system

Abstract

The system according to this embodiment aims to support optimal actions during disasters. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, and a support unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The provision unit provides the user with the analysis results obtained by the analysis unit. The support unit supports the optimal actions to take in the event of a disaster.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, a system for supporting optimal actions during disasters is not sufficiently developed, and there is room for improvement.

[0005] The system according to the embodiment aims to support optimal actions during disasters.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and a support unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The provision unit provides the analysis result obtained by the analysis unit to the user. The support unit supports optimal actions during disasters.

Effects of the Invention

[0007] The system according to this embodiment can support optimal actions during a disaster. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The smart city service according to an embodiment of the present invention is a system that streamlines urban life by utilizing communication networks and technologies, maps, news, weather, and disaster information, and a user base and notification functions. This system makes daily life more convenient and efficient by collecting, analyzing, and providing information necessary for urban life to users in real time. Furthermore, in the event of a disaster, an AI agent will guide users on evacuation routes and provide the latest information in real time, supporting optimal actions. For example, the system uses communication technologies and networks to collect information necessary for urban life in real time. Next, the system uses maps, weather, disaster, and real-time search functions to analyze the collected information and provide it to users. Furthermore, the system uses a user base and notification functions to notify users of necessary information. As a result, daily life becomes more convenient and urban life becomes more efficient. Furthermore, in the event of a disaster, an AI agent will guide users on evacuation routes and provide the latest information in real time, supporting optimal actions. For example, in the event of an earthquake, the AI ​​agent will calculate the optimal evacuation route based on the user's current location and provide guidance in real time. It will also analyze disaster information to provide users with the latest disaster information and encourage appropriate actions. This system improves the convenience of daily life for users and allows them to act with confidence during disasters. For example, in daily life, users can obtain necessary information in real time and act efficiently. In the event of a disaster, the AI ​​agent supports optimal actions, ensuring the user's safety. This service is available to everyone, but is particularly effective for people who are inadequately prepared for disasters. By combining communication technologies and assets, an integrated problem-solving engine can be created, utilizing generative AI to autonomously solve complex tasks and present users with multiple better practices. This allows users to act with confidence even during disasters. As a result, smart city services can streamline urban life and support optimal actions during disasters.

[0029] The smart city service according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a support unit. The collection unit collects information. The collection unit collects information necessary for urban life in real time, for example, using communication technology. The collection unit can also collect environmental data, for example, using sensors. The collection unit can also collect user location information, for example. The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the collected data, for example, using AI. The analysis unit can also analyze the data, for example, using machine learning algorithms. The analysis unit can also analyze text data, for example, using natural language processing technology. The provision unit provides the user with the analysis results obtained by the analysis unit. The provision unit notifies the user of the analysis results, for example. The provision unit can also send notifications to the user's smartphone, for example. The provision unit can also display the analysis results on the user's device, for example. The support unit supports optimal actions in the event of a disaster. The support unit calculates evacuation routes, for example, using AI. The support unit can also guide the user along evacuation routes, for example. The support unit can, for example, analyze disaster information and provide users with the latest information. This enables the smart city service according to the embodiment to efficiently collect, analyze, provide, and support information during disasters. Some or all of the above-described processes in the collection unit, analysis unit, provision unit, and support unit may be performed using AI, for example, or without AI. For example, the collection unit can input data acquired by sensors into a generation AI and have the generation AI perform data analysis. The analysis unit can input data collected by the collection unit into a generation AI and have the generation AI perform data analysis. The provision unit can input the analysis results obtained by the analysis unit into a generation AI and have the generation AI execute a method for providing the information to users. The support unit can input disaster information into a generation AI and have the generation AI execute a method for supporting optimal actions.

[0030] The data collection unit collects information. For example, the data collection unit uses communication technologies to collect information necessary for urban life in real time. Specifically, the data collection unit utilizes communication technologies such as Wi-Fi, Bluetooth®, and 5G to acquire data from various devices and sensors within the city. This includes environmental data such as traffic volume, air quality, noise levels, temperature, and humidity. Furthermore, the data collection unit can also collect user location information and activity data from smartphones and wearable devices. This allows for an understanding of overall city trends and individual user behavior patterns. The data collection unit can also collect environmental data using sensors, for example. This includes traffic sensors installed on roads, weather sensors installed on building rooftops, and noise sensors attached to streetlights. These sensors collect data in real time and transmit it to a central database. The data collection unit can also collect user location information, for example. This utilizes smartphones with GPS functionality and vehicle navigation systems. This allows for accurate tracking of the user's current location and travel route. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and provisioning departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit can analyze collected data using AI. Specifically, the AI ​​uses machine learning algorithms to analyze data and extract patterns and trends. For example, it can analyze traffic data to identify locations and times of congestion and propose optimal traffic routes. It can also analyze environmental data to detect changes in air quality and increases in noise levels and take appropriate measures. The analysis unit can also analyze text data using natural language processing technology. This includes analyzing text data collected from social media and news sites to understand urban trends and citizens' opinions. Furthermore, the analysis unit can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can predict risk fluctuations in specific areas and time periods based on past disaster data and formulate future countermeasures. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue early warnings. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The service provider provides users with the analysis results obtained by the analysis unit. For example, the service provider notifies users of the analysis results. Specifically, the service provider can send notifications to users' smartphones. This includes methods such as push notifications, SMS, and email. For example, it can notify users in real time of traffic congestion information or warnings about changes in air quality. The service provider can also display analysis results on users' devices. This includes displaying detailed analysis results and recommended actions through smartphone apps or web portals. For example, it can provide users with optimal traffic routes, evacuation routes, and suggestions for environmental improvement. The service provider can collect user feedback and continuously improve the accuracy and effectiveness of its services. For example, it can review notification content and display methods based on user feedback to provide a more user-friendly service. Furthermore, the service provider can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the service provider to provide users with information quickly and reliably, improving the convenience and safety of urban life.

[0033] The support department assists in taking optimal actions during disasters. For example, the support department uses AI to calculate evacuation routes. Specifically, the AI ​​calculates the optimal evacuation route in real time based on collected data and provides it to the user. This includes suggesting evacuation routes that take into account traffic conditions, environmental data, and the user's location information. The support department can also guide users along evacuation routes. This includes alerting users through voice guidance and vibration notifications via a smartphone app. Furthermore, the support department can analyze disaster information and provide users with the latest information. This includes collecting disaster information provided by the Japan Meteorological Agency and local governments in real time and notifying users. For example, it can quickly convey information such as earthquake and flood occurrences and the opening of evacuation centers to users. In this way, the support department can ensure the safety of users during disasters and support them in taking quick and appropriate actions. Furthermore, the support department can collect user feedback and continuously improve the accuracy and effectiveness of its support. For example, it can review the methods of suggesting evacuation routes and providing disaster information to provide more effective support. In addition, the support department can reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through smartphone notifications, but also through voice calls, SMS, and email. This allows the support department to provide users with quick and reliable instructions, minimizing risks during disasters.

[0034] The service provider includes a notification unit that notifies the user of necessary information. The notification unit, for example, sends a notification to the user's smartphone. The notification unit can also, for example, display the notification on the user's device. The notification unit can also, for example, send a notification to the user's email address. This ensures that the user is appropriately notified of the necessary information. Some or all of the above-described processes in the notification unit may be performed using AI, for example, or without AI. For example, when sending a notification to the user's smartphone, the notification unit can generate notification content using a generation AI.

[0035] The support unit includes a calculation unit that calculates evacuation routes. The calculation unit calculates evacuation routes using, for example, AI. The calculation unit can also calculate the optimal evacuation route based on, for example, the user's current location. The calculation unit can also calculate evacuation routes based on, for example, disaster information. This allows for the calculation of the optimal evacuation route in the event of a disaster. Some or all of the above-described processes in the calculation unit may be performed using, for example, AI, or without AI. For example, the calculation unit can input the user's current location and disaster information into a generating AI and have the generating AI perform the calculation of the optimal evacuation route.

[0036] The analysis unit includes an information provision unit that analyzes disaster information and provides the latest information. The information provision unit analyzes disaster information using, for example, AI. The information provision unit can also provide, for example, the latest disaster information to users. The information provision unit can also, for example, prompt users to take appropriate action based on the disaster information. This enables the provision of the latest information during a disaster. Some or all of the above-described processing in the information provision unit may be performed using, for example, AI, or without AI. For example, the information provision unit can input disaster information into a generating AI and have the generating AI perform the provision of the latest information.

[0037] The data collection unit analyzes the user's past behavior history and selects the optimal information collection method. For example, the data collection unit prioritizes collecting information sources that the user has frequently accessed in the past. The data collection unit can also analyze the user's past behavior patterns and select the optimal time for information collection. The data collection unit can also prioritize collecting specific information from the user's past behavior history. This allows the optimal information collection method to be selected based on the user's past behavior history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavior history data into a generating AI and have the generating AI select the optimal information collection method.

[0038] The data collection unit filters information based on the user's current location and areas of interest during data collection. For example, the data collection unit prioritizes collecting information about the user's surroundings based on the user's current location. The data collection unit can also filter and collect relevant information based on the user's areas of interest. The data collection unit can also combine the user's current location and areas of interest to collect optimal information. This allows the data collection unit to collect optimal information based on the user's current location and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current location and areas of interest data into a generating AI and have the generating AI perform the information filtering.

[0039] The data collection unit prioritizes collecting highly relevant information while considering the user's geographical location. For example, the data collection unit prioritizes collecting information about the surrounding area based on the user's current location. The data collection unit can also prioritize collecting highly relevant information while considering the user's geographical location. The data collection unit can also collect optimal information by combining the user's current location with their past behavioral history. This allows for the collection of highly relevant information while considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI collect highly relevant information.

[0040] The data collection unit analyzes the user's social media activity and collects relevant information during data collection. For example, the data collection unit analyzes the content of the user's social media posts and collects relevant information. The data collection unit can also analyze the activities of the user's social media followers and friends and collect relevant information. The data collection unit can also analyze the user's social media trends and collect relevant information. This allows the collection unit to collect relevant information based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect relevant information.

[0041] The analysis unit adjusts the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit performs a detailed analysis on information of high importance. For example, the analysis unit can also perform a simplified analysis on information of low importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the information. This allows the level of detail of the analysis to be adjusted according to the importance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0042] The analysis unit applies different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies a meteorological data analysis algorithm to weather information. For example, the analysis unit may also apply a disaster data analysis algorithm to disaster information. For example, the analysis unit may also apply a news data analysis algorithm to news information. This allows the optimal analysis algorithm to be applied according to the category of information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0043] The analysis unit determines the priority of analysis based on the timing of information collection during the analysis. For example, the analysis unit prioritizes the analysis of the most recent information. The analysis unit can also, for example, postpone the analysis of older information. The analysis unit can also dynamically adjust the priority of analysis according to the timing of information collection. This allows the analysis priority to be determined based on the timing of information collection. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information collection timing data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0044] The analysis unit adjusts the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant information. For example, the analysis unit may postpone the analysis of less relevant information. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the information. This allows the order of analysis to be adjusted based on the relevance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0045] The information provider adjusts the level of detail provided based on the importance of the information at the time of provision. For example, the provider provides detailed information for highly important information. For example, the provider may provide simplified information for less important information. The provider may also dynamically adjust the level of detail provided according to the importance of the information. This allows the level of detail provided to be adjusted according to the importance of the information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail provided.

[0046] The data delivery unit applies different delivery algorithms depending on the information category at the time of delivery. For example, the data delivery unit applies a meteorological data delivery algorithm to weather information. For example, the data delivery unit may also apply a disaster data delivery algorithm to disaster information. For example, the data delivery unit may also apply a news data delivery algorithm to news information. This allows the optimal delivery algorithm to be applied according to the information category. Some or all of the above processing in the data delivery unit may be performed using AI, for example, or without AI. For example, the data delivery unit can input information category data into a generating AI and have the generating AI execute the application of different delivery algorithms.

[0047] The information delivery unit determines the priority of information delivery based on when the information was collected. For example, the delivery unit may prioritize the delivery of the latest information. For example, the delivery unit may also postpone the delivery of older information. The delivery unit may also dynamically adjust the priority of information delivery according to when the information was collected. This allows the priority of information delivery to be determined based on when the information was collected. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit may input information collection time data into a generating AI and have the generating AI perform the determination of the priority of information delivery.

[0048] The information provider adjusts the order of information provision based on the relevance of the information at the time of provision. For example, the information provider may prioritize providing highly relevant information. For example, the information provider may also postpone providing less relevant information. The information provider may also dynamically adjust the order of provision according to the relevance of the information. This allows the order of provision to be adjusted based on the relevance of the information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider may input information relevance data into a generating AI and have the generating AI perform the adjustment of the order of provision.

[0049] The support unit analyzes the user's past behavior history to select the optimal support method during support. For example, the support unit may select the optimal support method based on the support methods the user has used in the past. The support unit may also suggest the optimal support method based on the user's past behavior history. For example, the support unit may analyze the user's past behavior patterns to select the optimal support method. This allows the support unit to select the optimal support method based on the user's past behavior history. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit may input the user's past behavior history data into a generating AI and have the generating AI select the optimal support method.

[0050] The support unit customizes the means of support based on the user's current location information during support. For example, the support unit may suggest the optimal evacuation route based on the user's current location. The support unit may also guide the user to a nearby evacuation shelter based on the user's current location. The support unit may also provide the optimal means of support based on the user's current location. This allows the support unit to provide the optimal means of support based on the user's current location information. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit may input the user's current location information data into a generating AI and have the generating AI perform the customization of the means of support.

[0051] The support unit selects the optimal support method when providing support, taking into account the user's geographical location information. For example, the support unit proposes the optimal evacuation route based on the user's current location. The support unit can also select the optimal support method by taking into account the user's geographical location information. For example, the support unit can provide the optimal support method by combining the user's current location with their past behavioral history. This allows the support unit to select the optimal support method based on the user's geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal support method.

[0052] The support unit analyzes the user's social media activity and proposes support measures when providing support. For example, the support unit can analyze the content of the user's social media posts and propose relevant support measures. The support unit can also analyze the activities of the user's social media followers and friends and propose relevant support measures. The support unit can also analyze the user's social media trends and propose relevant support measures. This allows the support unit to propose the most suitable support measures based on the user's social media activity. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's social media activity data into a generating AI and have the generating AI propose support measures.

[0053] The notification unit adjusts the level of detail of the notification based on the importance of the information when it sends a notification. For example, the notification unit provides a detailed notification for information of high importance. For example, the notification unit may provide a simplified notification for information of low importance. The notification unit may also dynamically adjust the level of detail of the notification according to the importance of the information. This allows the level of detail of the notification to be adjusted according to the importance of the information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the notification.

[0054] The notification unit, when sending a notification, prioritizes notifying the user of highly relevant information, taking into account the user's geographical location. For example, the notification unit prioritizes notifying the user of information about their surroundings based on the user's current location. The notification unit can also prioritize notifying the user of highly relevant information, taking into account the user's geographical location. The notification unit can also combine the user's current location with their past activity history to notify the user of the most relevant information. This allows the notification unit to notify the user of highly relevant information based on the user's geographical location. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's geographical location data into a generating AI and have the generating AI execute notifications of highly relevant information.

[0055] The calculation unit analyzes the user's past evacuation history during calculation to select the optimal evacuation route. For example, the calculation unit selects the optimal evacuation route based on evacuation routes previously used by the user. For example, the calculation unit can also select a route that avoids congestion based on the user's past evacuation history. For example, the calculation unit can analyze the user's past evacuation history and select the most efficient evacuation route. This allows the optimal evacuation route to be selected based on the user's past evacuation history. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input the user's past evacuation history data into a generating AI and have the generating AI perform the selection of the optimal evacuation route.

[0056] The calculation unit calculates the optimal evacuation route while considering the user's geographical location information. The calculation unit calculates the optimal evacuation route based on the user's current location, for example. The calculation unit can also calculate the optimal evacuation route by considering the user's geographical location information, for example. The calculation unit can also calculate the optimal evacuation route by combining the user's current location with past evacuation history, for example. This allows the calculation of the optimal evacuation route based on the user's geographical location information. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input the user's geographical location information data into a generating AI and have the generating AI perform the calculation of the optimal evacuation route.

[0057] The information provision unit adjusts the level of detail provided based on the importance of the information when providing it. For example, the information provision unit provides detailed information for information of high importance. For example, the information provision unit may provide simplified information for information of low importance. The information provision unit can also dynamically adjust the level of detail provided according to the importance of the information. This allows the level of detail provided to be adjusted according to the importance of the information. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without AI. For example, the information provision unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail provided.

[0058] The information provision unit, when providing information, prioritizes providing highly relevant information by taking into account the user's geographical location. For example, the information provision unit prioritizes providing information about the surrounding area based on the user's current location. The information provision unit can also prioritize providing highly relevant information by taking into account the user's geographical location. For example, the information provision unit can combine the user's current location with their past behavioral history to provide optimal information. This allows the information provision unit to provide highly relevant information based on the user's geographical location. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without AI. For example, the information provision unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing highly relevant information.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The data collection unit collects user health data, and the analysis unit analyzes the collected data to evaluate the user's health status. For example, the data collection unit can collect vital signs such as the user's heart rate, blood pressure, and body temperature. The analysis unit can analyze this data to evaluate the user's health status. The data provision unit can notify the user of the analysis results and, if necessary, encourage them to visit a medical institution. The support unit can suggest appropriate health management methods according to the user's health status. This allows users to manage their health in their daily lives and helps maintain and improve their health.

[0061] The information provider can deliver customized information based on the user's hobbies and interests. For example, the provider can provide information on events and activities that the user is interested in. The provider can also provide news and articles related to the user's hobbies. The provider can also provide the latest information on areas that the user is interested in. This allows users to receive information that matches their interests and enriches their daily lives.

[0062] The analysis unit can analyze a user's past behavioral history and predict their behavioral patterns. For example, the analysis unit can analyze data on places the user has visited and services they have used in the past. Based on this data, the analysis unit can predict the user's behavioral patterns and predict places they are likely to visit and services they are likely to use next. The service provider can then provide the user with appropriate information based on the predicted behavior. This allows users to receive information tailored to their own behavioral patterns, enabling them to live their daily lives more efficiently.

[0063] The data collection unit can understand the user's current activity status and select the optimal information collection method. For example, if the user is exercising, the data collection unit can prioritize collecting information related to exercise. If the user is working, the data collection unit can also prioritize collecting information related to work. If the user is resting, the data collection unit can also prioritize collecting information that promotes relaxation. This allows the system to collect the most relevant information according to the user's current activity status.

[0064] The information provider can select the most suitable method of providing information based on the user's current location. For example, if the user is outdoors, the provider can provide information about nearby events and activities. If the user is indoors, the provider can also provide information about things to enjoy indoors. If the user is on the move, the provider can also provide information related to their movement. This allows the provider to deliver the most relevant information based on the user's current location.

[0065] The information provider can analyze a user's past behavior history and customize the content of information provided based on the user's behavior patterns. For example, the provider can prioritize providing information that the user has previously shown interest in. The provider can also predict and provide the next information the user will need based on their past behavior patterns. The provider can also provide relevant information based on the user's past behavior history. This allows the provider to deliver the most relevant information based on the user's past behavior history.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The collection unit collects information. The collection unit can, for example, use communication technology to collect information necessary for urban life in real time. The collection unit can also, for example, use sensors to collect environmental data. The collection unit can also, for example, collect user location information. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit can, for example, analyze the collected data using AI. The analysis unit can also, for example, analyze the data using machine learning algorithms. The analysis unit can also, for example, analyze text data using natural language processing technology. Step 3: The service provider provides the user with the analysis results obtained by the analysis unit. The service provider, for example, notifies the user of the analysis results. The service provider can also, for example, send a notification to the user's smartphone. The service provider can also, for example, display the analysis results on the user's device. Step 4: The support unit assists in taking the optimal action during a disaster. For example, the support unit calculates evacuation routes using AI. For example, the support unit can also guide users along evacuation routes. For example, the support unit can analyze disaster information and provide users with the latest information.

[0068] (Example of form 2) The smart city service according to an embodiment of the present invention is a system that streamlines urban life by utilizing communication networks and technologies, maps, news, weather, and disaster information, and a user base and notification functions. This system makes daily life more convenient and efficient by collecting, analyzing, and providing information necessary for urban life to users in real time. Furthermore, in the event of a disaster, an AI agent will guide users on evacuation routes and provide the latest information in real time, supporting optimal actions. For example, the system uses communication technologies and networks to collect information necessary for urban life in real time. Next, the system uses maps, weather, disaster, and real-time search functions to analyze the collected information and provide it to users. Furthermore, the system uses a user base and notification functions to notify users of necessary information. As a result, daily life becomes more convenient and urban life becomes more efficient. Furthermore, in the event of a disaster, an AI agent will guide users on evacuation routes and provide the latest information in real time, supporting optimal actions. For example, in the event of an earthquake, the AI ​​agent will calculate the optimal evacuation route based on the user's current location and provide guidance in real time. It will also analyze disaster information to provide users with the latest disaster information and encourage appropriate actions. This system improves the convenience of daily life for users and allows them to act with confidence during disasters. For example, in daily life, users can obtain necessary information in real time and act efficiently. In the event of a disaster, the AI ​​agent supports optimal actions, ensuring the user's safety. This service is available to everyone, but is particularly effective for people who are inadequately prepared for disasters. By combining communication technologies and assets, an integrated problem-solving engine can be created, utilizing generative AI to autonomously solve complex tasks and present users with multiple better practices. This allows users to act with confidence even during disasters. As a result, smart city services can streamline urban life and support optimal actions during disasters.

[0069] The smart city service according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a support unit. The collection unit collects information. The collection unit collects information necessary for urban life in real time, for example, using communication technology. The collection unit can also collect environmental data, for example, using sensors. The collection unit can also collect user location information, for example. The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the collected data, for example, using AI. The analysis unit can also analyze the data, for example, using machine learning algorithms. The analysis unit can also analyze text data, for example, using natural language processing technology. The provision unit provides the user with the analysis results obtained by the analysis unit. The provision unit notifies the user of the analysis results, for example. The provision unit can also send notifications to the user's smartphone, for example. The provision unit can also display the analysis results on the user's device, for example. The support unit supports optimal actions in the event of a disaster. The support unit calculates evacuation routes, for example, using AI. The support unit can also guide the user along evacuation routes, for example. The support unit can, for example, analyze disaster information and provide users with the latest information. This enables the smart city service according to the embodiment to efficiently collect, analyze, provide, and support information during disasters. Some or all of the above-described processes in the collection unit, analysis unit, provision unit, and support unit may be performed using AI, for example, or without AI. For example, the collection unit can input data acquired by sensors into a generation AI and have the generation AI perform data analysis. The analysis unit can input data collected by the collection unit into a generation AI and have the generation AI perform data analysis. The provision unit can input the analysis results obtained by the analysis unit into a generation AI and have the generation AI execute a method for providing the information to users. The support unit can input disaster information into a generation AI and have the generation AI execute a method for supporting optimal actions.

[0070] The data collection unit collects information. For example, it uses communication technologies to collect information necessary for urban life in real time. Specifically, the data collection unit utilizes communication technologies such as Wi-Fi, Bluetooth, and 5G to acquire data from various devices and sensors within the city. This includes environmental data such as traffic volume, air quality, noise levels, temperature, and humidity. Furthermore, the data collection unit can also collect user location information and activity data from smartphones and wearable devices. This allows for an understanding of overall city trends and individual user behavior patterns. The data collection unit can also collect environmental data using sensors, for example. This includes traffic sensors installed on roads, weather sensors installed on building rooftops, and noise sensors attached to streetlights. These sensors collect data in real time and transmit it to a central database. The data collection unit can also collect user location information, for example. This utilizes smartphones with GPS functionality and vehicle navigation systems. This allows for accurate tracking of the user's current location and travel route. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and provisioning departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0071] The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit can analyze collected data using AI. Specifically, the AI ​​uses machine learning algorithms to analyze data and extract patterns and trends. For example, it can analyze traffic data to identify locations and times of congestion and propose optimal traffic routes. It can also analyze environmental data to detect changes in air quality and increases in noise levels and take appropriate measures. The analysis unit can also analyze text data using natural language processing technology. This includes analyzing text data collected from social media and news sites to understand urban trends and citizens' opinions. Furthermore, the analysis unit can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can predict risk fluctuations in specific areas and time periods based on past disaster data and formulate future countermeasures. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue early warnings. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0072] The service provider provides users with the analysis results obtained by the analysis unit. For example, the service provider notifies users of the analysis results. Specifically, the service provider can send notifications to users' smartphones. This includes methods such as push notifications, SMS, and email. For example, it can notify users in real time of traffic congestion information or warnings about changes in air quality. The service provider can also display analysis results on users' devices. This includes displaying detailed analysis results and recommended actions through smartphone apps or web portals. For example, it can provide users with optimal traffic routes, evacuation routes, and suggestions for environmental improvement. The service provider can collect user feedback and continuously improve the accuracy and effectiveness of its services. For example, it can review notification content and display methods based on user feedback to provide a more user-friendly service. Furthermore, the service provider can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the service provider to provide users with information quickly and reliably, improving the convenience and safety of urban life.

[0073] The support department assists in taking optimal actions during disasters. For example, the support department uses AI to calculate evacuation routes. Specifically, the AI ​​calculates the optimal evacuation route in real time based on collected data and provides it to the user. This includes suggesting evacuation routes that take into account traffic conditions, environmental data, and the user's location information. The support department can also guide users along evacuation routes. This includes alerting users through voice guidance and vibration notifications via a smartphone app. Furthermore, the support department can analyze disaster information and provide users with the latest information. This includes collecting disaster information provided by the Japan Meteorological Agency and local governments in real time and notifying users. For example, it can quickly convey information such as earthquake and flood occurrences and the opening of evacuation centers to users. In this way, the support department can ensure the safety of users during disasters and support them in taking quick and appropriate actions. Furthermore, the support department can collect user feedback and continuously improve the accuracy and effectiveness of its support. For example, it can review the methods of suggesting evacuation routes and providing disaster information to provide more effective support. In addition, the support department can reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through smartphone notifications, but also through voice calls, SMS, and email. This allows the support department to provide users with quick and reliable instructions, minimizing risks during disasters.

[0074] The service provider includes a notification unit that notifies the user of necessary information. The notification unit, for example, sends a notification to the user's smartphone. The notification unit can also, for example, display the notification on the user's device. The notification unit can also, for example, send a notification to the user's email address. This ensures that the user is appropriately notified of the necessary information. Some or all of the above-described processes in the notification unit may be performed using AI, for example, or without AI. For example, when sending a notification to the user's smartphone, the notification unit can generate notification content using a generation AI.

[0075] The support unit includes a calculation unit that calculates evacuation routes. The calculation unit calculates evacuation routes using, for example, AI. The calculation unit can also calculate the optimal evacuation route based on, for example, the user's current location. The calculation unit can also calculate evacuation routes based on, for example, disaster information. This allows for the calculation of the optimal evacuation route in the event of a disaster. Some or all of the above-described processes in the calculation unit may be performed using, for example, AI, or without AI. For example, the calculation unit can input the user's current location and disaster information into a generating AI and have the generating AI perform the calculation of the optimal evacuation route.

[0076] The analysis unit includes an information provision unit that analyzes disaster information and provides the latest information. The information provision unit analyzes disaster information using, for example, AI. The information provision unit can also provide, for example, the latest disaster information to users. The information provision unit can also, for example, prompt users to take appropriate action based on the disaster information. This enables the provision of the latest information during a disaster. Some or all of the above-described processing in the information provision unit may be performed using, for example, AI, or without AI. For example, the information provision unit can input disaster information into a generating AI and have the generating AI perform the provision of the latest information.

[0077] The data collection unit estimates the user's emotions and adjusts the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit reduces the frequency of information collection and collects only important information. For example, if the user is relaxed, the data collection unit can increase the frequency of information collection and collect more detailed information. For example, if the user is in a hurry, the data collection unit can quickly collect necessary information in real time. This allows the timing of information collection to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of information collection.

[0078] The data collection unit analyzes the user's past behavior history and selects the optimal information collection method. For example, the data collection unit prioritizes collecting information sources that the user has frequently accessed in the past. The data collection unit can also analyze the user's past behavior patterns and select the optimal time for information collection. The data collection unit can also prioritize collecting specific information from the user's past behavior history. This allows the optimal information collection method to be selected based on the user's past behavior history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavior history data into a generating AI and have the generating AI select the optimal information collection method.

[0079] The data collection unit filters information based on the user's current location and areas of interest during data collection. For example, the data collection unit prioritizes collecting information about the user's surroundings based on the user's current location. The data collection unit can also filter and collect relevant information based on the user's areas of interest. The data collection unit can also combine the user's current location and areas of interest to collect optimal information. This allows the data collection unit to collect optimal information based on the user's current location and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current location and areas of interest data into a generating AI and have the generating AI perform the information filtering.

[0080] The data collection unit estimates the user's emotions and determines the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting only important information. For example, if the user is relaxed, the data collection unit may prioritize collecting detailed information. For example, if the user is in a hurry, the data collection unit may prioritize collecting information needed in real time. This allows the system to determine the priority of information to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of information.

[0081] The data collection unit prioritizes collecting highly relevant information while considering the user's geographical location. For example, the data collection unit prioritizes collecting information about the surrounding area based on the user's current location. The data collection unit can also prioritize collecting highly relevant information while considering the user's geographical location. The data collection unit can also collect optimal information by combining the user's current location with their past behavioral history. This allows for the collection of highly relevant information while considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI collect highly relevant information.

[0082] The data collection unit analyzes the user's social media activity and collects relevant information during data collection. For example, the data collection unit analyzes the content of the user's social media posts and collects relevant information. The data collection unit can also analyze the activities of the user's social media followers and friends and collect relevant information. The data collection unit can also analyze the user's social media trends and collect relevant information. This allows the collection unit to collect relevant information based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect relevant information.

[0083] The analysis unit estimates the user's emotions and adjusts the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit provides a simple and easy-to-understand analysis result. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis result. For example, if the user is in a hurry, the analysis unit can also provide a concise analysis result. This allows the presentation of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0084] The analysis unit adjusts the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit performs a detailed analysis on information of high importance. For example, the analysis unit can also perform a simplified analysis on information of low importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the information. This allows the level of detail of the analysis to be adjusted according to the importance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0085] The analysis unit applies different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies a meteorological data analysis algorithm to weather information. For example, the analysis unit may also apply a disaster data analysis algorithm to disaster information. For example, the analysis unit may also apply a news data analysis algorithm to news information. This allows the optimal analysis algorithm to be applied according to the category of information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0086] The analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is excited, the analysis unit can also provide a visually stimulating analysis result. This allows the length of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0087] The analysis unit determines the priority of analysis based on the timing of information collection during the analysis. For example, the analysis unit prioritizes the analysis of the most recent information. The analysis unit can also, for example, postpone the analysis of older information. The analysis unit can also dynamically adjust the priority of analysis according to the timing of information collection. This allows the analysis priority to be determined based on the timing of information collection. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information collection timing data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0088] The analysis unit adjusts the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant information. For example, the analysis unit may postpone the analysis of less relevant information. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the information. This allows the order of analysis to be adjusted based on the relevance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0089] The delivery unit estimates the user's emotions and adjusts the presentation of the delivery based on the estimated emotions. For example, if the user is nervous, the delivery unit provides a simple and highly visible presentation. For example, if the user is relaxed, the delivery unit may provide a presentation that includes detailed information. For example, if the user is in a hurry, the delivery unit may provide a presentation that gets straight to the point. This allows the presentation of the delivery to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the delivery.

[0090] The information provider adjusts the level of detail provided based on the importance of the information at the time of provision. For example, the provider provides detailed information for highly important information. For example, the provider may provide simplified information for less important information. The provider may also dynamically adjust the level of detail provided according to the importance of the information. This allows the level of detail provided to be adjusted according to the importance of the information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail provided.

[0091] The data delivery unit applies different delivery algorithms depending on the information category at the time of delivery. For example, the data delivery unit applies a meteorological data delivery algorithm to weather information. For example, the data delivery unit may also apply a disaster data delivery algorithm to disaster information. For example, the data delivery unit may also apply a news data delivery algorithm to news information. This allows the optimal delivery algorithm to be applied according to the information category. Some or all of the above processing in the data delivery unit may be performed using AI, for example, or without AI. For example, the data delivery unit can input information category data into a generating AI and have the generating AI execute the application of different delivery algorithms.

[0092] The delivery unit estimates the user's emotions and adjusts the length of the delivery based on the estimated emotions. For example, if the user is in a hurry, the delivery unit will provide a short, concise delivery. For example, if the user is relaxed, the delivery unit may provide a detailed delivery. For example, if the user is excited, the delivery unit may provide a visually stimulating delivery. This allows the length of the delivery to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI or not using AI. For example, the delivery unit can input user emotion data into a generative AI and have the generative AI adjust the length of the delivery.

[0093] The information delivery unit determines the priority of information delivery based on when the information was collected. For example, the delivery unit may prioritize the delivery of the latest information. For example, the delivery unit may also postpone the delivery of older information. The delivery unit may also dynamically adjust the priority of information delivery according to when the information was collected. This allows the priority of information delivery to be determined based on when the information was collected. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit may input information collection time data into a generating AI and have the generating AI perform the determination of the priority of information delivery.

[0094] The information provider adjusts the order of information provision based on the relevance of the information at the time of provision. For example, the information provider may prioritize providing highly relevant information. For example, the information provider may also postpone providing less relevant information. The information provider may also dynamically adjust the order of provision according to the relevance of the information. This allows the order of provision to be adjusted based on the relevance of the information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider may input information relevance data into a generating AI and have the generating AI perform the adjustment of the order of provision.

[0095] The support unit estimates the user's emotions and adjusts its support methods based on the estimated emotions. For example, if the user is nervous, the support unit will provide support in a calm voice. If the user is relaxed, the support unit may also provide support in a cheerful voice. If the user is in a hurry, the support unit may also provide quick and concise support. This allows the support method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input user emotion data into a generative AI and have the generative AI adjust the support method.

[0096] The support unit analyzes the user's past behavior history to select the optimal support method during support. For example, the support unit may select the optimal support method based on the support methods the user has used in the past. The support unit may also suggest the optimal support method based on the user's past behavior history. For example, the support unit may analyze the user's past behavior patterns to select the optimal support method. This allows the support unit to select the optimal support method based on the user's past behavior history. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit may input the user's past behavior history data into a generating AI and have the generating AI select the optimal support method.

[0097] The support unit customizes the means of support based on the user's current location information during support. For example, the support unit may suggest the optimal evacuation route based on the user's current location. The support unit may also guide the user to a nearby evacuation shelter based on the user's current location. The support unit may also provide the optimal means of support based on the user's current location. This allows the support unit to provide the optimal means of support based on the user's current location information. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit may input the user's current location information data into a generating AI and have the generating AI perform the customization of the means of support.

[0098] The support unit estimates the user's emotions and determines the priority of support based on the estimated emotions. For example, if the user is stressed, the support unit will prioritize important support. For example, if the user is relaxed, the support unit may also provide detailed support. For example, if the user is in a hurry, the support unit may also provide rapid support. This allows the support unit to determine the priority of support according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI or not using AI. For example, the support unit can input user emotion data into a generative AI and have the generative AI determine the priority of support.

[0099] The support unit selects the optimal support method when providing support, taking into account the user's geographical location information. For example, the support unit proposes the optimal evacuation route based on the user's current location. The support unit can also select the optimal support method by taking into account the user's geographical location information. For example, the support unit can provide the optimal support method by combining the user's current location with their past behavioral history. This allows the support unit to select the optimal support method based on the user's geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal support method.

[0100] The support unit analyzes the user's social media activity and proposes support measures when providing support. For example, the support unit can analyze the content of the user's social media posts and propose relevant support measures. The support unit can also analyze the activities of the user's social media followers and friends and propose relevant support measures. The support unit can also analyze the user's social media trends and propose relevant support measures. This allows the support unit to propose the most suitable support measures based on the user's social media activity. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's social media activity data into a generating AI and have the generating AI propose support measures.

[0101] The notification unit estimates the user's emotions and adjusts the timing of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit may reduce the frequency of notifications and only send important notifications. If the user is relaxed, the notification unit may increase the frequency of notifications and send more detailed notifications. If the user is in a hurry, the notification unit may also send necessary notifications quickly in real time. This allows the timing of notifications to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit may input user emotion data into a generative AI and have the generative AI adjust the timing of notifications.

[0102] The notification unit adjusts the level of detail of the notification based on the importance of the information when it sends a notification. For example, the notification unit provides a detailed notification for information of high importance. For example, the notification unit may provide a simplified notification for information of low importance. The notification unit may also dynamically adjust the level of detail of the notification according to the importance of the information. This allows the level of detail of the notification to be adjusted according to the importance of the information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the notification.

[0103] The notification unit estimates the user's emotions and determines notification priorities based on the estimated emotions. For example, if the user is stressed, the notification unit will prioritize only important notifications. For example, if the user is relaxed, the notification unit may also prioritize detailed notifications. For example, if the user is in a hurry, the notification unit may also prioritize notifications that are needed in real time. This allows notification priorities to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into a generative AI and have the generative AI determine the notification priorities.

[0104] The notification unit, when sending a notification, prioritizes notifying the user of highly relevant information, taking into account the user's geographical location. For example, the notification unit prioritizes notifying the user of information about their surroundings based on the user's current location. The notification unit can also prioritize notifying the user of highly relevant information, taking into account the user's geographical location. The notification unit can also combine the user's current location with their past activity history to notify the user of the most relevant information. This allows the notification unit to notify the user of highly relevant information based on the user's geographical location. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's geographical location data into a generating AI and have the generating AI execute notifications of highly relevant information.

[0105] The calculation unit estimates the user's emotions and adjusts the evacuation route calculation method based on the estimated user emotions. For example, if the user is tense, the calculation unit prioritizes calculating the shortest route. For example, if the user is relaxed, the calculation unit may also prioritize calculating a route with good scenery. For example, if the user is in a hurry, the calculation unit may also prioritize calculating a route that allows for quick evacuation. This allows the evacuation route calculation method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the calculation unit may be performed using AI, for example, or not using AI. For example, the calculation unit can input user emotion data into a generative AI and have the generative AI adjust the evacuation route calculation method.

[0106] The calculation unit analyzes the user's past evacuation history during calculation to select the optimal evacuation route. For example, the calculation unit selects the optimal evacuation route based on evacuation routes previously used by the user. For example, the calculation unit can also select a route that avoids congestion based on the user's past evacuation history. For example, the calculation unit can analyze the user's past evacuation history and select the most efficient evacuation route. This allows the optimal evacuation route to be selected based on the user's past evacuation history. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input the user's past evacuation history data into a generating AI and have the generating AI perform the selection of the optimal evacuation route.

[0107] The calculation unit estimates the user's emotions and determines the priority of evacuation routes based on the estimated emotions. For example, if the user is tense, the calculation unit may prioritize the shortest route. For example, if the user is relaxed, the calculation unit may prioritize a route with good scenery. For example, if the user is in a hurry, the calculation unit may prioritize a route that allows for quick evacuation. This allows the priority of evacuation routes to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the calculation unit may be performed using AI, for example, or not using AI. For example, the calculation unit can input user emotion data into a generative AI and have the generative AI perform the determination of evacuation route priorities.

[0108] The calculation unit calculates the optimal evacuation route while considering the user's geographical location information. The calculation unit calculates the optimal evacuation route based on the user's current location, for example. The calculation unit can also calculate the optimal evacuation route by considering the user's geographical location information, for example. The calculation unit can also calculate the optimal evacuation route by combining the user's current location with past evacuation history, for example. This allows the calculation of the optimal evacuation route based on the user's geographical location information. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input the user's geographical location information data into a generating AI and have the generating AI perform the calculation of the optimal evacuation route.

[0109] The information delivery unit estimates the user's emotions and adjusts the method of information delivery based on the estimated emotions. For example, if the user is nervous, the information delivery unit provides a simple and highly visible method of information delivery. For example, if the user is relaxed, the information delivery unit may also provide a method of information delivery that includes detailed information. For example, if the user is in a hurry, the information delivery unit may also provide a method of information delivery that gets straight to the point. This allows the method of information delivery to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or not using AI. For example, the information delivery unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the method of information delivery.

[0110] The information provision unit adjusts the level of detail provided based on the importance of the information when providing it. For example, the information provision unit provides detailed information for information of high importance. For example, the information provision unit may provide simplified information for information of low importance. The information provision unit can also dynamically adjust the level of detail provided according to the importance of the information. This allows the level of detail provided to be adjusted according to the importance of the information. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without AI. For example, the information provision unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail provided.

[0111] The information provision unit estimates the user's emotions and determines the priority of information provision based on the estimated emotions. For example, if the user is stressed, the information provision unit may prioritize providing only important information. For example, if the user is relaxed, the information provision unit may also prioritize providing detailed information. For example, if the user is in a hurry, the information provision unit may also prioritize providing information needed in real time. This allows the information provision priority to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provision unit may be performed using AI, for example, or not using AI. For example, the information provision unit can input user emotion data into a generative AI and have the generative AI perform the determination of information provision priority.

[0112] The information provision unit, when providing information, prioritizes providing highly relevant information by taking into account the user's geographical location. For example, the information provision unit prioritizes providing information about the surrounding area based on the user's current location. The information provision unit can also prioritize providing highly relevant information by taking into account the user's geographical location. For example, the information provision unit can combine the user's current location with their past behavioral history to provide optimal information. This allows the information provision unit to provide highly relevant information based on the user's geographical location. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without AI. For example, the information provision unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing highly relevant information.

[0113] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0114] The data collection unit collects user health data, and the analysis unit analyzes the collected data to evaluate the user's health status. For example, the data collection unit can collect vital signs such as the user's heart rate, blood pressure, and body temperature. The analysis unit can analyze this data to evaluate the user's health status. The data provision unit can notify the user of the analysis results and, if necessary, encourage them to visit a medical institution. The support unit can suggest appropriate health management methods according to the user's health status. This allows users to manage their health in their daily lives and helps maintain and improve their health.

[0115] The information provider can deliver customized information based on the user's hobbies and interests. For example, the provider can provide information on events and activities that the user is interested in. The provider can also provide news and articles related to the user's hobbies. The provider can also provide the latest information on areas that the user is interested in. This allows users to receive information that matches their interests and enriches their daily lives.

[0116] The support unit can estimate the user's emotions and adjust the evacuation route guidance method based on the estimated emotions. For example, if the user is anxious, the support unit can guide them through the evacuation route in a calm voice. If the user is relaxed, the support unit can guide them through the evacuation route in a cheerful voice. If the user is in a hurry, the support unit can provide quick and concise guidance. This allows the system to provide the most appropriate evacuation route guidance method according to the user's emotions.

[0117] The analysis unit can analyze a user's past behavioral history and predict their behavioral patterns. For example, the analysis unit can analyze data on places the user has visited and services they have used in the past. Based on this data, the analysis unit can predict the user's behavioral patterns and predict places they are likely to visit and services they are likely to use next. The service provider can then provide the user with appropriate information based on the predicted behavior. This allows users to receive information tailored to their own behavioral patterns, enabling them to live their daily lives more efficiently.

[0118] The data collection unit can estimate the user's emotions and adjust the content of the information collected based on those emotions. For example, if the user is stressed, the data collection unit can prioritize collecting information that promotes relaxation. If the user is relaxed, the data collection unit can also prioritize collecting interesting information. If the user is in a hurry, the data collection unit can also quickly collect the necessary information. This allows for the collection of optimal information according to the user's emotions.

[0119] The data collection unit can understand the user's current activity status and select the optimal information collection method. For example, if the user is exercising, the data collection unit can prioritize collecting information related to exercise. If the user is working, the data collection unit can also prioritize collecting information related to work. If the user is resting, the data collection unit can also prioritize collecting information that promotes relaxation. This allows the system to collect the most relevant information according to the user's current activity status.

[0120] The data collection unit can estimate the user's emotions and adjust the frequency of information collection based on those emotions. For example, if the user is stressed, the unit can reduce the frequency of information collection and collect only essential information. If the user is relaxed, the unit can increase the frequency of information collection and collect more detailed information. If the user is in a hurry, the unit can quickly collect the necessary information in real time. This allows the frequency of information collection to be adjusted according to the user's emotions.

[0121] The information provider can select the most suitable method of providing information based on the user's current location. For example, if the user is outdoors, the provider can provide information about nearby events and activities. If the user is indoors, the provider can also provide information about things to enjoy indoors. If the user is on the move, the provider can also provide information related to their movement. This allows the provider to deliver the most relevant information based on the user's current location.

[0122] The information delivery system can estimate the user's emotions and adjust the timing of information delivery based on those estimates. For example, if the user is stressed, the system can reduce the frequency of information delivery and provide only essential information. If the user is relaxed, the system can increase the frequency of information delivery and provide more detailed information. If the user is in a hurry, the system can quickly provide the necessary information in real time. This allows the system to adjust the timing of information delivery according to the user's emotions.

[0123] The information provider can analyze a user's past behavior history and customize the content of information provided based on the user's behavior patterns. For example, the provider can prioritize providing information that the user has previously shown interest in. The provider can also predict and provide the next information the user will need based on their past behavior patterns. The provider can also provide relevant information based on the user's past behavior history. This allows the provider to deliver the most relevant information based on the user's past behavior history.

[0124] The following briefly describes the processing flow for example form 2.

[0125] Step 1: The collection unit collects information. The collection unit can, for example, use communication technology to collect information necessary for urban life in real time. The collection unit can also, for example, use sensors to collect environmental data. The collection unit can also, for example, collect user location information. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit can, for example, analyze the collected data using AI. The analysis unit can also, for example, analyze the data using machine learning algorithms. The analysis unit can also, for example, analyze text data using natural language processing technology. Step 3: The service provider provides the user with the analysis results obtained by the analysis unit. The service provider, for example, notifies the user of the analysis results. The service provider can also, for example, send a notification to the user's smartphone. The service provider can also, for example, display the analysis results on the user's device. Step 4: The support unit assists in taking the optimal action during a disaster. For example, the support unit calculates evacuation routes using AI. For example, the support unit can also guide users along evacuation routes. For example, the support unit can analyze disaster information and provide users with the latest information.

[0126] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0127] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0128] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0129] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects information using the sensors and communication technology of the smart device 14 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The provision unit is implemented in the control unit 46A of the smart device 14 and notifies the user of the analysis results. The support unit is implemented in the specific processing unit 290 of the data processing unit 12 and supports optimal actions during a disaster. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0130] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0131] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and support unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects information using the camera and microphone of the smart glasses 214 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and notifies the user of the analysis results. The support unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and supports optimal actions in the event of a disaster. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0146] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0147] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and support unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the collection unit collects information using the camera and microphone of the headset terminal 314 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The provision unit is implemented in the control unit 46A of the headset terminal 314 and notifies the user of the analysis results. The support unit is implemented in the specific processing unit 290 of the data processing unit 12 and supports optimal actions during a disaster. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0162] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0163] As shown in Figure 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.

[0164] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0165] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0166] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0167] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0168] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0169] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0170] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0171] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0172] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0173] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0174] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0175] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0176] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0177] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0178] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and support unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects information using the camera and microphone of the robot 414 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The provision unit is implemented in, for example, the control unit 46A of the robot 414 and notifies the user of the analysis results. The support unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12 and supports optimal actions in the event of a disaster. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0179] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0180] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0181] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0182] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0183] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0184] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0185] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0186] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0187] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0188] 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.

[0189] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0190] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0191] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0192] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0193] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0194] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0195] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0196] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0197] (Note 1) The information collection unit, An analysis unit analyzes the information collected by the aforementioned collection unit, A providing unit that provides the user with the analysis results obtained by the analysis unit, It includes a support unit that assists in taking optimal actions during a disaster. A system characterized by the following features. (Note 2) The aforementioned supply unit is, It includes a notification unit that notifies the user of necessary information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned support unit is It includes a calculation unit for calculating evacuation routes. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, It is equipped with an information provision department that analyzes disaster information and provides the latest information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Analyze the user's past behavior history and select the optimal method for collecting information. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting information, filtering is performed based on the user's current location and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, We estimate the user's emotions and adjust the way we present the content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing information, adjust the level of detail based on its importance. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing information, different delivery algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the service based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing information, the priority of provision will be determined based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing information, the order of provision will be adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned support unit is It estimates the user's emotions and adjusts the support method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned support unit is During support, we analyze the user's past behavior history to select the most appropriate support method. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned support unit is During support, customize the support method based on the user's current location. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned support unit is The system estimates the user's emotions and determines support priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned support unit is During support, the optimal support method will be selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned support unit is During support, we analyze the user's social media activity and suggest support methods. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned notification unit, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned notification unit, When sending notifications, adjust the level of detail based on the importance of the information. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned notification unit, When sending notifications, the system prioritizes sending highly relevant information, taking into account the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 33) The calculation unit, The system estimates the user's emotions and adjusts the evacuation route calculation method based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 34) The calculation unit, During calculation, the system analyzes the user's past evacuation history to select the optimal evacuation route. The system described in Appendix 3, characterized by the features described herein. (Note 35) The calculation unit, The system estimates the user's emotions and prioritizes evacuation routes based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The calculation unit, During calculations, the system takes the user's geographical location into account to calculate the optimal evacuation route. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned information provision unit, It estimates the user's emotions and adjusts the way information is provided based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 38) The aforementioned information provision unit, When providing information, adjust the level of detail based on the importance of the information. The system described in Appendix 4, characterized by the features described herein. (Note 39) The aforementioned information provision unit, The system estimates the user's emotions and prioritizes information provision based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned information provision unit, When providing information, we prioritize providing highly relevant information by taking into account the user's geographical location. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0198] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The information collection unit, An analysis unit analyzes the information collected by the aforementioned collection unit, A providing unit that provides the user with the analysis results obtained by the aforementioned analysis unit, It includes a support unit that assists in taking optimal actions during a disaster. A system characterized by the following features.

2. The aforementioned supply unit is, It includes a notification unit that notifies the user of necessary information. The system according to feature 1.

3. The aforementioned support unit is It includes a calculation unit for calculating evacuation routes. The system according to feature 1.

4. The aforementioned analysis unit, It has an information provision department that analyzes disaster information and provides the latest updates. The system according to feature 1.

5. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

6. The aforementioned collection unit is Analyze the user's past behavior history and select the optimal method for collecting information. The system according to feature 1.

7. The aforementioned collection unit is When collecting information, filtering is performed based on the user's current location and areas of interest. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

9. The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system according to feature 1.

10. The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system according to feature 1.

11. The aforementioned analysis unit, It estimates the user's emotions and adjusts the representation of the analysis based on the estimated user emotions. The system according to feature 1.

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