system

Abstract

The system according to this embodiment aims to provide integrated support for everything from creating travel plans and making reservations to providing information about travel destinations and managing travel history. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a navigation unit, a provision unit, a reservation unit, and a history unit. The reception unit receives user input. The generation unit generates a travel plan based on the information received by the reception unit. The navigation unit provides navigation based on the plan generated by the generation unit. The provision unit provides information about the travel destination. The reservation unit supports the reservation procedure. The history unit manages the travel history.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the creation of a travel plan, reservation procedures, provision of travel destination information, etc. are carried out individually, and there is a lack of a system for providing integrated support.

[0005] The system according to the embodiment aims to integrally support from the creation of a travel plan to reservation procedures, provision of travel destination information, and management of travel history.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a navigation unit, a provision unit, a reservation unit, and a history unit. The reception unit receives user input. The generation unit generates a travel plan based on the information received by the reception unit. The navigation unit provides navigation based on the plan generated by the generation unit. The provision unit provides information about the travel destination. The reservation unit supports the reservation procedure. The history unit manages the travel history. [Effects of the Invention]

[0007] The system according to this embodiment can provide integrated support for everything from creating travel plans and making reservations to providing information about travel destinations and managing travel history. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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) An AI travel concierge system according to an embodiment of the present invention is a system that provides travel-related information and advice using generative AI. The AI ​​travel concierge system allows users to ask questions about destinations and itineraries and receive information on tourist attractions, hotel reservations, transportation advice, and more. For example, when a user accesses the travel concierge service, the AI ​​travel concierge system provides a home screen displaying vivid images and attractive photos of travel destinations. It includes menu icons and a search bar, allowing users to search by entering keywords such as destinations or travel themes. Next, when the user enters questions or requests, a chatbot equipped with generative AI begins a conversation. The chatbot understands the user's requests through natural language processing and provides appropriate information and advice. Through interaction with the chatbot, the user can proceed with their travel planning. Furthermore, the chatbot provides the user with information about their travel destination, showcasing tourist attractions, popular activities, and recommended restaurants while displaying photos and maps. The user can view detailed information and select places and activities of interest. When the user enters their travel requirements and budget, the chatbot proposes a travel plan. Options and customizations are suggested based on the user's preferences and constraints. The user can review the suggestions and make modifications or adjustments as needed. Once a travel plan is finalized, the AI ​​travel concierge system supports booking procedures for hotels, flights, activities, and more. After booking is complete, users can make payments and receive travel details and tickets. Users can also review their travel history and view information and photos from past trips. This system caters to a variety of target groups, including individual travelers, families, business travelers, and international visitors to Japan. It provides personalized travel plans based on the traveler's preferences, budget, and constraints, and offers real-time information and booking support. This allows travelers to plan their trips efficiently, contributing to an improved travel experience.This allows the AI ​​travel concierge system to provide information and support tailored to the needs of travelers.

[0029] The AI ​​travel concierge system according to this embodiment comprises a reception unit, a generation unit, a navigation unit, a provision unit, a reservation unit, and a history unit. The reception unit receives user input. User input includes, but is not limited to, text input, voice input, and image input. The reception unit accepts, for example, user input of questions regarding destinations and itineraries. The generation unit generates a travel plan based on the information received by the reception unit using a generation AI. The generation unit generates a travel plan based, for example, the user's preferences and constraints. The generation AI analyzes the user's input and proposes the optimal travel plan. The navigation unit navigates the user based on the plan generated by the generation unit. The navigation unit guides the user using, for example, map display and voice guidance. The navigation unit can also provide real-time traffic information. The provision unit provides information about the travel destination. The provision unit provides, for example, information such as tourist attractions, popular activities, and recommended restaurants. The provision unit can provide information while displaying photos and maps. The reservation unit supports reservation procedures for hotels, airline tickets, activities, etc. The booking unit, for example, performs booking procedures based on the travel plan selected by the user. The booking unit can also provide links to booking sites. The history unit manages the user's travel history. The history unit manages information such as past travel itineraries, destinations, and photos. The history unit allows the user to view information and photos related to past trips. As a result, the AI ​​travel concierge system according to this embodiment can efficiently support travel planning by accepting user input, generating travel plans, providing navigation and information, handling booking procedures, and managing history.

[0030] The reception desk receives user input. User input includes, but is not limited to, text input, voice input, and image input. Specifically, with text input, users can use a keyboard or touchscreen to enter questions about their destination and itinerary. With voice input, users ask questions by voice through a microphone, and speech recognition technology converts them into text. With image input, users can upload photos or maps of their travel destination and ask questions based on them. The reception desk processes these inputs quickly and accurately and sends them to the generation desk. Furthermore, the reception desk can analyze the user's input and request additional information as needed. For example, if a user enters "I want to go to Paris," the reception desk may ask additional questions such as "How long is your trip?" or "What is your budget?" to gather more detailed information. This allows the reception desk to accurately understand the user's needs and desires and provide the foundational data for the generation desk to generate the optimal travel plan.

[0031] The generation unit uses a generation AI to generate travel plans based on information received by the reception unit. For example, the generation unit generates travel plans based on the user's preferences and constraints. The generation AI analyzes user input and proposes the optimal travel plan. Specifically, the generation AI analyzes the user's input using natural language processing technology to extract the user's preferences and constraints. For example, if a user inputs "I want to go on a family trip to a place where children can enjoy themselves," the generation AI extracts keywords such as "family trip" and "places where children can enjoy themselves" and proposes appropriate travel destinations and activities based on them. The generation AI can also refer to past travel data and user history data to generate travel plans that match the user's preferences. For example, it proposes similar travel destinations and activities based on data of places the user has visited and activities they have participated in in the past. Furthermore, the generation AI can utilize real-time information to generate travel plans that include the latest events and special offers. This allows the generation unit to quickly and accurately generate optimal travel plans that meet the user's needs.

[0032] The navigation unit guides the user based on the plan generated by the generation unit. The navigation unit guides the user using, for example, map display and voice guidance. Specifically, the navigation unit calculates the optimal route from the user's current location to the destination based on the generated travel plan and displays it on the map. The user can move while checking the map on the screen of their smartphone or tablet. In addition, the navigation unit can provide guidance to the user on the direction of travel and turns using the voice guidance function. The navigation unit can also provide real-time traffic information. For example, it can acquire traffic congestion and accident information in real time and suggest the optimal detour route. This allows the user to reach their destination smoothly. Furthermore, the navigation unit can also provide information on public transportation. For example, it can display train and bus timetables and operating status in real time to support the user in moving efficiently. In this way, the navigation unit can help the user move smoothly without getting lost during their trip.

[0033] The service provider offers information about travel destinations. For example, it provides information on tourist attractions, popular activities, and recommended restaurants. Specifically, it provides detailed information on tourist attractions that the user plans to visit and information on activities that can be enjoyed locally. For example, it displays information such as the history, highlights, opening hours, and admission fees of tourist attractions. For popular activities, it also provides detailed information such as how to make reservations, prices, and duration. The service provider can provide information while displaying photos and maps. For example, it can display photos of tourist attractions so that users can get an idea of ​​what they are like before visiting. It can also display the locations of tourist attractions and restaurants on a map to help users navigate smoothly at their destination. Furthermore, the service provider can provide information tailored to the user's preferences. For example, if a user is interested in food, it will prioritize displaying information on recommended restaurants and cafes. In this way, the service provider can provide information that allows users to maximize their enjoyment of their travel destination.

[0034] The reservations department supports booking procedures for hotels, flights, activities, and more. For example, it processes bookings based on the travel plan selected by the user. Specifically, the reservations department checks the availability of hotels and flights selected by the user in real time and proceeds with the booking process. Users can easily book their desired accommodations and flights through the reservations department. The reservations department also supports activity bookings. For example, it handles booking procedures for local tours and experience programs, ensuring users can participate smoothly. The reservations department can also provide links to booking sites. For example, if a user wants to book directly on the official website of a particular hotel or airline, the reservations department provides the link, allowing the user to easily access it. In this way, the reservations department can efficiently support users with the booking procedures necessary when planning their trips.

[0035] The History section manages the user's travel history. For example, it manages information such as past travel itineraries, destinations, and photos. Specifically, the History section saves data on places the user has visited and activities they have participated in, making it accessible to the user at any time. For instance, it can display a list of tourist attractions the user has visited and hotels they have stayed at, saving this information along with photos and comments. This allows the user to reminisce about past travel memories. Furthermore, the History section can suggest future travel plans based on the user's travel history. For example, it can analyze data on places visited and activities participated in to suggest new travel destinations and activities tailored to the user's preferences. In addition, the History section provides a function for users to share information about their past travels with other users. For example, users can post travel photos and comments to social media and share them with friends and family. This allows the History section to enrich the user's travel experience and support future travel planning.

[0036] The reception desk can receive questions from users regarding their destination and itinerary. For example, the reception desk can receive user input regarding their destination and itinerary. Based on the questions entered by the user, the reception desk can provide appropriate information. In this way, by receiving questions from users regarding their destination and itinerary, the reception desk can support the initial stages of travel planning. 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 user input into AI, which can then analyze the content of the questions and provide appropriate information.

[0037] The generation unit can generate travel plans based on the user's preferences and constraints. For example, the generation unit analyzes the user's input and proposes the optimal travel plan. The generation unit can also customize travel plans based on the user's preferences and constraints. For example, the generation unit proposes travel plans based on the user's budget and activities of interest. The generation unit uses a generation AI to analyze the user's input and generate the optimal travel plan. For example, the generation AI receives user input as a prompt and generates a travel plan. This allows for the provision of personalized travel plans by generating travel plans based on the user's preferences and constraints. Some or all of the above-described processes in the generation unit are performed using the generation AI. For example, the generation unit inputs user input into the generation AI, and the generation AI generates a travel plan.

[0038] The navigation unit can navigate the user based on the generated plan. The navigation unit guides the user using, for example, map display or voice guidance. The navigation unit can also provide real-time traffic information. For example, the navigation unit obtains the user's current location and guides them along the optimal route. The navigation unit can navigate the user until they reach their destination. This allows the navigation unit to guide the user during their journey by navigating them based on the generated plan. Some or all of the above processes in the navigation unit may be performed using AI or not. For example, the navigation unit inputs the user's current location into the AI, and the AI ​​guides them along the optimal route.

[0039] The information provider can provide information such as tourist attractions, popular activities, and recommended restaurants. For example, the information provider can provide information while displaying photos and maps of tourist attractions. The information provider can also provide relevant information based on the user's interests. For example, the information provider can provide information on tourist attractions and activities that the user is interested in. The information provider can provide detailed information about tourist attractions and activities selected by the user. This enriches the travel destination information by providing information on tourist attractions, popular activities, and recommended restaurants. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input the user's interests into the AI, and the AI ​​can provide relevant information.

[0040] The booking department can support the booking process for hotels, flights, activities, etc. For example, the booking department can process bookings based on the travel plan selected by the user. The booking department can also provide links to booking sites. For example, the booking department can provide links to the booking sites for hotels and flights selected by the user. The booking department can support the user until the booking process is completed. This streamlines travel arrangements by supporting the booking process for hotels, flights, activities, etc. Some or all of the above processes in the booking department may or may not be performed using AI. For example, the booking department inputs the user's selections into the AI, and the AI ​​supports the booking process.

[0041] The history section manages the user's travel history and makes information and photos from past trips viewable. For example, the history section manages information such as past travel itineraries, destinations, and photos. The history section allows users to view information and photos from past trips. For example, the history section displays places the user has visited and photos they have taken. The history section can also allow users to search their past travel history. This allows users to organize their travel records by managing their travel history and making information and photos from past trips viewable. Some or all of the above processing in the history section may be performed using AI or not. For example, the history section inputs the user's travel history into the AI, which then manages the information.

[0042] The reception desk can analyze the user's past question history and select the optimal question reception method. For example, the reception desk can automatically display frequently asked questions as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can predict and suggest questions to be asked at specific times based on the user's past question history. In this way, the reception desk can provide the optimal question reception method by analyzing the user's past question history. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past question history into AI, and the AI ​​can select the optimal question reception method.

[0043] The reception desk can filter questions based on the user's current travel plans and areas of interest when receiving them. For example, the reception desk can prioritize questions related to the travel destination the user is currently planning. The reception desk can also filter questions based on the user's areas of interest (e.g., history, nature, food, etc.). The reception desk can suggest appropriate questions according to the progress of the user's travel plans. This allows the reception desk to prioritize receiving highly relevant questions by filtering them based on the user's current travel plans and areas of interest. 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 user's current travel plans and areas of interest into the AI, and the AI ​​can filter the questions.

[0044] The reception desk can prioritize receiving questions that are highly relevant, taking into account the user's geographical location. For example, the reception desk can prioritize questions related to the user's current location. As the user approaches their travel destination, the reception desk can also prioritize questions related to that region. If the user is in a specific region, the reception desk can prioritize questions about tourist information for that region. This allows the reception desk to provide highly relevant information by considering the user's geographical location when receiving questions. 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 user's geographical location into the AI, which then prioritizes receiving relevant questions.

[0045] The reception desk can analyze the user's social media activity when receiving questions and accept relevant questions. For example, the reception desk can accept questions based on travel plans shared by the user on social media. The reception desk can also analyze the user's areas of interest from the content of their social media posts and accept relevant questions. The reception desk can accept relevant questions by referring to the content of posts by the user's social media followers and friends. In this way, relevant questions can be accepted by analyzing the user's social media activity. 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 user's social media activity into an AI, and the AI ​​can accept relevant questions.

[0046] The generation unit can generate the optimal travel plan by referring to the user's past travel history. For example, the generation unit can generate the optimal travel plan based on places the user has visited in the past. The generation unit can also generate a plan that avoids crowds based on the user's past travel history. The generation unit analyzes the user's past travel history and generates the most efficient plan. In this way, it can provide the optimal travel plan by referring to the user's past travel history. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs the user's past travel history into the generation AI, and the generation AI generates the optimal plan.

[0047] The generation unit can customize travel plans based on the user's current lifestyle and areas of interest when generating them. For example, the generation unit can generate a relaxing travel plan that suits the user's current lifestyle. The generation unit can also customize plans based on the user's areas of interest (e.g., history, nature, gourmet food, etc.). The generation unit can generate travel plans that fit the user's budget, according to the user's current lifestyle. In this way, by customizing plans based on the user's current lifestyle and areas of interest, the generation unit can provide the user with the most suitable travel plan. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs the user's current lifestyle and areas of interest into the generation AI, and the generation AI customizes the plan.

[0048] The generation unit can generate the optimal travel plan by considering the user's geographical location information. For example, the generation unit can generate a travel plan related to the user's current location. The generation unit can also generate a plan related to the region as the user approaches their travel destination. If the user is in a specific region, the generation unit can generate a plan based on tourist information for that region. In this way, by generating a plan while considering the user's geographical location information, it is possible to provide a highly relevant travel plan. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs the user's geographical location information into the generation AI, and the generation AI generates the optimal plan.

[0049] The generation unit can analyze a user's social media activity and generate relevant plans when generating travel plans. For example, the generation unit can generate plans based on travel plans shared by the user on social media. The generation unit can also analyze areas of interest from the user's social media posts and generate relevant plans. The generation unit can generate relevant plans by referring to the posts of the user's social media followers and friends. In this way, relevant travel plans can be provided by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs the user's social media activity into a generation AI, and the generation AI generates relevant plans.

[0050] The navigation unit can select the optimal navigation method by referring to the user's past travel history during navigation. For example, the navigation unit can suggest the optimal navigation method based on routes previously used by the user. The navigation unit can also suggest a navigation method that avoids congestion based on the user's past travel history. The navigation unit analyzes the user's past travel history and suggests the most efficient navigation method. In this way, the optimal navigation method can be provided by referring to the user's past travel history. Some or all of the above processes in the navigation unit may be performed using AI, or they may not be performed using AI. For example, the navigation unit inputs the user's past travel history into the AI, and the AI ​​selects the optimal navigation method.

[0051] The navigation unit can customize navigation based on the user's current location and traffic conditions during navigation. For example, the navigation unit updates the user's current location in real time while they are moving and provides navigation. The navigation unit can also update the user's current location in real time as they approach their destination and suggest the optimal route. If the user gets lost, the navigation unit updates their current location in real time and provides navigation again. This allows for optimal route guidance by customizing navigation based on the user's current location and traffic conditions. Some or all of the above processes in the navigation unit may be performed using AI or not. For example, the navigation unit inputs the user's current location and traffic conditions into the AI, and the AI ​​customizes the navigation.

[0052] The navigation unit can select the optimal navigation method during navigation, taking into account the user's geographical location information. For example, the navigation unit can provide a navigation method relevant to the user's current location. As the user approaches their travel destination, the navigation unit can also provide a navigation method relevant to that region. If the user is in a specific region, the navigation unit can provide a navigation method based on tourist information for that region. This enables highly relevant guidance by considering the user's geographical location information during navigation. Some or all of the above processing in the navigation unit may be performed using AI, or not. For example, the navigation unit inputs the user's geographical location information into the AI, and the AI ​​selects the optimal navigation method.

[0053] The navigation unit can analyze the user's social media activity during navigation and provide relevant navigation information. For example, the navigation unit can provide navigation based on travel plans shared by the user on social media. The navigation unit can also analyze the user's areas of interest from their social media posts and provide relevant navigation information. The navigation unit can provide relevant navigation information by referring to the posts of the user's social media followers and friends. In this way, relevant navigation information can be provided by analyzing the user's social media activity. Some or all of the above processing in the navigation unit may be performed using AI or not. For example, the navigation unit inputs the user's social media activity into AI, and the AI ​​provides relevant navigation information.

[0054] The information provider can provide the most relevant information by referring to the user's past travel history. For example, it can provide information related to places the user has visited in the past. Based on the user's past travel history, it can also suggest tourist attractions that the user might be interested in. The information provider analyzes the user's past travel history and provides the most relevant information. This allows the service to provide the most relevant information by referring to the user's past travel history. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input the user's past travel history into an AI, which then provides the most relevant information.

[0055] The information provider can customize the information provided based on the user's current areas of interest and travel plans. For example, the provider can customize the information based on the user's areas of interest (e.g., history, nature, food, etc.). The provider can also prioritize providing information relevant to the user's current travel plans. The provider can suggest relevant activities and tourist attractions based on the user's areas of interest. This allows for the provision of highly relevant information by customizing it based on the user's current areas of interest and travel plans. Some or all of the above processing in the information provider may be performed using AI or not. For example, the provider can input the user's current areas of interest and travel plans into the AI, which then customizes the information.

[0056] The information provider can provide optimal information by considering the user's geographical location when providing information. For example, the provider can provide information related to the user's current location. As the user approaches their travel destination, the provider can also provide information related to that region. If the user is in a specific region, the provider can provide tourist information for that region. In this way, by providing information while considering the user's geographical location, highly relevant information can be provided. Some or all of the above processing in the information provider may be performed using AI or not. For example, the provider can input the user's geographical location into the AI, and the AI ​​can provide optimal information.

[0057] The information provider can analyze the user's social media activity and provide relevant information when providing information. For example, the provider can provide information based on travel plans shared by the user on social media. The provider can also analyze the user's areas of interest from the content of their social media posts and provide relevant information. The provider can provide relevant information by referring to the content of posts by the user's social media followers and friends. In this way, relevant information can be provided by analyzing the user's social media activity. Some or all of the above processing in the information provider may be performed using AI or not. For example, the provider can input the user's social media activity into AI, and the AI ​​can provide relevant information.

[0058] The reservation department can select the optimal reservation method by referring to the user's past reservation history during the reservation process. For example, the reservation department may prioritize suggesting reservation methods that the user has used in the past. The reservation department can also suggest the optimal reservation method based on the user's past reservation history. The reservation department analyzes the user's past reservation history and suggests the most efficient reservation method. In this way, the optimal reservation method can be provided by referring to the user's past reservation history. Some or all of the above processes in the reservation department may be performed using AI or not. For example, the reservation department may input the user's past reservation history into the AI, and the AI ​​may select the optimal reservation method.

[0059] The booking department can customize bookings based on the user's current travel plans and budget during the booking process. For example, the booking department can suggest the best booking method based on the user's current travel plans. The booking department can also suggest the best booking method to fit the user's budget. The booking department customizes bookings according to the progress of the user's travel plans. This allows the system to provide the best booking process by customizing bookings based on the user's current travel plans and budget. Some or all of the above processes in the booking department may be performed using AI or not. For example, the booking department can input the user's current travel plans and budget into the AI, which then customizes the booking.

[0060] The reservation department can select the most suitable reservation method by considering the user's geographical location during the reservation process. For example, the reservation department can provide a reservation method relevant to the user's current location. As the user approaches their travel destination, the reservation department can also provide a reservation method relevant to that region. If the user is in a specific region, the reservation department can provide a reservation method based on tourist information for that region. This allows the reservation department to provide a highly relevant reservation method by considering the user's geographical location during the reservation process. Some or all of the above processes in the reservation department may be performed using AI or not. For example, the reservation department can input the user's geographical location information into the AI, which then selects the most suitable reservation method.

[0061] The reservation department can analyze a user's social media activity during the reservation process and provide relevant reservation information. For example, the reservation department can provide reservation information based on travel plans shared by the user on social media. The reservation department can also analyze a user's areas of interest from their social media posts and provide relevant reservation information. The reservation department can provide relevant reservation information by referring to posts from the user's social media followers and friends. In this way, relevant reservation information can be provided by analyzing the user's social media activity. Some or all of the above processes in the reservation department may be performed using AI or not. For example, the reservation department can input the user's social media activity into AI, and the AI ​​can provide relevant reservation information.

[0062] The history section can select the optimal management method by referring to the user's past travel history when managing history. For example, the history section may prioritize suggesting history management methods that the user has used in the past. The history section can also suggest the optimal management method based on the user's past travel history. The history section analyzes the user's past travel history and suggests the most efficient management method. In this way, it can provide the optimal history management method by referring to the user's past travel history. Some or all of the above processing in the history section may be performed using AI or not. For example, the history section may input the user's past travel history into the AI, and the AI ​​may select the optimal management method.

[0063] The history section can customize the history based on the user's current interests and travel plans when managing history. For example, the history section customizes the history based on the user's interests (e.g., history, nature, food, etc.). The history section can also prioritize managing history related to the user's current travel plans. The history section manages the history of relevant activities and tourist attractions based on the user's interests. This allows for highly relevant history management by customizing the history based on the user's current interests and travel plans. Some or all of the above processes in the history section may be performed using AI or not. For example, the history section inputs the user's current interests and travel plans into the AI, and the AI ​​customizes the history.

[0064] The history section can select the optimal management method when managing history, taking into account the user's geographical location information. For example, the history section can provide a history management method related to the user's current location. As the user approaches their travel destination, the history section can also provide a history management method related to that region. If the user is in a specific region, the history section can provide a history management method based on tourist information for that region. This allows for the provision of highly relevant management methods by considering the user's geographical location information during history management. Some or all of the above processing in the history section may be performed using AI or not. For example, the history section inputs the user's geographical location information into the AI, and the AI ​​selects the optimal management method.

[0065] The history section can analyze a user's social media activity and provide relevant history information during history management. For example, the history section can provide history information based on travel plans shared by the user on social media. The history section can also analyze a user's areas of interest from their social media posts and provide relevant history information. The history section can provide relevant history information by referring to posts from the user's social media followers and friends. In this way, relevant history information can be provided by analyzing the user's social media activity. Some or all of the above processing in the history section may be performed using AI or not. For example, the history section can input the user's social media activity into AI, and the AI ​​can provide relevant history information.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] The reception unit can be equipped with a function to translate user input in real time. For example, if a user enters a question in their native language, the reception unit can automatically translate it into English and send it to the generation unit. This allows users who speak different languages ​​to use the system smoothly. Furthermore, if a user uses voice input, the reception unit can convert that voice into text and translate it as needed. This accommodates users who prefer voice input. By analyzing user input and translating it into the appropriate language, the reception unit can serve a global user base.

[0068] The generation unit can consider the user's past reviews and ratings when generating travel plans based on the user's preferences and constraints. For example, it can refer to reviews of places the user has visited in the past and suggest travel destinations with similar ratings. This allows for the provision of personalized travel plans based on the user's past experiences. Furthermore, the generation unit can also suggest similar options based on information about activities and restaurants the user has rated in the past. This allows for the generation of travel plans that match the user's preferences. By analyzing the user's past reviews and ratings and suggesting the optimal travel plan, the generation unit can improve user satisfaction.

[0069] The navigation unit can provide navigation tailored to the user's mode of transportation when navigating the user based on the generated plan. For example, if the user is traveling on foot, it will guide them along a route suitable for walking. If the user is traveling by car, it will suggest the optimal route considering traffic conditions. Furthermore, the navigation unit can also provide transfer information and timetable information to users using public transportation. This allows it to provide optimal navigation tailored to the user's mode of transportation. By analyzing the user's mode of transportation and suggesting the optimal route, the navigation unit can smoothly support the user's journey.

[0070] The service provider can take the user's current weather information into account when providing information on tourist attractions, popular activities, and recommended restaurants. For example, if it's raining, it will prioritize suggesting indoor activities. If it's sunny, it will suggest outdoor tourist attractions and activities. Furthermore, the service provider can display the weather forecast for the user's travel destination and provide information to help with travel planning. This allows users to create the best travel plan according to the weather. By analyzing the user's current weather information and providing the most relevant information, the service provider can improve the user's travel experience.

[0071] The reservations department can provide users with reservation options tailored to their payment method when supporting them with booking procedures for hotels, flights, activities, etc. For example, if a user wishes to pay by credit card, it will prioritize displaying reservation options that accept credit cards. If a user wishes to pay with electronic money, it will suggest reservation options that accept electronic money. Furthermore, the reservations department can also guide users on how to use points or coupons if they wish to pay using those methods. This allows the department to provide the most suitable reservation procedure for each user's payment method. By analyzing the user's payment method and suggesting the most suitable reservation option, the reservations department can improve user convenience.

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

[0073] Step 1: The reception desk receives user input. User input includes text input, voice input, and image input. For example, it accepts user input regarding destinations and itineraries. Step 2: The generation unit generates a travel plan based on the information received by the reception unit. The generation unit uses a generation AI to generate a travel plan based on the user's preferences and constraints. The generation AI analyzes the user's input and proposes the optimal travel plan. Step 3: The navigation unit navigates the user based on the plan generated by the generation unit. The navigation unit can also guide the user using map displays and voice guidance, and can provide real-time traffic information. Step 4: The service provider provides information about the travel destination. The service provider can provide information such as tourist attractions, popular activities, and recommended restaurants, and can display photos and maps while providing the information. Step 5: The booking department supports the booking process for hotels, flights, activities, etc. The booking department can also process bookings based on the travel plan selected by the user and provide links to booking websites. Step 6: The History section manages the user's travel history. The History section manages information such as past travel itineraries, destinations, and photos, allowing users to view information and photos from their past trips.

[0074] (Example of form 2) An AI travel concierge system according to an embodiment of the present invention is a system that provides travel-related information and advice using generative AI. The AI ​​travel concierge system allows users to ask questions about destinations and itineraries and receive information on tourist attractions, hotel reservations, transportation advice, and more. For example, when a user accesses the travel concierge service, the AI ​​travel concierge system provides a home screen displaying vivid images and attractive photos of travel destinations. It includes menu icons and a search bar, allowing users to search by entering keywords such as destinations or travel themes. Next, when the user enters questions or requests, a chatbot equipped with generative AI begins a conversation. The chatbot understands the user's requests through natural language processing and provides appropriate information and advice. Through interaction with the chatbot, the user can proceed with their travel planning. Furthermore, the chatbot provides the user with information about their travel destination, showcasing tourist attractions, popular activities, and recommended restaurants while displaying photos and maps. The user can view detailed information and select places and activities of interest. When the user enters their travel requirements and budget, the chatbot proposes a travel plan. Options and customizations are suggested based on the user's preferences and constraints. The user can review the suggestions and make modifications or adjustments as needed. Once a travel plan is finalized, the AI ​​travel concierge system supports booking procedures for hotels, flights, activities, and more. After booking is complete, users can make payments and receive travel details and tickets. Users can also review their travel history and view information and photos from past trips. This system caters to a variety of target groups, including individual travelers, families, business travelers, and international visitors to Japan. It provides personalized travel plans based on the traveler's preferences, budget, and constraints, and offers real-time information and booking support. This allows travelers to plan their trips efficiently, contributing to an improved travel experience.This allows the AI ​​travel concierge system to provide information and support tailored to the needs of travelers.

[0075] The AI ​​travel concierge system according to this embodiment comprises a reception unit, a generation unit, a navigation unit, a provision unit, a reservation unit, and a history unit. The reception unit receives user input. User input includes, but is not limited to, text input, voice input, and image input. The reception unit accepts, for example, user input of questions regarding destinations and itineraries. The generation unit generates a travel plan based on the information received by the reception unit using a generation AI. The generation unit generates a travel plan based, for example, the user's preferences and constraints. The generation AI analyzes the user's input and proposes the optimal travel plan. The navigation unit navigates the user based on the plan generated by the generation unit. The navigation unit guides the user using, for example, map display and voice guidance. The navigation unit can also provide real-time traffic information. The provision unit provides information about the travel destination. The provision unit provides, for example, information such as tourist attractions, popular activities, and recommended restaurants. The provision unit can provide information while displaying photos and maps. The reservation unit supports reservation procedures for hotels, airline tickets, activities, etc. The booking unit, for example, performs booking procedures based on the travel plan selected by the user. The booking unit can also provide links to booking sites. The history unit manages the user's travel history. The history unit manages information such as past travel itineraries, destinations, and photos. The history unit allows the user to view information and photos related to past trips. As a result, the AI ​​travel concierge system according to this embodiment can efficiently support travel planning by accepting user input, generating travel plans, providing navigation and information, handling booking procedures, and managing history.

[0076] The reception desk receives user input. User input includes, but is not limited to, text input, voice input, and image input. Specifically, with text input, users can use a keyboard or touchscreen to enter questions about their destination and itinerary. With voice input, users ask questions by voice through a microphone, and speech recognition technology converts them into text. With image input, users can upload photos or maps of their travel destination and ask questions based on them. The reception desk processes these inputs quickly and accurately and sends them to the generation desk. Furthermore, the reception desk can analyze the user's input and request additional information as needed. For example, if a user enters "I want to go to Paris," the reception desk may ask additional questions such as "How long is your trip?" or "What is your budget?" to gather more detailed information. This allows the reception desk to accurately understand the user's needs and desires and provide the foundational data for the generation desk to generate the optimal travel plan.

[0077] The generation unit uses a generation AI to generate travel plans based on information received by the reception unit. For example, the generation unit generates travel plans based on the user's preferences and constraints. The generation AI analyzes user input and proposes the optimal travel plan. Specifically, the generation AI analyzes the user's input using natural language processing technology to extract the user's preferences and constraints. For example, if a user inputs "I want to go on a family trip to a place where children can enjoy themselves," the generation AI extracts keywords such as "family trip" and "places where children can enjoy themselves" and proposes appropriate travel destinations and activities based on them. The generation AI can also refer to past travel data and user history data to generate travel plans that match the user's preferences. For example, it proposes similar travel destinations and activities based on data of places the user has visited and activities they have participated in in the past. Furthermore, the generation AI can utilize real-time information to generate travel plans that include the latest events and special offers. This allows the generation unit to quickly and accurately generate optimal travel plans that meet the user's needs.

[0078] The navigation unit guides the user based on the plan generated by the generation unit. The navigation unit guides the user using, for example, map display and voice guidance. Specifically, the navigation unit calculates the optimal route from the user's current location to the destination based on the generated travel plan and displays it on the map. The user can move while checking the map on the screen of their smartphone or tablet. In addition, the navigation unit can provide guidance to the user on the direction of travel and turns using the voice guidance function. The navigation unit can also provide real-time traffic information. For example, it can acquire traffic congestion and accident information in real time and suggest the optimal detour route. This allows the user to reach their destination smoothly. Furthermore, the navigation unit can also provide information on public transportation. For example, it can display train and bus timetables and operating status in real time to support the user in moving efficiently. In this way, the navigation unit can help the user move smoothly without getting lost during their trip.

[0079] The service provider offers information about travel destinations. For example, it provides information on tourist attractions, popular activities, and recommended restaurants. Specifically, it provides detailed information on tourist attractions that the user plans to visit and information on activities that can be enjoyed locally. For example, it displays information such as the history, highlights, opening hours, and admission fees of tourist attractions. For popular activities, it also provides detailed information such as how to make reservations, prices, and duration. The service provider can provide information while displaying photos and maps. For example, it can display photos of tourist attractions so that users can get an idea of ​​what they are like before visiting. It can also display the locations of tourist attractions and restaurants on a map to help users navigate smoothly at their destination. Furthermore, the service provider can provide information tailored to the user's preferences. For example, if a user is interested in food, it will prioritize displaying information on recommended restaurants and cafes. In this way, the service provider can provide information that allows users to maximize their enjoyment of their travel destination.

[0080] The reservations department supports booking procedures for hotels, flights, activities, and more. For example, it processes bookings based on the travel plan selected by the user. Specifically, the reservations department checks the availability of hotels and flights selected by the user in real time and proceeds with the booking process. Users can easily book their desired accommodations and flights through the reservations department. The reservations department also supports activity bookings. For example, it handles booking procedures for local tours and experience programs, ensuring users can participate smoothly. The reservations department can also provide links to booking sites. For example, if a user wants to book directly on the official website of a particular hotel or airline, the reservations department provides the link, allowing the user to easily access it. In this way, the reservations department can efficiently support users with the booking procedures necessary when planning their trips.

[0081] The History section manages the user's travel history. For example, it manages information such as past travel itineraries, destinations, and photos. Specifically, the History section saves data on places the user has visited and activities they have participated in, making it accessible to the user at any time. For instance, it can display a list of tourist attractions the user has visited and hotels they have stayed at, saving this information along with photos and comments. This allows the user to reminisce about past travel memories. Furthermore, the History section can suggest future travel plans based on the user's travel history. For example, it can analyze data on places visited and activities participated in to suggest new travel destinations and activities tailored to the user's preferences. In addition, the History section provides a function for users to share information about their past travels with other users. For example, users can post travel photos and comments to social media and share them with friends and family. This allows the History section to enrich the user's travel experience and support future travel planning.

[0082] The reception desk can receive questions from users regarding their destination and itinerary. For example, the reception desk can receive user input regarding their destination and itinerary. Based on the questions entered by the user, the reception desk can provide appropriate information. In this way, by receiving questions from users regarding their destination and itinerary, the reception desk can support the initial stages of travel planning. 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 user input into AI, which can then analyze the content of the questions and provide appropriate information.

[0083] The generation unit can generate travel plans based on the user's preferences and constraints. For example, the generation unit analyzes the user's input and proposes the optimal travel plan. The generation unit can also customize travel plans based on the user's preferences and constraints. For example, the generation unit proposes travel plans based on the user's budget and activities of interest. The generation unit uses a generation AI to analyze the user's input and generate the optimal travel plan. For example, the generation AI receives user input as a prompt and generates a travel plan. This allows for the provision of personalized travel plans by generating travel plans based on the user's preferences and constraints. Some or all of the above-described processes in the generation unit are performed using the generation AI. For example, the generation unit inputs user input into the generation AI, and the generation AI generates a travel plan.

[0084] The navigation unit can navigate the user based on the generated plan. The navigation unit guides the user using, for example, map display or voice guidance. The navigation unit can also provide real-time traffic information. For example, the navigation unit obtains the user's current location and guides them along the optimal route. The navigation unit can navigate the user until they reach their destination. This allows the navigation unit to guide the user during their journey by navigating them based on the generated plan. Some or all of the above processes in the navigation unit may be performed using AI or not. For example, the navigation unit inputs the user's current location into the AI, and the AI ​​guides them along the optimal route.

[0085] The information provider can provide information such as tourist attractions, popular activities, and recommended restaurants. For example, the information provider can provide information while displaying photos and maps of tourist attractions. The information provider can also provide relevant information based on the user's interests. For example, the information provider can provide information on tourist attractions and activities that the user is interested in. The information provider can provide detailed information about tourist attractions and activities selected by the user. This enriches the travel destination information by providing information on tourist attractions, popular activities, and recommended restaurants. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input the user's interests into the AI, and the AI ​​can provide relevant information.

[0086] The booking department can support the booking process for hotels, flights, activities, etc. For example, the booking department can process bookings based on the travel plan selected by the user. The booking department can also provide links to booking sites. For example, the booking department can provide links to the booking sites for hotels and flights selected by the user. The booking department can support the user until the booking process is completed. This streamlines travel arrangements by supporting the booking process for hotels, flights, activities, etc. Some or all of the above processes in the booking department may or may not be performed using AI. For example, the booking department inputs the user's selections into the AI, and the AI ​​supports the booking process.

[0087] The history section manages the user's travel history and makes information and photos from past trips viewable. For example, the history section manages information such as past travel itineraries, destinations, and photos. The history section allows users to view information and photos from past trips. For example, the history section displays places the user has visited and photos they have taken. The history section can also allow users to search their past travel history. This allows users to organize their travel records by managing their travel history and making information and photos from past trips viewable. Some or all of the above processing in the history section may be performed using AI or not. For example, the history section inputs the user's travel history into the AI, which then manages the information.

[0088] The reception desk can estimate the user's emotions and adjust how questions are answered based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. If the user is in a hurry, the reception desk can prioritize voice input and answer questions quickly. This allows for situation-appropriate responses by adjusting how questions are answered based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, 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 inputs user emotion data into a generative AI, which then estimates the emotions.

[0089] The reception desk can analyze the user's past question history and select the optimal question reception method. For example, the reception desk can automatically display frequently asked questions as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can predict and suggest questions to be asked at specific times based on the user's past question history. In this way, the reception desk can provide the optimal question reception method by analyzing the user's past question history. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past question history into AI, and the AI ​​can select the optimal question reception method.

[0090] The reception desk can filter questions based on the user's current travel plans and areas of interest when receiving them. For example, the reception desk can prioritize questions related to the travel destination the user is currently planning. The reception desk can also filter questions based on the user's areas of interest (e.g., history, nature, food, etc.). The reception desk can suggest appropriate questions according to the progress of the user's travel plans. This allows the reception desk to prioritize receiving highly relevant questions by filtering them based on the user's current travel plans and areas of interest. 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 user's current travel plans and areas of interest into the AI, and the AI ​​can filter the questions.

[0091] The reception desk can estimate the user's emotions and determine the priority of questions to accept based on the estimated emotions. For example, if the user is feeling anxious, the reception desk will prioritize urgent questions. If the user is relaxed, the reception desk may also prioritize detailed questions. If the user is in a hurry, the reception desk will prioritize concise questions. This allows for a response tailored to the user's situation by prioritizing questions based on their 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 inputs user emotion data into a generative AI, which then estimates the emotions.

[0092] The reception desk can prioritize receiving questions that are highly relevant, taking into account the user's geographical location. For example, the reception desk can prioritize questions related to the user's current location. As the user approaches their travel destination, the reception desk can also prioritize questions related to that region. If the user is in a specific region, the reception desk can prioritize questions about tourist information for that region. This allows the reception desk to provide highly relevant information by considering the user's geographical location when receiving questions. 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 user's geographical location into the AI, which then prioritizes receiving relevant questions.

[0093] The reception desk can analyze the user's social media activity when receiving questions and accept relevant questions. For example, the reception desk can accept questions based on travel plans shared by the user on social media. The reception desk can also analyze the user's areas of interest from the content of their social media posts and accept relevant questions. The reception desk can accept relevant questions by referring to the content of posts by the user's social media followers and friends. In this way, relevant questions can be accepted by analyzing the user's social media activity. 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 user's social media activity into an AI, and the AI ​​can accept relevant questions.

[0094] The generation unit can estimate the user's emotions and adjust the travel plan generation method based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate a travel plan that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can also generate a travel plan that emphasizes the shortest route. If the user is excited, the generation unit will generate a travel plan with visually stimulating effects. In this way, by adjusting the travel plan generation method based on the user's emotions, a plan tailored to the user's situation can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation 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 generation unit is performed using the generation AI. For example, the generation unit inputs the user's emotion data into the generation AI, which estimates the emotions and adjusts the travel plan generation method.

[0095] The generation unit can generate the optimal travel plan by referring to the user's past travel history. For example, the generation unit can generate the optimal travel plan based on places the user has visited in the past. The generation unit can also generate a plan that avoids crowds based on the user's past travel history. The generation unit analyzes the user's past travel history and generates the most efficient plan. In this way, it can provide the optimal travel plan by referring to the user's past travel history. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs the user's past travel history into the generation AI, and the generation AI generates the optimal plan.

[0096] The generation unit can customize travel plans based on the user's current lifestyle and areas of interest when generating them. For example, the generation unit can generate a relaxing travel plan that suits the user's current lifestyle. The generation unit can also customize plans based on the user's areas of interest (e.g., history, nature, gourmet food, etc.). The generation unit can generate travel plans that fit the user's budget, according to the user's current lifestyle. In this way, by customizing plans based on the user's current lifestyle and areas of interest, the generation unit can provide the user with the most suitable travel plan. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs the user's current lifestyle and areas of interest into the generation AI, and the generation AI customizes the plan.

[0097] The generation unit can estimate the user's emotions and determine the priority of the plans to generate based on the estimated emotions. For example, if the user is feeling anxious, the generation unit will prioritize generating urgent plans. If the user is relaxed, the generation unit can also prioritize generating detailed plans. If the user is in a hurry, the generation unit will prioritize generating concise plans. This allows for a response tailored to the user's situation by prioritizing plans based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation 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 generation unit is performed using the generation AI. For example, the generation unit inputs the user's emotion data into the generation AI, which estimates the emotions and determines the priority of travel plans.

[0098] The generation unit can generate the optimal travel plan by considering the user's geographical location information. For example, the generation unit can generate a travel plan related to the user's current location. The generation unit can also generate a plan related to the region as the user approaches their travel destination. If the user is in a specific region, the generation unit can generate a plan based on tourist information for that region. In this way, by generating a plan while considering the user's geographical location information, it is possible to provide a highly relevant travel plan. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs the user's geographical location information into the generation AI, and the generation AI generates the optimal plan.

[0099] The generation unit can analyze a user's social media activity and generate relevant plans when generating travel plans. For example, the generation unit can generate plans based on travel plans shared by the user on social media. The generation unit can also analyze areas of interest from the user's social media posts and generate relevant plans. The generation unit can generate relevant plans by referring to the posts of the user's social media followers and friends. In this way, relevant travel plans can be provided by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs the user's social media activity into a generation AI, and the generation AI generates relevant plans.

[0100] The navigation unit can estimate the user's emotions and adjust the navigation method based on the estimated emotions. For example, if the user is nervous, the navigation unit can provide a simple and highly visible navigation method. If the user is relaxed, the navigation unit can also provide a navigation method that includes detailed information. If the user is in a hurry, the navigation unit can provide a concise navigation method. By adjusting the navigation method based on the user's emotions, it becomes possible to provide guidance that is appropriate to the user's situation. 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 navigation unit may be performed using AI or not using AI. For example, the navigation unit inputs user emotion data into the generative AI, the generative AI estimates the emotions, and adjusts the navigation method.

[0101] The navigation unit can select the optimal navigation method by referring to the user's past travel history during navigation. For example, the navigation unit can suggest the optimal navigation method based on routes previously used by the user. The navigation unit can also suggest a navigation method that avoids congestion based on the user's past travel history. The navigation unit analyzes the user's past travel history and suggests the most efficient navigation method. In this way, the optimal navigation method can be provided by referring to the user's past travel history. Some or all of the above processes in the navigation unit may be performed using AI, or they may not be performed using AI. For example, the navigation unit inputs the user's past travel history into the AI, and the AI ​​selects the optimal navigation method.

[0102] The navigation unit can customize navigation based on the user's current location and traffic conditions during navigation. For example, the navigation unit updates the user's current location in real time while they are moving and provides navigation. The navigation unit can also update the user's current location in real time as they approach their destination and suggest the optimal route. If the user gets lost, the navigation unit updates their current location in real time and provides navigation again. This allows for optimal route guidance by customizing navigation based on the user's current location and traffic conditions. Some or all of the above processes in the navigation unit may be performed using AI or not. For example, the navigation unit inputs the user's current location and traffic conditions into the AI, and the AI ​​customizes the navigation.

[0103] The navigation unit can estimate the user's emotions and determine navigation priorities based on those emotions. For example, if the user is feeling anxious, the navigation unit will prioritize urgent navigation. If the user is relaxed, the navigation unit may also prioritize detailed navigation. If the user is in a hurry, the navigation unit will prioritize concise navigation. This allows for guidance tailored to the user's situation by determining navigation priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the navigation unit may be performed using AI or not. For example, the navigation unit inputs user emotion data into a generative AI, which estimates the emotions and determines navigation priorities.

[0104] The navigation unit can select the optimal navigation method during navigation, taking into account the user's geographical location information. For example, the navigation unit can provide a navigation method relevant to the user's current location. As the user approaches their travel destination, the navigation unit can also provide a navigation method relevant to that region. If the user is in a specific region, the navigation unit can provide a navigation method based on tourist information for that region. This enables highly relevant guidance by considering the user's geographical location information during navigation. Some or all of the above processing in the navigation unit may be performed using AI, or not. For example, the navigation unit inputs the user's geographical location information into the AI, and the AI ​​selects the optimal navigation method.

[0105] The navigation unit can analyze the user's social media activity during navigation and provide relevant navigation information. For example, the navigation unit can provide navigation based on travel plans shared by the user on social media. The navigation unit can also analyze the user's areas of interest from their social media posts and provide relevant navigation information. The navigation unit can provide relevant navigation information by referring to the posts of the user's social media followers and friends. In this way, relevant navigation information can be provided by analyzing the user's social media activity. Some or all of the above processing in the navigation unit may be performed using AI or not. For example, the navigation unit inputs the user's social media activity into AI, and the AI ​​provides relevant navigation information.

[0106] The information provider can estimate the user's emotions and adjust the method of information delivery based on the estimated emotions. For example, if the user is relaxed, the provider can provide detailed information. If the user is in a hurry, the provider can also provide concise information. If the user is excited, the provider can provide visually stimulating information. This allows for information delivery tailored to the user's situation by adjusting the method of information delivery based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider inputs user emotion data into a generative AI, the generative AI estimates the emotions, and adjusts the method of information delivery.

[0107] The information provider can provide the most relevant information by referring to the user's past travel history. For example, it can provide information related to places the user has visited in the past. Based on the user's past travel history, it can also suggest tourist attractions that the user might be interested in. The information provider analyzes the user's past travel history and provides the most relevant information. This allows the service to provide the most relevant information by referring to the user's past travel history. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input the user's past travel history into an AI, which then provides the most relevant information.

[0108] The information provider can customize the information provided based on the user's current areas of interest and travel plans. For example, the provider can customize the information based on the user's areas of interest (e.g., history, nature, food, etc.). The provider can also prioritize providing information relevant to the user's current travel plans. The provider can suggest relevant activities and tourist attractions based on the user's areas of interest. This allows for the provision of highly relevant information by customizing it based on the user's current areas of interest and travel plans. Some or all of the above processing in the information provider may be performed using AI or not. For example, the provider can input the user's current areas of interest and travel plans into the AI, which then customizes the information.

[0109] The information provider can estimate the user's emotions and prioritize the information to be provided based on the estimated emotions. For example, if the user is feeling anxious, the information provider will prioritize providing urgent information. If the user is relaxed, the information provider may also prioritize providing detailed information. If the user is in a hurry, the information provider will prioritize providing concise information. This allows for the provision of information tailored to the user's situation by prioritizing information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider inputs user emotion data into a generative AI, which estimates the emotions and determines the priority of the information.

[0110] The information provider can provide optimal information by considering the user's geographical location when providing information. For example, the provider can provide information related to the user's current location. As the user approaches their travel destination, the provider can also provide information related to that region. If the user is in a specific region, the provider can provide tourist information for that region. In this way, by providing information while considering the user's geographical location, highly relevant information can be provided. Some or all of the above processing in the information provider may be performed using AI or not. For example, the provider can input the user's geographical location into the AI, and the AI ​​can provide optimal information.

[0111] The information provider can analyze the user's social media activity and provide relevant information when providing information. For example, the provider can provide information based on travel plans shared by the user on social media. The provider can also analyze the user's areas of interest from the content of their social media posts and provide relevant information. The provider can provide relevant information by referring to the content of posts by the user's social media followers and friends. In this way, relevant information can be provided by analyzing the user's social media activity. Some or all of the above processing in the information provider may be performed using AI or not. For example, the provider can input the user's social media activity into AI, and the AI ​​can provide relevant information.

[0112] The reservation system can estimate the user's emotions and adjust the reservation process based on those emotions. For example, if the user is relaxed, the system can provide a detailed reservation process. If the user is in a hurry, the system can provide a concise reservation process. If the user is feeling anxious, the system can provide a more supportive reservation process. By adjusting the reservation process based on the user's emotions, it becomes possible to provide a reservation process that is tailored to the user's situation. 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 reservation system may be performed using AI or not. For example, the reservation system inputs user emotion data into a generative AI, which estimates the emotion and adjusts the reservation process.

[0113] The reservation department can select the optimal reservation method by referring to the user's past reservation history during the reservation process. For example, the reservation department may prioritize suggesting reservation methods that the user has used in the past. The reservation department can also suggest the optimal reservation method based on the user's past reservation history. The reservation department analyzes the user's past reservation history and suggests the most efficient reservation method. In this way, the optimal reservation method can be provided by referring to the user's past reservation history. Some or all of the above processes in the reservation department may be performed using AI or not. For example, the reservation department may input the user's past reservation history into the AI, and the AI ​​may select the optimal reservation method.

[0114] The booking department can customize bookings based on the user's current travel plans and budget during the booking process. For example, the booking department can suggest the best booking method based on the user's current travel plans. The booking department can also suggest the best booking method to fit the user's budget. The booking department customizes bookings according to the progress of the user's travel plans. This allows the system to provide the best booking process by customizing bookings based on the user's current travel plans and budget. Some or all of the above processes in the booking department may be performed using AI or not. For example, the booking department can input the user's current travel plans and budget into the AI, which then customizes the booking.

[0115] The reservation system can estimate the user's emotions and determine the priority of the reservation process based on those emotions. For example, if the user is feeling anxious, the system will prioritize urgent reservations. If the user is relaxed, the system may also prioritize detailed reservations. If the user is in a hurry, the system will prioritize concise reservations. This allows for reservation processes tailored to the user's situation by prioritizing the reservation process based on their 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 reservation system may be performed using AI or not. For example, the reservation system inputs user emotion data into a generative AI, which estimates the emotions and determines the priority of the reservation process.

[0116] The reservation department can select the most suitable reservation method by considering the user's geographical location during the reservation process. For example, the reservation department can provide a reservation method relevant to the user's current location. As the user approaches their travel destination, the reservation department can also provide a reservation method relevant to that region. If the user is in a specific region, the reservation department can provide a reservation method based on tourist information for that region. This allows the reservation department to provide a highly relevant reservation method by considering the user's geographical location during the reservation process. Some or all of the above processes in the reservation department may be performed using AI or not. For example, the reservation department can input the user's geographical location information into the AI, which then selects the most suitable reservation method.

[0117] The reservation department can analyze a user's social media activity during the reservation process and provide relevant reservation information. For example, the reservation department can provide reservation information based on travel plans shared by the user on social media. The reservation department can also analyze a user's areas of interest from their social media posts and provide relevant reservation information. The reservation department can provide relevant reservation information by referring to posts from the user's social media followers and friends. In this way, relevant reservation information can be provided by analyzing the user's social media activity. Some or all of the above processes in the reservation department may be performed using AI or not. For example, the reservation department can input the user's social media activity into AI, and the AI ​​can provide relevant reservation information.

[0118] The history unit can estimate the user's emotions and adjust the history management method based on the estimated emotions. For example, if the user is relaxed, the history unit can provide detailed history management. If the user is in a hurry, the history unit can also provide concise history management. If the user is feeling anxious, the history unit can provide more supportive history management. This allows for history management tailored to the user's situation by adjusting the history management method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the history unit may be performed using AI or not. For example, the history unit inputs user emotion data into a generative AI, the generative AI estimates the emotions, and adjusts the history management method.

[0119] The history section can select the optimal management method by referring to the user's past travel history when managing history. For example, the history section may prioritize suggesting history management methods that the user has used in the past. The history section can also suggest the optimal management method based on the user's past travel history. The history section analyzes the user's past travel history and suggests the most efficient management method. In this way, it can provide the optimal history management method by referring to the user's past travel history. Some or all of the above processing in the history section may be performed using AI or not. For example, the history section may input the user's past travel history into the AI, and the AI ​​may select the optimal management method.

[0120] The history section can customize the history based on the user's current interests and travel plans when managing history. For example, the history section customizes the history based on the user's interests (e.g., history, nature, food, etc.). The history section can also prioritize managing history related to the user's current travel plans. The history section manages the history of relevant activities and tourist attractions based on the user's interests. This allows for highly relevant history management by customizing the history based on the user's current interests and travel plans. Some or all of the above processes in the history section may be performed using AI or not. For example, the history section inputs the user's current interests and travel plans into the AI, and the AI ​​customizes the history.

[0121] The history unit can estimate the user's emotions and determine the priority of history management based on the estimated emotions. For example, if the user is feeling anxious, the history unit will prioritize urgent history management. If the user is relaxed, the history unit may also prioritize detailed history management. If the user is in a hurry, the history unit will prioritize concise history management. This allows for history management tailored to the user's situation by determining the priority of history management based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the history unit may be performed using AI or not. For example, the history unit inputs user emotion data into a generative AI, which estimates the emotions and determines the priority of history management.

[0122] The history section can select the optimal management method when managing history, taking into account the user's geographical location information. For example, the history section can provide a history management method related to the user's current location. As the user approaches their travel destination, the history section can also provide a history management method related to that region. If the user is in a specific region, the history section can provide a history management method based on tourist information for that region. This allows for the provision of highly relevant management methods by considering the user's geographical location information during history management. Some or all of the above processing in the history section may be performed using AI or not. For example, the history section inputs the user's geographical location information into the AI, and the AI ​​selects the optimal management method.

[0123] The history section can analyze a user's social media activity and provide relevant history information during history management. For example, the history section can provide history information based on travel plans shared by the user on social media. The history section can also analyze a user's areas of interest from their social media posts and provide relevant history information. The history section can provide relevant history information by referring to posts from the user's social media followers and friends. In this way, relevant history information can be provided by analyzing the user's social media activity. Some or all of the above processing in the history section may be performed using AI or not. For example, the history section can input the user's social media activity into AI, and the AI ​​can provide relevant history information.

[0124] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0125] The reception unit can be equipped with a function to translate user input in real time. For example, if a user enters a question in their native language, the reception unit can automatically translate it into English and send it to the generation unit. This allows users who speak different languages ​​to use the system smoothly. Furthermore, if a user uses voice input, the reception unit can convert that voice into text and translate it as needed. This accommodates users who prefer voice input. By analyzing user input and translating it into the appropriate language, the reception unit can serve a global user base.

[0126] The generation unit can consider the user's past reviews and ratings when generating travel plans based on the user's preferences and constraints. For example, it can refer to reviews of places the user has visited in the past and suggest travel destinations with similar ratings. This allows for the provision of personalized travel plans based on the user's past experiences. Furthermore, the generation unit can also suggest similar options based on information about activities and restaurants the user has rated in the past. This allows for the generation of travel plans that match the user's preferences. By analyzing the user's past reviews and ratings and suggesting the optimal travel plan, the generation unit can improve user satisfaction.

[0127] The navigation unit can provide navigation tailored to the user's mode of transportation when navigating the user based on the generated plan. For example, if the user is traveling on foot, it will guide them along a route suitable for walking. If the user is traveling by car, it will suggest the optimal route considering traffic conditions. Furthermore, the navigation unit can also provide transfer information and timetable information to users using public transportation. This allows it to provide optimal navigation tailored to the user's mode of transportation. By analyzing the user's mode of transportation and suggesting the optimal route, the navigation unit can smoothly support the user's journey.

[0128] The service provider can take the user's current weather information into account when providing information on tourist attractions, popular activities, and recommended restaurants. For example, if it's raining, it will prioritize suggesting indoor activities. If it's sunny, it will suggest outdoor tourist attractions and activities. Furthermore, the service provider can display the weather forecast for the user's travel destination and provide information to help with travel planning. This allows users to create the best travel plan according to the weather. By analyzing the user's current weather information and providing the most relevant information, the service provider can improve the user's travel experience.

[0129] The reservations department can provide users with reservation options tailored to their payment method when supporting them with booking procedures for hotels, flights, activities, etc. For example, if a user wishes to pay by credit card, it will prioritize displaying reservation options that accept credit cards. If a user wishes to pay with electronic money, it will suggest reservation options that accept electronic money. Furthermore, the reservations department can also guide users on how to use points or coupons if they wish to pay using those methods. This allows the department to provide the most suitable reservation procedure for each user's payment method. By analyzing the user's payment method and suggesting the most suitable reservation option, the reservations department can improve user convenience.

[0130] The reception desk can estimate the user's emotions and adjust how questions are answered based on the estimated emotions. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. If the user is in a hurry, the reception desk can prioritize voice input and answer questions quickly. This allows for situation-appropriate responses by adjusting how questions are answered based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, 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 inputs user emotion data into a generative AI, which then estimates the emotions.

[0131] The generation unit can estimate the user's emotions and adjust the travel plan generation method based on the estimated emotions. For example, if the user is relaxed, it can generate a travel plan that proceeds at a leisurely pace. If the user is in a hurry, it can also generate a travel plan that emphasizes the shortest route. If the user is excited, the generation unit can generate a travel plan with visually stimulating effects. In this way, by adjusting the travel plan generation method based on the user's emotions, it is possible to provide a plan that is appropriate to the user's situation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit inputs the user's emotion data into the generation AI, the generation AI estimates the emotions, and adjusts the travel plan generation method.

[0132] The navigation unit can estimate the user's emotions and adjust the navigation method based on the estimated emotions. For example, if the user is nervous, it can provide a simple and highly visible navigation method. If the user is relaxed, the navigation unit can also provide a navigation method that includes detailed information. If the user is in a hurry, the navigation unit can provide a concise navigation method. By adjusting the navigation method based on the user's emotions, it becomes possible to provide guidance that is appropriate to the user's situation. 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 navigation unit may be performed using AI or not using AI. For example, the navigation unit inputs user emotion data into the generative AI, the generative AI estimates the emotions, and adjusts the navigation method.

[0133] The information provider can estimate the user's emotions and adjust the method of information delivery based on the estimated emotions. For example, if the user is relaxed, it can provide detailed information. If the user is in a hurry, it can provide concise information. If the user is excited, it can provide visually stimulating information. By adjusting the method of information delivery based on the user's emotions, it becomes possible to provide information that is appropriate to the user's situation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider inputs user emotion data into a generative AI, the generative AI estimates the emotions, and adjusts the method of information delivery.

[0134] The reservation system can estimate the user's emotions and adjust the reservation process based on those emotions. For example, if the user is relaxed, it can provide a detailed reservation process. If the user is in a hurry, it can provide a concise reservation process. If the user is feeling anxious, it can provide a more supportive reservation process. By adjusting the reservation process based on the user's emotions, it becomes possible to provide a reservation process tailored to the user's situation. 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 reservation system may be performed using AI or not. For example, the reservation system inputs user emotion data into a generative AI, which estimates the emotion and adjusts the reservation process.

[0135] The following briefly describes the processing flow for example form 2.

[0136] Step 1: The reception desk receives user input. User input includes text input, voice input, and image input. For example, it accepts user input regarding destinations and itineraries. Step 2: The generation unit generates a travel plan based on the information received by the reception unit. The generation unit uses a generation AI to generate a travel plan based on the user's preferences and constraints. The generation AI analyzes the user's input and proposes the optimal travel plan. Step 3: The navigation unit navigates the user based on the plan generated by the generation unit. The navigation unit can also guide the user using map displays and voice guidance, and can provide real-time traffic information. Step 4: The service provider provides information about the travel destination. The service provider can provide information such as tourist attractions, popular activities, and recommended restaurants, and can display photos and maps while providing the information. Step 5: The booking department supports the booking process for hotels, flights, activities, etc. The booking department can also process bookings based on the travel plan selected by the user and provide links to booking websites. Step 6: The History section manages the user's travel history. The History section manages information such as past travel itineraries, destinations, and photos, allowing users to view information and photos from their past trips.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the reception unit, generation unit, navigation unit, provision unit, reservation unit, and history 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 accepts text input and voice input from the user. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates a travel plan using generation AI. The navigation unit is implemented by, for example, the control unit 46A of the smart device 14 and provides map display and voice guidance. The provision unit is implemented by, for example, the control unit 46A of the smart device 14 and provides information on tourist attractions and restaurants. The reservation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and supports hotel and airline ticket reservation procedures. The history unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and manages the user's travel history. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0141] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the reception unit, generation unit, navigation unit, provision unit, reservation unit, and history unit, is implemented 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 accepts text and voice input from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a travel plan using generation AI. The navigation unit is implemented by the control unit 46A of the smart glasses 214 and provides map display and voice guidance. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides information on tourist attractions and restaurants. The reservation unit is implemented by the specific processing unit 290 of the data processing unit 12 and supports hotel and airline ticket reservation procedures. The history unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages the user's travel history. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0157] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Each of the multiple elements described above, including the reception unit, generation unit, navigation unit, provision unit, reservation unit, and history 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 accepts text and voice input from the user. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates a travel plan using a generation AI. The navigation unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides map display and voice guidance. The provision unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides information on tourist attractions and restaurants. The reservation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and supports hotel and airline ticket reservation procedures. The history unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and manages the user's travel history. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0173] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.).

[0186] 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.

[0187] 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.

[0188] 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.

[0189] Each of the multiple elements described above, including the reception unit, generation unit, navigation unit, provision unit, reservation unit, and history 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 accepts text and voice input from the user. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates a travel plan using generation AI. The navigation unit is implemented by, for example, the control unit 46A of the robot 414 and provides map display and voice guidance. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides information on tourist attractions and restaurants. The reservation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and supports hotel and airline ticket reservation procedures. The history unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and manages the user's travel history. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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."

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] (Note 1) A reception area that receives user input, A generation unit that generates a travel plan based on the information received by the reception unit, A navigation unit that performs navigation based on the plan generated by the generation unit, The information service department provides information about travel destinations, The reservation department supports the reservation process, It includes a history section for managing travel history. A system characterized by the following features. (Note 2) The aforementioned reception unit is We accept questions from users regarding their destinations and itineraries. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Generate travel plans based on user preferences and constraints. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned navigation unit is Navigate the user based on the generated plan. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, We provide information on tourist attractions, popular activities, recommended restaurants, and more. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reservation section is, Supports booking procedures for hotels, flights, activities, and more. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned history section is, Manage the user's travel history and allow them to view information and photos from past trips. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system estimates the user's emotions and adjusts how questions are answered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is Analyze the user's past question history and select the most suitable method for receiving questions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving a question, filtering is performed based on the user's current travel plans and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is It estimates the user's emotions and prioritizes the questions to be asked based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving questions, the system prioritizes questions that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When receiving questions, the system analyzes the user's social media activity and selects relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is We estimate the user's emotions and adjust the travel plan generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating a travel plan, the system references the user's past travel history to create the most suitable plan. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating travel plans, the plan is customized based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is It estimates the user's emotions and determines the priority of the plans generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating travel plans, the system takes the user's geographical location into consideration to create the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating travel plans, the system analyzes the user's social media activity and generates relevant plans. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned navigation unit is It estimates the user's emotions and adjusts the navigation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned navigation unit is During navigation, the system selects the optimal navigation method by referring to the user's past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned navigation unit is During navigation, the navigation system is customized based on the user's current location and traffic conditions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned navigation unit is It estimates the user's emotions and determines navigation priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned navigation unit is During navigation, the system selects the optimal navigation method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned navigation unit is During navigation, the system analyzes the user's social media activity and provides relevant navigation information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way information is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing information, we refer to the user's past travel history to provide the most relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing information, customize the information based on the user's current areas of interest and travel plans. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing information, we will consider the user's geographical location to provide the most relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing information, we analyze the user's social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned reservation section is, The system estimates the user's emotions and adjusts the booking process based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned reservation section is, During the booking process, the system will refer to the user's past booking history to select the most suitable booking method. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned reservation section is, During the booking process, the reservation is customized based on the user's current travel plans and budget. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned reservation section is, The system estimates the user's emotions and prioritizes the booking process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned reservation section is, During the booking process, the system selects the most suitable booking method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned reservation section is, During the booking process, we analyze the user's social media activity and provide relevant booking information. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned history section is, We estimate the user's emotions and adjust the history management method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned history section is, During history management, the system selects the optimal management method by referring to the user's past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned history section is, When managing travel history, customize the history based on the user's current areas of interest and travel plans. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned history section is, The system estimates the user's emotions and determines the priority of history management based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned history section is, When managing history, the optimal management method is selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned history section is, During history management, the system analyzes the user's social media activity and provides relevant historical information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0209] 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 area that receives user input, A generation unit that generates a travel plan based on the information received by the reception unit, A navigation unit that performs navigation based on the plan generated by the generation unit, The information provision department provides information about travel destinations, The reservation department supports the reservation process, It includes a history section for managing travel history. A system characterized by the following features.

2. The aforementioned reception unit is We accept questions from users regarding their destinations and itineraries. The system according to feature 1.

3. The generating unit is Generate travel plans based on user preferences and constraints. The system according to feature 1.

4. The aforementioned navigation unit is Navigate the user based on the generated plan. The system according to feature 1.

5. The aforementioned supply unit is, We provide information on tourist attractions, popular activities, recommended restaurants, and more. The system according to feature 1.

6. The aforementioned reservation section is, Supports booking procedures for hotels, flights, activities, and more. The system according to feature 1.

7. The aforementioned history section is, Manage the user's travel history and allow them to view information and photos from past trips. The system according to feature 1.

8. The aforementioned reception unit is The system estimates the user's emotions and adjusts how questions are answered based on those estimated emotions. The system according to feature 1.

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