System

Abstract

An object of a system according to an embodiment is to enable a user to respond quickly and appropriately when an emergency occurs.SOLUTION: A system includes a current location acquisition unit, a destination input unit, an information providing unit, and an emergency response information providing unit. The current location acquisition unit acquires a current location. The destination input unit inputs a destination. The information providing unit provides related information based on the information obtained by the current position acquisition unit and the destination input unit. The emergency response information providing unit provides specific response information when an emergency occurs.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not provide sufficient information to enable users to respond quickly and appropriately in the event of an emergency, and there is room for improvement.

[0005] The system according to the embodiment aims to enable a user to respond quickly and appropriately when an emergency occurs. [Means for solving the problem]

[0006] The system according to the embodiment includes a current location acquisition unit, a destination input unit, an information provision unit, and an emergency response information provision unit. The current location acquisition unit acquires a current location. The destination input unit inputs a destination. The information provision unit provides related information based on the information obtained by the current location acquisition unit and the destination input unit. The emergency response information provision unit provides specific response information in the event of an emergency. [Effects of the Invention]

[0007] The system according to the embodiment can enable a user to respond quickly and appropriately when an emergency occurs. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A real-time navigation system according to an embodiment of the present invention provides information related to a user's current location and destination, and provides appropriate response information in real time in the event of an emergency. In this system, a user wears AR glasses, acquires their current location, and inputs their destination. A generation AI then provides information related to the current location and destination in real time. This information includes basic information about the city, tourist attractions, restaurants, shopping centers, and more. Furthermore, in the event of an emergency, the generation AI provides appropriate response information in real time. For example, in a real-time navigation system, a user wears AR glasses and acquires their current location. For example, using GPS, the user identifies their current location. Next, the user inputs their destination. For example, the user may input "I want to go to the station." The generation AI then provides relevant information in real time based on the current location and destination information. For example, the generation AI provides information about restaurants the user can stop by on the way to the station. Furthermore, in the event of an emergency, the generation AI provides appropriate response information in real time. For example, in the event of an earthquake, the generation AI provides information about the nearest evacuation shelters and evacuation routes. This allows the real-time navigation system to quickly take the necessary actions to ensure safety, anytime and anywhere. This means that if an earthquake occurs while the user is sightseeing, the real-time navigation system will enable the generation AI to provide the optimal evacuation route in real time, allowing the user to evacuate quickly.

[0029] A real-time navigation system according to an embodiment includes a current location acquisition unit, a destination input unit, an information provision unit, and an emergency response information provision unit. The current location acquisition unit acquires the user's current location. For example, the current location acquisition unit can identify the user's current location using GPS. The current location acquisition unit can also acquire the user's current location using Wi-Fi or a cell tower. The destination input unit accepts destination information input by the user. For example, the destination input unit allows the user to input a destination by text input or voice input. The destination input unit can also select a destination using multiple-choice input. The information provision unit provides related information based on the information acquired by the current location acquisition unit and the destination input unit. For example, the information provision unit provides information such as basic information about a city, tourist spots, restaurants, and shopping centers. The information provision unit can provide information about restaurants the user can stop by on the way to a station. The information provision unit can also provide the user with information about the history and highlights of a tourist spot when the user is heading to the tourist spot. The emergency response information provision unit provides specific response information in the event of an emergency. For example, the emergency response information providing unit provides response information for emergencies such as earthquakes and typhoons. For example, the emergency response information providing unit can provide information on the nearest evacuation site and evacuation routes when an earthquake occurs. Furthermore, the emergency response information providing unit can also provide information on safe evacuation sites and evacuation routes when a typhoon occurs. As a result, the real-time navigation system according to the embodiment can provide information related to the user's current location and destination, and can provide appropriate response information in real time when an emergency occurs.

[0030] The current location acquisition unit can acquire the user's current location using GPS. GPS acquires the current location based on, for example, the number of satellites used and the frequency of updating location information. For example, the current location acquisition unit receives signals from multiple GPS satellites and identifies the user's current location. Furthermore, the current location acquisition unit can provide accurate location information while reducing battery consumption by adjusting the frequency of updating location information. For example, the current location acquisition unit can increase the frequency of updating location information when the user is moving and decrease the frequency of updating location information when the user is stationary. In this way, the use of GPS can accurately acquire the user's current location.

[0031] The destination input unit can accept destination information input by the user. The destination information includes, for example, an address, a landmark, and coordinates. For example, the destination input unit allows the user to input an address by text input. The destination input unit can also allow the user to input a landmark by voice input. Furthermore, the destination input unit can also allow the user to select coordinates on a map. For example, when the user voice inputs "I want to go to the station," the destination input unit can identify the coordinates of the station and set it as the destination. This allows the destination information input by the user to be accurately accepted.

[0032] The information providing unit can provide basic information about a city, information about tourist spots, restaurants, and shopping centers. The basic information about a city includes, for example, population, area, major facilities, etc. For example, the information providing unit can provide information about the population, area, and major facilities of a city in which the user is currently located. Tourist spots include, for example, historical buildings, natural landscapes, theme parks, etc. For example, the information providing unit can provide the history and highlights of tourist spots that the user plans to visit. The information about restaurants includes, for example, the types of cuisine, business hours, and ratings. For example, the information providing unit can provide the types of cuisine, business hours, and ratings of restaurants that the user plans to visit. The information about shopping centers includes, for example, a list of stores, business hours, and access methods. For example, the information providing unit can provide a list of stores, business hours, and access methods of a shopping center that the user plans to visit. This makes it possible to provide the user with basic information about a city, information about tourist spots, etc.

[0033] The emergency response information providing unit can provide response information for emergencies such as earthquakes and typhoons. Emergencies include, for example, earthquakes, typhoons, and fires. For example, the emergency response information providing unit can provide information on the nearest evacuation site and evacuation routes when an earthquake occurs. Furthermore, the emergency response information providing unit can provide information on safe evacuation sites and evacuation routes when a typhoon occurs. Furthermore, the emergency response information providing unit can provide information on emergency contacts and first aid methods when a fire occurs. For example, when an earthquake occurs, the emergency response information providing unit can identify the evacuation site nearest the user's current location and provide an evacuation route to that evacuation site. This makes it possible to provide appropriate response information when an emergency occurs.

[0034] The emergency response information providing unit can provide information on evacuation sites and evacuation routes. Examples of evacuation sites include parks, schools, public facilities, etc. For example, the emergency response information providing unit can provide the park, school, or public facility closest to the user's current location as an evacuation site. Examples of evacuation routes include maps, navigation, signs, etc. For example, the emergency response information providing unit can provide a route from the user's current location to an evacuation site using a map or navigation. The emergency response information providing unit can also guide the user safely to an evacuation site based on signs on the evacuation route. This makes it possible to provide information on the nearest evacuation site and evacuation route in the event of an emergency.

[0035] When acquiring the current location, the current location acquisition unit can analyze the user's past movement history and select the optimal acquisition method. The current location acquisition unit can adjust the accuracy of current location acquisition based on, for example, places that the user has frequently visited in the past. For example, the current location acquisition unit can analyze the user's past movement patterns and set the optimal acquisition timing. The current location acquisition unit can also select the optimal acquisition method by taking into account the means of transportation that the user has used in the past. For example, the current location acquisition unit can improve the accuracy of current location acquisition based on places that the user has frequently visited in the past. In this way, the optimal current location acquisition method can be selected by analyzing the user's past movement history.

[0036] When acquiring a current location, the current location acquisition unit can adjust the acquisition frequency taking into account the remaining battery level of the user's device. For example, when the remaining battery level of the device is low, the current location acquisition unit reduces the frequency of acquiring the current location to reduce battery consumption. For example, when the remaining battery level of the device is sufficient, the current location acquisition unit can increase the frequency of acquiring the current location to provide accurate location information. Furthermore, when the remaining battery level of the device is medium, the current location acquisition unit can set the frequency of acquiring the current location to a medium level to provide balanced location information. In this way, by adjusting the frequency of acquiring the current location according to the remaining battery level of the device, accurate location information can be provided while reducing battery consumption.

[0037] When acquiring the current location, the current location acquisition unit can select the optimal acquisition method depending on the user's means of transportation. For example, if the user is traveling on foot, the current location acquisition unit can acquire accurate location information using a GPS. For example, if the user is traveling by bicycle, the current location acquisition unit can acquire location information by combining a GPS and an acceleration sensor. Furthermore, if the user is traveling by car, the current location acquisition unit can also acquire location information by linking a GPS with an in-car navigation system. This allows more accurate location information to be provided by selecting the optimal current location acquisition method depending on the user's means of transportation.

[0038] When acquiring the current location, the current location acquisition unit can prioritize acquiring highly relevant location information in consideration of the user's geographical location information. For example, if the user is in an urban area, the current location acquisition unit can acquire location information based on surrounding buildings and landmarks. For example, if the user is in a suburban area, the current location acquisition unit can acquire location information based on geographical features such as roads and parks. Furthermore, if the user is in a tourist destination, the current location acquisition unit can also acquire location information based on tourist attractions and famous places. This allows for more appropriate location information to be provided by prioritizing acquisition of highly relevant location information based on the user's geographical location information.

[0039] When acquiring the current location, the current location acquisition unit can analyze the user's social media activity and acquire related location information. The current location acquisition unit acquires location information based on, for example, the location where the user checked in on social media. For example, the current location acquisition unit can analyze the content of the user's posts on social media and acquire related location information. The current location acquisition unit can also acquire related location information by referring to the activities of the user's friends on social media. In this way, related location information can be acquired by analyzing the user's social media activity.

[0040] The current location acquisition unit can customize the acquisition method by reflecting the user's past feedback when acquiring the current location. The current location acquisition unit adjusts the accuracy of current location acquisition, for example, based on feedback provided by the user in the past. For example, the current location acquisition unit can set the optimal acquisition timing based on the user's past feedback. The current location acquisition unit can also analyze the user's past feedback and select the optimal acquisition means. In this way, the optimal current location acquisition method can be customized by reflecting the user's past feedback.

[0041] When inputting a destination, the destination input unit can analyze the user's past destination history and select the optimal input method. For example, the destination input unit can automatically display destinations that the user has frequently input in the past as candidates. For example, the destination input unit can preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The destination input unit can also predict and suggest destinations to be used during a specific time period based on the user's past destination history. In this way, the optimal destination input method can be selected by analyzing the user's past destination history.

[0042] The destination input unit can present input candidates based on the user's current situation when inputting a destination. For example, when the user inputs a destination at night, the destination input unit can preferentially display places that are open at night as candidates. For example, when the user inputs a destination on rainy days, the destination input unit can preferentially display indoor facilities as candidates. Furthermore, when the user inputs a destination on a holiday, the destination input unit can also preferentially display tourist spots and leisure facilities as candidates. In this way, by presenting input candidates based on the user's current situation, it is possible to suggest a more appropriate destination.

[0043] The destination input unit can select the optimal input means depending on the user's input method when inputting a destination. For example, the destination input unit can automatically set the destination when the user simply inputs "I want to go to the station" by voice. For example, the destination input unit can allow the user to easily set the destination by performing a specific gesture on the smartphone screen. The destination input unit can also allow the user to set the destination more intuitively by combining voice input and gesture input. This allows the destination to be set more intuitively by selecting the optimal input means depending on the user's input method.

[0044] When inputting a destination, the destination input unit can prioritize inputting highly relevant destinations in consideration of the user's geographical location information. For example, when the user is in an urban area, the destination input unit can prioritize displaying surrounding tourist spots and restaurants as candidates. For example, when the user is in the suburbs, the destination input unit can prioritize displaying nearby parks and nature spots as candidates. Furthermore, when the user is in a tourist destination, the destination input unit can also prioritize displaying popular tourist spots and famous places as candidates. This makes it possible to suggest more appropriate destinations by preferentially inputting highly relevant destinations based on the user's geographical location information.

[0045] The destination input unit can analyze the user's social media activity when inputting a destination and input related destinations. The destination input unit can suggest destinations based on, for example, places where the user has checked in on social media. For example, the destination input unit can analyze the content of the user's social media posts and suggest related destinations. The destination input unit can also suggest related destinations by referring to the activities of the user's friends on social media. In this way, related destinations can be suggested by analyzing the user's social media activity.

[0046] The destination input unit can customize the input method by reflecting the user's past feedback when inputting a destination. The destination input unit can adjust the destination input interface, for example, based on feedback provided by the user in the past. For example, the destination input unit can suggest the optimal input method based on the user's past feedback. The destination input unit can also analyze the user's past feedback and display optimal input candidates. In this way, the optimal destination input method can be customized by reflecting the user's past feedback.

[0047] When providing information, the information providing unit can analyze the user's past information browsing history and select the most appropriate information. For example, the information providing unit can provide related information preferentially based on information the user has previously browsed. For example, the information providing unit can suggest information that the user is likely to be interested in based on the user's past information browsing history. The information providing unit can also analyze the user's past information browsing history and provide the most relevant information. In this way, the most appropriate information can be provided by analyzing the user's past information browsing history.

[0048] The information providing unit can customize information based on the user's current situation when providing information. For example, when the user views information at night, the information providing unit can prioritize providing information about places that are open at night. For example, when the user views information on rainy days, the information providing unit can prioritize providing information about indoor facilities. Furthermore, when the user views information on a holiday, the information providing unit can also prioritize providing information about tourist spots and leisure facilities. In this way, by customizing information based on the user's current situation, more appropriate information can be provided.

[0049] When providing information, the information providing unit can adjust the display method according to the screen size and resolution of the user's device. For example, if the user is using a smartphone, the information providing unit can provide a display method that matches the screen size. For example, if the user is using a tablet, the information providing unit can provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the information providing unit can also provide a simple and highly visible display method. In this way, by adjusting the display method according to the screen size and resolution of the user's device, it is possible to provide information that is easier to view.

[0050] When providing information, the information providing unit can prioritize providing highly relevant information taking into account the user's geographical location information. For example, when the user is in an urban area, the information providing unit can prioritize providing information about surrounding tourist spots and restaurants. For example, when the user is in the suburbs, the information providing unit can prioritize providing information about nearby parks and nature spots. Furthermore, when the user is in a tourist destination, the information providing unit can also prioritize providing information about popular tourist spots and famous places. This makes it possible to provide more appropriate information by preferentially providing highly relevant information based on the user's geographical location information.

[0051] When providing information, the information providing unit can analyze the user's social media activity and provide related information. The information providing unit can provide, for example, information about places where the user has checked in on social media. For example, the information providing unit can analyze the content of the user's social media posts and provide information about related tourist spots and stores. The information providing unit can also provide information about related places and events by referring to the activities of the user's friends on social media. In this way, related information can be provided by analyzing the user's social media activity.

[0052] The information providing unit can customize the information display method by reflecting the user's past feedback when providing information. The information providing unit adjusts the information display method based on, for example, feedback provided by the user in the past. For example, the information providing unit can suggest an optimal information display means based on the user's past feedback. The information providing unit can also analyze the user's past feedback and provide optimal information. In this way, the optimal information display method can be customized by reflecting the user's past feedback.

[0053] When providing emergency response information, the emergency response information providing unit can optimize current response information by referring to past emergency situation data. The emergency response information providing unit can, for example, propose an optimal evacuation site based on past earthquake data. For example, the emergency response information providing unit can propose an optimal evacuation route based on past typhoon data. The emergency response information providing unit can also propose an optimal evacuation method based on past fire data. In this way, current response information can be optimized by referring to past emergency situation data.

[0054] The emergency response information providing unit can customize the information based on the user's current situation when providing the emergency response information. For example, if the user receives emergency response information at night, the emergency response information providing unit can suggest an evacuation site suitable for nighttime. For example, if the user receives emergency response information during rainy weather, the emergency response information providing unit can preferentially suggest an indoor evacuation site. Furthermore, if the user receives emergency response information on a holiday, the emergency response information providing unit can also suggest an evacuation route that avoids crowds. In this way, by customizing the information based on the user's current situation, more appropriate emergency response information can be provided.

[0055] When providing emergency response information, the emergency response information providing unit can adjust the information display frequency taking into account the remaining battery level of the user's device. For example, when the remaining battery level of the device is low, the emergency response information providing unit reduces the information display frequency to reduce battery consumption. For example, when the remaining battery level of the device is sufficient, the emergency response information providing unit can increase the information display frequency to provide accurate information. Furthermore, when the remaining battery level of the device is medium, the emergency response information providing unit can set the information display frequency to a medium level to provide balanced information. In this way, by adjusting the information display frequency according to the remaining battery level of the device, accurate information can be provided while reducing battery consumption.

[0056] When providing emergency response information, the emergency response information providing unit can prioritize providing highly relevant information taking into account the user's geographical location information. For example, when the user is in an urban area, the emergency response information providing unit can prioritize providing information on surrounding evacuation sites. For example, when the user is in a suburban area, the emergency response information providing unit can prioritize providing information on nearby evacuation routes. Furthermore, when the user is in a tourist destination, the emergency response information providing unit can also prioritize providing information on the nearest evacuation sites and evacuation routes. In this way, by preferentially providing highly relevant information based on the user's geographical location information, more appropriate emergency response information can be provided.

[0057] When providing emergency response information, the emergency response information providing unit can analyze the user's social media activities and provide related information. The emergency response information providing unit can provide, for example, emergency response information related to places where the user has checked in on social media. For example, the emergency response information providing unit can analyze the content of the user's posts on social media and provide related emergency response information. The emergency response information providing unit can also provide related emergency response information by referring to the activities of the user's friends on social media. In this way, it is possible to provide related emergency response information by analyzing the user's social media activities.

[0058] The emergency response information providing unit can customize the display method of the information by reflecting the user's past feedback when providing the emergency response information. The emergency response information providing unit adjusts the display method of the emergency response information based on, for example, feedback provided by the user in the past. For example, the emergency response information providing unit can suggest an optimal information display means based on the user's past feedback. The emergency response information providing unit can also analyze the user's past feedback and provide optimal emergency response information. In this way, the display method of the emergency response information can be customized optimally by reflecting the user's past feedback.

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

[0060] The real-time navigation system may further include a health management unit that monitors the user's health condition. The health management unit acquires the user's vital signs, such as heart rate and blood pressure, in real time, and can provide appropriate response information if an abnormality is detected. For example, if the user's heart rate suddenly increases, the health management unit can provide information about the nearest medical institution. Also, if the user's blood pressure is abnormally high, the health management unit can send a notification to an emergency contact. Furthermore, the health management unit can provide information about appropriate rest areas and relaxation spots based on the user's health condition. This enables navigation that takes the user's health condition into consideration, supporting safer and more comfortable travel.

[0061] The real-time navigation system may further include a route optimization unit that analyzes the user's past travel history and proposes the optimal route. The route optimization unit can propose the optimal route based on the routes and means of transportation used by the user in the past. For example, it can prioritize the proposal of routes that the user has used frequently in the past. It can also select the optimal route by taking into account the means of transportation (walking, bicycle, car, etc.) used by the user in the past. Furthermore, the route optimization unit can propose the optimal route for a specific time period based on the user's past travel history. In this way, it is possible to provide a more efficient and comfortable route by utilizing the user's past travel history.

[0062] The real-time navigation system may further include a battery management unit that adjusts the frequency and content of information provision in consideration of the remaining battery level of the user's device. When the remaining battery level of the device is low, the battery management unit can reduce the frequency of information provision and save battery consumption. For example, when the remaining battery level of the device is low, the battery management unit can provide only important information and suppress the provision of unnecessary information. In addition, when the remaining battery level of the device is sufficient, the battery management unit can provide detailed information. Furthermore, the battery management unit can adjust the timing of information provision according to the remaining battery level of the device. This makes it possible to provide information taking into consideration the remaining battery level of the device, and to provide necessary information while saving battery consumption.

[0063] The real-time navigation system may further include a feedback reflecting unit that reflects the user's past feedback to customize the content and method of information provision. The feedback reflecting unit can adjust the content and method of information provision based on feedback provided by the user in the past. For example, the feedback reflecting unit can prioritize providing information that the user has previously preferred. The feedback reflecting unit can also improve an information provision method that the user has previously been dissatisfied with. Furthermore, the feedback reflecting unit can analyze the user's past feedback and suggest an optimal information provision method. This makes it possible to provide information that reflects the user's past feedback, thereby achieving information provision with a higher level of satisfaction.

[0064] The real-time navigation system may further include a social media integration unit that analyzes the user's social media activity and provides related information. The social media integration unit can provide related information based on the places the user has checked in to and the content of posts on social media. For example, the social media integration unit can provide information about restaurants the user has checked in to. The social media integration unit can also provide information about tourist spots visited by the user's friends. Furthermore, the social media integration unit can analyze the user's social media activity and suggest events and places that may be of interest to the user. This makes it possible to provide information that utilizes the user's social media activity, resulting in more personalized information.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The current location acquisition unit acquires the user's current location. For example, the current location acquisition unit can identify the user's current location using GPS. It can also acquire the current location using Wi-Fi or cell towers. Step 2: The destination input unit accepts destination information entered by the user. For example, the destination input unit allows the user to enter a destination by text input or voice input. Alternatively, the user can select a destination using multiple-choice input. Step 3: The information provider provides relevant information based on the information obtained by the current location acquisition unit and the destination input unit. For example, it provides basic information about the city, tourist spots, restaurants, shopping centers, etc. It can provide information about restaurants the user can stop by on the way to the station, as well as the history and highlights of tourist spots. Step 4: The emergency response information provider provides specific response information in the event of an emergency. For example, it provides response information for emergencies such as earthquakes and typhoons. In the event of an earthquake, it can provide information on the nearest evacuation site and evacuation routes, and in the event of a typhoon, it can provide information on safe evacuation sites and evacuation routes.

[0067] (Example 2) A real-time navigation system according to an embodiment of the present invention provides information related to a user's current location and destination, and provides appropriate response information in real time in the event of an emergency. In this system, a user wears AR glasses, acquires their current location, and inputs their destination. A generation AI then provides information related to the current location and destination in real time. This information includes basic information about the city, tourist attractions, restaurants, shopping centers, and more. Furthermore, in the event of an emergency, the generation AI provides appropriate response information in real time. For example, in a real-time navigation system, a user wears AR glasses and acquires their current location. For example, using GPS, the user identifies their current location. Next, the user inputs their destination. For example, the user may input "I want to go to the station." The generation AI then provides relevant information in real time based on the current location and destination information. For example, the generation AI provides information about restaurants the user can stop by on the way to the station. Furthermore, in the event of an emergency, the generation AI provides appropriate response information in real time. For example, in the event of an earthquake, the generation AI provides information about the nearest evacuation shelters and evacuation routes. This allows the real-time navigation system to quickly take the necessary actions to ensure safety, anytime and anywhere. This means that if an earthquake occurs while the user is sightseeing, the real-time navigation system will enable the generation AI to provide the optimal evacuation route in real time, allowing the user to evacuate quickly.

[0068] A real-time navigation system according to an embodiment includes a current location acquisition unit, a destination input unit, an information provision unit, and an emergency response information provision unit. The current location acquisition unit acquires the user's current location. For example, the current location acquisition unit can identify the user's current location using GPS. The current location acquisition unit can also acquire the user's current location using Wi-Fi or a cell tower. The destination input unit accepts destination information input by the user. For example, the destination input unit allows the user to input a destination by text input or voice input. The destination input unit can also select a destination using multiple-choice input. The information provision unit provides related information based on the information acquired by the current location acquisition unit and the destination input unit. For example, the information provision unit provides information such as basic information about a city, tourist spots, restaurants, and shopping centers. The information provision unit can provide information about restaurants the user can stop by on the way to a station. The information provision unit can also provide the user with information about the history and highlights of a tourist spot when the user is heading to the tourist spot. The emergency response information provision unit provides specific response information in the event of an emergency. For example, the emergency response information providing unit provides response information for emergencies such as earthquakes and typhoons. For example, the emergency response information providing unit can provide information on the nearest evacuation site and evacuation routes when an earthquake occurs. Furthermore, the emergency response information providing unit can also provide information on safe evacuation sites and evacuation routes when a typhoon occurs. As a result, the real-time navigation system according to the embodiment can provide information related to the user's current location and destination, and can provide appropriate response information in real time when an emergency occurs.

[0069] The current location acquisition unit can acquire the user's current location using GPS. GPS acquires the current location based on, for example, the number of satellites used and the frequency of updating location information. For example, the current location acquisition unit receives signals from multiple GPS satellites and identifies the user's current location. Furthermore, the current location acquisition unit can provide accurate location information while reducing battery consumption by adjusting the frequency of updating location information. For example, the current location acquisition unit can increase the frequency of updating location information when the user is moving and decrease the frequency of updating location information when the user is stationary. In this way, the use of GPS can accurately acquire the user's current location.

[0070] The destination input unit can accept destination information input by the user. The destination information includes, for example, an address, a landmark, and coordinates. For example, the destination input unit allows the user to input an address by text input. The destination input unit can also allow the user to input a landmark by voice input. Furthermore, the destination input unit can also allow the user to select coordinates on a map. For example, when the user voice inputs "I want to go to the station," the destination input unit can identify the coordinates of the station and set it as the destination. This allows the destination information input by the user to be accurately accepted.

[0071] The information providing unit can provide basic information about a city, information about tourist spots, restaurants, and shopping centers. The basic information about a city includes, for example, population, area, major facilities, etc. For example, the information providing unit can provide information about the population, area, and major facilities of a city in which the user is currently located. Tourist spots include, for example, historical buildings, natural landscapes, theme parks, etc. For example, the information providing unit can provide the history and highlights of tourist spots that the user plans to visit. The information about restaurants includes, for example, the types of cuisine, business hours, and ratings. For example, the information providing unit can provide the types of cuisine, business hours, and ratings of restaurants that the user plans to visit. The information about shopping centers includes, for example, a list of stores, business hours, and access methods. For example, the information providing unit can provide a list of stores, business hours, and access methods of a shopping center that the user plans to visit. This makes it possible to provide the user with basic information about a city, information about tourist spots, etc.

[0072] The emergency response information providing unit can provide response information for emergencies such as earthquakes and typhoons. Emergencies include, for example, earthquakes, typhoons, and fires. For example, the emergency response information providing unit can provide information on the nearest evacuation site and evacuation routes when an earthquake occurs. Furthermore, the emergency response information providing unit can provide information on safe evacuation sites and evacuation routes when a typhoon occurs. Furthermore, the emergency response information providing unit can provide information on emergency contacts and first aid methods when a fire occurs. For example, when an earthquake occurs, the emergency response information providing unit can identify the evacuation site nearest the user's current location and provide an evacuation route to that evacuation site. This makes it possible to provide appropriate response information when an emergency occurs.

[0073] The emergency response information providing unit can provide information on evacuation sites and evacuation routes. Examples of evacuation sites include parks, schools, public facilities, etc. For example, the emergency response information providing unit can provide the park, school, or public facility closest to the user's current location as an evacuation site. Examples of evacuation routes include maps, navigation, signs, etc. For example, the emergency response information providing unit can provide a route from the user's current location to an evacuation site using a map or navigation. The emergency response information providing unit can also guide the user safely to an evacuation site based on signs on the evacuation route. This makes it possible to provide information on the nearest evacuation site and evacuation route in the event of an emergency.

[0074] The current location acquisition unit can estimate the user's emotions and adjust the frequency of current location acquisition based on the estimated user emotions. For example, if the user is nervous, the current location acquisition unit can increase the frequency of current location acquisition to provide more accurate location information. For example, if the user is relaxed, the current location acquisition unit can reduce the frequency of current location acquisition to reduce battery consumption. Furthermore, if the user is in a hurry, the current location acquisition unit can set the frequency of current location acquisition to a medium level to provide balanced location information. In this way, by adjusting the frequency of current location acquisition according to the user's emotions, more appropriate location information can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] When acquiring the current location, the current location acquisition unit can analyze the user's past movement history and select the optimal acquisition method. The current location acquisition unit can adjust the accuracy of current location acquisition based on, for example, places that the user has frequently visited in the past. For example, the current location acquisition unit can analyze the user's past movement patterns and set the optimal acquisition timing. The current location acquisition unit can also select the optimal acquisition method by taking into account the means of transportation that the user has used in the past. For example, the current location acquisition unit can improve the accuracy of current location acquisition based on places that the user has frequently visited in the past. In this way, the optimal current location acquisition method can be selected by analyzing the user's past movement history.

[0076] When acquiring a current location, the current location acquisition unit can adjust the acquisition frequency taking into account the remaining battery level of the user's device. For example, when the remaining battery level of the device is low, the current location acquisition unit reduces the frequency of acquiring the current location to reduce battery consumption. For example, when the remaining battery level of the device is sufficient, the current location acquisition unit can increase the frequency of acquiring the current location to provide accurate location information. Furthermore, when the remaining battery level of the device is medium, the current location acquisition unit can set the frequency of acquiring the current location to a medium level to provide balanced location information. In this way, by adjusting the frequency of acquiring the current location according to the remaining battery level of the device, accurate location information can be provided while reducing battery consumption.

[0077] When acquiring the current location, the current location acquisition unit can select the optimal acquisition method depending on the user's means of transportation. For example, if the user is traveling on foot, the current location acquisition unit can acquire accurate location information using a GPS. For example, if the user is traveling by bicycle, the current location acquisition unit can acquire location information by combining a GPS and an acceleration sensor. Furthermore, if the user is traveling by car, the current location acquisition unit can also acquire location information by linking a GPS with an in-car navigation system. This allows more accurate location information to be provided by selecting the optimal current location acquisition method depending on the user's means of transportation.

[0078] The current location acquisition unit can estimate the user's emotions and adjust the accuracy of the acquired current location based on the estimated user emotions. For example, if the user is nervous, the current location acquisition unit can increase the accuracy of the current location to provide more accurate location information. For example, if the user is relaxed, the current location acquisition unit can reduce the accuracy of the current location to reduce battery consumption. Furthermore, if the user is in a hurry, the current location acquisition unit can set the accuracy of the current location to a medium level to provide balanced location information. This allows for adjusting the accuracy of the current location according to the user's emotions to provide more appropriate location information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0079] When acquiring the current location, the current location acquisition unit can prioritize acquiring highly relevant location information in consideration of the user's geographical location information. For example, if the user is in an urban area, the current location acquisition unit can acquire location information based on surrounding buildings and landmarks. For example, if the user is in a suburban area, the current location acquisition unit can acquire location information based on geographical features such as roads and parks. Furthermore, if the user is in a tourist destination, the current location acquisition unit can also acquire location information based on tourist attractions and famous places. This allows for more appropriate location information to be provided by prioritizing acquisition of highly relevant location information based on the user's geographical location information.

[0080] When acquiring the current location, the current location acquisition unit can analyze the user's social media activity and acquire related location information. The current location acquisition unit acquires location information based on, for example, the location where the user checked in on social media. For example, the current location acquisition unit can analyze the content of the user's posts on social media and acquire related location information. The current location acquisition unit can also acquire related location information by referring to the activities of the user's friends on social media. In this way, related location information can be acquired by analyzing the user's social media activity.

[0081] The current location acquisition unit can customize the acquisition method by reflecting the user's past feedback when acquiring the current location. The current location acquisition unit adjusts the accuracy of current location acquisition, for example, based on feedback provided by the user in the past. For example, the current location acquisition unit can set the optimal acquisition timing based on the user's past feedback. The current location acquisition unit can also analyze the user's past feedback and select the optimal acquisition means. In this way, the optimal current location acquisition method can be customized by reflecting the user's past feedback.

[0082] The destination input unit can estimate the user's emotions and adjust the destination input interface based on the estimated user emotions. For example, if the user is feeling stressed, the destination input unit can provide a simple interface and minimize input steps. For example, if the user is relaxed, the destination input unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the destination input unit can prioritize voice input to enable quick destination input. This allows the destination input interface to be adjusted according to the user's emotions, providing a more user-friendly interface. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] When inputting a destination, the destination input unit can analyze the user's past destination history and select the optimal input method. For example, the destination input unit can automatically display destinations that the user has frequently input in the past as candidates. For example, the destination input unit can preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The destination input unit can also predict and suggest destinations to be used during a specific time period based on the user's past destination history. In this way, the optimal destination input method can be selected by analyzing the user's past destination history.

[0084] The destination input unit can present input candidates based on the user's current situation when inputting a destination. For example, when the user inputs a destination at night, the destination input unit can preferentially display places that are open at night as candidates. For example, when the user inputs a destination on rainy days, the destination input unit can preferentially display indoor facilities as candidates. Furthermore, when the user inputs a destination on a holiday, the destination input unit can also preferentially display tourist spots and leisure facilities as candidates. In this way, by presenting input candidates based on the user's current situation, it is possible to suggest a more appropriate destination.

[0085] The destination input unit can select the optimal input means depending on the user's input method when inputting a destination. For example, the destination input unit can automatically set the destination when the user simply inputs "I want to go to the station" by voice. For example, the destination input unit can allow the user to easily set the destination by performing a specific gesture on the smartphone screen. The destination input unit can also allow the user to set the destination more intuitively by combining voice input and gesture input. This allows the destination to be set more intuitively by selecting the optimal input means depending on the user's input method.

[0086] The destination input unit can estimate the user's emotions and determine the priority of destinations to be input based on the estimated user emotions. For example, if the user is nervous, the destination input unit can prioritize nearby and safe places as candidate locations. For example, if the user is relaxed, the destination input unit can prioritize distant but interesting places as candidate locations. Furthermore, if the user is in a hurry, the destination input unit can also prioritize places that can be reached in the shortest time as candidate locations. This allows more appropriate destinations to be suggested by prioritizing destinations according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] When inputting a destination, the destination input unit can prioritize inputting highly relevant destinations in consideration of the user's geographical location information. For example, when the user is in an urban area, the destination input unit can prioritize displaying surrounding tourist spots and restaurants as candidates. For example, when the user is in the suburbs, the destination input unit can prioritize displaying nearby parks and nature spots as candidates. Furthermore, when the user is in a tourist destination, the destination input unit can also prioritize displaying popular tourist spots and famous places as candidates. This makes it possible to suggest more appropriate destinations by preferentially inputting highly relevant destinations based on the user's geographical location information.

[0088] The destination input unit can analyze the user's social media activity when inputting a destination and input related destinations. The destination input unit can suggest destinations based on, for example, places where the user has checked in on social media. For example, the destination input unit can analyze the content of the user's social media posts and suggest related destinations. The destination input unit can also suggest related destinations by referring to the activities of the user's friends on social media. In this way, related destinations can be suggested by analyzing the user's social media activity.

[0089] The destination input unit can customize the input method by reflecting the user's past feedback when inputting a destination. The destination input unit can adjust the destination input interface, for example, based on feedback provided by the user in the past. For example, the destination input unit can suggest the optimal input method based on the user's past feedback. The destination input unit can also analyze the user's past feedback and display optimal input candidates. In this way, the optimal destination input method can be customized by reflecting the user's past feedback.

[0090] The information providing unit can estimate the user's emotions and adjust the information display method based on the estimated user's emotions. For example, if the user is nervous, the information providing unit can provide a simple, highly visible display method. For example, if the user is relaxed, the information providing unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the information providing unit can also provide a display method that focuses on the main points. This allows the information display method to be adjusted according to the user's emotions, making it possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0091] When providing information, the information providing unit can analyze the user's past information browsing history and select the most appropriate information. For example, the information providing unit can provide related information preferentially based on information the user has previously browsed. For example, the information providing unit can suggest information that the user is likely to be interested in based on the user's past information browsing history. The information providing unit can also analyze the user's past information browsing history and provide the most relevant information. In this way, the most appropriate information can be provided by analyzing the user's past information browsing history.

[0092] The information providing unit can customize information based on the user's current situation when providing information. For example, when the user views information at night, the information providing unit can prioritize providing information about places that are open at night. For example, when the user views information on rainy days, the information providing unit can prioritize providing information about indoor facilities. Furthermore, when the user views information on a holiday, the information providing unit can also prioritize providing information about tourist spots and leisure facilities. In this way, by customizing information based on the user's current situation, more appropriate information can be provided.

[0093] When providing information, the information providing unit can adjust the display method according to the screen size and resolution of the user's device. For example, if the user is using a smartphone, the information providing unit can provide a display method that matches the screen size. For example, if the user is using a tablet, the information providing unit can provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the information providing unit can also provide a simple and highly visible display method. In this way, by adjusting the display method according to the screen size and resolution of the user's device, it is possible to provide information that is easier to view.

[0094] The information providing unit can estimate the user's emotions and determine the priority of information based on the estimated user's emotions. For example, when the user is nervous, the information providing unit can prioritize displaying important information. For example, when the user is relaxed, the information providing unit can prioritize displaying interesting information. Furthermore, when the user is in a hurry, the information providing unit can also prioritize displaying information that covers the main points. In this way, by determining the priority of information according to the user's emotions, more appropriate information can be provided. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0095] When providing information, the information providing unit can prioritize providing highly relevant information taking into account the user's geographical location information. For example, when the user is in an urban area, the information providing unit can prioritize providing information about surrounding tourist spots and restaurants. For example, when the user is in the suburbs, the information providing unit can prioritize providing information about nearby parks and nature spots. Furthermore, when the user is in a tourist destination, the information providing unit can also prioritize providing information about popular tourist spots and famous places. This makes it possible to provide more appropriate information by preferentially providing highly relevant information based on the user's geographical location information.

[0096] When providing information, the information providing unit can analyze the user's social media activity and provide related information. The information providing unit can provide, for example, information about places where the user has checked in on social media. For example, the information providing unit can analyze the content of the user's social media posts and provide information about related tourist spots and stores. The information providing unit can also provide information about related places and events by referring to the activities of the user's friends on social media. In this way, related information can be provided by analyzing the user's social media activity.

[0097] The information providing unit can customize the information display method by reflecting the user's past feedback when providing information. The information providing unit adjusts the information display method based on, for example, feedback provided by the user in the past. For example, the information providing unit can suggest an optimal information display means based on the user's past feedback. The information providing unit can also analyze the user's past feedback and provide optimal information. In this way, the optimal information display method can be customized by reflecting the user's past feedback.

[0098] The emergency response information providing unit can estimate the user's emotions and adjust the display method of the emergency response information based on the estimated user's emotions. For example, if the user is nervous, the emergency response information providing unit can provide a simple, highly visible display method. For example, if the user is relaxed, the emergency response information providing unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the emergency response information providing unit can also provide a display method that focuses on the main points. This allows for adjusting the display method of the emergency response information according to the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0099] When providing emergency response information, the emergency response information providing unit can optimize current response information by referring to past emergency situation data. The emergency response information providing unit can, for example, propose an optimal evacuation site based on past earthquake data. For example, the emergency response information providing unit can propose an optimal evacuation route based on past typhoon data. The emergency response information providing unit can also propose an optimal evacuation method based on past fire data. In this way, current response information can be optimized by referring to past emergency situation data.

[0100] The emergency response information providing unit can customize the information based on the user's current situation when providing the emergency response information. For example, if the user receives emergency response information at night, the emergency response information providing unit can suggest an evacuation site suitable for nighttime. For example, if the user receives emergency response information during rainy weather, the emergency response information providing unit can preferentially suggest an indoor evacuation site. Furthermore, if the user receives emergency response information on a holiday, the emergency response information providing unit can also suggest an evacuation route that avoids crowds. In this way, by customizing the information based on the user's current situation, more appropriate emergency response information can be provided.

[0101] When providing emergency response information, the emergency response information providing unit can adjust the information display frequency taking into account the remaining battery level of the user's device. For example, when the remaining battery level of the device is low, the emergency response information providing unit reduces the information display frequency to reduce battery consumption. For example, when the remaining battery level of the device is sufficient, the emergency response information providing unit can increase the information display frequency to provide accurate information. Furthermore, when the remaining battery level of the device is medium, the emergency response information providing unit can set the information display frequency to a medium level to provide balanced information. In this way, by adjusting the information display frequency according to the remaining battery level of the device, accurate information can be provided while reducing battery consumption.

[0102] The emergency response information providing unit can estimate the user's emotions and determine the priority of emergency response information based on the estimated user's emotions. For example, when the user is nervous, the emergency response information providing unit can prioritize displaying the most important information. For example, when the user is relaxed, the emergency response information providing unit can prioritize displaying detailed information. Furthermore, when the user is in a hurry, the emergency response information providing unit can also prioritize displaying information that covers the main points. In this way, by determining the priority of emergency response information according to the user's emotions, more appropriate information can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0103] When providing emergency response information, the emergency response information providing unit can prioritize providing highly relevant information taking into account the user's geographical location information. For example, when the user is in an urban area, the emergency response information providing unit can prioritize providing information on surrounding evacuation sites. For example, when the user is in a suburban area, the emergency response information providing unit can prioritize providing information on nearby evacuation routes. Furthermore, when the user is in a tourist destination, the emergency response information providing unit can also prioritize providing information on the nearest evacuation sites and evacuation routes. In this way, by preferentially providing highly relevant information based on the user's geographical location information, more appropriate emergency response information can be provided.

[0104] When providing emergency response information, the emergency response information providing unit can analyze the user's social media activities and provide related information. The emergency response information providing unit can provide, for example, emergency response information related to places where the user has checked in on social media. For example, the emergency response information providing unit can analyze the content of the user's posts on social media and provide related emergency response information. The emergency response information providing unit can also provide related emergency response information by referring to the activities of the user's friends on social media. In this way, it is possible to provide related emergency response information by analyzing the user's social media activities.

[0105] The emergency response information providing unit can customize the display method of the information by reflecting the user's past feedback when providing the emergency response information. The emergency response information providing unit adjusts the display method of the emergency response information based on, for example, feedback provided by the user in the past. For example, the emergency response information providing unit can suggest an optimal information display means based on the user's past feedback. The emergency response information providing unit can also analyze the user's past feedback and provide optimal emergency response information. In this way, the display method of the emergency response information can be customized optimally by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the current location acquisition unit, destination input unit, information provision unit, and emergency response information provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the current location acquisition unit identifies the user's current location using the GPS function, Wi-Fi, and cell towers of the smart device 14. The destination input unit receives the user's destination information using the touch panel 38A and microphone 38B of the smart device 14. The information provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides basic information about the city and information about tourist spots. The emergency response information provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides response information for emergencies such as earthquakes and typhoons. === Hard Collateral 1-2 === Each of the multiple elements including the above-described current location acquisition unit, destination input unit, information provision unit, and emergency response information provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the current location acquisition unit identifies the user's current location using the GPS function, Wi-Fi, and cell towers of the smart glasses 214. The destination input unit receives the user's destination information using the microphone 238 of the smart glasses 214. The information provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides basic information about the city and information about tourist spots. The emergency response information provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides response information for emergencies such as earthquakes and typhoons. === Hard Collateral 1-3 === Each of the multiple elements including the above-described current location acquisition unit, destination input unit, information provision unit, and emergency response information provision unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the current location acquisition unit identifies the user's current location using the GPS function, Wi-Fi, or cell tower of the headset terminal 314. The destination input unit receives the user's destination information using the microphone 238 of the headset terminal 314. The information provision unit is realized by the identification processing unit 290 of the data processing device 12 and provides basic information about the city and information about tourist spots. The emergency response information provision unit is realized by the identification processing unit 290 of the data processing device 12 and provides response information for emergencies such as earthquakes and typhoons. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned current location acquisition unit, destination input unit, information provision unit, and emergency response information provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the current location acquisition unit identifies the user's current location using the GPS function, Wi-Fi, and cell towers of the robot 414. The destination input unit receives the user's destination information using the microphone 238 of the robot 414. The information provision unit is realized by the identification processing unit 290 of the data processing device 12 and provides basic information about the city and information about tourist spots. The emergency response information provision unit is realized by the identification processing unit 290 of the data processing device 12 and provides response information for emergencies such as earthquakes and typhoons.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The real-time navigation system may further include a health management unit that monitors the user's health condition. The health management unit acquires the user's vital signs, such as heart rate and blood pressure, in real time, and can provide appropriate response information if an abnormality is detected. For example, if the user's heart rate suddenly increases, the health management unit can provide information about the nearest medical institution. Also, if the user's blood pressure is abnormally high, the health management unit can send a notification to an emergency contact. Furthermore, the health management unit can provide information about appropriate rest areas and relaxation spots based on the user's health condition. This enables navigation that takes the user's health condition into consideration, supporting safer and more comfortable travel.

[0108] The real-time navigation system may further include a music providing unit that estimates the user's emotions and provides music based on the estimated emotions. The music providing unit may provide relaxing music when the user is relaxed, and music with a relaxing effect when the user is tense. For example, when the user is feeling stressed, the music providing unit may provide classical music with a relaxing effect. Furthermore, when the user is relaxed, the music providing unit may also provide music tailored to the user's preferences. Furthermore, the music providing unit may select appropriate music depending on the situation during the user's journey. This allows for a more comfortable travel experience by providing music tailored to the user's emotions.

[0109] The real-time navigation system may further include a route optimization unit that analyzes the user's past travel history and proposes the optimal route. The route optimization unit can propose the optimal route based on the routes and means of transportation used by the user in the past. For example, it can prioritize the proposal of routes that the user has used frequently in the past. It can also select the optimal route by taking into account the means of transportation (walking, bicycle, car, etc.) used by the user in the past. Furthermore, the route optimization unit can propose the optimal route for a specific time period based on the user's past travel history. In this way, it is possible to provide a more efficient and comfortable route by utilizing the user's past travel history.

[0110] The real-time navigation system may further include a notification management unit that estimates the user's emotions and adjusts the frequency of notifications based on the estimated emotions. The notification management unit can reduce the frequency of notifications when the user is relaxed, and provide only important notifications when the user is tense. For example, when the user is relaxed, the notification management unit can suppress unnecessary notifications, providing an environment in which the user can concentrate. Also, when the user is tense, the notification management unit can provide only important notifications, reducing the user's stress. Furthermore, the notification management unit can customize the content of notifications according to the user's emotions. This enables notification management according to the user's emotions, providing a more comfortable travel experience.

[0111] The real-time navigation system may further include a battery management unit that adjusts the frequency and content of information provision in consideration of the remaining battery level of the user's device. When the remaining battery level of the device is low, the battery management unit can reduce the frequency of information provision and save battery consumption. For example, when the remaining battery level of the device is low, the battery management unit can provide only important information and suppress the provision of unnecessary information. In addition, when the remaining battery level of the device is sufficient, the battery management unit can provide detailed information. Furthermore, the battery management unit can adjust the timing of information provision according to the remaining battery level of the device. This makes it possible to provide information taking into consideration the remaining battery level of the device, and to provide necessary information while saving battery consumption.

[0112] The real-time navigation system may further include an advertisement management unit that estimates the user's emotions and adjusts the content of advertisements to be displayed based on the estimated emotions. The advertisement management unit may display advertisements with a relaxing effect when the user is relaxed, and may display advertisements with a stress-reducing effect when the user is tense. For example, when the user is relaxed, the advertisement management unit may display a travel advertisement with a relaxing effect. Also, when the user is tense, the advertisement management unit may display an advertisement for a relaxation product with a stress-reducing effect. Furthermore, the advertisement management unit may adjust the frequency of advertisement display based on the user's emotions. This makes it possible to display advertisements according to the user's emotions, thereby achieving more effective advertisement delivery.

[0113] The real-time navigation system may further include a feedback reflecting unit that reflects the user's past feedback to customize the content and method of information provision. The feedback reflecting unit can adjust the content and method of information provision based on feedback provided by the user in the past. For example, the feedback reflecting unit can prioritize providing information that the user has previously preferred. The feedback reflecting unit can also improve an information provision method that the user has previously been dissatisfied with. Furthermore, the feedback reflecting unit can analyze the user's past feedback and suggest an optimal information provision method. This makes it possible to provide information that reflects the user's past feedback, thereby achieving information provision with a higher level of satisfaction.

[0114] The real-time navigation system may further include a destination suggestion unit that estimates the user's emotions and suggests destinations based on the estimated emotions. The destination suggestion unit may suggest relaxing places when the user is relaxed, and may suggest places that have a stress-reducing effect when the user is tense. For example, if the user is relaxed, the destination suggestion unit may suggest relaxing places such as parks or cafes. Also, if the user is tense, the destination suggestion unit may suggest places that have a stress-reducing effect such as spas or relaxation facilities. Furthermore, the destination suggestion unit may customize the types of destinations to suggest based on the user's emotions. This makes it possible to suggest destinations that suit the user's emotions, thereby providing a more comfortable travel experience.

[0115] The real-time navigation system may further include a social media integration unit that analyzes the user's social media activity and provides related information. The social media integration unit can provide related information based on the places the user has checked in to and the content of posts on social media. For example, the social media integration unit can provide information about restaurants the user has checked in to. The social media integration unit can also provide information about tourist spots visited by the user's friends. Furthermore, the social media integration unit can analyze the user's social media activity and suggest events and places that may be of interest to the user. This makes it possible to provide information that utilizes the user's social media activity, resulting in more personalized information.

[0116] The real-time navigation system may further include an emergency response display unit that estimates the user's emotions and adjusts the display method of emergency response information based on the estimated emotions. The emergency response display unit may provide a simple, highly visible display method when the user is nervous, and a display method including detailed information when the user is relaxed. For example, when the user is nervous, the emergency response display unit may display only important information in a large size, allowing the user to respond quickly. On the other hand, when the user is relaxed, the emergency response display unit may provide detailed information, making it easier for the user to understand the situation. Furthermore, the emergency response display unit may customize the display method according to the user's emotions. This makes it possible to display emergency response information according to the user's emotions, thereby realizing more appropriate information provision.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The current location acquisition unit acquires the user's current location. For example, the current location acquisition unit can identify the user's current location using GPS. It can also acquire the current location using Wi-Fi or cell towers. Step 2: The destination input unit accepts destination information entered by the user. For example, the destination input unit allows the user to enter a destination by text input or voice input. Alternatively, the user can select a destination using multiple-choice input. Step 3: The information provider provides relevant information based on the information obtained by the current location acquisition unit and the destination input unit. For example, it provides basic information about the city, tourist spots, restaurants, shopping centers, etc. It can provide information about restaurants the user can stop by on the way to the station, as well as the history and highlights of tourist spots. Step 4: The emergency response information provider provides specific response information in the event of an emergency. For example, it provides response information for emergencies such as earthquakes and typhoons. In the event of an earthquake, it can provide information on the nearest evacuation site and evacuation routes, and in the event of a typhoon, it can provide information on safe evacuation sites and evacuation routes.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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 current location acquisition unit that acquires a current location; a destination input section for inputting a destination; an information providing unit that provides related information based on the information obtained by the current location obtaining unit and the destination input unit; an emergency response information providing unit that provides specific response information when an emergency occurs. A system characterized by:

2. The current location acquisition unit Use GPS to get the user's location The system of claim 1 .

3. The destination input unit Accepts destination information entered by the user The system of claim 1 .

4. The information providing unit Provides basic information about the city, tourist attractions, restaurants, and shopping centers The system of claim 1 .

5. The emergency response information providing unit Providing emergency response information for earthquakes and typhoons The system of claim 1 .

6. The emergency response information providing unit Providing information on evacuation sites and routes The system of claim 1 .

7. The current location acquisition unit Estimate user emotions and adjust location acquisition frequency based on the estimated user emotions The system of claim 1 .

8. The current location acquisition unit When obtaining the current location, the system analyzes the user's past movement history and selects the optimal method of obtaining the location. The system of claim 1 .

Images