the user can find an appropriate place to stay. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0069] The collection unit can estimate the user's emotions and adjust the timing of collecting typhoon forecast data based on the estimated user's emotions. The collection unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the collection unit analyzes facial expression data of the user captured by a camera to estimate the emotions. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit analyzes the tone and speed of the user's voice to estimate the emotions. The collection unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor to estimate the emotions. For example, the collection unit estimates the emotions based on heart rate fluctuations. The collection unit adjusts the timing of collecting typhoon forecast data based on the estimated user's emotions. For example, if the user is feeling anxious, the collection unit can frequently collect typhoon forecast data and provide the latest information. Alternatively, if the user is relaxed, the collection unit can collect typhoon forecast data at a normal collection frequency. Alternatively, if the user is in a hurry, the collection unit can prioritize collecting only important data and provide it quickly. This allows the timing of collection to be adjusted according to the user's emotions, thereby providing information at a more appropriate time. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may 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 collection unit may be performed using, for example, an AI, or may be performed without using an AI.

[0070] The collection unit can analyze the past collection history of typhoon data and select the optimal collection method. The collection history includes, for example, past collection dates and times, types of collected data, etc., but is not limited to these examples. The collection unit, for example, selects the most reliable data source from the past collection history. The collection unit can also determine the optimal collection frequency based on the past collection history. For example, the collection unit analyzes the past collection history and selects a collection method that minimizes data loss. Furthermore, the collection unit can evaluate the reliability of data based on the past collection history and select the optimal collection method. For example, the collection unit analyzes the past collection history and preferentially selects a highly reliable data source. In this way, the optimal collection method can be selected by analyzing the past collection history. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without using AI.

[0071] When collecting typhoon forecast data, the collection unit can filter the data based on the user's current location information and area of ​​interest. Examples of filtering include, but are not limited to, the range of location information and the method for setting the area of ​​interest. For example, the collection unit prioritizes the collection of typhoon forecast data that will have the greatest impact on the user's current location. The collection unit can also collect related typhoon forecast data based on the area of ​​interest set by the user. For example, the collection unit filters and collects the most relevant typhoon forecast data based on the user's location information. Furthermore, the collection unit can determine the priority of the data to be collected based on the user's location information and area of ​​interest. For example, the collection unit prioritizes the collection of typhoon forecast data closest to the user's current location. This allows for the collection of highly relevant data based on the user's location information and area of ​​interest. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0072] When collecting typhoon forecast data, the collection unit can evaluate the reliability of the data and prioritize collection of highly reliable data. Data reliability includes, but is not limited to, the data source and the frequency of data updates. For example, the collection unit can evaluate the reliability of each data source and prioritize collection of highly reliable data. The collection unit can also evaluate the reliability of data in real time and select data to collect. For example, the collection unit collects the most reliable data based on the reliability evaluation results of past data. Furthermore, the collection unit can develop an algorithm for evaluating data reliability and select the optimal data source. For example, the collection unit selects highly reliable data based on the data source and update frequency. By prioritizing collection of highly reliable data, the accuracy of predictions is improved. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0073] The collection unit can estimate the user's emotions and determine the priority of typhoon forecast data to be collected based on the estimated user's emotions. The collection unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the collection unit analyzes facial expression data of the user captured by a camera to estimate the emotions. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit analyzes the tone and speed of the user's voice to estimate the emotions. The collection unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor to estimate the emotions. For example, the collection unit estimates the emotions based on heart rate fluctuations. The collection unit determines the priority of typhoon forecast data to be collected based on the estimated user's emotions. For example, if the user is feeling anxious, the collection unit prioritizes collecting the most important typhoon forecast data. Alternatively, if the user is relaxed, the collection unit can collect typhoon forecast data with normal priority. Alternatively, if the user is in a hurry, the collection unit can prioritize collecting data that can be provided quickly. This allows for more appropriate information to be provided by determining the priority of data according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may 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 may be performed using, for example, an AI, or may be performed without using an AI.

[0074] When collecting typhoon forecast data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. Highly relevant data includes, but is not limited to, data based on the user's location information and data based on a region of interest. For example, the collection unit prioritizes collecting typhoon forecast data that has the greatest impact on the user's current location. The collection unit can also collect related typhoon forecast data based on a region of interest set by the user. For example, the collection unit filters and collects the most relevant typhoon forecast data based on the user's location information. Furthermore, the collection unit can also determine the priority of data to be collected by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting typhoon forecast data closest to the user's current location. This allows highly relevant data to be collected by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0075] When collecting typhoon forecast data, the collection unit can analyze the user's social media activity and collect related data. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. The collection unit can collect related typhoon forecast data, for example, based on location information shared by the user on social media. The collection unit can also analyze the content of the user's social media posts to collect related typhoon forecast data. For example, the collection unit can collect related typhoon forecast data based on the activities of the user's friends on social media. Furthermore, the collection unit can determine the priority of data to be collected based on the user's social media activity. For example, the collection unit can prioritize collection of highly relevant data based on the frequency of the user's social media activity. This allows highly relevant data to be collected by analyzing the user's social media activity. Some or all of the above-described processing by the collection unit can be performed, for example, using AI or without AI.

[0076] When collecting typhoon forecast data, the collection unit can adjust the collection method by reflecting the user's past feedback. Past feedback includes, but is not limited to, user ratings, comments, and usage history. For example, the collection unit customizes the type of data to be collected based on the user's past feedback. The collection unit can also adjust the collection frequency by reflecting the user's past feedback. For example, the collection unit selects an optimal data source based on the user's past feedback. Furthermore, the collection unit can develop an algorithm for optimizing the collection method based on the user's past feedback. For example, the collection unit analyzes the user's ratings and comments and adjusts the collection method. In this way, the collection method can be optimized by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0077] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. The analysis unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the analysis unit analyzes facial expression data of the user captured by a camera to estimate the emotions. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the user's voice to estimate the emotions. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor to estimate the emotions. For example, the analysis unit estimates the emotions based on heart rate fluctuations. The analysis unit adjusts the way the analysis is presented based on the estimated user's emotions. For example, if the user is feeling anxious, the analysis unit can provide a simple, highly visible analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. If the user is in a hurry, the analysis unit can provide a concise analysis result. This allows the analysis unit to adjust the way the analysis is presented based on the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI.

[0078] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the typhoon. Examples of the importance of a typhoon include, but are not limited to, the typhoon's strength, area of ​​impact, and path. For example, the analysis unit performs a detailed analysis for a typhoon with a high level of importance. Furthermore, the analysis unit can also perform a simplified analysis for a typhoon with a low level of importance. For example, the analysis unit dynamically adjusts the level of detail of the analysis based on the typhoon's importance. Furthermore, the analysis unit can develop an algorithm for evaluating the typhoon's importance and select an optimal analysis method. For example, the analysis unit evaluates the importance based on the typhoon's strength and area of ​​impact, and adjusts the level of detail of the analysis. By adjusting the level of detail of the analysis based on the typhoon's importance, appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI.

[0079] During analysis, the analysis unit can apply different analysis algorithms depending on the typhoon category. Examples of typhoon categories include, but are not limited to, Category 1 and Category 2. The analysis unit selects an optimal analysis algorithm depending on, for example, the typhoon category (strength, path, etc.). The analysis unit can also apply different analysis methods based on the typhoon category. For example, the analysis unit dynamically switches analysis algorithms depending on the typhoon category. Furthermore, the analysis unit can develop an algorithm for evaluating the typhoon category and select the optimal analysis method. For example, the analysis unit evaluates the category based on the typhoon's strength and path, and applies the optimal analysis algorithm. This improves the accuracy of the analysis by applying the optimal analysis algorithm depending on the typhoon category. 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.

[0080] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. Past analysis results include, but are not limited to, past prediction accuracy and successful analysis examples. 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. For example, the analysis unit analyzes the user's past analysis results and selects an optimal analysis method. Furthermore, the analysis unit can develop an algorithm for improving the accuracy of the analysis based on the past analysis results. For example, the analysis unit evaluates the accuracy of past predictions and applies an optimal analysis algorithm. This improves the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI or without AI.

[0081] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the analysis unit analyzes facial expression data of the user captured by a camera to estimate the emotion. The analysis unit can also estimate the user's emotion using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the user's voice to estimate the emotion. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor to estimate the emotion. For example, the analysis unit estimates the emotion based on heart rate fluctuations. The analysis unit adjusts the length of the analysis based on the estimated user's emotion. For example, if the user is feeling anxious, the analysis unit can provide a short and concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. If the user is in a hurry, the analysis unit can provide a concise analysis result. In this way, by adjusting the length of the analysis according to the user's emotion, more appropriate analysis results can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI.

[0082] During analysis, the analysis unit can determine the priority of analysis based on the timing of typhoon occurrence. Examples of typhoon occurrence timing include, but are not limited to, seasons, months, and weeks. For example, the analysis unit prioritizes analysis of typhoons with the greatest impact based on the timing of typhoon occurrence. The analysis unit can also dynamically adjust the priority of analysis according to the timing of typhoon occurrence. For example, the analysis unit optimally allocates analysis resources taking into account the timing of typhoon occurrence. Furthermore, the analysis unit can develop an algorithm for evaluating the timing of typhoon occurrence and select an optimal analysis method. For example, the analysis unit applies an optimal analysis algorithm based on the timing of typhoon occurrence. This allows for determining the priority of analysis based on the timing of typhoon occurrence, thereby providing appropriate analysis results. 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.

[0083] During analysis, the analysis unit can adjust the order of analysis based on the relevance of typhoons. Examples of the relevance of typhoons include, but are not limited to, the typhoon's path, area of ​​impact, and strength. For example, the analysis unit prioritizes analysis of the most important typhoons based on the relevance of typhoons. The analysis unit can also dynamically adjust the order of analysis according to the relevance of typhoons. For example, the analysis unit optimally allocates analysis resources taking into account the relevance of typhoons. Furthermore, the analysis unit can develop an algorithm for evaluating the relevance of typhoons and select an optimal analysis method. For example, the analysis unit evaluates the relevance based on the typhoon's path and area of ​​impact and adjusts the order of analysis. In this way, by adjusting the order of analysis based on the relevance of typhoons, appropriate analysis results 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 using AI.

[0084] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Technical terms include, but are not limited to, meteorological terms and analytical terms. For example, if the user has specialized knowledge, the analysis unit uses detailed technical terms. Furthermore, if the user does not have specialized knowledge, the analysis unit can provide analysis results in simple language. For example, the analysis unit can adjust the way the analysis results are expressed according to the user's level of expertise. Furthermore, the analysis unit can develop an algorithm for adjusting the use of technical terms and select the optimal analysis method. For example, the analysis unit can evaluate the user's level of expertise and apply the optimal expression method. By adjusting the use of technical terms in the analysis according to the user's level of expertise, more understandable analysis results can be provided. Some or all of the above-described processing in the analysis unit can be performed, for example, using AI or without AI.

[0085] The providing unit can estimate the user's emotions and adjust the presentation method of the information to be provided based on the estimated user's emotions. The providing unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the providing unit analyzes facial expression data of the user captured by a camera to estimate the emotions. The providing unit can also estimate the user's emotions using voice analysis technology. For example, the providing unit analyzes the tone and speed of the user's voice to estimate the emotions. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor to estimate the emotions. For example, the providing unit estimates the emotions based on heart rate fluctuations. The providing unit adjusts the presentation method of the information to be provided based on the estimated user's emotions. For example, if the user is feeling anxious, the providing unit can provide simple, highly visible information. If the user is relaxed, the providing unit can provide detailed information. If the user is in a hurry, the providing unit can provide information that focuses on the main points. This allows the provision of more appropriate information by adjusting the presentation method of information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may 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 providing unit may be performed using, for example, AI, or may be performed without using AI.

[0086] The providing unit can adjust the level of detail of the information to be provided based on the impact of the typhoon when providing the information. The level of detail of the information includes, but is not limited to, the impact of the typhoon, a user request, and the like. For example, the providing unit provides detailed information for a typhoon with high impact. The providing unit can also provide simplified information for a typhoon with low impact. For example, the providing unit dynamically adjusts the level of detail of the information according to the impact of the typhoon. Furthermore, the providing unit can develop an algorithm for evaluating the impact of the typhoon and select an optimal information provision method. For example, the providing unit applies an optimal information provision algorithm based on the impact of the typhoon. This allows appropriate information to be provided by adjusting the level of detail of the information based on the impact of the typhoon. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without AI.

[0087] The providing unit can apply different information provision algorithms depending on the typhoon category when providing information. Examples of information provision algorithms include, but are not limited to, recommendation algorithms and filtering algorithms. The providing unit selects an optimal information provision algorithm depending on, for example, the typhoon category (strength, path, etc.). The providing unit can also apply different information provision methods based on the typhoon category. For example, the providing unit dynamically switches the information provision algorithm depending on the typhoon category. Furthermore, the providing unit can develop an algorithm for evaluating the typhoon category and select the optimal information provision method. For example, the providing unit evaluates the category based on the typhoon's strength and path and applies the optimal information provision algorithm. This improves the accuracy of information provision by applying the optimal information provision algorithm depending on the typhoon category. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0088] The providing unit can improve the accuracy of information provision by referring to the user's past provision results when providing information. Past provision results include, but are not limited to, the user's ratings, feedback, and usage history. For example, the providing unit adjusts an information provision algorithm based on the user's past provision results. The providing unit can also improve the accuracy of information provision by referring to the user's past provision results. For example, the providing unit analyzes the user's past provision results and selects an optimal information provision method. Furthermore, the providing unit can develop an algorithm for improving the accuracy of information provision based on the past provision results. For example, the providing unit applies an optimal information provision algorithm based on the user's past ratings and feedback. As a result, the accuracy of information provision is improved by referring to the user's past provision results. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0089] The providing unit can estimate the user's emotion and adjust the length of information to be provided based on the estimated user's emotion. The providing unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the providing unit analyzes facial expression data of the user captured by a camera to estimate the emotion. The providing unit can also estimate the user's emotion using voice analysis technology. For example, the providing unit analyzes the tone and speed of the user's voice to estimate the emotion. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor to estimate the emotion. For example, the providing unit estimates the emotion based on heart rate fluctuations. The providing unit adjusts the length of information to be provided based on the estimated user's emotion. For example, if the user is feeling anxious, the providing unit can provide short, to-the-point information. If the user is relaxed, the providing unit can provide detailed information. If the user is in a hurry, the providing unit can provide concise information. In this way, by adjusting the length of information according to the user's emotion, more appropriate information can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may 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 providing unit may be performed using, for example, AI, or may be performed without using AI.

[0090] The providing unit can determine the priority of information provision based on the timing of the typhoon's occurrence when providing information. The priority of information provision includes, but is not limited to, the timing of the typhoon's occurrence and the degree of impact, for example. The providing unit can, for example, prioritize providing information about typhoons with the greatest impact based on the timing of the typhoon's occurrence. The providing unit can also dynamically adjust the priority of information provision according to the timing of the typhoon's occurrence. For example, the providing unit optimally allocates information provision resources taking into account the timing of the typhoon's occurrence. Furthermore, the providing unit can develop an algorithm for evaluating the timing of the typhoon's occurrence and select an optimal information provision method. For example, the providing unit applies an optimal information provision algorithm based on the timing of the typhoon's occurrence. This allows appropriate information to be provided by determining the priority of information provision based on the timing of the typhoon's occurrence. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0091] The providing unit can adjust the order of information provision based on the relevance of the typhoon when providing the information. The order of information provision includes, but is not limited to, the relevance and impact of the typhoon. For example, the providing unit prioritizes providing the most important information based on the relevance of the typhoon. The providing unit can also dynamically adjust the order of information provision according to the relevance of the typhoon. For example, the providing unit optimally allocates information provision resources taking into account the relevance of the typhoon. Furthermore, the providing unit can develop an algorithm for evaluating the relevance of the typhoon and select the optimal information provision method. For example, the providing unit evaluates the relevance based on the typhoon's path and impact area and adjusts the order of information provision. As a result, appropriate information can be provided by adjusting the order of information provision based on the relevance of the typhoon. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or without AI.

[0092] The providing unit can adjust the use of technical terms in providing information according to the user's level of expertise when providing the information. Examples of the use of technical terms include, but are not limited to, the user's level of expertise and the importance of the information. For example, the providing unit uses detailed technical terms when the user has specialized knowledge. Furthermore, the providing unit can provide information in simple language when the user does not have specialized knowledge. For example, the providing unit can adjust the way the information is presented according to the user's level of expertise. Furthermore, the providing unit can develop an algorithm for adjusting the use of technical terms and select the optimal way to present the information. For example, the providing unit can evaluate the user's level of expertise and apply the optimal way of presentation. By adjusting the use of technical terms in providing information according to the user's level of expertise, more understandable information can be provided. Some or all of the above-described processing by the providing unit can be performed, for example, using AI or without AI. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and provision 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 the user's facial expressions and voice using the camera 42 and microphone 38B of the smart device 14, and estimates the user's emotions using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to predict the typhoon's path, strength, and impact area. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides the prediction results to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and provision 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 the user's facial expressions and voice using the camera 42 and microphone 238 of the smart glasses 214, and estimates the user's emotions using the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and predicts the typhoon's path, strength, and impact area. The provision unit, realized, for example, by the control unit 46A of the smart glasses 214, provides the user with the prediction results. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and provision 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 the user's facial expressions and voice using the camera 42 and microphone 238 of the headset-type terminal 314, and estimates the user's emotions using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to predict the typhoon's path, strength, and impact area. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and provides the user with the prediction results. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and provision 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 the user's facial expressions and voice using the camera 42 and microphone 238 of the robot 414, and estimates the user's emotions using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to predict the typhoon's path, strength, and impact area. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the user with the prediction results.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The typhoon forecast system further collects data on the user's past evacuation behavior, and the analysis unit uses the data to predict the user's evacuation behavior pattern. For example, the past evacuation behavior data includes evacuation locations, evacuation routes, evacuation times, etc. The analysis unit uses this data to predict what actions the user will take under what circumstances. This allows the provision unit to provide more appropriate evacuation information to the user. For example, if the user previously selected a specific evacuation location, the provision unit can preferentially suggest that evacuation location. Also, if the user previously used a specific evacuation route, the provision unit can preferentially suggest that route. Furthermore, the provision unit can suggest an appropriate evacuation start time by taking into account the time periods when the user previously began evacuating. This allows the provision of more personalized evacuation information by utilizing the user's past evacuation behavior data.

[0095] In addition to predicting the path of a typhoon, the analysis unit can evaluate the vulnerability of infrastructure (e.g., power grids, communication networks, transportation networks, etc.) that may be affected by the typhoon. For example, the analysis unit can predict the vulnerability of infrastructure in a specific area using past typhoon data and infrastructure damage data. This allows the provision unit to provide users with information about infrastructure vulnerability. For example, if a power outage is expected due to the typhoon, the provision unit can suggest countermeasures to the user to prevent the power outage. Furthermore, if there is a possibility that the communication network will be affected, the provision unit can suggest alternative means of communication to the user. Furthermore, if there is a possibility that the transportation network will be affected, the provision unit can also suggest alternative means of transportation to the user. This makes it possible to provide information that takes into account the vulnerability of infrastructure.

[0096] The providing unit can estimate the user's emotions and adjust the method of providing evacuation information based on the estimated user's emotions. For example, if the user is feeling anxious, the providing unit can provide simple, highly visible evacuation information. If the user is relaxed, the providing unit can provide detailed evacuation information. If the user is in a hurry, the providing unit can provide evacuation information that covers the main points. This allows for adjusting the method of providing evacuation information according to the user's emotions, making it possible to provide more appropriate evacuation information. Emotions can be estimated using, for example, facial expression recognition technology or voice analysis technology. For example, facial expression data of the user captured by a camera can be analyzed to estimate emotions. Emotions can also be estimated by analyzing the tone and speed of the user's voice. This allows for adjusting the method of providing evacuation information based on the user's emotions.

[0097] The providing unit can estimate the user's emotions and adjust the method of suggesting alternative means of transportation or accommodation based on the estimated user's emotions. For example, if the user is feeling anxious, the providing unit can suggest simple, highly visible alternative means. If the user is relaxed, the providing unit can suggest detailed alternative means. If the user is in a hurry, the providing unit can suggest alternative means that focus on the main points. This allows the method of suggesting alternative means to be adjusted according to the user's emotions, making it possible to provide more appropriate alternative means. Emotions can be estimated using, for example, facial expression recognition technology or voice analysis technology. For example, facial expression data of the user captured by a camera can be analyzed to estimate emotions. Emotions can also be estimated by analyzing the tone and speed of the user's voice. This allows the method of suggesting alternative means to be adjusted based on the user's emotions.

[0098] The providing unit can estimate the user's emotions and adjust the method of suggesting alternative transportation means based on the estimated user's emotions. For example, if the user is feeling anxious, the providing unit can suggest simple and highly visible alternative transportation means. Furthermore, if the user is relaxed, the providing unit can suggest detailed alternative transportation means. Furthermore, if the user is in a hurry, the providing unit can suggest alternative transportation means that focus on the main points. In this way, by adjusting the method of suggesting alternative transportation means according to the user's emotions, more appropriate alternative transportation means can be provided. Emotion estimation is performed using, for example, facial expression recognition technology or voice analysis technology. For example, facial expression data of the user captured by a camera can be analyzed to estimate emotions. Emotion can also be estimated by analyzing the tone and speed of the user's voice. In this way, the method of suggesting alternative transportation means can be adjusted based on the user's emotions.

[0099] The providing unit can estimate the user's emotions and adjust the method of providing information about accommodations based on the estimated user emotions. For example, if the user is feeling anxious, the providing unit can provide simple, highly visible information about accommodations. If the user is relaxed, the providing unit can provide detailed information about accommodations. If the user is in a hurry, the providing unit can provide information about accommodations that focuses on the main points. This allows the method of providing information about accommodations to be adjusted according to the user's emotions, making it possible to provide more appropriate information about accommodations. Emotions can be estimated using, for example, facial expression recognition technology or voice analysis technology. For example, facial expression data of the user captured by a camera can be analyzed to estimate emotions. Emotions can also be estimated by analyzing the tone and speed of the user's voice. This allows the method of providing information about accommodations to be adjusted based on the user's emotions.

[0100] The typhoon forecast system also collects users' social media activity, and the analysis unit uses the data to predict the user's interests and behavioral patterns. For example, a user's social media activity includes the content of posts, the number of likes, the number of followers, etc. The analysis unit uses this data to predict what information the user is interested in. This allows the provision unit to provide more appropriate information to the user. For example, if a user is interested in a specific region, the provision unit can prioritize providing typhoon information related to that region. Also, if a user is interested in a specific topic, the provision unit can provide information related to that topic. Furthermore, based on the user's social media activity, the provision unit can predict the user's behavioral patterns and provide information at the appropriate time. This makes it possible to provide more personalized information by utilizing the user's social media activity.

[0101] The typhoon forecast system can further collect users' past feedback and use the data in the analysis unit to improve the prediction algorithm. For example, users' past feedback includes ratings, comments, usage history, etc. The analysis unit can use this data to improve the accuracy of the prediction algorithm. This allows the provision unit to provide more accurate typhoon forecasts to users. For example, based on the users' past feedback, the analysis unit can adjust the prediction algorithm and improve the prediction accuracy. Furthermore, by analyzing the users' past feedback, the analysis unit can identify and improve weaknesses in the prediction algorithm. Furthermore, based on the users' past feedback, the analysis unit can develop a new prediction algorithm. This allows the accuracy of the prediction algorithm to be improved by utilizing the users' past feedback.

[0102] The typhoon forecast system can further collect the user's geographical location information, and the analysis unit can use that data to predict the user's evacuation behavior. For example, the user's geographical location information includes the user's current location, past movement history, areas of interest, etc. The analysis unit can use this data to predict what actions the user will take under what circumstances. This allows the provision unit to provide more appropriate evacuation information to the user. For example, if the user evacuates from their current location, the provision unit can suggest the optimal evacuation route. Also, if the user has evacuated to a specific area in the past, the provision unit can preferentially suggest that area. Furthermore, the provision unit can also suggest appropriate evacuation locations taking into account the user's areas of interest. This makes it possible to provide more personalized evacuation information by utilizing the user's geographical location information.

[0103] The typhoon forecast system further collects data on the user's past evacuation behavior, and the analysis unit uses the data to predict the user's evacuation behavior pattern. For example, the past evacuation behavior data includes evacuation locations, evacuation routes, evacuation times, etc. The analysis unit uses this data to predict what actions the user will take under what circumstances. This allows the provision unit to provide more appropriate evacuation information to the user. For example, if the user previously selected a specific evacuation location, the provision unit can preferentially suggest that evacuation location. Also, if the user previously used a specific evacuation route, the provision unit can preferentially suggest that route. Furthermore, the provision unit can suggest an appropriate evacuation start time by taking into account the time periods when the user previously began evacuating. This allows the provision of more personalized evacuation information by utilizing the user's past evacuation behavior data.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The collection unit collects typhoon forecast data provided by the Japan Meteorological Agency. Typhoon forecast data includes wind speed, precipitation, and predicted path. The collection unit obtains data through the Japan Meteorological Agency's API and can collect data periodically. For example, it can obtain the latest typhoon forecast data every hour. Step 2: The analysis unit analyzes the data collected by the collection unit and predicts the typhoon's path, strength, and area of ​​impact. The analysis unit uses AI to study past typhoon data and weather patterns, and makes predictions using machine learning algorithms, deep learning, and statistical models. Step 3: The provider provides the prediction results obtained by the analysis unit to the user. The provider displays or notifies the user of the prediction results via an app, website, email, SMS, etc. Based on the conditions set by the user, the provider can notify the user in real time of the typhoon's path, strength, and area of ​​impact.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[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 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.

[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 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.

[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 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.

[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] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] [Explanation of symbols]

[0178] 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 typhoon forecast data provided by the Japan Meteorological Agency; an analysis unit that analyzes the data collected by the collection unit and predicts the course, strength, and range of impact of the typhoon; a providing unit that provides a user with the prediction result obtained by the analysis unit. A system characterized by:

2. The analysis unit Learn from past typhoon data or weather patterns and make predictions based on this 2. The system of claim 1.

3. The providing unit Predicts the typhoon until it passes, and provides users with information on evacuation and preparation.

2. The system of claim 1.

4. The providing unit Suggest alternative transportation or accommodation options 2. The system of claim 1.

5. The providing unit If public transport is suspended, suggest alternative means of transport 2. The system of claim 1.

6. The providing unit Providing accommodation information 2. The system of claim 1.

7. The collecting unit Estimate user emotions and adjust the timing of collecting typhoon forecast data based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Analyze past typhoon data collection history and select the optimal collection method 2. The system of claim 1.

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