system

Abstract

The system according to the embodiment aims to enable travelers to quickly obtain appropriate tourist information and help in case of trouble while they are in the area. [Solution] The system according to the embodiment comprises a detection unit, a connection unit, a request unit, and a provision unit. The detection unit detects the current location of a traveler. The connection unit connects to a local generation AI based on the current location detected by the detection unit. The request unit requests tourist information, recommended shops, or help in case of trouble from the generation AI connected by the connection unit. The provision unit provides the information requested by the request unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult for travelers to quickly obtain appropriate tourist information or help in case of trouble while they are in the area.

[0005] The system according to the embodiment aims to enable travelers to quickly obtain appropriate tourist information and help in case of trouble while they are in the area. [Means for solving the problem]

[0006] The system according to the embodiment includes a detection unit, a connection unit, a request unit, and a provision unit. The detection unit detects the current location of a traveler. The connection unit connects to a local generation AI based on the current location detected by the detection unit. The request unit requests tourist information, recommended shops, or help in case of trouble from the generation AI connected by the connection unit. The provision unit provides the information requested by the request unit. [Effects of the Invention]

[0007] The system according to the embodiment enables travelers to quickly obtain appropriate tourist information and help in case of trouble while they are in the area. [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 travel support system according to an embodiment of the present invention provides international travelers with tourist information, recommended shops, and troubleshooting help by linking with a local AI. This system allows travelers to connect with the local AI, enabling them to receive more effective responses based on local knowledge, unlike AIs in other countries. First, upon arrival, travelers connect with the local AI through a dedicated application. This application automatically detects the traveler's current location and provides an interface for connecting with the local AI. Through the application, travelers can request tourist information, recommended shops, and troubleshooting help. For example, when a traveler requests tourist information, the AI ​​suggests the most suitable tourist spots based on the traveler's current location and interests. The AI ​​provides detailed descriptions of tourist spots and access information based on the latest local information. Furthermore, when a traveler requests recommended shops, the AI ​​suggests restaurants and shopping spots that suit the traveler's preferences and budget. Furthermore, when a traveler requests troubleshooting help, the AI ​​provides local emergency contact information and procedures, helping the traveler respond quickly. This system allows travelers to connect with the local AI, enabling them to receive more effective responses based on local knowledge, unlike AIs in other countries, thereby improving traveler satisfaction. In addition, by connecting with local generative AI, travelers can also obtain information about local culture and customs, providing a deeper travel experience. This allows the travel support system to provide effective answers based on local information by connecting travelers with local generative AI.

[0029] A travel support system according to an embodiment includes a detection unit, a connection unit, a request unit, and a provision unit. The detection unit detects the current location of a traveler. The current location of the traveler is detected using, for example, GPS, Wi-Fi location information, cell tower information, etc. For example, the detection unit detects the current location of the traveler with high accuracy using GPS. The detection unit can also detect the current location of the traveler using Wi-Fi location information. The detection unit can also detect the current location of the traveler using cell tower information. For example, the detection unit receives a GPS signal to identify the current location of the traveler. The Wi-Fi location information identifies the current location based on information about surrounding Wi-Fi access points. The cell tower information identifies the current location based on the signal strength of surrounding cell towers. The connection unit connects to a local generation AI based on the current location detected by the detection unit. The connection unit connects to the local generation AI through, for example, a mobile application or a web application. For example, the connection unit connects to the local generation AI using a mobile application. The connection unit can also connect to the local generation AI using a web application. The connection unit can also connect to the local generation AI using a voice assistant. For example, the connection unit detects the traveler's current location through a mobile application and connects to the local generation AI. The request unit requests tourist information, recommended shops, and help in case of trouble from the generation AI connected by the connection unit. The request unit makes the request through, for example, a mobile application or a web application. For example, the request unit requests tourist information using a mobile application. The request unit can also request recommended shops using a web application. Furthermore, the request unit can request help in case of trouble using a voice assistant. For example, the request unit requests tourist information through a mobile application and receives a response from the generation AI. The provision unit provides the information requested by the request unit. The provision unit provides, for example, tourist information, recommended shops, and help in case of trouble. For example, the provision unit suggests the best tourist spots based on the traveler's current location and interests.The providing unit can also suggest restaurants and shopping spots that suit the traveler's preferences and budget. Furthermore, the providing unit can also provide local emergency contact information and procedures. For example, the providing unit can suggest optimal tourist spots based on the traveler's current location and provide detailed descriptions and access methods. This allows the travel support system according to the embodiment to enable the traveler to obtain effective answers based on local information by linking with the local generation AI. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can generate tourist spot information using the generation AI to suggest optimal tourist spots based on the traveler's current location and interests.

[0030] The providing unit can suggest tourist spots based on the traveler's current location and interests. The providing unit, for example, suggests optimal tourist spots based on the traveler's current location. For example, the providing unit suggests tourist spots close to the traveler's current location. The providing unit can also suggest tourist spots based on the traveler's interests. For example, the providing unit can suggest historical tourist spots based on the traveler's interests. The providing unit can also suggest tourist spots with natural scenery based on the traveler's interests. For example, the providing unit can suggest museums and art galleries based on the traveler's interests. This can improve traveler satisfaction by suggesting optimal tourist spots based on the traveler's current location and interests. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can generate tourist spot information using a generation AI in order to suggest optimal tourist spots based on the traveler's current location and interests.

[0031] The providing unit can suggest restaurants and shopping spots according to the traveler's preferences and budget. The providing unit, for example, suggests restaurants based on the traveler's preferences. For example, the providing unit suggests restaurants serving local cuisine based on the traveler's preferences. The providing unit can also suggest high-end restaurants based on the traveler's preferences. The providing unit can also suggest restaurants according to the traveler's budget. For example, the providing unit can suggest affordable restaurants based on the traveler's budget. The providing unit can also suggest high-end restaurants based on the traveler's budget. The providing unit can also suggest shopping spots according to the traveler's preferences and budget. For example, the providing unit can suggest shopping spots selling local specialties based on the traveler's preferences. The providing unit can also suggest affordable shopping spots based on the traveler's budget. This makes it possible to improve traveler satisfaction by making suggestions according to the traveler's preferences and budget. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can generate information using a generation AI to suggest optimal restaurants and shopping spots based on the traveler's preferences and budget.

[0032] The providing unit can provide local emergency contact information and response methods. The providing unit, for example, provides local emergency contact information. For example, the providing unit can provide contact information for the local police. The providing unit can also provide contact information for local hospitals. Furthermore, the providing unit can also provide contact information for the local embassy. For example, the providing unit can provide contact information for the local police to support travelers in responding quickly in the event of an emergency. The providing unit can also provide contact information for local hospitals to support travelers in responding quickly in the event of an emergency. The providing unit can also provide contact information for the local embassy to support travelers in responding quickly in the event of an emergency, such as a lost passport. This makes it possible to support travelers in responding quickly in the event of an emergency. Some or all of the above-described processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can generate information using a generation AI to provide local emergency contact information and response methods.

[0033] The connection unit can automatically detect the traveler's current location and provide an interface for connecting to the local generation AI. The connection unit, for example, automatically detects the traveler's current location. For example, the connection unit detects the traveler's current location with high accuracy using GPS. The connection unit can also detect the traveler's current location using Wi-Fi location information. Furthermore, the connection unit can detect the traveler's current location using cell tower information. For example, the connection unit receives a GPS signal to identify the traveler's current location. The Wi-Fi location information identifies the current location based on information about surrounding Wi-Fi access points. The cell tower information identifies the current location based on the signal strength of surrounding cell towers. After detecting the traveler's current location, the connection unit provides an interface for connecting to the local generation AI. For example, the connection unit connects to the local generation AI through a mobile application. The connection unit can also connect to the local generation AI through a web application. Furthermore, the connection unit can connect to the local generation AI through a voice assistant. For example, the connection unit detects the traveler's current location through a mobile application and connects to the local generation AI. This allows the traveler to easily connect to the local generation AI. Some or all of the above-described processing in the connection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the connection unit may provide an interface using the generation AI to detect the traveler's current location and connect to the local generation AI.

[0034] The request unit can provide an interface through which travelers request tourist information, recommended shops, and help in case of trouble. The request unit requests tourist information, for example, through a mobile application. For example, the request unit provides an interface through which travelers request tourist information. The request unit can also request recommended shops through a web application. For example, the request unit provides an interface through which travelers request recommended shops. The request unit can also request help in case of trouble through a voice assistant. For example, the request unit provides an interface through which travelers request help in case of trouble. This allows travelers to easily make requests. Some or all of the above-mentioned processing in the request unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the request unit can provide an interface using a generation AI through which travelers request tourist information, recommended shops, and help in case of trouble.

[0035] The detection unit can analyze the traveler's past movement history and select an optimal current location detection method. The detection unit can, for example, improve the accuracy of current location detection based on places the traveler has frequently visited in the past. For example, the detection unit can improve the accuracy of current location detection based on places the traveler has frequently visited in the past. The detection unit can also analyze the traveler's past movement patterns and select an optimal detection method. For example, the detection unit can analyze the traveler's past movement patterns and select an optimal detection method. Furthermore, the detection unit can adjust the current location detection method based on the means of transportation used by the traveler in the past. For example, the detection unit adjusts the current location detection method based on the means of transportation used by the traveler in the past. This can improve the accuracy of current location detection based on the past movement history. Some or all of the above-described processing in the detection unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the detection unit can analyze the traveler's past movement history and use a generation AI to analyze the movement history to select an optimal current location detection method.

[0036] When detecting the current location, the detection unit can adjust the detection accuracy based on the traveler's means of transportation. For example, when the traveler is traveling on foot, the detection unit increases the GPS accuracy to provide detailed location information. For example, when the traveler is traveling on foot, the detection unit increases the GPS accuracy. Furthermore, when the traveler is traveling by car, the detection unit can set the GPS accuracy to a medium level to reduce battery consumption. For example, when the traveler is traveling by car, the detection unit sets the GPS accuracy to a medium level. Furthermore, when the traveler is using public transportation, the detection unit can also set an optimal detection accuracy according to the means of transportation. For example, when the traveler is using public transportation, the detection unit sets an optimal detection accuracy according to the means of transportation. This makes it possible to provide an optimal detection accuracy according to the means of transportation. Some or all of the above-described processing in the detection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the detection unit can identify the means of transportation using a generation AI in order to adjust the detection accuracy based on the traveler's means of transportation.

[0037] When detecting the current location, the detection unit can adjust the detection frequency taking into account the remaining battery level of the traveler's device. For example, when the remaining battery level of the device is low, the detection unit reduces the detection frequency of the current location to reduce battery consumption. For example, when the remaining battery level of the device is low, the detection unit reduces the detection frequency of the current location. Furthermore, when the remaining battery level of the device is sufficient, the detection unit can increase the detection frequency of the current location to provide detailed location information. For example, when the remaining battery level of the device is sufficient, the detection unit can increase the detection frequency of the current location. Furthermore, when the remaining battery level of the device is medium, the detection unit can set an appropriate detection frequency to achieve a balance. For example, when the remaining battery level of the device is medium, the detection unit sets an appropriate detection frequency. This makes it possible to provide an optimal detection frequency according to the remaining battery level. Some or all of the above-described processing in the detection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the detection unit can detect the remaining battery level of the device using a generation AI to detect the remaining battery level and adjust the detection frequency according to the remaining battery level.

[0038] When detecting the current location, the detection unit can select the optimal detection timing by referring to the traveler's schedule information. The detection unit, for example, detects the current location before an important event based on the traveler's schedule. For example, the detection unit detects the current location before an important event based on the traveler's schedule. The detection unit can also detect the current location during a time period when travel is expected based on the traveler's schedule information. For example, the detection unit detects the current location during a time period when travel is expected based on the traveler's schedule information. The detection unit can also set the optimal detection timing according to the traveler's schedule. For example, the detection unit sets the optimal detection timing according to the traveler's schedule. This makes it possible to provide the optimal detection timing based on the schedule information. Some or all of the above-described processing in the detection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the detection unit can refer to the traveler's schedule information and analyze the schedule information using a generation AI to select the optimal detection timing.

[0039] When connecting, the connection unit can analyze the traveler's past connection history and select the optimal connection method. For example, the connection unit selects the optimal connection method based on the connection methods used by the traveler in the past. For example, the connection unit selects the optimal connection method based on the connection methods used by the traveler in the past. The connection unit can also suggest a stable connection method based on the traveler's past connection history. For example, the connection unit suggests a stable connection method based on the traveler's past connection history. Furthermore, the connection unit can analyze the traveler's past connection history and select the most efficient connection method. For example, the connection unit analyzes the traveler's past connection history and selects the most efficient connection method. This makes it possible to provide the optimal connection method based on the past connection history. Some or all of the above-mentioned processing in the connection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the connection unit can analyze the traveler's past connection history and use a generation AI to analyze the connection history and select the optimal connection method.

[0040] The connection unit can adjust the connection method based on the traveler's communication environment when connecting. For example, when the traveler is using Wi-Fi, the connection unit selects a stable connection method. For example, when the traveler is using Wi-Fi, the connection unit selects a stable connection method. Furthermore, when the traveler is using mobile data, the connection unit can select a connection method that reduces data consumption. For example, when the traveler is using mobile data, the connection unit selects a connection method that reduces data consumption. Furthermore, the connection unit can adjust the optimal connection method according to the traveler's communication environment. For example, the connection unit adjusts the optimal connection method according to the traveler's communication environment. This makes it possible to provide the optimal connection method according to the communication environment. Some or all of the above-mentioned processing in the connection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the connection unit can detect the communication environment of the traveler and adjust the connection method according to the communication environment.

[0041] When connecting, the connection unit can select a connection method taking into account the communication speed of the traveler's device. For example, if the communication speed of the device is high, the connection unit selects a high-speed connection method. For example, if the communication speed of the device is high, the connection unit selects a high-speed connection method. Furthermore, if the communication speed of the device is low, the connection unit can select a stable connection method. For example, if the communication speed of the device is low, the connection unit selects a stable connection method. Furthermore, the connection unit can select the optimal connection method depending on the communication speed of the device. For example, the connection unit selects the optimal connection method depending on the communication speed. This makes it possible to provide the optimal connection method depending on the communication speed. Some or all of the above-mentioned processing in the connection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the connection unit can detect the communication speed of the device and use the generation AI to detect the communication speed in order to select a connection method depending on the communication speed.

[0042] When connecting, the connection unit can select the optimal connection method by referring to the security settings of the traveler's device. For example, if the security settings of the device are high, the connection unit selects a secure connection method. For example, if the security settings of the device are high, the connection unit selects a secure connection method. Furthermore, if the security settings of the device are low, the connection unit can select a general connection method. For example, if the security settings of the device are low, the connection unit selects a general connection method. Furthermore, the connection unit can select the optimal connection method depending on the security settings of the device. For example, the connection unit selects the optimal connection method depending on the security settings. This makes it possible to provide the optimal connection method depending on the security settings. Some or all of the above-mentioned processing in the connection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the connection unit can detect the security settings using a generation AI in order to refer to the security settings of the device and select a connection method depending on the security settings.

[0043] At the time of a request, the request unit can analyze the traveler's past request history and select the optimal request method. For example, the request unit selects the optimal request method based on the request methods used by the traveler in the past. For example, the request unit selects the optimal request method based on the request methods used by the traveler in the past. The request unit can also suggest an efficient request method based on the traveler's past request history. For example, the request unit suggests an efficient request method based on the traveler's past request history. Furthermore, the request unit can analyze the traveler's past request history and select the most appropriate request method. For example, the request unit analyzes the traveler's past request history and selects the most appropriate request method. This makes it possible to provide the optimal request method based on the past request history. Some or all of the above-described processing in the request unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the request unit can analyze the traveler's past request history using a generation AI to select the optimal request method.

[0044] The request unit can adjust the request content based on the traveler's current situation at the time of the request. For example, if the traveler makes a request at night, the request unit prioritizes suggesting safe locations. For example, if the traveler makes a request at night, the request unit prioritizes suggesting safe locations. Furthermore, the request unit can also suggest indoor tourist spots when the traveler makes a request during rainy weather. For example, the request unit can suggest indoor tourist spots when the traveler makes a request during rainy weather. Furthermore, the request unit can also adjust the optimal request content according to the traveler's current situation. For example, the request unit adjusts the optimal request content according to the traveler's current situation. This makes it possible to provide the optimal request content according to the current situation. Some or all of the above-mentioned processing in the request unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the request unit can detect the situation using a generation AI in order to detect the traveler's current situation and adjust the request content according to the current situation.

[0045] At the time of request, the request unit can select a request method taking into consideration the traveler's device usage status. For example, if the device's battery level is low, the request unit selects a simple request method. For example, if the device's battery level is low, the request unit selects a simple request method. Furthermore, the request unit can also select a detailed request method if the device's battery level is sufficient. For example, if the device's battery level is sufficient, the request unit selects a detailed request method. Furthermore, the request unit can also select the optimal request method depending on the device usage status. For example, the request unit selects the optimal request method depending on the device usage status. This makes it possible to provide the optimal request method depending on the device usage status. Some or all of the above-mentioned processing in the request unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the request unit can detect the device usage status using a generation AI in order to detect the usage status and select a request method depending on the usage status.

[0046] At the time of a request, the request unit can select the optimal request method by referring to the traveler's language setting. The request unit, for example, automatically sets the language of the request based on the language setting of the traveler's device. For example, the request unit automatically sets the language of the request based on the language setting of the traveler's device. The request unit can also provide a language switching function if the traveler uses multiple languages. For example, the request unit provides a language switching function if the traveler uses multiple languages. Furthermore, the request unit can also provide the request in a specific language if the traveler selects that language. For example, the request unit provides the request in that language if the traveler selects a specific language. This makes it possible to provide the optimal request method according to the language setting. Some or all of the above-described processing in the request unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the request unit can refer to the traveler's language setting and detect the language setting using a generation AI in order to select a request method according to the language setting.

[0047] When providing information, the providing unit can analyze the traveler's past information provision history and select the optimal provision method. The providing unit, for example, selects the optimal provision method based on the information provision method used by the traveler in the past. For example, the providing unit selects the optimal provision method based on the information provision method used by the traveler in the past. The providing unit can also suggest an efficient provision method based on the traveler's past information provision history. For example, the providing unit suggests an efficient provision method based on the traveler's past information provision history. The providing unit can also analyze the traveler's past information provision history and select the most appropriate provision method. For example, the providing unit analyzes the traveler's past information provision history and selects the most appropriate provision method. This makes it possible to provide the optimal provision method based on the past information provision history. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can analyze the traveler's past information provision history and select the optimal provision method using a generation AI.

[0048] When providing information, the providing unit can adjust the provided content based on the traveler's current situation. For example, when a traveler requests information at night, the providing unit prioritizes providing safe locations. For example, when a traveler requests information at night, the providing unit prioritizes providing safe locations. Furthermore, when a traveler requests information in rainy weather, the providing unit can provide indoor tourist spots. For example, when a traveler requests information in rainy weather, the providing unit provides indoor tourist spots. Furthermore, the providing unit can adjust the optimal provided content according to the traveler's current situation. For example, the providing unit adjusts the optimal provided content according to the traveler's current situation. This makes it possible to provide the optimal provided content according to the current situation. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can detect the current situation of the traveler and adjust the provided content according to the current situation.

[0049] When providing information, the providing unit can select a providing method taking into consideration the display settings of the traveler's device. For example, when the display setting of the device is high resolution, the providing unit provides detailed information. For example, when the display setting of the device is high resolution, the providing unit provides detailed information. Furthermore, when the display setting of the device is low resolution, the providing unit can also provide concise information. For example, when the display setting of the device is low resolution, the providing unit provides concise information. Furthermore, the providing unit can also select an optimal providing method according to the display setting of the device. For example, the providing unit selects an optimal providing method according to the display setting. This makes it possible to provide an optimal providing method according to the display setting. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can detect the display setting of the device and select a providing method according to the display setting.

[0050] When providing information, the providing unit can select the optimal providing method by referring to the traveler's language setting. The providing unit, for example, automatically sets the language of the information based on the language setting of the traveler's device. For example, the providing unit automatically sets the language of the information based on the language setting of the traveler's device. The providing unit can also provide a language switching function when the traveler uses multiple languages. For example, the providing unit provides a language switching function when the traveler uses multiple languages. Furthermore, the providing unit can also provide information in a specific language when the traveler selects that language. For example, the providing unit provides information in that language when the traveler selects a specific language. This makes it possible to provide the optimal providing method according to the language setting. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can refer to the traveler's language setting and detect the language setting using a generation AI in order to select a providing method according to the language setting.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The provision unit can analyze the traveler's past information provision history and select the optimal provision method. For example, the optimal provision method is selected based on the information provision methods used by the traveler in the past. It can also suggest an efficient provision method based on the traveler's past information provision history. Furthermore, it can analyze the traveler's past information provision history and select the most appropriate provision method. This makes it possible to provide the optimal provision method based on the past information provision history.

[0053] The providing unit can adjust the content to be provided based on the traveler's current situation. For example, if a traveler requests information at night, safe locations can be provided with priority. Also, if a traveler requests information during rainy weather, indoor tourist spots can be provided. Furthermore, the providing unit can adjust the optimal content to be provided based on the traveler's current situation. This makes it possible to provide the optimal content to be provided according to the current situation.

[0054] The providing unit can select the providing method taking into consideration the display settings of the traveler's device. For example, if the device's display setting is high resolution, detailed information can be provided. On the other hand, if the device's display setting is low resolution, concise information can be provided. Furthermore, the optimal providing method can be selected depending on the device's display setting. This makes it possible to provide the optimal providing method depending on the display setting.

[0055] The providing unit can select the optimal providing method by referring to the traveler's language setting. For example, the language of the information can be automatically set based on the language setting of the traveler's device. In addition, if the traveler uses multiple languages, a language switching function can be provided. Furthermore, if the traveler selects a specific language, the information can be provided in that language. This makes it possible to provide the optimal providing method according to the language setting.

[0056] The providing unit can analyze the traveler's past movement history and select the optimal information provision method. For example, the accuracy of information provision can be improved based on places that the traveler has frequently visited in the past. The providing unit can also analyze the traveler's past movement patterns and select the optimal information provision method. Furthermore, the information provision method can be adjusted based on the means of transportation that the traveler has used in the past. This makes it possible to provide the optimal information provision method based on the travel history in the past.

[0057] The providing unit can select an information providing method taking into consideration the remaining battery level of the traveler's device. For example, if the remaining battery level of the device is low, brief information can be provided to reduce battery consumption. Also, if the remaining battery level of the device is sufficient, detailed information can be provided. Furthermore, if the remaining battery level of the device is medium, an appropriate information providing method can be selected to achieve a balance. This makes it possible to provide the optimal information providing method according to the remaining battery level.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The detection unit detects the current location of the traveler. The current location of the traveler is detected using GPS, Wi-Fi location information, cell tower information, etc. For example, the detection unit uses GPS to detect the current location of the traveler with high accuracy. The current location can also be identified using Wi-Fi location information and cell tower information. Step 2: The connection unit connects to the local generation AI based on the current location detected by the detection unit. The connection unit can connect to the local generation AI through a mobile application, a web application, or a voice assistant. Step 3: The request unit requests tourist information, recommended shops, and help in case of trouble from the generated AI connected by the connection unit. The request unit can make requests through a mobile application, a web application, or a voice assistant. Step 4: The provider provides the information requested by the requester. The provider provides tourist information, recommended shops, and help in case of trouble. For example, the provider suggests the best tourist spots based on the traveler's current location and interests, and provides detailed descriptions and access instructions.

[0060] (Example 2) A travel support system according to an embodiment of the present invention provides international travelers with tourist information, recommended shops, and troubleshooting help by linking with a local AI. This system allows travelers to connect with the local AI, enabling them to receive more effective responses based on local knowledge, unlike AIs in other countries. First, upon arrival, travelers connect with the local AI through a dedicated application. This application automatically detects the traveler's current location and provides an interface for connecting with the local AI. Through the application, travelers can request tourist information, recommended shops, and troubleshooting help. For example, when a traveler requests tourist information, the AI ​​suggests the most suitable tourist spots based on the traveler's current location and interests. The AI ​​provides detailed descriptions of tourist spots and access information based on the latest local information. Furthermore, when a traveler requests recommended shops, the AI ​​suggests restaurants and shopping spots that suit the traveler's preferences and budget. Furthermore, when a traveler requests troubleshooting help, the AI ​​provides local emergency contact information and procedures, helping the traveler respond quickly. This system allows travelers to connect with the local AI, enabling them to receive more effective responses based on local knowledge, unlike AIs in other countries, thereby improving traveler satisfaction. In addition, by connecting with local generative AI, travelers can also obtain information about local culture and customs, providing a deeper travel experience. This allows the travel support system to provide effective answers based on local information by connecting travelers with local generative AI.

[0061] A travel support system according to an embodiment includes a detection unit, a connection unit, a request unit, and a provision unit. The detection unit detects the current location of a traveler. The current location of the traveler is detected using, for example, GPS, Wi-Fi location information, cell tower information, etc. For example, the detection unit detects the current location of the traveler with high accuracy using GPS. The detection unit can also detect the current location of the traveler using Wi-Fi location information. The detection unit can also detect the current location of the traveler using cell tower information. For example, the detection unit receives a GPS signal to identify the current location of the traveler. The Wi-Fi location information identifies the current location based on information about surrounding Wi-Fi access points. The cell tower information identifies the current location based on the signal strength of surrounding cell towers. The connection unit connects to a local generation AI based on the current location detected by the detection unit. The connection unit connects to the local generation AI through, for example, a mobile application or a web application. For example, the connection unit connects to the local generation AI using a mobile application. The connection unit can also connect to the local generation AI using a web application. The connection unit can also connect to the local generation AI using a voice assistant. For example, the connection unit detects the traveler's current location through a mobile application and connects to the local generation AI. The request unit requests tourist information, recommended shops, and help in case of trouble from the generation AI connected by the connection unit. The request unit makes the request through, for example, a mobile application or a web application. For example, the request unit requests tourist information using a mobile application. The request unit can also request recommended shops using a web application. Furthermore, the request unit can request help in case of trouble using a voice assistant. For example, the request unit requests tourist information through a mobile application and receives a response from the generation AI. The provision unit provides the information requested by the request unit. The provision unit provides, for example, tourist information, recommended shops, and help in case of trouble. For example, the provision unit suggests the best tourist spots based on the traveler's current location and interests.The providing unit can also suggest restaurants and shopping spots that suit the traveler's preferences and budget. Furthermore, the providing unit can also provide local emergency contact information and procedures. For example, the providing unit can suggest optimal tourist spots based on the traveler's current location and provide detailed descriptions and access methods. This allows the travel support system according to the embodiment to enable the traveler to obtain effective answers based on local information by linking with the local generation AI. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can generate tourist spot information using the generation AI to suggest optimal tourist spots based on the traveler's current location and interests.

[0062] The providing unit can suggest tourist spots based on the traveler's current location and interests. The providing unit, for example, suggests optimal tourist spots based on the traveler's current location. For example, the providing unit suggests tourist spots close to the traveler's current location. The providing unit can also suggest tourist spots based on the traveler's interests. For example, the providing unit can suggest historical tourist spots based on the traveler's interests. The providing unit can also suggest tourist spots with natural scenery based on the traveler's interests. For example, the providing unit can suggest museums and art galleries based on the traveler's interests. This can improve traveler satisfaction by suggesting optimal tourist spots based on the traveler's current location and interests. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can generate tourist spot information using a generation AI in order to suggest optimal tourist spots based on the traveler's current location and interests.

[0063] The providing unit can suggest restaurants and shopping spots according to the traveler's preferences and budget. The providing unit, for example, suggests restaurants based on the traveler's preferences. For example, the providing unit suggests restaurants serving local cuisine based on the traveler's preferences. The providing unit can also suggest high-end restaurants based on the traveler's preferences. The providing unit can also suggest restaurants according to the traveler's budget. For example, the providing unit can suggest affordable restaurants based on the traveler's budget. The providing unit can also suggest high-end restaurants based on the traveler's budget. The providing unit can also suggest shopping spots according to the traveler's preferences and budget. For example, the providing unit can suggest shopping spots selling local specialties based on the traveler's preferences. The providing unit can also suggest affordable shopping spots based on the traveler's budget. This makes it possible to improve traveler satisfaction by making suggestions according to the traveler's preferences and budget. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can generate information using a generation AI to suggest optimal restaurants and shopping spots based on the traveler's preferences and budget.

[0064] The providing unit can provide local emergency contact information and response methods. The providing unit, for example, provides local emergency contact information. For example, the providing unit can provide contact information for the local police. The providing unit can also provide contact information for local hospitals. Furthermore, the providing unit can also provide contact information for the local embassy. For example, the providing unit can provide contact information for the local police to support travelers in responding quickly in the event of an emergency. The providing unit can also provide contact information for local hospitals to support travelers in responding quickly in the event of an emergency. The providing unit can also provide contact information for the local embassy to support travelers in responding quickly in the event of an emergency, such as a lost passport. This makes it possible to support travelers in responding quickly in the event of an emergency. Some or all of the above-described processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can generate information using a generation AI to provide local emergency contact information and response methods.

[0065] The connection unit can automatically detect the traveler's current location and provide an interface for connecting to the local generation AI. The connection unit, for example, automatically detects the traveler's current location. For example, the connection unit detects the traveler's current location with high accuracy using GPS. The connection unit can also detect the traveler's current location using Wi-Fi location information. Furthermore, the connection unit can detect the traveler's current location using cell tower information. For example, the connection unit receives a GPS signal to identify the traveler's current location. The Wi-Fi location information identifies the current location based on information about surrounding Wi-Fi access points. The cell tower information identifies the current location based on the signal strength of surrounding cell towers. After detecting the traveler's current location, the connection unit provides an interface for connecting to the local generation AI. For example, the connection unit connects to the local generation AI through a mobile application. The connection unit can also connect to the local generation AI through a web application. Furthermore, the connection unit can connect to the local generation AI through a voice assistant. For example, the connection unit detects the traveler's current location through a mobile application and connects to the local generation AI. This allows the traveler to easily connect to the local generation AI. Some or all of the above-described processing in the connection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the connection unit may provide an interface using the generation AI to detect the traveler's current location and connect to the local generation AI.

[0066] The request unit can provide an interface through which travelers request tourist information, recommended shops, and help in case of trouble. The request unit requests tourist information, for example, through a mobile application. For example, the request unit provides an interface through which travelers request tourist information. The request unit can also request recommended shops through a web application. For example, the request unit provides an interface through which travelers request recommended shops. The request unit can also request help in case of trouble through a voice assistant. For example, the request unit provides an interface through which travelers request help in case of trouble. This allows travelers to easily make requests. Some or all of the above-mentioned processing in the request unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the request unit can provide an interface using a generation AI through which travelers request tourist information, recommended shops, and help in case of trouble.

[0067] The detection unit can estimate the traveler's emotions and adjust the frequency of detecting the current location based on the estimated traveler's emotions. For example, if the traveler feels anxious, the detection unit increases the frequency of detecting the current location and updates the location information in real time. For example, if the traveler feels anxious, the detection unit increases the frequency of detecting the current location. Furthermore, if the traveler feels relaxed, the detection unit can reduce the frequency of detecting the current location to reduce battery consumption. For example, if the traveler feels relaxed, the detection unit reduces the frequency of detecting the current location. Furthermore, if the traveler is in a hurry, the detection unit can set the frequency of detecting the current location to a medium level and maintain a moderate update frequency. For example, if the traveler is in a hurry, the detection unit sets the frequency of detecting the current location to a medium level. This allows for adjusting the frequency of detecting the current location according to the traveler's emotions, thereby providing more appropriate location information. Emotion estimation is realized 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. Some or all of the above-described processing in the detection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the detection unit may estimate the traveler's emotion using the generation AI in order to estimate the emotion and adjust the detection frequency of the current location based on the estimated emotion.

[0068] The detection unit can analyze the traveler's past movement history and select an optimal current location detection method. The detection unit can, for example, improve the accuracy of current location detection based on places the traveler has frequently visited in the past. For example, the detection unit can improve the accuracy of current location detection based on places the traveler has frequently visited in the past. The detection unit can also analyze the traveler's past movement patterns and select an optimal detection method. For example, the detection unit can analyze the traveler's past movement patterns and select an optimal detection method. Furthermore, the detection unit can adjust the current location detection method based on the means of transportation used by the traveler in the past. For example, the detection unit adjusts the current location detection method based on the means of transportation used by the traveler in the past. This can improve the accuracy of current location detection based on the past movement history. Some or all of the above-described processing in the detection unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the detection unit can analyze the traveler's past movement history and use a generation AI to analyze the movement history to select an optimal current location detection method.

[0069] When detecting the current location, the detection unit can adjust the detection accuracy based on the traveler's means of transportation. For example, when the traveler is traveling on foot, the detection unit increases the GPS accuracy to provide detailed location information. For example, when the traveler is traveling on foot, the detection unit increases the GPS accuracy. Furthermore, when the traveler is traveling by car, the detection unit can set the GPS accuracy to a medium level to reduce battery consumption. For example, when the traveler is traveling by car, the detection unit sets the GPS accuracy to a medium level. Furthermore, when the traveler is using public transportation, the detection unit can also set an optimal detection accuracy according to the means of transportation. For example, when the traveler is using public transportation, the detection unit sets an optimal detection accuracy according to the means of transportation. This makes it possible to provide an optimal detection accuracy according to the means of transportation. Some or all of the above-described processing in the detection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the detection unit can identify the means of transportation using a generation AI in order to adjust the detection accuracy based on the traveler's means of transportation.

[0070] The detection unit can estimate the traveler's emotions and adjust the timing of detecting the current location based on the estimated traveler's emotions. For example, if the traveler feels anxious, the detection unit frequently detects the traveler's current location to provide a sense of security. For example, if the traveler feels anxious, the detection unit frequently detects the traveler's current location. Furthermore, if the traveler is relaxed, the detection unit can delay the timing of detecting the current location to reduce battery consumption. For example, if the traveler is relaxed, the detection unit delays the timing of detecting the current location. Furthermore, if the traveler is in a hurry, the detection unit can detect the current location at an appropriate timing to provide efficient navigation. For example, if the traveler is in a hurry, the detection unit detects the current location at an appropriate timing. As a result, by adjusting the timing of detecting the current location according to the traveler'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 a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the detection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the detection unit may estimate the traveler's emotion using the generation AI in order to estimate the emotion and adjust the timing of detecting the current location based on the estimated emotion.

[0071] When detecting the current location, the detection unit can adjust the detection frequency taking into account the remaining battery level of the traveler's device. For example, when the remaining battery level of the device is low, the detection unit reduces the detection frequency of the current location to reduce battery consumption. For example, when the remaining battery level of the device is low, the detection unit reduces the detection frequency of the current location. Furthermore, when the remaining battery level of the device is sufficient, the detection unit can increase the detection frequency of the current location to provide detailed location information. For example, when the remaining battery level of the device is sufficient, the detection unit can increase the detection frequency of the current location. Furthermore, when the remaining battery level of the device is medium, the detection unit can set an appropriate detection frequency to achieve a balance. For example, when the remaining battery level of the device is medium, the detection unit sets an appropriate detection frequency. This makes it possible to provide an optimal detection frequency according to the remaining battery level. Some or all of the above-described processing in the detection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the detection unit can detect the remaining battery level of the device using a generation AI to detect the remaining battery level and adjust the detection frequency according to the remaining battery level.

[0072] When detecting the current location, the detection unit can select the optimal detection timing by referring to the traveler's schedule information. The detection unit, for example, detects the current location before an important event based on the traveler's schedule. For example, the detection unit detects the current location before an important event based on the traveler's schedule. The detection unit can also detect the current location during a time period when travel is expected based on the traveler's schedule information. For example, the detection unit detects the current location during a time period when travel is expected based on the traveler's schedule information. The detection unit can also set the optimal detection timing according to the traveler's schedule. For example, the detection unit sets the optimal detection timing according to the traveler's schedule. This makes it possible to provide the optimal detection timing based on the schedule information. Some or all of the above-described processing in the detection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the detection unit can refer to the traveler's schedule information and analyze the schedule information using a generation AI to select the optimal detection timing.

[0073] The connection unit can estimate the traveler's emotions and adjust the connection stability based on the estimated traveler's emotions. For example, if the traveler feels anxious, the connection unit increases the connection stability to provide reliability. For example, if the traveler feels anxious, the connection unit increases the connection stability. Furthermore, if the traveler is relaxed, the connection unit can set the connection stability to a medium level to reduce battery consumption. For example, if the traveler is relaxed, the connection unit sets the connection stability to a medium level. Furthermore, if the traveler is in a hurry, the connection unit can increase the connection stability to provide information quickly. For example, if the traveler is in a hurry, the connection unit increases the connection stability. This makes it possible to provide optimal connection stability according to the traveler's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the connection unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the connection unit can use generative AI to estimate emotions of the traveler and adjust the stability of the connection based on the estimated emotions.

[0074] When connecting, the connection unit can analyze the traveler's past connection history and select the optimal connection method. For example, the connection unit selects the optimal connection method based on the connection methods used by the traveler in the past. For example, the connection unit selects the optimal connection method based on the connection methods used by the traveler in the past. The connection unit can also suggest a stable connection method based on the traveler's past connection history. For example, the connection unit suggests a stable connection method based on the traveler's past connection history. Furthermore, the connection unit can analyze the traveler's past connection history and select the most efficient connection method. For example, the connection unit analyzes the traveler's past connection history and selects the most efficient connection method. This makes it possible to provide the optimal connection method based on the past connection history. Some or all of the above-mentioned processing in the connection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the connection unit can analyze the traveler's past connection history and use a generation AI to analyze the connection history and select the optimal connection method.

[0075] The connection unit can adjust the connection method based on the traveler's communication environment when connecting. For example, when the traveler is using Wi-Fi, the connection unit selects a stable connection method. For example, when the traveler is using Wi-Fi, the connection unit selects a stable connection method. Furthermore, when the traveler is using mobile data, the connection unit can select a connection method that reduces data consumption. For example, when the traveler is using mobile data, the connection unit selects a connection method that reduces data consumption. Furthermore, the connection unit can adjust the optimal connection method according to the traveler's communication environment. For example, the connection unit adjusts the optimal connection method according to the traveler's communication environment. This makes it possible to provide the optimal connection method according to the communication environment. Some or all of the above-mentioned processing in the connection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the connection unit can detect the communication environment of the traveler and adjust the connection method according to the communication environment.

[0076] The connection unit can estimate the traveler's emotions and determine connection priorities based on the estimated traveler's emotions. For example, if the traveler is feeling anxious, the connection unit prioritizes the connection of important information. For example, if the traveler is feeling anxious, the connection unit prioritizes the connection of important information. The connection unit can also prioritize the connection of general information if the traveler is relaxed. For example, if the traveler is relaxed, the connection unit prioritizes the connection of general information. The connection unit can also prioritize the connection of emergency information if the traveler is in a hurry. For example, if the traveler is in a hurry, the connection unit prioritizes the connection of emergency information. This makes it possible to provide optimal connection priorities according to the traveler's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the connection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the connection unit can use generative AI to estimate emotions to estimate a traveler's emotions and prioritize connections based on the estimated emotions.

[0077] When connecting, the connection unit can select a connection method taking into account the communication speed of the traveler's device. For example, if the communication speed of the device is high, the connection unit selects a high-speed connection method. For example, if the communication speed of the device is high, the connection unit selects a high-speed connection method. Furthermore, if the communication speed of the device is low, the connection unit can select a stable connection method. For example, if the communication speed of the device is low, the connection unit selects a stable connection method. Furthermore, the connection unit can select the optimal connection method depending on the communication speed of the device. For example, the connection unit selects the optimal connection method depending on the communication speed. This makes it possible to provide the optimal connection method depending on the communication speed. Some or all of the above-mentioned processing in the connection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the connection unit can detect the communication speed of the device and use the generation AI to detect the communication speed in order to select a connection method depending on the communication speed.

[0078] When connecting, the connection unit can select the optimal connection method by referring to the security settings of the traveler's device. For example, if the security settings of the device are high, the connection unit selects a secure connection method. For example, if the security settings of the device are high, the connection unit selects a secure connection method. Furthermore, if the security settings of the device are low, the connection unit can select a general connection method. For example, if the security settings of the device are low, the connection unit selects a general connection method. Furthermore, the connection unit can select the optimal connection method depending on the security settings of the device. For example, the connection unit selects the optimal connection method depending on the security settings. This makes it possible to provide the optimal connection method depending on the security settings. Some or all of the above-mentioned processing in the connection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the connection unit can detect the security settings using a generation AI in order to refer to the security settings of the device and select a connection method depending on the security settings.

[0079] The request unit can estimate the traveler's emotions and adjust the way the request is expressed based on the estimated traveler's emotions. For example, if the traveler is feeling anxious, the request unit can provide a concise and clear expression. For example, if the traveler is feeling anxious, the request unit can provide a concise and clear expression. Furthermore, if the traveler is relaxed, the request unit can provide a detailed expression. For example, if the traveler is relaxed, the request unit can provide a detailed expression. Furthermore, if the traveler is in a hurry, the request unit can provide a quickly understandable expression. For example, if the traveler is in a hurry, the request unit can provide a quickly understandable expression. This makes it possible to provide an optimal way to express the request according to the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the request unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the request department can use generative AI to estimate emotions of the traveler and adjust the way the request is expressed based on the estimated emotions.

[0080] At the time of a request, the request unit can analyze the traveler's past request history and select the optimal request method. For example, the request unit selects the optimal request method based on the request methods used by the traveler in the past. For example, the request unit selects the optimal request method based on the request methods used by the traveler in the past. The request unit can also suggest an efficient request method based on the traveler's past request history. For example, the request unit suggests an efficient request method based on the traveler's past request history. Furthermore, the request unit can analyze the traveler's past request history and select the most appropriate request method. For example, the request unit analyzes the traveler's past request history and selects the most appropriate request method. This makes it possible to provide the optimal request method based on the past request history. Some or all of the above-described processing in the request unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the request unit can analyze the traveler's past request history using a generation AI to select the optimal request method.

[0081] The request unit can adjust the request content based on the traveler's current situation at the time of the request. For example, if the traveler makes a request at night, the request unit prioritizes suggesting safe locations. For example, if the traveler makes a request at night, the request unit prioritizes suggesting safe locations. Furthermore, the request unit can also suggest indoor tourist spots when the traveler makes a request during rainy weather. For example, the request unit can suggest indoor tourist spots when the traveler makes a request during rainy weather. Furthermore, the request unit can also adjust the optimal request content according to the traveler's current situation. For example, the request unit adjusts the optimal request content according to the traveler's current situation. This makes it possible to provide the optimal request content according to the current situation. Some or all of the above-mentioned processing in the request unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the request unit can detect the situation using a generation AI in order to detect the traveler's current situation and adjust the request content according to the current situation.

[0082] The request unit can estimate the traveler's emotions and prioritize requests based on the estimated traveler's emotions. For example, if the traveler is feeling anxious, the request unit prioritizes urgent requests. For example, if the traveler is feeling anxious, the request unit prioritizes urgent requests. The request unit can also prioritize general requests if the traveler is relaxed. For example, if the traveler is relaxed, the request unit prioritizes general requests. The request unit can also prioritize requests requiring a quick response if the traveler is in a hurry. For example, if the traveler is in a hurry, the request unit prioritizes requests requiring a quick response. This makes it possible to provide optimal request prioritization according to the traveler's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the request unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the request department can use generative AI to estimate emotions of travelers and prioritize requests based on the estimated emotions.

[0083] At the time of request, the request unit can select a request method taking into consideration the traveler's device usage status. For example, if the device's battery level is low, the request unit selects a simple request method. For example, if the device's battery level is low, the request unit selects a simple request method. Furthermore, the request unit can also select a detailed request method if the device's battery level is sufficient. For example, if the device's battery level is sufficient, the request unit selects a detailed request method. Furthermore, the request unit can also select the optimal request method depending on the device usage status. For example, the request unit selects the optimal request method depending on the device usage status. This makes it possible to provide the optimal request method depending on the device usage status. Some or all of the above-mentioned processing in the request unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the request unit can detect the device usage status using a generation AI in order to detect the usage status and select a request method depending on the usage status.

[0084] At the time of a request, the request unit can select the optimal request method by referring to the traveler's language setting. The request unit, for example, automatically sets the language of the request based on the language setting of the traveler's device. For example, the request unit automatically sets the language of the request based on the language setting of the traveler's device. The request unit can also provide a language switching function if the traveler uses multiple languages. For example, the request unit provides a language switching function if the traveler uses multiple languages. Furthermore, the request unit can also provide the request in a specific language if the traveler selects that language. For example, the request unit provides the request in that language if the traveler selects a specific language. This makes it possible to provide the optimal request method according to the language setting. Some or all of the above-described processing in the request unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the request unit can refer to the traveler's language setting and detect the language setting using a generation AI in order to select a request method according to the language setting.

[0085] The providing unit can estimate the traveler's emotions and adjust the information provision method based on the estimated traveler's emotions. For example, if the traveler is feeling anxious, the providing unit provides concise and clear information. For example, if the traveler is feeling anxious, the providing unit provides concise and clear information. Furthermore, if the traveler is relaxed, the providing unit can provide detailed information. For example, if the traveler is relaxed, the providing unit can provide detailed information. Furthermore, if the traveler is in a hurry, the providing unit can provide information that can be quickly understood. For example, if the traveler is in a hurry, the providing unit provides information that can be quickly understood. This makes it possible to provide an optimal information provision method according to the traveler's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can use the generating AI to estimate emotions in order to estimate the traveler's emotions and adjust the way information is provided based on the estimated emotions.

[0086] When providing information, the providing unit can analyze the traveler's past information provision history and select the optimal provision method. The providing unit, for example, selects the optimal provision method based on the information provision method used by the traveler in the past. For example, the providing unit selects the optimal provision method based on the information provision method used by the traveler in the past. The providing unit can also suggest an efficient provision method based on the traveler's past information provision history. For example, the providing unit suggests an efficient provision method based on the traveler's past information provision history. The providing unit can also analyze the traveler's past information provision history and select the most appropriate provision method. For example, the providing unit analyzes the traveler's past information provision history and selects the most appropriate provision method. This makes it possible to provide the optimal provision method based on the past information provision history. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can analyze the traveler's past information provision history and select the optimal provision method using a generation AI.

[0087] When providing information, the providing unit can adjust the provided content based on the traveler's current situation. For example, when a traveler requests information at night, the providing unit prioritizes providing safe locations. For example, when a traveler requests information at night, the providing unit prioritizes providing safe locations. Furthermore, when a traveler requests information in rainy weather, the providing unit can provide indoor tourist spots. For example, when a traveler requests information in rainy weather, the providing unit provides indoor tourist spots. Furthermore, the providing unit can adjust the optimal provided content according to the traveler's current situation. For example, the providing unit adjusts the optimal provided content according to the traveler's current situation. This makes it possible to provide the optimal provided content according to the current situation. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can detect the current situation of the traveler and adjust the provided content according to the current situation.

[0088] The providing unit can estimate the traveler's emotions and determine the priority of information based on the estimated traveler's emotions. For example, if the traveler is feeling anxious, the providing unit prioritizes providing emergency information. For example, if the traveler is feeling anxious, the providing unit prioritizes providing emergency information. The providing unit can also prioritize providing general information if the traveler is relaxed. For example, if the traveler is relaxed, the providing unit prioritizes providing general information. Furthermore, if the traveler is in a hurry, the providing unit can also prioritize providing information requiring a quick response. For example, if the traveler is in a hurry, the providing unit prioritizes providing information requiring a quick response. This makes it possible to provide optimal information priorities according to the traveler's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can use the generating AI to estimate emotions in order to estimate the emotions of the traveler and determine the priority of information based on the estimated emotions.

[0089] When providing information, the providing unit can select a providing method taking into consideration the display settings of the traveler's device. For example, when the display setting of the device is high resolution, the providing unit provides detailed information. For example, when the display setting of the device is high resolution, the providing unit provides detailed information. Furthermore, when the display setting of the device is low resolution, the providing unit can also provide concise information. For example, when the display setting of the device is low resolution, the providing unit provides concise information. Furthermore, the providing unit can also select an optimal providing method according to the display setting of the device. For example, the providing unit selects an optimal providing method according to the display setting. This makes it possible to provide an optimal providing method according to the display setting. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can detect the display setting of the device and select a providing method according to the display setting.

[0090] When providing information, the providing unit can select the optimal providing method by referring to the traveler's language setting. The providing unit, for example, automatically sets the language of the information based on the language setting of the traveler's device. For example, the providing unit automatically sets the language of the information based on the language setting of the traveler's device. The providing unit can also provide a language switching function when the traveler uses multiple languages. For example, the providing unit provides a language switching function when the traveler uses multiple languages. Furthermore, the providing unit can also provide information in a specific language when the traveler selects that language. For example, the providing unit provides information in that language when the traveler selects a specific language. This makes it possible to provide the optimal providing method according to the language setting. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can refer to the traveler's language setting and detect the language setting using a generation AI in order to select a providing method according to the language setting. === Hard Collateral 1-1 === Each of the multiple elements, including the detection unit, connection unit, request unit, and provision unit, described above, is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the detection unit detects the traveler's current location using GPS or Wi-Fi location information of the smart device 14. The connection unit connects to a local generation AI through a mobile application of the smart device 14. The request unit requests tourist information, recommended shops, and help in case of trouble using the mobile application of the smart device 14. The provision unit provides the information generated by the specific processing unit 290 of the data processing device 12 to the traveler through the display 40A and speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned detection unit, connection unit, request unit, and provision unit, is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the detection unit detects the traveler's current location using GPS or Wi-Fi location information of the smart glasses 214. The connection unit connects to a local generation AI through a mobile application of the smart glasses 214. The request unit requests tourist information, recommended shops, and help in case of trouble using the mobile application of the smart glasses 214. The provision unit provides the information generated by the specific processing unit 290 of the data processing device 12 to the traveler through the display or speaker of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned detection unit, connection unit, request unit, and provision unit is realized, for example, in at least one of the headset terminal 314 and the data processing device 12. For example, the detection unit detects the traveler's current location using the GPS or Wi-Fi location information of the headset terminal 314. The connection unit connects to a local generation AI through a mobile application of the headset terminal 314. The request unit requests tourist information, recommended shops, and help in case of trouble using the mobile application of the headset terminal 314. The provision unit provides the information generated by the specific processing unit 290 of the data processing device 12 to the traveler through the display or speaker of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned detection unit, connection unit, request unit, and provision unit is realized, for example, in at least one of the robot 414 and the data processing device 12. For example, the detection unit detects the traveler's current location using the robot 414's GPS or Wi-Fi location information. The connection unit connects to a local generation AI through the robot 414's mobile application. The request unit uses the robot 414's mobile application to request tourist information, recommended shops, and help in case of trouble. The provision unit provides the information generated by the specific processing unit 290 of the data processing device 12 to the traveler through the robot 414's display and speaker.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The information providing unit can estimate the traveler's emotions and adjust the way information is provided based on the estimated emotions. For example, if the traveler is feeling anxious, the information providing unit can provide concise and clear information. If the traveler is relaxed, the information providing unit can select a way of providing information that includes detailed information. Furthermore, if the traveler is in a hurry, the information providing unit can provide information that can be quickly understood. This makes it possible to provide the optimal way of providing information according to the traveler's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc.

[0093] The provision unit can analyze the traveler's past information provision history and select the optimal provision method. For example, the optimal provision method is selected based on the information provision methods used by the traveler in the past. It can also suggest an efficient provision method based on the traveler's past information provision history. Furthermore, it can analyze the traveler's past information provision history and select the most appropriate provision method. This makes it possible to provide the optimal provision method based on the past information provision history.

[0094] The providing unit can adjust the content to be provided based on the traveler's current situation. For example, if a traveler requests information at night, safe locations can be provided with priority. Also, if a traveler requests information during rainy weather, indoor tourist spots can be provided. Furthermore, the providing unit can adjust the optimal content to be provided based on the traveler's current situation. This makes it possible to provide the optimal content to be provided according to the current situation.

[0095] The providing unit can estimate the traveler's emotions and determine the priority of information based on the estimated emotions. For example, if the traveler is feeling anxious, emergency information can be provided first. If the traveler is relaxed, general information can be provided first. Furthermore, if the traveler is in a hurry, information requiring a quick response can be provided first. This makes it possible to provide optimal information priorities according to the traveler's emotions. Emotion estimation is achieved using an emotion engine or generative AI.

[0096] The providing unit can select the providing method taking into consideration the display settings of the traveler's device. For example, if the device's display setting is high resolution, detailed information can be provided. On the other hand, if the device's display setting is low resolution, concise information can be provided. Furthermore, the optimal providing method can be selected depending on the device's display setting. This makes it possible to provide the optimal providing method depending on the display setting.

[0097] The providing unit can select the optimal providing method by referring to the traveler's language setting. For example, the language of the information can be automatically set based on the language setting of the traveler's device. In addition, if the traveler uses multiple languages, a language switching function can be provided. Furthermore, if the traveler selects a specific language, the information can be provided in that language. This makes it possible to provide the optimal providing method according to the language setting.

[0098] The providing unit can estimate the traveler's emotions and adjust the frequency of information provision based on the estimated emotions. For example, if the traveler is feeling anxious, information can be provided more frequently to give a sense of security. Also, if the traveler is relaxed, the frequency of information provision can be reduced to reduce battery consumption. Furthermore, if the traveler is in a hurry, information can be provided at an appropriate frequency to provide efficient navigation. This makes it possible to provide the optimal frequency of information provision according to the traveler's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc.

[0099] The providing unit can analyze the traveler's past movement history and select the optimal information provision method. For example, the accuracy of information provision can be improved based on places that the traveler has frequently visited in the past. The providing unit can also analyze the traveler's past movement patterns and select the optimal information provision method. Furthermore, the information provision method can be adjusted based on the means of transportation that the traveler has used in the past. This makes it possible to provide the optimal information provision method based on the travel history in the past.

[0100] The providing unit can estimate the traveler's emotions and adjust the level of detail of information based on the estimated emotions. For example, if the traveler is feeling anxious, detailed information can be provided to give a sense of security. If the traveler is relaxed, concise information can be provided to reduce battery consumption. Furthermore, if the traveler is in a hurry, information that can be quickly understood can be provided. This makes it possible to provide the optimal level of information detail according to the traveler's emotions. Emotion estimation is achieved using an emotion engine or generative AI.

[0101] The providing unit can select an information providing method taking into consideration the remaining battery level of the traveler's device. For example, if the remaining battery level of the device is low, brief information can be provided to reduce battery consumption. Also, if the remaining battery level of the device is sufficient, detailed information can be provided. Furthermore, if the remaining battery level of the device is medium, an appropriate information providing method can be selected to achieve a balance. This makes it possible to provide the optimal information providing method according to the remaining battery level.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The detection unit detects the current location of the traveler. The current location of the traveler is detected using GPS, Wi-Fi location information, cell tower information, etc. For example, the detection unit uses GPS to detect the current location of the traveler with high accuracy. The current location can also be identified using Wi-Fi location information and cell tower information. Step 2: The connection unit connects to the local generation AI based on the current location detected by the detection unit. The connection unit can connect to the local generation AI through a mobile application, a web application, or a voice assistant. Step 3: The request unit requests tourist information, recommended shops, and help in case of trouble from the generated AI connected by the connection unit. The request unit can make requests through a mobile application, a web application, or a voice assistant. Step 4: The provider provides the information requested by the requester. The provider provides tourist information, recommended shops, and help in case of trouble. For example, the provider suggests the best tourist spots based on the traveler's current location and interests, and provides detailed descriptions and access instructions.

[0104] 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.

[0105] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0106] 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.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] [Explanation of symbols]

[0176] 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 detection unit for detecting the current location of a traveler; a connection unit that connects to a local generation AI based on the current location detected by the detection unit; a request unit that requests tourist information, recommended shops, or help in case of trouble from the generated AI connected by the connection unit; a providing unit that provides the information requested by the requesting unit. A system characterized by:

2. The providing unit Suggest attractions based on a traveler's location or interests 2. The system of claim 1.

3. The providing unit Suggest restaurants or shopping spots that suit your tastes or budget 2. The system of claim 1.

4. The providing unit Provide local emergency contact information or instructions 2. The system of claim 1.

5. The connection portion is Automatically detects the traveler's current location and provides an interface to connect to local AI generation 2. The system of claim 1.

6. The request unit Providing an interface for travelers to request tourist information, recommendations, or help in case of a problem 2. The system of claim 1.

7. The detection unit Estimate traveler sentiment and adjust location detection frequency based on the estimated traveler sentiment.

2. The system of claim 1.

8. The detection unit Analyze the traveler's past movement history and select a location detection method 2. The system of claim 1.

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