system

Abstract

The system according to this embodiment aims to respond quickly and accurately to unexpected problems during travel. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, and a proposal unit. The reception unit receives input from travelers. The analysis unit analyzes the local situation in real time based on the information received by the reception unit. The proposal unit proposes the optimal course of action based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to quickly and accurately respond to unexpected troubles during travel.

[0005] The system according to the embodiment aims to quickly and accurately respond to unexpected troubles during travel.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a proposal unit. The reception unit receives the input from the traveler. The analysis unit analyzes the local situation in real time based on the information received by the reception unit. The proposal unit proposes an optimal countermeasure based on the analysis result obtained by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can respond quickly and accurately to unexpected problems during travel. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​agent system according to an embodiment of the present invention is a system that responds quickly and accurately to unexpected troubles during travel. When a traveler encounters trouble, this AI agent system analyzes the local situation in real time and proposes the optimal solution. This mechanism allows travelers to enjoy their trip with peace of mind. For example, if a traveler gets lost, the AI ​​agent system identifies their current location based on GPS data and proposes the optimal route. Furthermore, if a traveler faces a language barrier, the AI ​​agent system uses natural language processing technology to translate the local language and provides appropriate advice. In addition, if a traveler faces an emergency, the AI ​​agent system suggests local emergency contacts and the nearest medical facilities. If a traveler faces cultural troubles, the AI ​​agent system provides advice based on local culture and customs. This allows travelers to deal with troubles quickly and accurately. Because the AI ​​agent system provides personalized support according to the traveler's needs, travelers can enjoy their trip at their own pace. Furthermore, the AI ​​agent system improves user satisfaction by reducing the time required to resolve problems, thereby enhancing the traveler's sense of security. For example, if a traveler faces an emergency, the AI ​​agent system suggests local emergency contacts and the nearest medical facilities. This allows travelers to take quick and appropriate action. Furthermore, if travelers encounter cultural difficulties, the AI ​​agent system provides advice based on local culture and customs. This allows travelers to adapt to the local culture and avoid problems. In this way, the AI ​​agent system is an innovative solution that provides travelers with a safe and comfortable travel experience. With the support of the AI ​​agent system, travelers can enjoy their trip with peace of mind. In addition, the AI ​​agent system can broaden the range of activities and experiences of travelers and promote cross-cultural understanding. As a result, the AI ​​agent system can respond quickly and accurately to unexpected problems that travelers may encounter.

[0029] The AI ​​agent system according to this embodiment comprises a reception unit, an analysis unit, and a suggestion unit. The reception unit receives input from travelers. Traveler input includes, but is not limited to, text input, voice input, and image input. For example, the reception unit can receive text messages from travelers using their smartphones. The reception unit can also receive voice input from travelers. Furthermore, the reception unit can also receive image uploads from travelers. For example, the reception unit analyzes photos taken by travelers to understand the nature of the problem. The analysis unit analyzes the local situation in real time based on the information received by the reception unit. For example, the analysis unit uses GPS data to identify the traveler's current location. The analysis unit can also translate the local language using natural language processing technology. Furthermore, the analysis unit can acquire and analyze local weather information and traffic conditions in real time. For example, the analysis unit identifies the traveler's current location and suggests the optimal route. The analysis unit also translates the local language and provides appropriate advice to travelers. The suggestion department proposes the optimal course of action based on the analysis results obtained by the analysis department. For example, the suggestion department may suggest emergency contacts or the nearest medical facilities. The suggestion department can also provide advice based on local culture and customs. Furthermore, the suggestion department can provide personalized support according to the traveler's needs. For example, if a traveler faces an emergency, the suggestion department will suggest the nearest medical facilities. Also, if a traveler faces cultural troubles, the suggestion department will provide advice based on local culture and customs. As a result, the AI ​​agent system according to this embodiment can respond quickly and accurately to unexpected troubles faced by travelers.

[0030] The reception desk accepts input from travelers. This input includes, but is not limited to, text input, voice input, and image input. For example, the reception desk accepts text messages from travelers using their smartphones. Specifically, travelers can input problems or questions in text format through a smartphone application. This allows travelers to easily communicate their situation and problems to the system. The reception desk can also accept voice input from travelers. In the case of voice input, travelers can use their smartphone's microphone to communicate questions or problems verbally. Voice input is particularly useful when hands are occupied or when text input is difficult. Furthermore, the reception desk can accept image uploads from travelers. For example, it can analyze photos taken by travelers to understand the nature of the problem. In the case of image input, travelers can use their smartphone camera to take photos of the local situation or problem and upload them to the system. This allows the system to understand the situation more accurately based on visual information. By supporting these diverse input methods, the reception desk can flexibly respond to the various situations travelers face. Furthermore, the reception department is also responsible for quickly processing the entered information and transmitting it to the analysis department. This allows the reception department to efficiently receive traveler input and support a rapid response throughout the entire system.

[0031] The analysis department analyzes local conditions in real time based on information received by the reception department. For example, the analysis department uses GPS data to identify the traveler's current location. Specifically, it analyzes GPS data obtained from the traveler's smartphone to determine the traveler's precise location. This allows the system to know where the traveler is in real time and provide appropriate support. The analysis department can also translate local languages ​​using natural language processing technology. For example, to enable travelers to communicate smoothly with locals without language barriers, the system translates the traveler's input and provides advice and information in the local language. Furthermore, the analysis department can acquire and analyze local weather information and traffic conditions in real time. For example, if the weather suddenly changes or traffic congestion occurs while a traveler is traveling, the system can quickly acquire this information and suggest the optimal route and actions for the traveler. The analysis department integrates this diverse information to comprehensively understand the traveler's situation and build a foundation for providing optimal support. In addition, the analysis department can utilize historical data and statistical information to analyze traveler behavior patterns and trends, and formulate future predictions and countermeasures. This allows the analysis department to not only grasp the situation in real time, but also to handle long-term risk management and forecasting, thereby improving the reliability and security of the entire system.

[0032] The suggestion department proposes the most suitable course of action based on the analysis results obtained by the analysis department. For example, the suggestion department may suggest emergency contacts and the nearest medical facilities. Specifically, if a traveler faces an emergency, the suggestion department will quickly provide the medical facilities and emergency contacts closest to the traveler's current location. This allows the traveler to receive prompt and appropriate medical assistance. The suggestion department can also provide advice based on local culture and customs. For example, if a traveler faces cultural troubles in a local area, the suggestion department will advise on appropriate actions and responses based on local culture and customs. This allows the traveler to communicate smoothly with local people. Furthermore, the suggestion department can provide personalized support according to the traveler's needs. For example, if a traveler wants to visit a specific tourist destination, the suggestion department will provide information about that destination and the best route to visit. Also, if a traveler has specific dietary restrictions, the suggestion department will suggest restaurants and meals that accommodate those restrictions. By making optimal suggestions tailored to individual needs based on the traveler's input and analysis results, the suggestion department can improve the traveler's experience. Furthermore, the suggestion department can collect feedback from travelers and continuously improve the accuracy and effectiveness of its suggestions. This allows the proposal department to provide travelers with prompt and accurate support and to respond quickly and accurately to any unexpected problems travelers may encounter.

[0033] The analysis unit includes a location identification unit that determines the current location based on GPS data. The location identification unit determines the current location using, for example, GPS data acquired from the traveler's smartphone. If, for example, the traveler gets lost, the location identification unit determines the current location and suggests the optimal route. The location identification unit can also identify the nearest medical facility if the traveler faces an emergency. Furthermore, when the traveler visits a tourist destination, the location identification unit can suggest the optimal route from the current location to the tourist destination. For example, the location identification unit determines the traveler's current location and suggests the optimal route to the tourist destination. This allows for quick identification of the current location based on GPS data, enabling appropriate action even if the traveler gets lost. Some or all of the above processing in the location identification unit may be performed using, for example, AI, or without AI. For example, the location identification unit can input GPS data acquired from the traveler's smartphone into a generating AI and have the generating AI perform the determination of the current location.

[0034] The analysis unit includes a translation unit that translates local languages ​​using natural language processing technology. For example, if a traveler does not understand the local language, the translation unit uses natural language processing technology to translate it. For example, when a traveler communicates with locals, the translation unit translates the local language and provides appropriate advice. The translation unit can also translate local languages ​​when a traveler understands local signs or menus. Furthermore, the translation unit can translate local languages ​​when a traveler understands local culture and customs. For example, when a traveler communicates with locals, the translation unit translates the local language and provides appropriate advice. This ensures that travelers can receive appropriate advice even when faced with language barriers, by translating local languages ​​using natural language processing technology. Some or all of the above-described processes in the translation unit may be performed using AI, or not. For example, the translation unit can input text data entered by a traveler into a generating AI, which can then perform the translation into the local language.

[0035] The suggestion unit includes an emergency response unit that suggests emergency contacts and the nearest medical facilities. For example, if a traveler faces an emergency, the emergency response unit suggests emergency contacts and the nearest medical facilities. For example, if a traveler is involved in an accident, the emergency response unit suggests the nearest medical facilities. The emergency response unit can also suggest the nearest medical facilities if a traveler becomes ill. Furthermore, the emergency response unit can also suggest emergency contacts if a traveler is involved in a crime. For example, if a traveler is involved in an accident, the emergency response unit suggests the nearest medical facilities. This enables a quick and appropriate response even when a traveler faces an emergency by suggesting emergency contacts and the nearest medical facilities. Some or all of the above processing in the emergency response unit may be performed using AI, for example, or without AI. For example, the emergency response unit can input the traveler's current location into a generating AI and have the generating AI identify the nearest medical facilities.

[0036] The proposal department includes a cultural advice department that provides advice based on local culture and customs. For example, if a traveler encounters cultural trouble, the cultural advice department will provide advice based on local culture and customs. For example, the cultural advice department will provide appropriate advice when a traveler is trying to understand local religious customs. The cultural advice department can also provide appropriate advice when a traveler is trying to understand local social manners. Furthermore, the cultural advice department can also provide appropriate advice when a traveler is trying to understand local food culture. For example, the cultural advice department will provide appropriate advice when a traveler is trying to understand local religious customs. By providing advice based on local culture and customs, travelers will be able to respond appropriately even if they encounter cultural troubles. Some or all of the above processing in the cultural advice department may be performed using AI, for example, or not using AI. For example, the cultural advice department can input text data entered by a traveler into a generating AI and have the generating AI generate advice based on local culture and customs.

[0037] The reception department can analyze a traveler's past trouble history and select the most appropriate reception method. For example, the reception department may prioritize a particular reception method based on the troubles the traveler has frequently encountered in the past. For example, the reception department may suggest the most effective reception method based on the traveler's past trouble history. For example, the reception department may analyze a traveler's past trouble history and select a reception method appropriate to the type of trouble. This allows for the selection of the most appropriate reception method and more effective response by analyzing the traveler's past trouble history. Some or all of the above processes in the reception department may be performed using AI, for example, or not using AI. For example, the reception department may input the traveler's past trouble history data into a generating AI and have the generating AI select the most appropriate reception method.

[0038] The reception unit can filter information based on the traveler's current situation and areas of interest when receiving input. For example, the reception unit can prioritize receiving information that is highly relevant to the traveler based on their current situation. For example, the reception unit can filter and receive relevant information based on the traveler's areas of interest. For example, the reception unit can combine the traveler's current situation and areas of interest to receive the most relevant information. This allows the reception unit to prioritize receiving more relevant information by filtering based on the traveler's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the traveler's current situation data and areas of interest data into a generating AI and have the generating AI perform the filtering.

[0039] The reception unit can prioritize receiving highly relevant information by considering the traveler's geographical location information when receiving input. For example, the reception unit can prioritize receiving highly relevant information based on the traveler's current location. For example, the reception unit can prioritize receiving optimal information by considering the traveler's geographical location information. For example, the reception unit can filter and receive information related to the traveler's current location. This allows for the provision of more appropriate information by prioritizing the receipt of highly relevant information by considering the traveler's geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the traveler's geographical location information data into a generating AI and have the generating AI perform the filtering of highly relevant information.

[0040] The reception unit can analyze the traveler's social media activity and receive relevant information when data is received. For example, the reception unit can analyze the traveler's social media activity and prioritize receiving relevant information. For example, the reception unit can receive information based on the traveler's areas of interest from the traveler's social media activity. For example, the reception unit can receive the most relevant information by referring to the traveler's social media activity. This allows the reception unit to receive more relevant information by analyzing the traveler's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the traveler's social media activity data into a generating AI and have the generating AI perform the receiving of relevant information.

[0041] The analysis unit can adjust the level of detail of the analysis based on the severity of the problem during the analysis. For example, the analysis unit performs a detailed analysis for high-severity problems. For example, the analysis unit performs a concise analysis for low-severity problems. The analysis unit adjusts the level of detail of the analysis according to the severity of the problem. By adjusting the level of detail of the analysis based on the severity of the problem, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input problem severity data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the trouble category during analysis. For example, the analysis unit selects the optimal analysis algorithm depending on the trouble category. For example, the analysis unit applies different analysis algorithms for each trouble category. For example, the analysis unit applies the optimal analysis algorithm based on the trouble category. By applying different analysis algorithms depending on the trouble category, more appropriate analysis results can be provided. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input trouble category data into a generating AI and have the generating AI execute the application of the optimal analysis algorithm.

[0043] The analysis unit can determine the priority of analysis based on the timing of the troubles during the analysis. For example, the analysis unit prioritizes the analysis of recently occurring troubles. For example, the analysis unit determines the priority of analysis based on the timing of the troubles. For example, the analysis unit determines the optimal analysis order by considering the timing of the troubles. By determining the priority of analysis based on the timing of the troubles, more appropriate analysis results can be provided. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input trouble timing data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0044] The analysis unit can adjust the order of analysis based on the relevance of the problems during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant problems. For example, the analysis unit adjusts the order of analysis based on the relevance of the problems. For example, the analysis unit determines the optimal order of analysis by considering the relevance of the problems. By adjusting the order of analysis based on the relevance of the problems, more appropriate analysis results can be provided. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input problem relevance data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0045] The proposal unit can adjust the level of detail of its proposals based on the importance of the solutions. For example, the proposal unit provides detailed proposals for high-importance solutions. For example, it provides concise proposals for low-importance solutions. The proposal unit adjusts the level of detail of its proposals according to the importance of the solutions. By adjusting the level of detail of proposals based on the importance of the solutions, it is possible to provide more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input data on the importance of solutions into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposals.

[0046] The proposal unit can apply different proposal algorithms depending on the category of the solution when making a proposal. For example, the proposal unit selects the optimal proposal algorithm depending on the category of the solution. For example, the proposal unit applies a different proposal algorithm for each category of solution. For example, the proposal unit applies the optimal proposal algorithm based on the category of solution. By applying different proposal algorithms depending on the category of solution, it is possible to provide more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input solution category data into a generating AI and have the generating AI execute the application of the optimal proposal algorithm.

[0047] The proposal unit can determine the priority of proposals based on when the solutions occurred. For example, the proposal unit will prioritize recently occurring solutions. For example, the proposal unit will determine the priority of proposals based on when the solutions occurred. For example, the proposal unit will determine the optimal order of proposals, taking into account when the solutions occurred. This allows for the provision of more appropriate proposals by determining the priority of proposals based on when the solutions occurred. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input data on when the solutions occurred into a generating AI and have the generating AI perform the determination of proposal priorities.

[0048] The proposal unit can adjust the order of proposals based on the relevance of the solutions when making a proposal. For example, the proposal unit will prioritize proposing solutions that are highly relevant. For example, the proposal unit will adjust the order of proposals based on the relevance of the solutions. For example, the proposal unit will determine the optimal order of proposals by considering the relevance of the solutions. In this way, by adjusting the order of proposals based on the relevance of the solutions, it is possible to provide more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI. For example, the proposal unit can input the relevance data of the solutions into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0049] The location identification unit can select the optimal location identification method by referring to the traveler's past travel history when determining the location. For example, the location identification unit selects the optimal location identification method based on the traveler's past travel history. For example, the location identification unit proposes the most effective location identification method from the traveler's past travel history. For example, the location identification unit analyzes the traveler's past travel history and selects the optimal location identification method. This makes it possible to select the optimal location identification method by referring to the traveler's past travel history, enabling more effective location identification. Some or all of the above processing in the location identification unit may be performed using AI, for example, or without using AI. For example, the location identification unit can input the traveler's past travel history data into a generating AI and have the generating AI perform the selection of the optimal location identification method.

[0050] The location tracking unit can customize the location tracking method based on the traveler's current situation when determining the location. For example, the location tracking unit provides the optimal location tracking method based on the traveler's current situation. For example, the location tracking unit customizes the location tracking method considering the traveler's current situation. For example, the location tracking unit selects the optimal location tracking method according to the traveler's current situation. This makes it possible to determine the location more appropriately by customizing the location tracking method based on the traveler's current situation. Some or all of the above processing in the location tracking unit may be performed using AI, for example, or without AI. For example, the location tracking unit can input the traveler's current situation data into a generating AI and have the generating AI perform the customization of the location tracking method.

[0051] The location identification unit can select the optimal location identification method when determining the traveler's location, taking into account the traveler's geographical location information. For example, the location identification unit can select the optimal location identification method based on the traveler's current location. For example, the location identification unit can select the optimal location identification method taking into account the traveler's geographical location information. For example, the location identification unit can select the optimal location identification method based on information related to the traveler's current location. By selecting the optimal location identification method while taking into account the traveler's geographical location information, more appropriate location identification becomes possible. Some or all of the above processing in the location identification unit may be performed using AI, for example, or without using AI. For example, the location identification unit can input the traveler's geographical location information data into a generating AI and have the generating AI perform the selection of the optimal location identification method.

[0052] The location identification unit can analyze the traveler's social media activity and propose a method for location identification during location identification. For example, the location identification unit analyzes the traveler's social media activity and proposes the optimal method for location identification. For example, the location identification unit proposes relevant method for location identification based on the traveler's social media activity. For example, the location identification unit proposes the optimal method for location identification by referring to the traveler's social media activity. In this way, by analyzing the traveler's social media activity, a more appropriate method for location identification can be proposed. Some or all of the above processing in the location identification unit may be performed using AI, for example, or without AI. For example, the location identification unit can input the traveler's social media activity data into a generating AI and have the generating AI execute the proposal of the optimal method for location identification.

[0053] The translation unit can select the optimal translation method by referring to the traveler's past language usage history during translation. For example, the translation unit selects the optimal translation method based on the traveler's past language usage history. For example, the translation unit proposes the most effective translation method from the traveler's past language usage history. For example, the translation unit analyzes the traveler's past language usage history and selects the optimal translation method. This makes it possible to select the optimal translation method by referring to the traveler's past language usage history, resulting in more effective translation. Some or all of the above processes in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input the traveler's past language usage history data into a generating AI and have the generating AI select the optimal translation method.

[0054] The translation unit can customize the translation method based on the traveler's current situation during translation. For example, the translation unit provides the optimal translation method based on the traveler's current situation. For example, the translation unit customizes the translation method considering the traveler's current situation. For example, the translation unit selects the optimal translation method according to the traveler's current situation. This makes it possible to provide more appropriate translations by customizing the translation method based on the traveler's current situation. Some or all of the above processes in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input the traveler's current situation data into a generating AI and have the generating AI perform the customization of the translation method.

[0055] The translation unit can select the optimal translation method while considering the traveler's geographical location information. For example, the translation unit selects the optimal translation method based on the traveler's current location. For example, the translation unit selects the optimal translation method while considering the traveler's geographical location information. For example, the translation unit selects the optimal translation method based on information related to the traveler's current location. By selecting the optimal translation method while considering the traveler's geographical location information, more appropriate translations become possible. Some or all of the above processing in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input the traveler's geographical location data into a generating AI and have the generating AI perform the selection of the optimal translation method.

[0056] The translation unit can analyze the traveler's social media activity during translation and suggest translation methods. For example, the translation unit can analyze the traveler's social media activity and suggest the most appropriate translation method. For example, the translation unit can suggest relevant translation methods based on the traveler's social media activity. For example, the translation unit can suggest the most appropriate translation method by referring to the traveler's social media activity. In this way, by analyzing the traveler's social media activity, it is possible to suggest a more appropriate translation method. Some or all of the above processing in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input the traveler's social media activity data into a generating AI and have the generating AI suggest the most appropriate translation method.

[0057] The emergency response unit can select the optimal emergency response method by referring to the traveler's past emergency history during an emergency. For example, the emergency response unit selects the optimal emergency response method based on the traveler's past emergency history. For example, the emergency response unit proposes the most effective emergency response method from the traveler's past emergency history. For example, the emergency response unit analyzes the traveler's past emergency history and selects the optimal emergency response method. This makes it possible to select the optimal emergency response method by referring to the traveler's past emergency history, enabling a more effective emergency response. Some or all of the above processes in the emergency response unit may be performed using AI, for example, or without AI. For example, the emergency response unit can input the traveler's past emergency history data into a generating AI and have the generating AI perform the selection of the optimal emergency response method.

[0058] The emergency response unit can customize emergency response measures based on the traveler's current situation during an emergency. For example, the emergency response unit provides the optimal emergency response measures based on the traveler's current situation. For example, the emergency response unit customizes emergency response measures considering the traveler's current situation. For example, the emergency response unit selects the optimal emergency response measures according to the traveler's current situation. This makes it possible to provide a more appropriate emergency response by customizing emergency response measures based on the traveler's current situation. Some or all of the above processes in the emergency response unit may be performed using AI, for example, or without AI. For example, the emergency response unit can input the traveler's current situation data into a generating AI and have the generating AI perform the customization of emergency response measures.

[0059] The emergency response unit can select the optimal emergency response method in the event of an emergency, taking into account the traveler's geographical location information. For example, the emergency response unit selects the optimal emergency response method based on the traveler's current location. For example, the emergency response unit selects the optimal emergency response method by taking into account the traveler's geographical location information. For example, the emergency response unit selects the optimal emergency response method based on information related to the traveler's current location. By selecting the optimal emergency response method while taking into account the traveler's geographical location information, a more appropriate emergency response becomes possible. Some or all of the above processing in the emergency response unit may be performed using AI, for example, or without using AI. For example, the emergency response unit can input the traveler's geographical location information data into a generating AI and have the generating AI perform the selection of the optimal emergency response method.

[0060] The emergency response unit can analyze a traveler's social media activity during an emergency and propose emergency response measures. For example, the emergency response unit can analyze a traveler's social media activity and propose the most appropriate emergency response measures. For example, the emergency response unit can propose relevant emergency response measures based on the traveler's social media activity. For example, the emergency response unit can propose the most appropriate emergency response measures by referring to the traveler's social media activity. In this way, by analyzing the traveler's social media activity, it is possible to propose more appropriate emergency response measures. Some or all of the above processing in the emergency response unit may be performed using AI, for example, or without AI. For example, the emergency response unit can input traveler's social media activity data into a generating AI and have the generating AI propose the most appropriate emergency response measures.

[0061] The cultural advice department can select the most appropriate advice method when providing cultural advice by referring to the traveler's past cultural experiences. For example, the cultural advice department selects the most appropriate advice method based on the traveler's past cultural experiences. For example, the cultural advice department proposes the most effective advice method based on the traveler's past cultural experiences. For example, the cultural advice department analyzes the traveler's past cultural experiences and selects the most appropriate advice method. This allows for the selection of the most appropriate advice method by referring to the traveler's past cultural experiences, enabling more effective cultural advice. Some or all of the above processes in the cultural advice department may be performed using AI, for example, or without AI. For example, the cultural advice department can input the traveler's past cultural experience data into a generating AI and have the generating AI select the most appropriate advice method.

[0062] The Cultural Advice Department can customize the means of advice given based on the traveler's current situation when providing cultural advice. For example, the Cultural Advice Department can provide the most appropriate means of advice based on the traveler's current situation. For example, the Cultural Advice Department can customize the means of advice considering the traveler's current situation. For example, the Cultural Advice Department can select the most appropriate means of advice according to the traveler's current situation. This makes it possible to provide more appropriate cultural advice by customizing the means of advice based on the traveler's current situation. Some or all of the above processes in the Cultural Advice Department may be performed using AI, for example, or without AI. For example, the Cultural Advice Department can input data on the traveler's current situation into a generating AI and have the generating AI perform the customization of the means of advice.

[0063] The cultural advice unit can select the optimal advice method when providing cultural advice, taking into account the traveler's geographical location information. For example, the cultural advice unit can select the optimal advice method based on the traveler's current location. For example, the cultural advice unit can select the optimal advice method by taking into account the traveler's geographical location information. For example, the cultural advice unit can select the optimal advice method based on information related to the traveler's current location. By selecting the optimal advice method while taking into account the traveler's geographical location information, more appropriate cultural advice becomes possible. Some or all of the above processing in the cultural advice unit may be performed using AI, for example, or without using AI. For example, the cultural advice unit can input the traveler's geographical location data into a generating AI and have the generating AI select the optimal advice method.

[0064] The Cultural Advice Department can analyze a traveler's social media activity and propose methods of advice when providing cultural advice. For example, the Cultural Advice Department can analyze a traveler's social media activity and propose the most appropriate method of advice. For example, the Cultural Advice Department can propose relevant methods of advice based on a traveler's social media activity. For example, the Cultural Advice Department can propose the most appropriate method of advice by referring to a traveler's social media activity. In this way, by analyzing a traveler's social media activity, it is possible to propose more appropriate methods of advice. Some or all of the above processing in the Cultural Advice Department may be performed using AI, for example, or not using AI. For example, the Cultural Advice Department can input traveler's social media activity data into a generating AI and have the generating AI propose the most appropriate method of advice.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The reception desk can monitor the traveler's health status and adjust the input method based on that status. For example, if the traveler is tired, the reception desk can prioritize voice input, and if the traveler is healthy, it can prioritize text input. Furthermore, if the traveler is ill, the reception desk can provide a simplified input method, and if the traveler is healthy, it can provide a more detailed input method. This allows the reception desk to provide the optimal input method for the traveler by adjusting the input method based on their health status. Health status monitoring can be performed using devices such as smartwatches or fitness trackers. Some or all of the above processing at the reception desk may be performed using AI, or not. For example, the reception desk can input the traveler's health data into a generating AI and have the generating AI adjust the input method.

[0067] The analysis unit can refer to a traveler's past travel history and improve the accuracy of the analysis based on that history. For example, it can determine the traveler's current location more quickly and accurately based on data of places the traveler has visited in the past. It can also analyze the traveler's preferred tourist destinations and routes from their past travel history and suggest the optimal route. Furthermore, by referring to past travel history, it can understand patterns of problems the traveler has encountered in the past and respond quickly if similar problems occur. In this way, by referring to the traveler's past travel history, the accuracy of the analysis can be improved and more appropriate countermeasures can be suggested. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the traveler's past travel history data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0068] The suggestion unit can suggest local restaurants and meals, taking into account the traveler's dietary preferences. For example, if the traveler is vegetarian, the suggestion unit will suggest vegetarian restaurants. It can also suggest allergy-friendly restaurants if the traveler has specific allergies. Furthermore, if the traveler prefers a particular dish, it can suggest restaurants that serve that dish. This allows for optimal restaurant and meal suggestions based on the traveler's dietary preferences. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For instance, the suggestion unit can input the traveler's dietary preference data into a generating AI and have the AI ​​generate restaurant and meal suggestions.

[0069] The reception desk can provide the optimal input method, taking into account the traveler's language proficiency. For example, if the traveler is multilingual, the reception desk can accept input in multiple languages. If the traveler is unfamiliar with a particular language, the reception desk can prioritize input in a simpler language. Furthermore, if the traveler uses sign language, the reception desk can accept sign language input. This allows the reception desk to provide the optimal input method based on the traveler's language proficiency. Some or all of the above processing at the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the traveler's language proficiency data into a generating AI and have the generating AI provide the optimal input method.

[0070] The suggestion unit can refer to the traveler's past suggestion history and improve the accuracy of suggestions based on that history. For example, it can make suggestions best suited to the current situation based on data from suggestions the traveler has received in the past. It can also analyze the patterns of suggestions preferred by travelers from their past suggestion history and provide optimal suggestions. Furthermore, by referring to past suggestion history, it can understand the effectiveness of suggestions the traveler has received in the past and make more effective suggestions for similar situations. In this way, by referring to the traveler's past suggestion history, the accuracy of suggestions can be improved and more appropriate suggestions can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the traveler's past suggestion history data into a generating AI and have the generating AI perform the suggestion accuracy improvement.

[0071] The suggestion unit can make optimal suggestions by considering the traveler's current activity status. For example, if the traveler is sightseeing, it will prioritize suggestions related to tourist destinations. If the traveler is resting, it can also suggest places to relax or activities. Furthermore, if the traveler is on the move, it can also make suggestions related to travel. In this way, it can make optimal suggestions based on the traveler's current activity status. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the traveler's current activity status data into a generating AI and have the generating AI execute the optimal suggestions.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The reception desk receives input from travelers. This input includes text, voice, and image input. For example, travelers can enter text messages, voice input, or upload images using their smartphones. The reception desk can also analyze photos taken by travelers to understand the nature of the problem. Step 2: The analysis department analyzes local conditions in real time based on the information received by the reception department. For example, it can use GPS data to identify the traveler's current location and natural language processing technology to translate the local language. It can also acquire and analyze local weather information and traffic conditions in real time. This makes it possible to identify the traveler's current location, suggest the optimal route, and provide appropriate advice by translating the local language. Step 3: The proposal department proposes the best course of action based on the analysis results obtained by the analysis department. For example, they can suggest emergency contacts and the nearest medical facilities, or provide advice based on local culture and customs. They can also provide personalized support tailored to the traveler's needs. This allows for a quick and accurate response when travelers face emergencies or cultural difficulties.

[0074] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system that responds quickly and accurately to unexpected troubles during travel. When a traveler encounters trouble, this AI agent system analyzes the local situation in real time and proposes the optimal solution. This mechanism allows travelers to enjoy their trip with peace of mind. For example, if a traveler gets lost, the AI ​​agent system identifies their current location based on GPS data and proposes the optimal route. Furthermore, if a traveler faces a language barrier, the AI ​​agent system uses natural language processing technology to translate the local language and provides appropriate advice. In addition, if a traveler faces an emergency, the AI ​​agent system suggests local emergency contacts and the nearest medical facilities. If a traveler faces cultural troubles, the AI ​​agent system provides advice based on local culture and customs. This allows travelers to deal with troubles quickly and accurately. Because the AI ​​agent system provides personalized support according to the traveler's needs, travelers can enjoy their trip at their own pace. Furthermore, the AI ​​agent system improves user satisfaction by reducing the time required to resolve problems, thereby enhancing the traveler's sense of security. For example, if a traveler faces an emergency, the AI ​​agent system suggests local emergency contacts and the nearest medical facilities. This allows travelers to take quick and appropriate action. Furthermore, if travelers encounter cultural difficulties, the AI ​​agent system provides advice based on local culture and customs. This allows travelers to adapt to the local culture and avoid problems. In this way, the AI ​​agent system is an innovative solution that provides travelers with a safe and comfortable travel experience. With the support of the AI ​​agent system, travelers can enjoy their trip with peace of mind. In addition, the AI ​​agent system can broaden the range of activities and experiences of travelers and promote cross-cultural understanding. As a result, the AI ​​agent system can respond quickly and accurately to unexpected problems that travelers may encounter.

[0075] The AI ​​agent system according to this embodiment comprises a reception unit, an analysis unit, and a suggestion unit. The reception unit receives input from travelers. Traveler input includes, but is not limited to, text input, voice input, and image input. For example, the reception unit can receive text messages from travelers using their smartphones. The reception unit can also receive voice input from travelers. Furthermore, the reception unit can also receive image uploads from travelers. For example, the reception unit analyzes photos taken by travelers to understand the nature of the problem. The analysis unit analyzes the local situation in real time based on the information received by the reception unit. For example, the analysis unit uses GPS data to identify the traveler's current location. The analysis unit can also translate the local language using natural language processing technology. Furthermore, the analysis unit can acquire and analyze local weather information and traffic conditions in real time. For example, the analysis unit identifies the traveler's current location and suggests the optimal route. The analysis unit also translates the local language and provides appropriate advice to travelers. The suggestion department proposes the optimal course of action based on the analysis results obtained by the analysis department. For example, the suggestion department may suggest emergency contacts or the nearest medical facilities. The suggestion department can also provide advice based on local culture and customs. Furthermore, the suggestion department can provide personalized support according to the traveler's needs. For example, if a traveler faces an emergency, the suggestion department will suggest the nearest medical facilities. Also, if a traveler faces cultural troubles, the suggestion department will provide advice based on local culture and customs. As a result, the AI ​​agent system according to this embodiment can respond quickly and accurately to unexpected troubles faced by travelers.

[0076] The reception desk accepts input from travelers. This input includes, but is not limited to, text input, voice input, and image input. For example, the reception desk accepts text messages from travelers using their smartphones. Specifically, travelers can input problems or questions in text format through a smartphone application. This allows travelers to easily communicate their situation and problems to the system. The reception desk can also accept voice input from travelers. In the case of voice input, travelers can use their smartphone's microphone to communicate questions or problems verbally. Voice input is particularly useful when hands are occupied or when text input is difficult. Furthermore, the reception desk can accept image uploads from travelers. For example, it can analyze photos taken by travelers to understand the nature of the problem. In the case of image input, travelers can use their smartphone camera to take photos of the local situation or problem and upload them to the system. This allows the system to understand the situation more accurately based on visual information. By supporting these diverse input methods, the reception desk can flexibly respond to the various situations travelers face. Furthermore, the reception department is also responsible for quickly processing the entered information and transmitting it to the analysis department. This allows the reception department to efficiently receive traveler input and support a rapid response throughout the entire system.

[0077] The analysis department analyzes local conditions in real time based on information received by the reception department. For example, the analysis department uses GPS data to identify the traveler's current location. Specifically, it analyzes GPS data obtained from the traveler's smartphone to determine the traveler's precise location. This allows the system to know where the traveler is in real time and provide appropriate support. The analysis department can also translate local languages ​​using natural language processing technology. For example, to enable travelers to communicate smoothly with locals without language barriers, the system translates the traveler's input and provides advice and information in the local language. Furthermore, the analysis department can acquire and analyze local weather information and traffic conditions in real time. For example, if the weather suddenly changes or traffic congestion occurs while a traveler is traveling, the system can quickly acquire this information and suggest the optimal route and actions for the traveler. The analysis department integrates this diverse information to comprehensively understand the traveler's situation and build a foundation for providing optimal support. In addition, the analysis department can utilize historical data and statistical information to analyze traveler behavior patterns and trends, and formulate future predictions and countermeasures. This allows the analysis department to not only grasp the situation in real time, but also to handle long-term risk management and forecasting, thereby improving the reliability and security of the entire system.

[0078] The suggestion department proposes the most suitable course of action based on the analysis results obtained by the analysis department. For example, the suggestion department may suggest emergency contacts and the nearest medical facilities. Specifically, if a traveler faces an emergency, the suggestion department will quickly provide the medical facilities and emergency contacts closest to the traveler's current location. This allows the traveler to receive prompt and appropriate medical assistance. The suggestion department can also provide advice based on local culture and customs. For example, if a traveler faces cultural troubles in a local area, the suggestion department will advise on appropriate actions and responses based on local culture and customs. This allows the traveler to communicate smoothly with local people. Furthermore, the suggestion department can provide personalized support according to the traveler's needs. For example, if a traveler wants to visit a specific tourist destination, the suggestion department will provide information about that destination and the best route to visit. Also, if a traveler has specific dietary restrictions, the suggestion department will suggest restaurants and meals that accommodate those restrictions. By making optimal suggestions tailored to individual needs based on the traveler's input and analysis results, the suggestion department can improve the traveler's experience. Furthermore, the suggestion department can collect feedback from travelers and continuously improve the accuracy and effectiveness of its suggestions. This allows the proposal department to provide travelers with prompt and accurate support and to respond quickly and accurately to any unexpected problems travelers may encounter.

[0079] The analysis unit includes a location identification unit that determines the current location based on GPS data. The location identification unit determines the current location using, for example, GPS data acquired from the traveler's smartphone. If, for example, the traveler gets lost, the location identification unit determines the current location and suggests the optimal route. The location identification unit can also identify the nearest medical facility if the traveler faces an emergency. Furthermore, when the traveler visits a tourist destination, the location identification unit can suggest the optimal route from the current location to the tourist destination. For example, the location identification unit determines the traveler's current location and suggests the optimal route to the tourist destination. This allows for quick identification of the current location based on GPS data, enabling appropriate action even if the traveler gets lost. Some or all of the above processing in the location identification unit may be performed using, for example, AI, or without AI. For example, the location identification unit can input GPS data acquired from the traveler's smartphone into a generating AI and have the generating AI perform the determination of the current location.

[0080] The analysis unit includes a translation unit that translates local languages ​​using natural language processing technology. For example, if a traveler does not understand the local language, the translation unit uses natural language processing technology to translate it. For example, when a traveler communicates with locals, the translation unit translates the local language and provides appropriate advice. The translation unit can also translate local languages ​​when a traveler understands local signs or menus. Furthermore, the translation unit can translate local languages ​​when a traveler understands local culture and customs. For example, when a traveler communicates with locals, the translation unit translates the local language and provides appropriate advice. This ensures that travelers can receive appropriate advice even when faced with language barriers, by translating local languages ​​using natural language processing technology. Some or all of the above-described processes in the translation unit may be performed using AI, or not. For example, the translation unit can input text data entered by a traveler into a generating AI, which can then perform the translation into the local language.

[0081] The suggestion unit includes an emergency response unit that suggests emergency contacts and the nearest medical facilities. For example, if a traveler faces an emergency, the emergency response unit suggests emergency contacts and the nearest medical facilities. For example, if a traveler is involved in an accident, the emergency response unit suggests the nearest medical facilities. The emergency response unit can also suggest the nearest medical facilities if a traveler becomes ill. Furthermore, the emergency response unit can also suggest emergency contacts if a traveler is involved in a crime. For example, if a traveler is involved in an accident, the emergency response unit suggests the nearest medical facilities. This enables a quick and appropriate response even when a traveler faces an emergency by suggesting emergency contacts and the nearest medical facilities. Some or all of the above processing in the emergency response unit may be performed using AI, for example, or without AI. For example, the emergency response unit can input the traveler's current location into a generating AI and have the generating AI identify the nearest medical facilities.

[0082] The proposal department includes a cultural advice department that provides advice based on local culture and customs. For example, if a traveler encounters cultural trouble, the cultural advice department will provide advice based on local culture and customs. For example, the cultural advice department will provide appropriate advice when a traveler is trying to understand local religious customs. The cultural advice department can also provide appropriate advice when a traveler is trying to understand local social manners. Furthermore, the cultural advice department can also provide appropriate advice when a traveler is trying to understand local food culture. For example, the cultural advice department will provide appropriate advice when a traveler is trying to understand local religious customs. By providing advice based on local culture and customs, travelers will be able to respond appropriately even if they encounter cultural troubles. Some or all of the above processing in the cultural advice department may be performed using AI, for example, or not using AI. For example, the cultural advice department can input text data entered by a traveler into a generating AI and have the generating AI generate advice based on local culture and customs.

[0083] The reception desk can estimate the traveler's emotions and adjust the timing of input processing based on the estimated emotions. For example, if the traveler is stressed, the reception desk can delay the input processing to allow time to relax. For example, if the traveler is relaxed, the reception desk can speed up the input processing to provide a quick response. For example, if the traveler is in a hurry, the reception desk can process the input immediately to enable a quick response. By adjusting the timing of input processing based on the traveler's emotions, it is possible to reduce the traveler's stress and enable input processing at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the traveler's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0084] The reception department can analyze a traveler's past trouble history and select the most appropriate reception method. For example, the reception department may prioritize a particular reception method based on the troubles the traveler has frequently encountered in the past. For example, the reception department may suggest the most effective reception method based on the traveler's past trouble history. For example, the reception department may analyze a traveler's past trouble history and select a reception method appropriate to the type of trouble. This allows for the selection of the most appropriate reception method and more effective response by analyzing the traveler's past trouble history. Some or all of the above processes in the reception department may be performed using AI, for example, or not using AI. For example, the reception department may input the traveler's past trouble history data into a generating AI and have the generating AI select the most appropriate reception method.

[0085] The reception unit can filter information based on the traveler's current situation and areas of interest when receiving input. For example, the reception unit can prioritize receiving information that is highly relevant to the traveler based on their current situation. For example, the reception unit can filter and receive relevant information based on the traveler's areas of interest. For example, the reception unit can combine the traveler's current situation and areas of interest to receive the most relevant information. This allows the reception unit to prioritize receiving more relevant information by filtering based on the traveler's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the traveler's current situation data and areas of interest data into a generating AI and have the generating AI perform the filtering.

[0086] The reception desk can estimate the traveler's emotions and prioritize the information to be received based on the estimated emotions. For example, if the traveler is stressed, the reception desk will prioritize important information. For example, if the traveler is relaxed, the reception desk will prioritize detailed information. For example, if the traveler is in a hurry, the reception desk will prioritize information that requires a quick response. By prioritizing the information to be received based on the traveler's emotions, more appropriate information can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the traveler's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0087] The reception unit can prioritize receiving highly relevant information by considering the traveler's geographical location information when receiving input. For example, the reception unit can prioritize receiving highly relevant information based on the traveler's current location. For example, the reception unit can prioritize receiving optimal information by considering the traveler's geographical location information. For example, the reception unit can filter and receive information related to the traveler's current location. This allows for the provision of more appropriate information by prioritizing the receipt of highly relevant information by considering the traveler's geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the traveler's geographical location information data into a generating AI and have the generating AI perform the filtering of highly relevant information.

[0088] The reception unit can analyze the traveler's social media activity and receive relevant information when data is received. For example, the reception unit can analyze the traveler's social media activity and prioritize receiving relevant information. For example, the reception unit can receive information based on the traveler's areas of interest from the traveler's social media activity. For example, the reception unit can receive the most relevant information by referring to the traveler's social media activity. This allows the reception unit to receive more relevant information by analyzing the traveler's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the traveler's social media activity data into a generating AI and have the generating AI perform the receiving of relevant information.

[0089] The analysis unit can estimate the traveler's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the traveler is stressed, the analysis unit provides a simple and easy-to-understand presentation. For example, if the traveler is relaxed, the analysis unit provides detailed analysis results. For example, if the traveler is in a hurry, the analysis unit provides concise analysis results. By adjusting the presentation of the analysis based on the traveler's emotions, the analysis unit can provide analysis results that are easy for the traveler to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input the traveler's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0090] The analysis unit can adjust the level of detail of the analysis based on the severity of the problem during the analysis. For example, the analysis unit performs a detailed analysis for high-severity problems. For example, the analysis unit performs a concise analysis for low-severity problems. The analysis unit adjusts the level of detail of the analysis according to the severity of the problem. By adjusting the level of detail of the analysis based on the severity of the problem, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input problem severity data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0091] The analysis unit can apply different analysis algorithms depending on the trouble category during analysis. For example, the analysis unit selects the optimal analysis algorithm depending on the trouble category. For example, the analysis unit applies different analysis algorithms for each trouble category. For example, the analysis unit applies the optimal analysis algorithm based on the trouble category. By applying different analysis algorithms depending on the trouble category, more appropriate analysis results can be provided. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input trouble category data into a generating AI and have the generating AI execute the application of the optimal analysis algorithm.

[0092] The analysis unit can estimate the traveler's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the traveler is stressed, the analysis unit provides a short, concise analysis. For example, if the traveler is relaxed, the analysis unit provides a detailed analysis. For example, if the traveler is in a hurry, the analysis unit provides a short, easily understandable analysis. By adjusting the length of the analysis based on the traveler's emotions, the analysis results can be made easier for the traveler to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input the traveler's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0093] The analysis unit can determine the priority of analysis based on the timing of the troubles during the analysis. For example, the analysis unit prioritizes the analysis of recently occurring troubles. For example, the analysis unit determines the priority of analysis based on the timing of the troubles. For example, the analysis unit determines the optimal analysis order by considering the timing of the troubles. By determining the priority of analysis based on the timing of the troubles, more appropriate analysis results can be provided. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input trouble timing data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0094] The analysis unit can adjust the order of analysis based on the relevance of the problems during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant problems. For example, the analysis unit adjusts the order of analysis based on the relevance of the problems. For example, the analysis unit determines the optimal order of analysis by considering the relevance of the problems. By adjusting the order of analysis based on the relevance of the problems, more appropriate analysis results can be provided. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input problem relevance data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0095] The suggestion unit can estimate the traveler's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the traveler is stressed, the suggestion unit will provide simple and easy-to-understand suggestions. If the traveler is relaxed, the suggestion unit will provide detailed suggestions. If the traveler is in a hurry, the suggestion unit will provide concise suggestions. By adjusting the way it presents suggestions based on the traveler's emotions, it is possible to provide suggestions that are easy for the traveler to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the traveler's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0096] The proposal unit can adjust the level of detail of its proposals based on the importance of the solutions. For example, the proposal unit provides detailed proposals for high-importance solutions. For example, it provides concise proposals for low-importance solutions. The proposal unit adjusts the level of detail of its proposals according to the importance of the solutions. By adjusting the level of detail of proposals based on the importance of the solutions, it is possible to provide more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input data on the importance of solutions into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposals.

[0097] The proposal unit can apply different proposal algorithms depending on the category of the solution when making a proposal. For example, the proposal unit selects the optimal proposal algorithm depending on the category of the solution. For example, the proposal unit applies a different proposal algorithm for each category of solution. For example, the proposal unit applies the optimal proposal algorithm based on the category of solution. By applying different proposal algorithms depending on the category of solution, it is possible to provide more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input solution category data into a generating AI and have the generating AI execute the application of the optimal proposal algorithm.

[0098] The suggestion unit can estimate the traveler's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the traveler is stressed, the suggestion unit will provide short, concise suggestions. For example, if the traveler is relaxed, the suggestion unit will provide detailed suggestions. For example, if the traveler is in a hurry, the suggestion unit will provide short, easily understandable suggestions. By adjusting the length of suggestions based on the traveler's emotions, the suggestion unit can provide suggestions that are easy for the traveler to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input the traveler's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0099] The proposal unit can determine the priority of proposals based on when the solutions occurred. For example, the proposal unit will prioritize recently occurring solutions. For example, the proposal unit will determine the priority of proposals based on when the solutions occurred. For example, the proposal unit will determine the optimal order of proposals, taking into account when the solutions occurred. This allows for the provision of more appropriate proposals by determining the priority of proposals based on when the solutions occurred. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input data on when the solutions occurred into a generating AI and have the generating AI perform the determination of proposal priorities.

[0100] The proposal unit can adjust the order of proposals based on the relevance of the solutions when making a proposal. For example, the proposal unit will prioritize proposing solutions that are highly relevant. For example, the proposal unit will adjust the order of proposals based on the relevance of the solutions. For example, the proposal unit will determine the optimal order of proposals by considering the relevance of the solutions. In this way, by adjusting the order of proposals based on the relevance of the solutions, it is possible to provide more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI. For example, the proposal unit can input the relevance data of the solutions into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0101] The location tracking unit can estimate the traveler's emotions and adjust the location tracking method based on the estimated emotions. For example, if the traveler is stressed, the location tracking unit provides a simple and rapid location tracking method. For example, if the traveler is relaxed, the location tracking unit provides a detailed location tracking method. For example, if the traveler is in a hurry, the location tracking unit provides a rapid location tracking method. By adjusting the location tracking method based on the traveler's emotions, a location tracking method that is easy for the traveler to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the location tracking unit may be performed using AI, for example, or without AI. For example, the location tracking unit can input the traveler's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0102] The location identification unit can select the optimal location identification method by referring to the traveler's past travel history when determining the location. For example, the location identification unit selects the optimal location identification method based on the traveler's past travel history. For example, the location identification unit proposes the most effective location identification method from the traveler's past travel history. For example, the location identification unit analyzes the traveler's past travel history and selects the optimal location identification method. This makes it possible to select the optimal location identification method by referring to the traveler's past travel history, enabling more effective location identification. Some or all of the above processing in the location identification unit may be performed using AI, for example, or without using AI. For example, the location identification unit can input the traveler's past travel history data into a generating AI and have the generating AI perform the selection of the optimal location identification method.

[0103] The location tracking unit can customize the location tracking method based on the traveler's current situation when determining the location. For example, the location tracking unit provides the optimal location tracking method based on the traveler's current situation. For example, the location tracking unit customizes the location tracking method considering the traveler's current situation. For example, the location tracking unit selects the optimal location tracking method according to the traveler's current situation. This makes it possible to determine the location more appropriately by customizing the location tracking method based on the traveler's current situation. Some or all of the above processing in the location tracking unit may be performed using AI, for example, or without AI. For example, the location tracking unit can input the traveler's current situation data into a generating AI and have the generating AI perform the customization of the location tracking method.

[0104] The location tracking unit can estimate the traveler's emotions and determine location tracking priorities based on the estimated emotions. For example, if the traveler is stressed, the location tracking unit will perform location tracking quickly. For example, if the traveler is relaxed, the location tracking unit will perform detailed location tracking. For example, if the traveler is in a hurry, the location tracking unit will prioritize location tracking. This allows for more appropriate location tracking by determining location tracking priorities based on the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the location tracking unit may be performed using AI, or not using AI. For example, the location tracking unit can input the traveler's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0105] The location identification unit can select the optimal location identification method when determining the traveler's location, taking into account the traveler's geographical location information. For example, the location identification unit can select the optimal location identification method based on the traveler's current location. For example, the location identification unit can select the optimal location identification method taking into account the traveler's geographical location information. For example, the location identification unit can select the optimal location identification method based on information related to the traveler's current location. By selecting the optimal location identification method while taking into account the traveler's geographical location information, more appropriate location identification becomes possible. Some or all of the above processing in the location identification unit may be performed using AI, for example, or without using AI. For example, the location identification unit can input the traveler's geographical location information data into a generating AI and have the generating AI perform the selection of the optimal location identification method.

[0106] The location identification unit can analyze the traveler's social media activity and propose a method for location identification during location identification. For example, the location identification unit analyzes the traveler's social media activity and proposes the optimal method for location identification. For example, the location identification unit proposes relevant method for location identification based on the traveler's social media activity. For example, the location identification unit proposes the optimal method for location identification by referring to the traveler's social media activity. In this way, by analyzing the traveler's social media activity, a more appropriate method for location identification can be proposed. Some or all of the above processing in the location identification unit may be performed using AI, for example, or without AI. For example, the location identification unit can input the traveler's social media activity data into a generating AI and have the generating AI execute the proposal of the optimal method for location identification.

[0107] The translation unit can estimate the traveler's emotions and adjust the translation's expression based on the estimated emotions. For example, if the traveler is stressed, the translation unit will provide a simple and easy-to-understand translation. For example, if the traveler is relaxed, the translation unit will provide a detailed translation. For example, if the traveler is in a hurry, the translation unit will provide a concise translation. By adjusting the translation's expression based on the traveler's emotions, the translation can be made easier for the traveler to understand. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the translation unit may be performed using AI or not using AI. For example, the translation unit can input the traveler's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0108] The translation unit can select the optimal translation method by referring to the traveler's past language usage history during translation. For example, the translation unit selects the optimal translation method based on the traveler's past language usage history. For example, the translation unit proposes the most effective translation method from the traveler's past language usage history. For example, the translation unit analyzes the traveler's past language usage history and selects the optimal translation method. This makes it possible to select the optimal translation method by referring to the traveler's past language usage history, resulting in more effective translation. Some or all of the above processes in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input the traveler's past language usage history data into a generating AI and have the generating AI select the optimal translation method.

[0109] The translation unit can customize the translation method based on the traveler's current situation during translation. For example, the translation unit provides the optimal translation method based on the traveler's current situation. For example, the translation unit customizes the translation method considering the traveler's current situation. For example, the translation unit selects the optimal translation method according to the traveler's current situation. This makes it possible to provide more appropriate translations by customizing the translation method based on the traveler's current situation. Some or all of the above processes in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input the traveler's current situation data into a generating AI and have the generating AI perform the customization of the translation method.

[0110] The translation unit can estimate the traveler's emotions and prioritize translations based on the estimated emotions. For example, if the traveler is stressed, the translation unit will prioritize important translations. For example, if the traveler is relaxed, the translation unit will prioritize detailed translations. For example, if the traveler is in a hurry, the translation unit will prioritize translations that require immediate attention. This allows for more appropriate translations by prioritizing translations based on the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the translation unit may be performed using AI or not. For example, the translation unit can input traveler facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0111] The translation unit can select the optimal translation method while considering the traveler's geographical location information. For example, the translation unit selects the optimal translation method based on the traveler's current location. For example, the translation unit selects the optimal translation method while considering the traveler's geographical location information. For example, the translation unit selects the optimal translation method based on information related to the traveler's current location. By selecting the optimal translation method while considering the traveler's geographical location information, more appropriate translations become possible. Some or all of the above processing in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input the traveler's geographical location data into a generating AI and have the generating AI perform the selection of the optimal translation method.

[0112] The translation unit can analyze the traveler's social media activity during translation and suggest translation methods. For example, the translation unit can analyze the traveler's social media activity and suggest the most appropriate translation method. For example, the translation unit can suggest relevant translation methods based on the traveler's social media activity. For example, the translation unit can suggest the most appropriate translation method by referring to the traveler's social media activity. In this way, by analyzing the traveler's social media activity, it is possible to suggest a more appropriate translation method. Some or all of the above processing in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input the traveler's social media activity data into a generating AI and have the generating AI suggest the most appropriate translation method.

[0113] The emergency response unit can estimate the traveler's emotions and adjust the emergency response method based on the estimated emotions. For example, if the traveler is stressed, the emergency response unit will provide a quick and concise emergency response method. For example, if the traveler is relaxed, the emergency response unit will provide a detailed emergency response method. For example, if the traveler is in a hurry, the emergency response unit will provide an emergency response method that requires immediate attention. By adjusting the emergency response method based on the traveler's emotions, it is possible to provide an emergency response method that is easy for the traveler to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the emergency response unit may be performed using AI or not using AI. For example, the emergency response unit can input the traveler's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0114] The emergency response unit can select the optimal emergency response method by referring to the traveler's past emergency history during an emergency. For example, the emergency response unit selects the optimal emergency response method based on the traveler's past emergency history. For example, the emergency response unit proposes the most effective emergency response method from the traveler's past emergency history. For example, the emergency response unit analyzes the traveler's past emergency history and selects the optimal emergency response method. This makes it possible to select the optimal emergency response method by referring to the traveler's past emergency history, enabling a more effective emergency response. Some or all of the above processes in the emergency response unit may be performed using AI, for example, or without AI. For example, the emergency response unit can input the traveler's past emergency history data into a generating AI and have the generating AI perform the selection of the optimal emergency response method.

[0115] The emergency response unit can customize emergency response measures based on the traveler's current situation during an emergency. For example, the emergency response unit provides the optimal emergency response measures based on the traveler's current situation. For example, the emergency response unit customizes emergency response measures considering the traveler's current situation. For example, the emergency response unit selects the optimal emergency response measures according to the traveler's current situation. This makes it possible to provide a more appropriate emergency response by customizing emergency response measures based on the traveler's current situation. Some or all of the above processes in the emergency response unit may be performed using AI, for example, or without AI. For example, the emergency response unit can input the traveler's current situation data into a generating AI and have the generating AI perform the customization of emergency response measures.

[0116] The emergency response unit can estimate the traveler's emotions and determine the priority of emergency response based on the estimated emotions. For example, if the traveler is stressed, the emergency response unit will provide a rapid emergency response. For example, if the traveler is relaxed, the emergency response unit will provide a detailed emergency response. For example, if the traveler is in a hurry, the emergency response unit will provide the highest priority emergency response. This allows for more appropriate emergency responses by determining the priority of emergency response based on the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the emergency response unit may be performed using AI or not using AI. For example, the emergency response unit can input the traveler's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0117] The emergency response unit can select the optimal emergency response method in the event of an emergency, taking into account the traveler's geographical location information. For example, the emergency response unit selects the optimal emergency response method based on the traveler's current location. For example, the emergency response unit selects the optimal emergency response method by taking into account the traveler's geographical location information. For example, the emergency response unit selects the optimal emergency response method based on information related to the traveler's current location. By selecting the optimal emergency response method while taking into account the traveler's geographical location information, a more appropriate emergency response becomes possible. Some or all of the above processing in the emergency response unit may be performed using AI, for example, or without using AI. For example, the emergency response unit can input the traveler's geographical location information data into a generating AI and have the generating AI perform the selection of the optimal emergency response method.

[0118] The emergency response unit can analyze a traveler's social media activity during an emergency and propose emergency response measures. For example, the emergency response unit can analyze a traveler's social media activity and propose the most appropriate emergency response measures. For example, the emergency response unit can propose relevant emergency response measures based on the traveler's social media activity. For example, the emergency response unit can propose the most appropriate emergency response measures by referring to the traveler's social media activity. In this way, by analyzing the traveler's social media activity, it is possible to propose more appropriate emergency response measures. Some or all of the above processing in the emergency response unit may be performed using AI, for example, or without AI. For example, the emergency response unit can input traveler's social media activity data into a generating AI and have the generating AI propose the most appropriate emergency response measures.

[0119] The cultural advice unit can estimate the traveler's emotions and adjust the way cultural advice is expressed based on the estimated emotions. For example, if the traveler is stressed, the cultural advice unit will provide simple and easy-to-understand cultural advice. For example, if the traveler is relaxed, the cultural advice unit will provide detailed cultural advice. For example, if the traveler is in a hurry, the cultural advice unit will provide concise cultural advice. By adjusting the way cultural advice is expressed based on the traveler's emotions, it is possible to provide cultural advice that is easy for the traveler to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the cultural advice unit may be performed using AI or not using AI. For example, the cultural advice unit can input the traveler's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0120] The cultural advice department can select the most appropriate advice method when providing cultural advice by referring to the traveler's past cultural experiences. For example, the cultural advice department selects the most appropriate advice method based on the traveler's past cultural experiences. For example, the cultural advice department proposes the most effective advice method based on the traveler's past cultural experiences. For example, the cultural advice department analyzes the traveler's past cultural experiences and selects the most appropriate advice method. This allows for the selection of the most appropriate advice method by referring to the traveler's past cultural experiences, enabling more effective cultural advice. Some or all of the above processes in the cultural advice department may be performed using AI, for example, or without AI. For example, the cultural advice department can input the traveler's past cultural experience data into a generating AI and have the generating AI select the most appropriate advice method.

[0121] The Cultural Advice Department can customize the means of advice given based on the traveler's current situation when providing cultural advice. For example, the Cultural Advice Department can provide the most appropriate means of advice based on the traveler's current situation. For example, the Cultural Advice Department can customize the means of advice considering the traveler's current situation. For example, the Cultural Advice Department can select the most appropriate means of advice according to the traveler's current situation. This makes it possible to provide more appropriate cultural advice by customizing the means of advice based on the traveler's current situation. Some or all of the above processes in the Cultural Advice Department may be performed using AI, for example, or without AI. For example, the Cultural Advice Department can input data on the traveler's current situation into a generating AI and have the generating AI perform the customization of the means of advice.

[0122] The cultural advice unit can estimate the traveler's emotions and prioritize cultural advice based on those emotions. For example, if the traveler is stressed, the cultural advice unit will prioritize important cultural advice. If the traveler is relaxed, the cultural advice unit will prioritize detailed cultural advice. If the traveler is in a hurry, the cultural advice unit will prioritize cultural advice that requires immediate attention. By prioritizing cultural advice based on the traveler's emotions, more appropriate cultural advice can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the cultural advice unit may be performed using AI or not. For example, the cultural advice unit can input the traveler's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0123] The cultural advice unit can select the optimal advice method when providing cultural advice, taking into account the traveler's geographical location information. For example, the cultural advice unit can select the optimal advice method based on the traveler's current location. For example, the cultural advice unit can select the optimal advice method by taking into account the traveler's geographical location information. For example, the cultural advice unit can select the optimal advice method based on information related to the traveler's current location. By selecting the optimal advice method while taking into account the traveler's geographical location information, more appropriate cultural advice becomes possible. Some or all of the above processing in the cultural advice unit may be performed using AI, for example, or without using AI. For example, the cultural advice unit can input the traveler's geographical location data into a generating AI and have the generating AI select the optimal advice method.

[0124] The Cultural Advice Department can analyze a traveler's social media activity and propose methods of advice when providing cultural advice. For example, the Cultural Advice Department can analyze a traveler's social media activity and propose the most appropriate method of advice. For example, the Cultural Advice Department can propose relevant methods of advice based on a traveler's social media activity. For example, the Cultural Advice Department can propose the most appropriate method of advice by referring to a traveler's social media activity. In this way, by analyzing a traveler's social media activity, it is possible to propose more appropriate methods of advice. Some or all of the above processing in the Cultural Advice Department may be performed using AI, for example, or not using AI. For example, the Cultural Advice Department can input traveler's social media activity data into a generating AI and have the generating AI propose the most appropriate method of advice.

[0125] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0126] The reception desk can monitor the traveler's health status and adjust the input method based on that status. For example, if the traveler is tired, the reception desk can prioritize voice input, and if the traveler is healthy, it can prioritize text input. Furthermore, if the traveler is ill, the reception desk can provide a simplified input method, and if the traveler is healthy, it can provide a more detailed input method. This allows the reception desk to provide the optimal input method for the traveler by adjusting the input method based on their health status. Health status monitoring can be performed using devices such as smartwatches or fitness trackers. Some or all of the above processing at the reception desk may be performed using AI, or not. For example, the reception desk can input the traveler's health data into a generating AI and have the generating AI adjust the input method.

[0127] The analysis unit can refer to a traveler's past travel history and improve the accuracy of the analysis based on that history. For example, it can determine the traveler's current location more quickly and accurately based on data of places the traveler has visited in the past. It can also analyze the traveler's preferred tourist destinations and routes from their past travel history and suggest the optimal route. Furthermore, by referring to past travel history, it can understand patterns of problems the traveler has encountered in the past and respond quickly if similar problems occur. In this way, by referring to the traveler's past travel history, the accuracy of the analysis can be improved and more appropriate countermeasures can be suggested. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the traveler's past travel history data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0128] The translation unit can estimate the traveler's emotions and adjust the tone of the translation based on those emotions. For example, if the traveler is stressed, the translation unit will provide a gentle tone; if the traveler is relaxed, it will provide a casual tone. If the traveler is in a hurry, it can also provide a concise and direct tone. By adjusting the tone of the translation based on the traveler's emotions, the translation can be made easier for the traveler to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the translation unit may be performed using AI or not. For example, the translation unit can input the traveler's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0129] The suggestion unit can suggest local restaurants and meals, taking into account the traveler's dietary preferences. For example, if the traveler is vegetarian, the suggestion unit will suggest vegetarian restaurants. It can also suggest allergy-friendly restaurants if the traveler has specific allergies. Furthermore, if the traveler prefers a particular dish, it can suggest restaurants that serve that dish. This allows for optimal restaurant and meal suggestions based on the traveler's dietary preferences. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For instance, the suggestion unit can input the traveler's dietary preference data into a generating AI and have the AI ​​generate restaurant and meal suggestions.

[0130] The suggestion unit can estimate the traveler's emotions and adjust the timing of suggestions based on those emotions. For example, if the traveler is stressed, the suggestion unit can delay the suggestion; if the traveler is relaxed, it can speed up the suggestion. If the traveler is in a hurry, it can also make an immediate suggestion to enable a quick response. By adjusting the timing of suggestions based on the traveler's emotions, the system can provide suggestions at the optimal time for the traveler. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the traveler's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0131] The reception desk can provide the optimal input method, taking into account the traveler's language proficiency. For example, if the traveler is multilingual, the reception desk can accept input in multiple languages. If the traveler is unfamiliar with a particular language, the reception desk can prioritize input in a simpler language. Furthermore, if the traveler uses sign language, the reception desk can accept sign language input. This allows the reception desk to provide the optimal input method based on the traveler's language proficiency. Some or all of the above processing at the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the traveler's language proficiency data into a generating AI and have the generating AI provide the optimal input method.

[0132] The analysis unit can estimate the traveler's emotions and prioritize analyses based on those estimated emotions. For example, if the traveler is stressed, important analyses are prioritized; if the traveler is relaxed, detailed analyses are performed. Similarly, if the traveler is in a hurry, analyses requiring quick attention are prioritized. This allows for more appropriate analysis results by prioritizing analyses based on the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input traveler facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0133] The suggestion unit can refer to the traveler's past suggestion history and improve the accuracy of suggestions based on that history. For example, it can make suggestions best suited to the current situation based on data from suggestions the traveler has received in the past. It can also analyze the patterns of suggestions preferred by travelers from their past suggestion history and provide optimal suggestions. Furthermore, by referring to past suggestion history, it can understand the effectiveness of suggestions the traveler has received in the past and make more effective suggestions for similar situations. In this way, by referring to the traveler's past suggestion history, the accuracy of suggestions can be improved and more appropriate suggestions can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the traveler's past suggestion history data into a generating AI and have the generating AI perform the suggestion accuracy improvement.

[0134] The reception desk can estimate the traveler's emotions and prioritize the information to be received based on the estimated emotions. For example, if the traveler is stressed, important information will be prioritized. If the traveler is relaxed, detailed information will be prioritized. If the traveler is in a hurry, information requiring a quick response will be prioritized. This allows for the prioritization of more relevant information based on the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the traveler's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0135] The suggestion unit can make optimal suggestions by considering the traveler's current activity status. For example, if the traveler is sightseeing, it will prioritize suggestions related to tourist destinations. If the traveler is resting, it can also suggest places to relax or activities. Furthermore, if the traveler is on the move, it can also make suggestions related to travel. In this way, it can make optimal suggestions based on the traveler's current activity status. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the traveler's current activity status data into a generating AI and have the generating AI execute the optimal suggestions.

[0136] The following briefly describes the processing flow for example form 2.

[0137] Step 1: The reception desk receives input from travelers. This input includes text, voice, and image input. For example, travelers can enter text messages, voice input, or upload images using their smartphones. The reception desk can also analyze photos taken by travelers to understand the nature of the problem. Step 2: The analysis department analyzes local conditions in real time based on the information received by the reception department. For example, it can use GPS data to identify the traveler's current location and natural language processing technology to translate the local language. It can also acquire and analyze local weather information and traffic conditions in real time. This makes it possible to identify the traveler's current location, suggest the optimal route, and provide appropriate advice by translating the local language. Step 3: The proposal department proposes the best course of action based on the analysis results obtained by the analysis department. For example, they can suggest emergency contacts and the nearest medical facilities, or provide advice based on local culture and customs. They can also provide personalized support tailored to the traveler's needs. This allows for a quick and accurate response when travelers face emergencies or cultural difficulties.

[0138] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0140] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] Each of the multiple elements described above, including the reception unit, analysis unit, and proposal unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives input from travelers. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the local situation in real time. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal course of action. The reception unit may be implemented by, for example, the control unit 46A of the smart device 14, and the analysis unit and proposal unit may be implemented by, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0142] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0143] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0145] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

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

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0149] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements described above, including the reception unit, analysis unit, and proposal unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives input from travelers. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the local situation in real time. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes the optimal course of action. The reception unit may be implemented, for example, by the control unit 46A of the smart glasses 214, and the analysis unit and proposal unit may be implemented, for example, by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0158] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0159] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0160] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0161] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0162] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0164] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0165] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0166] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements described above, including the reception unit, analysis unit, and proposal unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives input from travelers. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the local situation in real time. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal course of action. The reception unit may be implemented by, for example, the control unit 46A of the headset terminal 314, and the analysis unit and proposal unit may be implemented by, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0174] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0175] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0176] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0177] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0178] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0179] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0180] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0181] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0182] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0183] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0184] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0185] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0186] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0187] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0188] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0189] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0190] Each of the multiple elements described above, including the reception unit, analysis unit, and proposal unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives input from travelers. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the local situation in real time. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal course of action. The reception unit may be implemented by, for example, the control unit 46A of the robot 414, and the analysis unit and proposal unit may be implemented by, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0191] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0192] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0193] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0194] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0195] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0196] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0197] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0198] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0199] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0200] 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.

[0201] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0202] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0203] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0204] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0205] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0206] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0207] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0208] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0209] (Note 1) A reception desk that accepts travelers' information, Based on the information received by the aforementioned reception department, the analysis department analyzes the local situation in real time. Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes the optimal countermeasure, Equipped with A system characterized by the following features. (Note 2) The aforementioned analysis unit is It is equipped with a location identification unit that determines the current location based on GPS data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is It is equipped with a translation unit that uses natural language processing technology to translate the local language. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, The department has an emergency response team that can suggest emergency contacts and the nearest medical facilities. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We have a cultural advice department that provides advice based on local culture and customs. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates the traveler's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We analyze travelers' past trouble histories and select the most suitable check-in method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving input, the system filters the information based on the traveler's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the traveler's emotions and prioritizes the information to be received based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving input, the system prioritizes processing highly relevant information, taking into account the traveler's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving input, the system analyzes the traveler's social media activity and collects relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is We estimate the travelers' emotions and adjust the way the analysis is presented based on the estimated travelers' emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During the analysis, adjust the level of detail based on the severity of the problem. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, different analysis algorithms are applied depending on the category of the problem. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is Estimate the traveler's sentiment and adjust the length of the analysis based on the estimated traveler's sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During the analysis, prioritize the analysis based on when the problem occurred. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During the analysis, adjust the order of analysis based on the relevance of the problems. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, We estimate the traveler's emotions and adjust the way the proposal is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the importance of the solution. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the solution. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, Estimate the traveler's sentiment and adjust the length of the suggestion based on the estimated traveler's sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, prioritize the proposal based on when the solution is needed. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making proposals, adjust the order of the suggestions based on the relevance of the solutions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned position identification unit is The system estimates the traveler's emotions and adjusts the location method based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 25) The aforementioned position identification unit is When determining a traveler's location, the system selects the optimal location method by referring to the traveler's past travel history. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned position identification unit is When determining location, customize the location tracking method based on the traveler's current situation. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned position identification unit is It estimates the traveler's emotions and determines location priorities based on the estimated traveler's emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned position identification unit is When determining location, the optimal location method is selected considering the traveler's geographical location information. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned position identification unit is When determining a traveler's location, we analyze their social media activity and suggest methods for location tracking. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned translation department, The system estimates the traveler's emotions and adjusts the translation's expression based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned translation department, During translation, the system selects the most suitable translation method by referring to the traveler's past language usage history. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned translation department, During translation, the translation method is customized based on the traveler's current situation. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned translation department, The system estimates the traveler's sentiment and determines translation priorities based on the estimated sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned translation department, During translation, the optimal translation method is selected by considering the traveler's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned translation department, During translation, we analyze travelers' social media activity and suggest translation methods. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned emergency response unit, Estimate the traveler's emotions and adjust emergency response methods based on the estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 37) The aforementioned emergency response unit, During an emergency, the system will refer to the traveler's past emergency history to select the most appropriate emergency response method. The system described in Appendix 4, characterized by the features described herein. (Note 38) The aforementioned emergency response unit, In emergency situations, customize emergency response measures based on the traveler's current situation. The system described in Appendix 4, characterized by the features described herein. (Note 39) The aforementioned emergency response unit, Estimate travelers' emotions and prioritize emergency responses based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned emergency response unit, In emergency situations, the optimal emergency response method will be selected considering the traveler's geographical location. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned emergency response unit, During emergencies, we analyze travelers' social media activity to propose emergency response strategies. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned Cultural Advice Department, We estimate the traveler's emotions and adjust the way cultural advice is expressed based on those estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 43) The aforementioned Cultural Advice Department, When providing cultural advice, we select the most appropriate advice method by referring to the traveler's past cultural experiences. The system described in Appendix 5, characterized by the features described herein. (Note 44) The aforementioned Cultural Advice Department, When providing cultural advice, customize the advice based on the traveler's current situation. The system described in Appendix 5, characterized by the features described herein. (Note 45) The aforementioned Cultural Advice Department, Estimate travelers' sentiments and prioritize cultural advice based on those estimated sentiments. The system described in Appendix 5, characterized by the features described herein. (Note 46) The aforementioned Cultural Advice Department, When providing cultural advice, the most appropriate advice method is selected considering the traveler's geographical location. The system described in Appendix 5, characterized by the features described herein. (Note 47) The aforementioned Cultural Advice Department, When providing cultural advice, we analyze travelers' social media activity and suggest methods for offering advice. The system described in Appendix 5, characterized by the features described herein. [Explanation of symbols]

[0210] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts travelers' information, Based on the information received by the aforementioned reception department, the analysis department analyzes the local situation in real time. Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes the optimal countermeasure, Equipped with A system characterized by the following features.

2. The aforementioned analysis unit is It is equipped with a location identification unit that determines the current location based on GPS data. The system according to feature 1.

3. The aforementioned analysis unit is It is equipped with a translation unit that uses natural language processing technology to translate the local language. The system according to feature 1.

4. The aforementioned proposal section is, The department has an emergency response team that can suggest emergency contacts and the nearest medical facilities. The system according to feature 1.

5. The aforementioned proposal section is, We have a cultural advice department that provides advice based on local culture and customs. The system according to feature 1.

6. The aforementioned reception unit is The system estimates the traveler's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is We analyze travelers' past trouble histories and select the most suitable check-in method. The system according to feature 1.

8. The aforementioned reception unit is When receiving input, the system filters the information based on the traveler's current situation and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is The system estimates the traveler's emotions and prioritizes the information to be received based on those estimated emotions. The system according to feature 1.

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