System

Abstract

An object of a system according to an embodiment is to perform quick and appropriate evacuation guidance in an emergency situation.SOLUTION: A system includes a generation part, a navigation part, and a voice guide part. The generation unit generates information corresponding to the emergency situation scenario. The navigation unit performs navigation based on the information generated by the generation unit. The voice guide unit provides the navigation information provided by the navigation unit as a voice guide.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to provide quick and appropriate evacuation guidance in an emergency.

[0005] The system according to the embodiment aims to provide prompt and appropriate evacuation guidance in an emergency situation. [Means for solving the problem]

[0006] A system according to an embodiment includes a generation unit, a navigation unit, and a voice guidance unit. The generation unit generates information corresponding to an emergency scenario. The navigation unit performs navigation based on the information generated by the generation unit. The voice guidance unit provides navigation information provided by the navigation unit as voice guidance. [Effects of the Invention]

[0007] The system according to the embodiment can provide prompt and appropriate evacuation guidance in an emergency situation. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An emergency support system according to an embodiment of the present invention uses a generative AI embedded in AR glasses to assist a user in evacuating in an emergency. In this system, the generative AI generates information corresponding to an emergency scenario, and the AR glasses provide navigation and audio guidance to guide the user to a safe evacuation shelter. For example, the emergency support system generates information corresponding to emergencies such as earthquakes, fires, floods, and terrorist attacks. Based on the generated information, the AR glasses then provide navigation and audio guidance to the user. For example, an evacuation route is displayed on the AR glasses' display, allowing the user to evacuate by following the route. Audio guidance also allows the user to confirm the evacuation route both visually and aurally. Furthermore, the AR glasses can track the user's current location in real time and dynamically update the evacuation route. This allows the user to always use the optimal evacuation route. This allows the emergency support system to enable users to evacuate quickly and safely in an emergency. For example, in the event of an earthquake, the user can quickly evacuate to a safe evacuation shelter by following the navigation and audio guidance provided by the AR glasses. Similarly, in the event of a fire, the user can safely evacuate using the optimal evacuation route. In this way, by using generative AI built into AR glasses, user support can be provided in emergencies, ensuring user safety.

[0029] An emergency support system according to an embodiment includes a generation unit, a navigation unit, and an audio guide unit. The generation unit generates information corresponding to emergency scenarios using a generation AI. The generation unit generates information corresponding to emergency situations such as earthquakes, fires, floods, and terrorist attacks. The generation unit inputs a prompt to the generation AI, such as "Please calculate an evacuation route in the event of an earthquake," and the generation AI calculates and generates information about an optimal evacuation route. The navigation unit performs navigation based on the information generated by the generation unit. The navigation unit displays the evacuation route on a display of AR glasses, for example. The navigation unit displays the generated evacuation route on a display of the AR glasses, allowing a user to visually confirm the evacuation route. The navigation unit can also track a user's current location in real time and dynamically update the evacuation route. For example, the navigation unit tracks a user's current location using GPS data, and if an obstacle occurs on the evacuation route, calculates and displays a new route. The audio guide unit provides the navigation information provided by the navigation unit as audio guidance. The audio guide unit provides the evacuation route using, for example, both visual and auditory senses. The audio guidance unit provides audio guidance along the generated evacuation route, allowing the user to confirm the evacuation route not only visually but also aurally. The audio guidance unit can also estimate the user's emotions and adjust the tone and speed of the audio guidance based on the estimated emotions. For example, if the user is in a panic, the audio guidance unit provides guidance in a calm tone at a slow speed. This allows the emergency support system according to the embodiment to evacuate the user quickly and safely.

[0030] The generation unit can generate information to respond to emergencies such as earthquakes, fires, floods, and terrorist attacks. For example, when an earthquake occurs, the generation unit uses the generation AI to calculate the route to the nearest safe evacuation shelter from the user's current location and generate that information. In addition, when a fire occurs, the generation unit can use the generation AI to calculate a safe evacuation route from the location of the fire and generate that information. Furthermore, when a flood occurs, the generation unit can use the generation AI to calculate an evacuation route to higher ground based on water level information and generate that information. For example, the generation unit provides the location of the flood and water level information to the generation AI as input, causing it to calculate the optimal evacuation route to higher ground. This makes it possible to provide information to respond to a variety of emergency situations.

[0031] The navigation unit can display an evacuation route on the display of the AR glasses. For example, the navigation unit displays the evacuation route generated by the generation unit on the display of the AR glasses. The navigation unit displays arrows and route lines on the display so that the user can visually confirm the evacuation route. The navigation unit can also highlight important points on the evacuation route (for example, corners and the locations of evacuation shelters). For example, the navigation unit displays an arrow on the display to show the user the direction before reaching a corner on the evacuation route. This allows the user to easily evacuate by visually providing the evacuation route.

[0032] The audio guidance unit can provide an evacuation route by utilizing both visual and auditory senses. For example, the audio guidance unit provides audio guidance of the evacuation route generated by the generation unit. The audio guidance unit provides audio guidance of the direction and distance of the evacuation route so that the user can confirm the evacuation route not only visually but also auditorily. The audio guidance unit can also emphasize important points on the evacuation route (e.g., corners and the location of evacuation shelters) by audio. For example, the audio guidance unit provides audio guidance such as "Turn right at the next intersection." Furthermore, the audio guidance unit can estimate the user's emotions and adjust the tone and speed of the audio guidance based on the estimated emotions. For example, if the user is in a panic, the audio guidance unit provides guidance in a calm tone at a slow speed. This makes it possible to more reliably communicate the evacuation route by utilizing both visual and auditory senses.

[0033] The navigation unit can track the user's current location in real time and dynamically update the evacuation route. The navigation unit tracks the user's current location in real time using, for example, GPS data. The navigation unit dynamically updates the evacuation route generated by the generation unit based on the user's current location. For example, if an obstacle occurs on the evacuation route, the navigation unit calculates and displays a new route. The navigation unit can also analyze the user's movement speed and direction in real time to provide the optimal route. For example, the navigation unit provides the shortest route if the user is walking fast, and provides a more detailed route if the user is walking slowly. In this way, by updating the evacuation route in real time, the optimal evacuation route can always be provided.

[0034] The generation unit can generate information based on past emergency data or real-time sensor data. For example, the generation unit generates information using past emergency data. The generation unit calculates optimal evacuation routes based on past disaster records and evacuation history. The generation unit can also generate information using real-time sensor data. For example, the generation unit identifies the location and direction of a fire based on data from a temperature sensor or humidity sensor, and generates that information. The generation unit can also generate information by combining past data and real-time data. For example, the generation unit calculates evacuation routes to higher ground based on past flood data and current water level information. This makes it possible to provide more accurate information by utilizing past data and real-time data.

[0035] The generation unit can apply different information generation algorithms depending on the type of emergency. For example, in the case of an earthquake, the generation unit uses a generation AI to generate an evacuation route that takes into account the risk of building collapse. In the case of a fire, the generation unit can also use a generation AI to generate a route that avoids the spread of smoke. Furthermore, in the case of a flood, the generation unit can use a generation AI to generate an evacuation route to higher ground based on water level information. For example, the generation unit provides the location of the earthquake and building collapse risk data as input to the generation AI, causing it to calculate the optimal evacuation route. This makes it possible to provide optimal information according to the type of emergency.

[0036] The generation unit can adjust the level of detail of the information generated based on the frequency of occurrence and impact of the emergency. For example, in the case of frequently occurring earthquakes, the generation unit uses the generation AI to provide detailed evacuation routes. In the case of a terrorist attack with high impact, the generation unit can also use the generation AI to provide quick evacuation information. Furthermore, in the case of a small-scale fire with low impact, the generation unit can also use the generation AI to provide simplified evacuation information. For example, the generation unit evaluates the frequency of occurrence and impact of emergency situations based on past statistical data, and adjusts the level of detail of the information generated based on the evaluation results. This makes it possible to provide appropriate information according to the frequency of occurrence and impact of the emergency.

[0037] The generation unit can analyze the user's past evacuation history and select the optimal information generation method. For example, the generation unit generates the optimal route based on evacuation routes used by the user in the past. The generation unit can also generate a route that avoids congestion from the user's past evacuation history. Furthermore, the generation unit can analyze the user's past evacuation history and generate the most efficient route. For example, the generation unit provides the user's past evacuation route and evacuation time as input to the generation AI and selects the optimal information generation method. This makes it possible to provide more appropriate information by utilizing the user's past evacuation history.

[0038] The generation unit can generate region-specific information based on the location of the emergency. For example, the generation unit generates evacuation shelter information for an area where an earthquake has occurred. The generation unit can also generate firefighting activity information for an area where a fire has occurred. Furthermore, the generation unit can generate water level information for an area where a flood has occurred. For example, the generation unit provides the location of the emergency as input to the generation AI to generate region-specific information. This allows for the provision of region-specific information, thereby providing more appropriate evacuation information.

[0039] The generation unit can adjust the timing of information generation based on the time of day when the emergency occurs. For example, if an earthquake occurs at night, the generation AI can provide information on nighttime response at evacuation shelters. If a fire occurs during the day, the generation unit can also provide information on congestion on evacuation routes. Furthermore, if a flood occurs early in the morning, the generation unit can also provide information on the opening of evacuation shelters. For example, the generation unit provides the time of day when the emergency occurs as input to the generation AI and adjusts the timing of information generation. This makes it possible to provide appropriate information according to the time of day when the emergency occurs.

[0040] The generation unit can select an appropriate information generation means depending on the type of emergency. For example, in the case of an earthquake, the generation AI generates a video showing evacuation routes. In the case of a fire, the generation unit can also generate an image showing evacuation procedures. Furthermore, in the case of a flood, the generation unit can also generate text information showing the location of evacuation shelters. For example, the generation unit provides the type of emergency as input to the generation AI and selects an appropriate information generation means. This makes it possible to provide the optimal information generation means depending on the type of emergency.

[0041] During navigation, the navigation unit can analyze the user's movement speed and direction in real time and provide the optimal route. The navigation unit, for example, uses GPS data and an acceleration sensor to analyze the user's movement speed and direction in real time. The navigation unit provides the optimal route based on the user's movement speed and direction. For example, the navigation unit provides the shortest route if the user is walking fast. The navigation unit can also provide a more detailed route if the user is walking slowly. Furthermore, the navigation unit can update the route in real time if the user changes direction. For example, the navigation unit provides the user's movement speed and direction as input to a generation AI to calculate the optimal route. This makes it possible to provide the optimal route according to the user's movement conditions.

[0042] The navigation unit can detect obstacles and dangerous spots on the evacuation route during navigation and dynamically update the route. For example, the navigation unit uses cameras and sensors to detect obstacles and dangerous spots on the evacuation route. The navigation unit dynamically updates the route based on the obstacles and dangerous spots on the evacuation route. For example, the navigation unit provides a new route in real time if an obstacle appears on the evacuation route. The navigation unit can also provide a detour route if there is a dangerous spot on the evacuation route. Furthermore, the navigation unit can detect congestion on the evacuation route and provide the optimal route. For example, the navigation unit provides camera and sensor data as input to a generation AI to calculate the optimal route. This makes it possible to provide an optimal route that avoids obstacles and dangerous spots on the evacuation route.

[0043] During navigation, the navigation unit can suggest the optimal route by referring to the user's past travel history. For example, the navigation unit suggests the optimal route using the user's past travel history. The navigation unit provides the optimal route based on evacuation routes the user has used in the past. The navigation unit can also provide a route that avoids congestion based on the user's past travel history. Furthermore, the navigation unit can analyze the user's past travel history and provide the most efficient route. For example, the navigation unit provides the user's past travel routes and travel times as input to a generation AI to calculate the optimal route. In this way, a more appropriate route can be provided by utilizing the user's past travel history.

[0044] During navigation, the navigation unit can provide an optimal route by taking into account the user's geographical location information. For example, the navigation unit provides an optimal route using the user's geographical location information. The navigation unit provides a route to the nearest evacuation shelter from the user's current location. The navigation unit can also provide a safe evacuation route from the user's current location. Furthermore, the navigation unit can also provide a route from the user's current location that avoids congestion. For example, the navigation unit provides the user's geographical location information as input to a generation AI to calculate the optimal route. This makes it possible to provide an optimal evacuation route based on the user's current location.

[0045] The navigation unit can adjust the route during navigation, taking into account the user's device information. For example, the navigation unit adjusts the route using the user's device information. The navigation unit provides the shortest route when the user's device has low battery. The navigation unit can also provide a route that can be used offline when the user's device has poor communication conditions. Furthermore, the navigation unit can provide the optimal route depending on the performance of the user's device. For example, the navigation unit provides the user's device information as input to a generation AI to calculate the optimal route. This makes it possible to provide the optimal evacuation route depending on the device's status.

[0046] During navigation, the navigation unit can provide an optimal route according to the user's current activity status. For example, the navigation unit provides the optimal route using the user's current activity status. If the user is walking, the navigation unit provides an evacuation route exclusively for pedestrians. Furthermore, if the user is traveling by vehicle, the navigation unit can also provide an evacuation route exclusively for vehicles. Furthermore, if the user is traveling by bicycle, the navigation unit can also provide an evacuation route exclusively for bicycles. For example, the navigation unit provides the user's current activity status as input to a generation AI, which calculates the optimal route. This makes it possible to provide an optimal evacuation route according to the user's activity status.

[0047] The audio guide unit can customize the audio guidance content according to the user's hearing characteristics when providing audio guidance. For example, the audio guide unit adjusts the volume according to the user's hearing characteristics. The audio guide unit can also adjust the frequency according to the user's hearing characteristics. Furthermore, the audio guide unit can customize the audio guidance content according to the user's hearing characteristics. For example, the audio guide unit provides the user's hearing characteristics as input to a generation AI, which calculates the optimal audio guidance content. This makes it possible to provide the optimal audio guidance according to the user's hearing characteristics.

[0048] When providing audio guidance, the audio guide unit can select the optimal guidance method by referring to the user's past audio guide usage history. For example, the audio guide unit selects the optimal guidance method by using the user's past audio guide usage history. The audio guide unit provides the optimal guidance method based on the tone and speed of the audio guide used by the user in the past. The audio guide unit can also provide the user's preferred guidance method based on the user's past audio guide usage history. Furthermore, the audio guide unit can analyze the user's past audio guide usage history and provide the most efficient guidance method. For example, the audio guide unit provides the user's past audio guide usage history as input to a generation AI to calculate the optimal guidance method. This makes it possible to provide the optimal audio guide based on the user's past usage history.

[0049] The audio guide unit can provide different audio guidance content depending on the type of emergency. For example, in the case of an earthquake, the audio guide unit provides audio guidance indicating evacuation routes. In the case of a fire, the audio guide unit can also provide audio guidance indicating evacuation procedures. Furthermore, in the case of a flood, the audio guide unit can also provide audio guidance indicating the location of evacuation shelters. For example, the audio guide unit provides the type of emergency as input to a generation AI, which calculates the optimal audio guidance content. This makes it possible to provide an appropriate audio guidance depending on the type of emergency.

[0050] The audio guide unit can provide multilingual guidance according to the user's language setting during audio guidance. For example, the audio guide unit automatically sets the audio guidance language based on the language setting of the user's device. The audio guide unit can also provide a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the audio guide unit can provide audio guidance in that language. For example, the audio guide unit provides the user's language setting as input to a generation AI and provides guidance in the most appropriate language. This makes it possible to provide multilingual audio guidance according to the user's language setting.

[0051] The voice guidance unit can adjust the guidance content taking into account the user's device information when providing voice guidance. For example, the voice guidance unit adjusts the volume according to the speaker performance of the user's device. The voice guidance unit can also adjust the sound quality according to the speaker performance of the user's device. Furthermore, the voice guidance unit can customize the guidance content according to the speaker performance of the user's device. For example, the voice guidance unit provides the user's device information as input to a generation AI, which calculates the optimal guidance content. This makes it possible to provide optimal voice guidance according to the device performance.

[0052] The audio guidance unit can analyze the user's current environmental sounds and provide optimal audio guidance when providing audio guidance. For example, if the user is in a noisy place, the audio guidance unit can increase the volume of the audio guidance when providing guidance. If the user is in a quiet place, the audio guidance unit can also decrease the volume of the audio guidance when providing guidance. Furthermore, the audio guidance unit can analyze the environmental sounds around the user and provide optimal audio guidance. For example, the audio guidance unit can provide the user's environmental sound data as input to a generation AI to calculate the optimal audio guidance. This makes it possible to provide optimal audio guidance according to the environmental sounds.

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

[0054] The generation unit can monitor the user's health condition and adjust the evacuation route in an emergency. For example, the generation unit can measure the user's heart rate and blood pressure in real time and, if the user's health condition worsens, provide the quickest and safest evacuation route. Furthermore, if the user has a chronic illness, the generation unit can also generate an evacuation route taking that information into account. Furthermore, the generation unit can add medical support points on the evacuation route based on the user's health condition. This makes it possible to provide the optimal evacuation route according to the user's health condition.

[0055] The audio guide unit can select the optimal guidance method by referring to the user's past audio guide usage history. For example, it can provide the optimal guidance method based on the tone and speed of the audio guide used by the user in the past. It can also provide the user's preferred guidance method based on the user's past audio guide usage history. It can also analyze the user's past audio guide usage history and provide the most efficient guidance method. This makes it possible to provide the optimal audio guide based on the user's past usage history.

[0056] The generation unit can generate region-specific information based on the location of the emergency. For example, it generates evacuation shelter information for an area where an earthquake has occurred. The generation unit can also generate firefighting activity information for an area where a fire has occurred. Furthermore, it can generate water level information for an area where a flood has occurred. By providing region-specific information, more appropriate evacuation information can be provided.

[0057] The navigation unit can analyze the user's movement speed and direction in real time during navigation and provide the optimal route. For example, GPS data and an acceleration sensor are used to analyze the user's movement speed and direction in real time. If the user is walking fast, the shortest route is provided. Also, if the user is walking slowly, a detailed route can be provided. This makes it possible to provide the optimal route according to the user's movement situation.

[0058] The navigation unit can detect obstacles and dangerous spots on the evacuation route during navigation and dynamically update the route. For example, it uses cameras and sensors to detect obstacles and dangerous spots on the evacuation route. If an obstacle appears on the evacuation route, it can provide a new route in real time. It can also provide a detour route if there is a dangerous spot on the evacuation route. This makes it possible to provide the optimal route that avoids obstacles and dangerous spots on the evacuation route.

[0059] The generator can adjust the timing of information generation based on the time of day an emergency occurs. For example, if an earthquake occurs at night, the generator AI will provide information on nighttime response at evacuation shelters. If a fire occurs during the day, the generator AI can also provide information on congestion on evacuation routes. This allows the generator to provide appropriate information according to the time of day.

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

[0061] Step 1: The generation unit uses the generation AI to generate information corresponding to emergency scenarios. The generation unit generates information corresponding to emergencies such as earthquakes, fires, floods, and terrorist attacks. For example, a prompt such as "Please calculate an evacuation route in the event of an earthquake" is input to the generation AI, and the generation AI calculates the optimal evacuation route and generates information. Step 2: The navigation unit performs navigation based on the information generated by the generation unit. The navigation unit displays an evacuation route on the AR glasses' display, allowing the user to visually confirm the evacuation route. The navigation unit can also track the user's current location in real time and dynamically update the evacuation route. For example, the navigation unit can track the user's current location using GPS data, and if an obstacle appears on the evacuation route, it calculates and displays a new route. Step 3: The voice guidance unit provides the navigation information provided by the navigation unit as voice guidance. The voice guidance unit provides evacuation routes using both visual and auditory senses and provides voice guidance along the generated evacuation route. Furthermore, the voice guidance unit can estimate the user's emotions and adjust the tone and speed of the voice guidance based on the estimated emotions. For example, if the user is in a panic, the voice guidance unit provides guidance in a calm tone at a slow speed.

[0062] (Example 2) An emergency support system according to an embodiment of the present invention uses a generative AI embedded in AR glasses to assist a user in evacuating in an emergency. In this system, the generative AI generates information corresponding to an emergency scenario, and the AR glasses provide navigation and audio guidance to guide the user to a safe evacuation shelter. For example, the emergency support system generates information corresponding to emergencies such as earthquakes, fires, floods, and terrorist attacks. Based on the generated information, the AR glasses then provide navigation and audio guidance to the user. For example, an evacuation route is displayed on the AR glasses' display, allowing the user to evacuate by following the route. Audio guidance also allows the user to confirm the evacuation route both visually and aurally. Furthermore, the AR glasses can track the user's current location in real time and dynamically update the evacuation route. This allows the user to always use the optimal evacuation route. This allows the emergency support system to enable users to evacuate quickly and safely in an emergency. For example, in the event of an earthquake, the user can quickly evacuate to a safe evacuation shelter by following the navigation and audio guidance provided by the AR glasses. Similarly, in the event of a fire, the user can safely evacuate using the optimal evacuation route. In this way, by using generative AI built into AR glasses, user support can be provided in emergencies, ensuring user safety.

[0063] An emergency support system according to an embodiment includes a generation unit, a navigation unit, and an audio guide unit. The generation unit generates information corresponding to emergency scenarios using a generation AI. The generation unit generates information corresponding to emergency situations such as earthquakes, fires, floods, and terrorist attacks. The generation unit inputs a prompt to the generation AI, such as "Please calculate an evacuation route in the event of an earthquake," and the generation AI calculates and generates information about an optimal evacuation route. The navigation unit performs navigation based on the information generated by the generation unit. The navigation unit displays the evacuation route on a display of AR glasses, for example. The navigation unit displays the generated evacuation route on a display of the AR glasses, allowing a user to visually confirm the evacuation route. The navigation unit can also track a user's current location in real time and dynamically update the evacuation route. For example, the navigation unit tracks a user's current location using GPS data, and if an obstacle occurs on the evacuation route, calculates and displays a new route. The audio guide unit provides the navigation information provided by the navigation unit as audio guidance. The audio guide unit provides the evacuation route using, for example, both visual and auditory senses. The audio guidance unit provides audio guidance along the generated evacuation route, allowing the user to confirm the evacuation route not only visually but also aurally. The audio guidance unit can also estimate the user's emotions and adjust the tone and speed of the audio guidance based on the estimated emotions. For example, if the user is in a panic, the audio guidance unit provides guidance in a calm tone at a slow speed. This allows the emergency support system according to the embodiment to evacuate the user quickly and safely.

[0064] The generation unit can generate information to respond to emergencies such as earthquakes, fires, floods, and terrorist attacks. For example, when an earthquake occurs, the generation unit uses the generation AI to calculate the route to the nearest safe evacuation shelter from the user's current location and generate that information. In addition, when a fire occurs, the generation unit can use the generation AI to calculate a safe evacuation route from the location of the fire and generate that information. Furthermore, when a flood occurs, the generation unit can use the generation AI to calculate an evacuation route to higher ground based on water level information and generate that information. For example, the generation unit provides the location of the flood and water level information to the generation AI as input, causing it to calculate the optimal evacuation route to higher ground. This makes it possible to provide information to respond to a variety of emergency situations.

[0065] The navigation unit can display an evacuation route on the display of the AR glasses. For example, the navigation unit displays the evacuation route generated by the generation unit on the display of the AR glasses. The navigation unit displays arrows and route lines on the display so that the user can visually confirm the evacuation route. The navigation unit can also highlight important points on the evacuation route (for example, corners and the locations of evacuation shelters). For example, the navigation unit displays an arrow on the display to show the user the direction before reaching a corner on the evacuation route. This allows the user to easily evacuate by visually providing the evacuation route.

[0066] The audio guidance unit can provide an evacuation route by utilizing both visual and auditory senses. For example, the audio guidance unit provides audio guidance of the evacuation route generated by the generation unit. The audio guidance unit provides audio guidance of the direction and distance of the evacuation route so that the user can confirm the evacuation route not only visually but also auditorily. The audio guidance unit can also emphasize important points on the evacuation route (e.g., corners and the location of evacuation shelters) by audio. For example, the audio guidance unit provides audio guidance such as "Turn right at the next intersection." Furthermore, the audio guidance unit can estimate the user's emotions and adjust the tone and speed of the audio guidance based on the estimated emotions. For example, if the user is in a panic, the audio guidance unit provides guidance in a calm tone at a slow speed. This makes it possible to more reliably communicate the evacuation route by utilizing both visual and auditory senses.

[0067] The navigation unit can track the user's current location in real time and dynamically update the evacuation route. The navigation unit tracks the user's current location in real time using, for example, GPS data. The navigation unit dynamically updates the evacuation route generated by the generation unit based on the user's current location. For example, if an obstacle occurs on the evacuation route, the navigation unit calculates and displays a new route. The navigation unit can also analyze the user's movement speed and direction in real time to provide the optimal route. For example, the navigation unit provides the shortest route if the user is walking fast, and provides a more detailed route if the user is walking slowly. In this way, by updating the evacuation route in real time, the optimal evacuation route can always be provided.

[0068] The generation unit can generate information based on past emergency data or real-time sensor data. For example, the generation unit generates information using past emergency data. The generation unit calculates optimal evacuation routes based on past disaster records and evacuation history. The generation unit can also generate information using real-time sensor data. For example, the generation unit identifies the location and direction of a fire based on data from a temperature sensor or humidity sensor, and generates that information. The generation unit can also generate information by combining past data and real-time data. For example, the generation unit calculates evacuation routes to higher ground based on past flood data and current water level information. This makes it possible to provide more accurate information by utilizing past data and real-time data.

[0069] The generation unit can estimate the user's emotions and adjust the priority of information generation for emergency scenarios based on the estimated user emotions. The generation unit, for example, uses facial expression recognition technology to estimate the user's emotions. The generation unit analyzes the user's facial expression data and calculates an emotion score. For example, if the user is in a panic, the generation AI can prioritize generating the most important evacuation information. Also, if the user is calm, the generation unit can provide detailed evacuation information. Furthermore, if the user is feeling anxious, the generation AI can prioritize generating information that provides a sense of security. For example, the generation unit provides the user's facial expression data as input to the generation AI and adjusts the priority of information generation based on the emotion score. This allows more appropriate information to be provided by adjusting the priority of information generation according to the user's emotions.

[0070] The generation unit can apply different information generation algorithms depending on the type of emergency. For example, in the case of an earthquake, the generation unit uses a generation AI to generate an evacuation route that takes into account the risk of building collapse. In the case of a fire, the generation unit can also use a generation AI to generate a route that avoids the spread of smoke. Furthermore, in the case of a flood, the generation unit can use a generation AI to generate an evacuation route to higher ground based on water level information. For example, the generation unit provides the location of the earthquake and building collapse risk data as input to the generation AI, causing it to calculate the optimal evacuation route. This makes it possible to provide optimal information according to the type of emergency.

[0071] The generation unit can adjust the level of detail of the information generated based on the frequency of occurrence and impact of the emergency. For example, in the case of frequently occurring earthquakes, the generation unit uses the generation AI to provide detailed evacuation routes. In the case of a terrorist attack with high impact, the generation unit can also use the generation AI to provide quick evacuation information. Furthermore, in the case of a small-scale fire with low impact, the generation unit can also use the generation AI to provide simplified evacuation information. For example, the generation unit evaluates the frequency of occurrence and impact of emergency situations based on past statistical data, and adjusts the level of detail of the information generated based on the evaluation results. This makes it possible to provide appropriate information according to the frequency of occurrence and impact of the emergency.

[0072] The generation unit can analyze the user's past evacuation history and select the optimal information generation method. For example, the generation unit generates the optimal route based on evacuation routes used by the user in the past. The generation unit can also generate a route that avoids congestion from the user's past evacuation history. Furthermore, the generation unit can analyze the user's past evacuation history and generate the most efficient route. For example, the generation unit provides the user's past evacuation route and evacuation time as input to the generation AI and selects the optimal information generation method. This makes it possible to provide more appropriate information by utilizing the user's past evacuation history.

[0073] The generation unit can estimate the user's emotions and adjust the way the information is presented based on the estimated user emotions. For example, the generation unit uses facial expression recognition technology to estimate the user's emotions. The generation unit analyzes the user's facial expression data and calculates an emotion score. For example, if the user is in a panic, the generation AI can provide concise, visually easy-to-understand information. If the user is calm, the generation unit can also provide detailed text information. Furthermore, if the user is feeling anxious, the generation AI can provide images or videos that give a sense of security. For example, the generation unit provides the user's facial expression data as input to the generation AI and adjusts the way the information is presented based on the emotion score. This makes it possible to provide an appropriate information presentation method according to the user's emotions.

[0074] The generation unit can generate region-specific information based on the location of the emergency. For example, the generation unit generates evacuation shelter information for an area where an earthquake has occurred. The generation unit can also generate firefighting activity information for an area where a fire has occurred. Furthermore, the generation unit can generate water level information for an area where a flood has occurred. For example, the generation unit provides the location of the emergency as input to the generation AI to generate region-specific information. This allows for the provision of region-specific information, thereby providing more appropriate evacuation information.

[0075] The generation unit can adjust the timing of information generation based on the time of day when the emergency occurs. For example, if an earthquake occurs at night, the generation AI can provide information on nighttime response at evacuation shelters. If a fire occurs during the day, the generation unit can also provide information on congestion on evacuation routes. Furthermore, if a flood occurs early in the morning, the generation unit can also provide information on the opening of evacuation shelters. For example, the generation unit provides the time of day when the emergency occurs as input to the generation AI and adjusts the timing of information generation. This makes it possible to provide appropriate information according to the time of day when the emergency occurs.

[0076] The generation unit can select an appropriate information generation means depending on the type of emergency. For example, in the case of an earthquake, the generation AI generates a video showing evacuation routes. In the case of a fire, the generation unit can also generate an image showing evacuation procedures. Furthermore, in the case of a flood, the generation unit can also generate text information showing the location of evacuation shelters. For example, the generation unit provides the type of emergency as input to the generation AI and selects an appropriate information generation means. This makes it possible to provide the optimal information generation means depending on the type of emergency.

[0077] The navigation unit can estimate the user's emotions and adjust the navigation instruction method based on the estimated user's emotions. The navigation unit, for example, uses facial expression recognition technology to estimate the user's emotions. The navigation unit analyzes the user's facial expression data and calculates an emotion score. For example, the navigation unit provides simple and clear instructions when the user is in a panic. The navigation unit can also provide detailed navigation information when the user is calm. Furthermore, the navigation unit can provide navigation instructions that give a sense of security when the user is feeling anxious. For example, the navigation unit provides the user's facial expression data as input to a generation AI and adjusts the navigation instruction method based on the emotion score. This makes it possible to provide appropriate navigation instructions according to the user's emotions.

[0078] During navigation, the navigation unit can analyze the user's movement speed and direction in real time and provide the optimal route. The navigation unit, for example, uses GPS data and an acceleration sensor to analyze the user's movement speed and direction in real time. The navigation unit provides the optimal route based on the user's movement speed and direction. For example, the navigation unit provides the shortest route if the user is walking fast. The navigation unit can also provide a more detailed route if the user is walking slowly. Furthermore, the navigation unit can update the route in real time if the user changes direction. For example, the navigation unit provides the user's movement speed and direction as input to a generation AI to calculate the optimal route. This makes it possible to provide the optimal route according to the user's movement conditions.

[0079] The navigation unit can detect obstacles and dangerous spots on the evacuation route during navigation and dynamically update the route. For example, the navigation unit uses cameras and sensors to detect obstacles and dangerous spots on the evacuation route. The navigation unit dynamically updates the route based on the obstacles and dangerous spots on the evacuation route. For example, the navigation unit provides a new route in real time if an obstacle appears on the evacuation route. The navigation unit can also provide a detour route if there is a dangerous spot on the evacuation route. Furthermore, the navigation unit can detect congestion on the evacuation route and provide the optimal route. For example, the navigation unit provides camera and sensor data as input to a generation AI to calculate the optimal route. This makes it possible to provide an optimal route that avoids obstacles and dangerous spots on the evacuation route.

[0080] During navigation, the navigation unit can suggest the optimal route by referring to the user's past travel history. For example, the navigation unit suggests the optimal route using the user's past travel history. The navigation unit provides the optimal route based on evacuation routes the user has used in the past. The navigation unit can also provide a route that avoids congestion based on the user's past travel history. Furthermore, the navigation unit can analyze the user's past travel history and provide the most efficient route. For example, the navigation unit provides the user's past travel routes and travel times as input to a generation AI to calculate the optimal route. In this way, a more appropriate route can be provided by utilizing the user's past travel history.

[0081] The navigation unit can estimate the user's emotions and adjust the navigation display method based on the estimated user emotions. The navigation unit, for example, uses facial expression recognition technology to estimate the user's emotions. The navigation unit analyzes the user's facial expression data and calculates an emotion score. For example, if the user is in a panicked state, the navigation unit provides a simple, highly visible display method. Furthermore, if the user is calm, the navigation unit can also provide a display method that includes detailed information. Furthermore, if the user is feeling anxious, the navigation unit can also provide a display method that gives a sense of security. For example, the navigation unit provides the user's facial expression data as input to a generation AI and adjusts the navigation display method based on the emotion score. This makes it possible to provide an appropriate navigation display method according to the user's emotions.

[0082] During navigation, the navigation unit can provide an optimal route by taking into account the user's geographical location information. For example, the navigation unit provides an optimal route using the user's geographical location information. The navigation unit provides a route to the nearest evacuation shelter from the user's current location. The navigation unit can also provide a safe evacuation route from the user's current location. Furthermore, the navigation unit can also provide a route from the user's current location that avoids congestion. For example, the navigation unit provides the user's geographical location information as input to a generation AI to calculate the optimal route. This makes it possible to provide an optimal evacuation route based on the user's current location.

[0083] The navigation unit can adjust the route during navigation, taking into account the user's device information. For example, the navigation unit adjusts the route using the user's device information. The navigation unit provides the shortest route when the user's device has low battery. The navigation unit can also provide a route that can be used offline when the user's device has poor communication conditions. Furthermore, the navigation unit can provide the optimal route depending on the performance of the user's device. For example, the navigation unit provides the user's device information as input to a generation AI to calculate the optimal route. This makes it possible to provide the optimal evacuation route depending on the device's status.

[0084] During navigation, the navigation unit can provide an optimal route according to the user's current activity status. For example, the navigation unit provides the optimal route using the user's current activity status. If the user is walking, the navigation unit provides an evacuation route exclusively for pedestrians. Furthermore, if the user is traveling by vehicle, the navigation unit can also provide an evacuation route exclusively for vehicles. Furthermore, if the user is traveling by bicycle, the navigation unit can also provide an evacuation route exclusively for bicycles. For example, the navigation unit provides the user's current activity status as input to a generation AI, which calculates the optimal route. This makes it possible to provide an optimal evacuation route according to the user's activity status.

[0085] The voice guidance unit can estimate the user's emotions and adjust the tone and speed of the voice guidance based on the estimated user's emotions. The voice guidance unit, for example, uses facial expression recognition technology to estimate the user's emotions. The voice guidance unit analyzes the user's facial expression data and calculates an emotion score. For example, if the user is in a panicked state, the voice guidance unit can provide guidance in a calm tone at a slow speed. If the user is calm, the voice guidance unit can also provide guidance in a normal tone and speed. Furthermore, if the user is feeling anxious, the voice guidance unit can provide guidance in a tone that gives a sense of security. For example, the voice guidance unit provides the user's facial expression data as input to a generation AI and adjusts the tone and speed of the voice guidance based on the emotion score. This makes it possible to provide appropriate voice guidance according to the user's emotions.

[0086] The audio guide unit can customize the audio guidance content according to the user's hearing characteristics when providing audio guidance. For example, the audio guide unit adjusts the volume according to the user's hearing characteristics. The audio guide unit can also adjust the frequency according to the user's hearing characteristics. Furthermore, the audio guide unit can customize the audio guidance content according to the user's hearing characteristics. For example, the audio guide unit provides the user's hearing characteristics as input to a generation AI, which calculates the optimal audio guidance content. This makes it possible to provide the optimal audio guidance according to the user's hearing characteristics.

[0087] When providing audio guidance, the audio guide unit can select the optimal guidance method by referring to the user's past audio guide usage history. For example, the audio guide unit selects the optimal guidance method by using the user's past audio guide usage history. The audio guide unit provides the optimal guidance method based on the tone and speed of the audio guide used by the user in the past. The audio guide unit can also provide the user's preferred guidance method based on the user's past audio guide usage history. Furthermore, the audio guide unit can analyze the user's past audio guide usage history and provide the most efficient guidance method. For example, the audio guide unit provides the user's past audio guide usage history as input to a generation AI to calculate the optimal guidance method. This makes it possible to provide the optimal audio guide based on the user's past usage history.

[0088] The audio guide unit can provide different audio guidance content depending on the type of emergency. For example, in the case of an earthquake, the audio guide unit provides audio guidance indicating evacuation routes. In the case of a fire, the audio guide unit can also provide audio guidance indicating evacuation procedures. Furthermore, in the case of a flood, the audio guide unit can also provide audio guidance indicating the location of evacuation shelters. For example, the audio guide unit provides the type of emergency as input to a generation AI, which calculates the optimal audio guidance content. This makes it possible to provide an appropriate audio guidance depending on the type of emergency.

[0089] The voice guidance unit can estimate the user's emotions and adjust the content of the voice guidance based on the estimated user's emotions. The voice guidance unit, for example, uses facial expression recognition technology to estimate the user's emotions. The voice guidance unit analyzes the user's facial expression data and calculates an emotion score. For example, if the user is in a panic, the voice guidance unit can provide reassuring content in a calm voice. If the user is calm, the voice guidance unit can also provide detailed evacuation information. Furthermore, if the user is feeling anxious, the voice guidance unit can provide guidance content including words of encouragement. For example, the voice guidance unit provides the user's facial expression data as input to a generation AI and adjusts the content of the voice guidance based on the emotion score. This makes it possible to provide appropriate voice guidance content according to the user's emotions.

[0090] The audio guide unit can provide multilingual guidance according to the user's language setting during audio guidance. For example, the audio guide unit automatically sets the audio guidance language based on the language setting of the user's device. The audio guide unit can also provide a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the audio guide unit can provide audio guidance in that language. For example, the audio guide unit provides the user's language setting as input to a generation AI and provides guidance in the most appropriate language. This makes it possible to provide multilingual audio guidance according to the user's language setting.

[0091] The voice guidance unit can adjust the guidance content taking into account the user's device information when providing voice guidance. For example, the voice guidance unit adjusts the volume according to the speaker performance of the user's device. The voice guidance unit can also adjust the sound quality according to the speaker performance of the user's device. Furthermore, the voice guidance unit can customize the guidance content according to the speaker performance of the user's device. For example, the voice guidance unit provides the user's device information as input to a generation AI, which calculates the optimal guidance content. This makes it possible to provide optimal voice guidance according to the device performance.

[0092] The audio guidance unit can analyze the user's current environmental sounds and provide optimal audio guidance when providing audio guidance. For example, if the user is in a noisy place, the audio guidance unit can increase the volume of the audio guidance when providing guidance. If the user is in a quiet place, the audio guidance unit can also decrease the volume of the audio guidance when providing guidance. Furthermore, the audio guidance unit can analyze the environmental sounds around the user and provide optimal audio guidance. For example, the audio guidance unit can provide the user's environmental sound data as input to a generation AI to calculate the optimal audio guidance. This makes it possible to provide optimal audio guidance according to the environmental sounds. === Hard Collateral 1-1 === Each of the multiple elements including the generation unit, navigation unit, and voice guide unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the generation unit is realized by a specific processing unit 290 of the data processing device 12. For example, the generation unit is also realized by a control unit 46A of the smart device 14. The navigation unit is realized, for example, by a display 40A of the smart device 14. The voice guide unit is realized, for example, by a speaker 40B of the smart device 14. The navigation unit and the voice guide unit are also realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned generation unit, navigation unit, and voice guidance unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the generation unit is realized by a specific processing unit 290 of the data processing device 12. For example, the generation unit is also realized by a control unit 46A of the smart glasses 214. The navigation unit is realized, for example, by a display of the smart glasses 214. The voice guidance unit is realized, for example, by a speaker 240 of the smart glasses 214. The navigation unit and the voice guidance unit are also realized by a specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the generation unit, navigation unit, and voice guidance unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the generation unit is also realized by the control unit 46A of the headset type terminal 314. The navigation unit is realized, for example, by the display 343 of the headset type terminal 314. The voice guidance unit is realized, for example, by the speaker 240 of the headset type terminal 314. The navigation unit and voice guidance unit are also realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned generation unit, navigation unit, and audio guide unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the generation unit is also realized by the control unit 46A of the robot 414. The navigation unit is realized, for example, by the display of the robot 414. The audio guide unit is realized, for example, by the speaker 240 of the robot 414. The navigation unit and audio guide unit are also realized by the specific processing unit 290 of the data processing device 12.

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

[0094] The generation unit can monitor the user's health condition and adjust the evacuation route in an emergency. For example, the generation unit can measure the user's heart rate and blood pressure in real time and, if the user's health condition worsens, provide the quickest and safest evacuation route. Furthermore, if the user has a chronic illness, the generation unit can also generate an evacuation route taking that information into account. Furthermore, the generation unit can add medical support points on the evacuation route based on the user's health condition. This makes it possible to provide the optimal evacuation route according to the user's health condition.

[0095] The navigation unit can estimate the user's emotions and adjust the display method of evacuation routes based on the estimated emotions. For example, if the user is in a panic, the navigation unit can provide a simple, highly visible display method. If the user is calm, the navigation unit can also provide a display method that includes detailed information. Furthermore, if the user is feeling anxious, the navigation unit can also provide a display method that gives a sense of security. This makes it possible to provide an appropriate navigation display method according to the user's emotions.

[0096] The audio guide unit can select the optimal guidance method by referring to the user's past audio guide usage history. For example, it can provide the optimal guidance method based on the tone and speed of the audio guide used by the user in the past. It can also provide the user's preferred guidance method based on the user's past audio guide usage history. It can also analyze the user's past audio guide usage history and provide the most efficient guidance method. This makes it possible to provide the optimal audio guide based on the user's past usage history.

[0097] The generation unit can generate region-specific information based on the location of the emergency. For example, it generates evacuation shelter information for an area where an earthquake has occurred. The generation unit can also generate firefighting activity information for an area where a fire has occurred. Furthermore, it can generate water level information for an area where a flood has occurred. By providing region-specific information, more appropriate evacuation information can be provided.

[0098] The navigation unit can analyze the user's movement speed and direction in real time during navigation and provide the optimal route. For example, GPS data and an acceleration sensor are used to analyze the user's movement speed and direction in real time. If the user is walking fast, the shortest route is provided. Also, if the user is walking slowly, a detailed route can be provided. This makes it possible to provide the optimal route according to the user's movement situation.

[0099] The generation unit can estimate the user's emotions and adjust the way the information is presented based on the estimated user emotions. For example, if the user is in a panic, the generation AI can provide concise, visually easy-to-understand information. If the user is calm, the generation AI can also provide detailed text information. Furthermore, if the user is feeling anxious, the generation AI can provide images or videos that give a sense of security. This makes it possible to provide an appropriate way of presenting information according to the user's emotions.

[0100] The navigation unit can detect obstacles and dangerous spots on the evacuation route during navigation and dynamically update the route. For example, it uses cameras and sensors to detect obstacles and dangerous spots on the evacuation route. If an obstacle appears on the evacuation route, it can provide a new route in real time. It can also provide a detour route if there is a dangerous spot on the evacuation route. This makes it possible to provide the optimal route that avoids obstacles and dangerous spots on the evacuation route.

[0101] The voice guidance unit can estimate the user's emotions and adjust the content of the voice guidance based on the estimated user emotions. For example, if the user is in a panic, it can provide reassuring content in a calm voice. If the user is calm, it can also provide detailed evacuation information. Furthermore, if the user is feeling anxious, it can provide guidance content including encouraging words. This makes it possible to provide appropriate voice guidance content according to the user's emotions.

[0102] The generator can adjust the timing of information generation based on the time of day an emergency occurs. For example, if an earthquake occurs at night, the generator AI will provide information on nighttime response at evacuation shelters. If a fire occurs during the day, the generator AI can also provide information on congestion on evacuation routes. This allows the generator to provide appropriate information according to the time of day.

[0103] The navigation unit can estimate the user's emotions and adjust the navigation instruction method based on the estimated user's emotions. For example, if the user is in a panic, simple and clear instructions can be provided. If the user is calm, detailed navigation information can be provided. Furthermore, if the user is feeling anxious, navigation instructions that give a sense of security can be provided. This makes it possible to provide an appropriate navigation instruction method according to the user's emotions.

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

[0105] Step 1: The generation unit uses the generation AI to generate information corresponding to emergency scenarios. The generation unit generates information corresponding to emergencies such as earthquakes, fires, floods, and terrorist attacks. For example, a prompt such as "Please calculate an evacuation route in the event of an earthquake" is input to the generation AI, and the generation AI calculates the optimal evacuation route and generates information. Step 2: The navigation unit performs navigation based on the information generated by the generation unit. The navigation unit displays an evacuation route on the AR glasses' display, allowing the user to visually confirm the evacuation route. The navigation unit can also track the user's current location in real time and dynamically update the evacuation route. For example, the navigation unit can track the user's current location using GPS data, and if an obstacle appears on the evacuation route, it calculates and displays a new route. Step 3: The voice guidance unit provides the navigation information provided by the navigation unit as voice guidance. The voice guidance unit provides evacuation routes using both visual and auditory senses and provides voice guidance along the generated evacuation route. Furthermore, the voice guidance unit can estimate the user's emotions and adjust the tone and speed of the voice guidance based on the estimated emotions. For example, if the user is in a panic, the voice guidance unit provides guidance in a calm tone at a slow speed.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0111] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0143] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0150] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0153] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0159] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0160] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0161] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0162] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0163] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0164] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0166] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0167] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0168] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0169] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0170] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0171] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0172] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0173] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0174] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0175] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0176] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0177] [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a generating unit that generates information corresponding to an emergency scenario; a navigation unit that performs navigation based on the information generated by the generation unit; a voice guide unit that provides the navigation information provided by the navigation unit as a voice guide; A system characterized by:

2. The generation unit Generate information to respond to earthquake, fire, flood, and terrorism emergencies 2. The system of claim 1.

3. The navigation unit Displaying evacuation routes on the AR glasses display 2. The system of claim 1.

4. The voice guide unit Providing evacuation routes using visual and auditory senses 2. The system of claim 1.

5. The navigation unit Track your location in real time and dynamically update your evacuation route 2. The system of claim 1.

6. The generation unit Generate information based on historical emergency data or real-time sensor data 2. The system of claim 1.

7. The generation unit Estimating user emotions and adjusting the priority of information generation in emergency scenarios based on the estimated user emotions 2. The system of claim 1.

8. The generation unit Apply different information generation algorithms depending on the type of emergency.

2. The system of claim 1.

9. The generation unit Adjust the level of detail of information generated based on the frequency and impact of emergencies 2. The system of claim 1.

Images