system

Abstract

The system according to this embodiment aims to propose itineraries tailored to individual preferences, generate travel guides summarizing the itineraries, and provide tour guides and navigation services tailored to the individuals. [Solution] The system according to the embodiment comprises a reception unit, a proposal unit, a generation unit, a guide unit, and a navigation unit. The reception unit receives input of the desired area or tourist genre, date and time, or number of people. The proposal unit analyzes the information received by the reception unit and proposes a travel itinerary tailored to the individual's preferences. The generation unit generates a travel guide summarizing the travel schedule based on the itinerary proposed by the proposal unit. The guide unit provides a tour guide tailored to the age and knowledge level of the target person based on the travel guide generated by the generation unit. The navigation unit performs navigation in conjunction with a map application based on the information provided by the guide unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including 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, a journey specialized for an individual's wishes has not been sufficiently proposed, a itinerary summarizing a travel schedule has not been generated, and a tour guide or navigator tailored to the target person has not been provided, leaving room for improvement.

[0005] The system according to the embodiment aims to propose a journey specialized for an individual's wishes, generate a itinerary summarizing a travel schedule, and provide a tour guide or navigator tailored to the target person.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a proposal unit, a generation unit, a guide unit, and a navigation unit. The reception unit receives input of the desired area or tourist genre, date and time, or number of people. The proposal unit analyzes the information received by the reception unit and proposes a travel itinerary tailored to the individual's preferences. The generation unit generates a travel guide summarizing the travel schedule based on the itinerary proposed by the proposal unit. The guide unit provides a tour guide tailored to the age and knowledge level of the target person based on the travel guide generated by the generation unit. The navigation unit performs navigation in conjunction with a map application based on the information provided by the guide unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose itineraries tailored to individual preferences, generate travel guides summarizing the itinerary, and provide tour guides and navigation services tailored to the individual. [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 tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memories (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

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

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

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, a specific processing unit 290 (see FIG. 2) acquires data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example of form 1) The information platform according to an embodiment of the present invention is an information platform that utilizes generative AI to propose a personalized itinerary to travelers, generates an original travel guide summarizing the travel schedule, and further provides a unique tour guide tailored to the age and knowledge level of the target person. This information platform allows users to input their desired area, sightseeing genre, date and time, number of people, etc., using free text. The generative AI analyzes the input information, matches it with data from various travel agencies, and proposes a personalized itinerary tailored to the individual's preferences. Furthermore, the generative AI generates an original travel guide summarizing the travel schedule. This guide includes the travel schedule, places to visit, routes, dates, etc. The generative AI also provides a unique tour guide tailored to the age and knowledge level of the target person. For example, it can provide simple explanations for children and detailed historical and cultural explanations for adults. Furthermore, it integrates with a map application to provide optimal navigation using an agent. This allows users to easily understand transportation options to their destination and enjoy their trip with peace of mind. By sharing schedules and information on the information platform, it is also possible to provide better reference information to future visitors. This allows the information platform to propose personalized itineraries tailored to the user's preferences, generate original travel guides summarizing the travel schedule, provide unique tour guides suited to the age and knowledge level of the target audience, and integrate with map applications for optimal navigation.

[0029] The information platform according to this embodiment comprises a reception unit, a proposal unit, a generation unit, a guide unit, and a navigation unit. The reception unit accepts input of the desired area or tourism genre, date and time, or number of people. For example, the reception unit allows users to input their desired area or tourism genre, date and time, number of people, etc., using free text. The proposal unit analyzes the information received by the reception unit and proposes a travel itinerary tailored to the individual's preferences. For example, the proposal unit uses a generation AI to analyze the input information, compare it with data from various travel agencies, and propose a personalized travel itinerary tailored to the individual's preferences. The generation unit generates a travel guide summarizing the travel schedule based on the itinerary proposed by the proposal unit. For example, the generation unit uses a generation AI to generate an original travel guide summarizing the travel schedule. This guide includes the travel schedule, places to visit, routes, dates and times, etc. The guide unit provides a tour guide tailored to the age and knowledge level of the target person based on the guide generated by the generation unit. For example, the guide unit uses AI to provide a unique tour guide tailored to the age and knowledge level of the target person. For example, it can provide simple explanations for children and detailed historical and cultural explanations for adults. The navigation unit works in conjunction with a map application to provide navigation based on the information provided by the guide unit. The navigation unit can use AI, for example, to provide optimal navigation in conjunction with the map application. As a result, the information platform according to the embodiment can propose a personalized itinerary tailored to the user's wishes, generate an original travel guide summarizing the travel schedule, provide a unique tour guide tailored to the age and knowledge level of the target person, and provide optimal navigation in conjunction with a map application.

[0030] The reception desk accepts input from users regarding their desired area or type of tourism, date and time, and number of people. For example, users can freely input their desired area, type of tourism, date and time, and number of people using keywords. Specifically, when planning a trip, users can freely input their desired tourist destinations, places they want to visit, purpose of travel, travel dates, and number of companions. This allows users to easily input a travel plan that suits their preferences. Furthermore, the reception desk has a function to automatically analyze the information entered by the user and extract necessary information. For example, if a user enters "Family trip to Tokyo, 3 days, 4 people," the reception desk will extract the keywords "Tokyo," "Family trip," "3 days," and "4 people," and send this information to the next step, the suggestion desk. The reception desk also has a function to automatically display suggestions for related tourist destinations, events, and activities based on the information entered by the user. This allows users to easily find a travel plan that suits their preferences. Additionally, the reception desk has a function to save the information entered by the user and reuse it later. This allows users to create new travel plans by referring to past travel plans.

[0031] The Proposal Department analyzes the information received by the Reception Department and proposes itineraries tailored to individual preferences. For example, using a generative AI, the Proposal Department analyzes the input information, matches it with data from various travel agencies, and proposes a personalized itinerary tailored to the individual's wishes. Specifically, the generative AI analyzes past travel data and plans provided by travel agencies based on information such as the desired area, type of sightseeing, date, and number of people entered by the user, and generates the optimal itinerary. For example, if a user enters "Family trip to Tokyo, 3 days, 4 people," the generative AI will propose a family-friendly travel plan based on information such as tourist attractions, events, and activities in Tokyo. Furthermore, the generative AI can make even more personalized suggestions by considering the user's past travel history and preferences. For example, based on data of places the user has visited and activities they have participated in in the past, it will suggest new tourist destinations and events that the user might be interested in. In addition, the Proposal Department can also propose detailed plans such as travel budget, transportation, and accommodation based on the information entered by the user. This makes it easy for users to find a travel plan that suits their preferences. Furthermore, the Proposal Department also has a function to provide feedback to users on the proposed plans. This allows users to customize the proposed plan and create one that better suits their needs.

[0032] The generation unit generates a travel itinerary based on the itinerary proposed by the suggestion unit. For example, the generation unit uses a generation AI to create an original travel itinerary. This itinerary includes the travel schedule, places to visit, routes, and dates. Specifically, the generation AI creates a detailed travel schedule based on the itinerary proposed by the suggestion unit. For example, if a user selects a plan such as "Family trip to Tokyo, 3 days, 4 people," the generation AI creates a specific schedule, such as visiting Senso-ji Temple in the morning of the first day and sightseeing at Tokyo Skytree in the afternoon. The generation AI also calculates the optimal mode of transportation and travel time based on information such as places to visit, routes, and dates, creating an efficient travel plan. Furthermore, the generation unit can customize the itinerary based on information entered by the user and feedback from the suggestion unit. For example, the user can add specific tourist destinations or change visit times. This allows users to create an original travel itinerary tailored to their preferences. Additionally, the generation unit has the functionality to provide the itinerary content in digital format. This allows users to check their bookmarks on devices such as smartphones and tablets and use them during their trip.

[0033] The guide unit provides tour guides tailored to the age and knowledge level of the target audience, based on bookmarks generated by the generation unit. The guide unit, for example, uses AI to provide unique tour guides tailored to the age and knowledge level of the target audience. Specifically, the AI ​​generates optimal guide content based on information such as the user's age, knowledge level, and interests. For example, it can provide simple explanations and quiz-style guides for children, and detailed historical and cultural explanations for adults. Furthermore, the AI ​​can adjust the guide content based on the user's real-time reactions and feedback. For example, if a user shows interest in a particular place, it can provide detailed information about that place. In addition, the guide unit can provide guides in various formats, such as audio guides, text guides, and video guides. This allows users to choose the guide format that suits their preferences. Moreover, the guide unit has a function to update the guide content in real time based on the places and routes the user visits. This ensures that users always receive guides based on the latest information.

[0034] The navigation unit provides navigation in conjunction with a map application based on information provided by the guide unit. For example, the navigation unit uses AI to provide optimal navigation in conjunction with the map application. Specifically, the AI ​​calculates the optimal route based on the user's current location, destination, and places to visit, and displays it on the map application. For example, if a user is traveling from Senso-ji Temple to Tokyo Skytree, the AI ​​calculates the optimal mode of transport and travel time and displays it on the map application. The navigation unit also has a function to acquire real-time traffic and weather information and optimize the route. This ensures that users always receive navigation based on the latest information. Furthermore, the navigation unit has a function to update the navigation content in real time based on the places and routes the user is visiting. This ensures that users always receive navigation based on the latest information. In addition, the navigation unit can provide various forms of navigation, such as voice guidance and vibration notifications. This allows users to choose the navigation format that suits their preferences.

[0035] The information platform includes a sharing section for sharing schedules or information on the platform. The sharing section allows users to share information such as travel schedules, places to visit, routes, and dates with other users. Users can share travel schedules and information through, for example, a website or mobile app. This allows for the provision of better reference information to future visitors by sharing schedules and information. Some or all of the above processing in the sharing section may be performed using, for example, AI, or not. For example, the sharing section may use AI to analyze information shared by users and automatically extract and provide useful information to other users.

[0036] The reception desk can analyze the user's past input history and provide input assistance functions. For example, the reception desk can automatically display areas and tourist genres that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict specific dates, times, and numbers of people based on the user's past input history and provide input assistance functions accordingly. In this way, by analyzing the user's past input history, the reception desk can provide optimal input assistance functions. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI perform the provision of input assistance functions.

[0037] The reception desk can automatically suggest relevant areas and tourist genres by considering the user's current location information during input. For example, when a user opens the app, the reception desk can automatically acquire the user's current location and suggest relevant tourist spots. Furthermore, when a user enters a destination, the reception desk can suggest the most suitable tourist genre by considering the distance from the user's current location. Additionally, if the user uses the app while on the move, the reception desk can update the user's current location in real time and suggest relevant areas and tourist genres. This allows the system to provide optimal suggestions to the user by automatically suggesting relevant areas and tourist genres based on the user's current location information. 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 location data into a generating AI and have the generating AI suggest relevant areas and tourist genres.

[0038] The reception desk can analyze the user's social media activity during input and suggest relevant travel genres. For example, it can suggest relevant travel genres based on places the user frequently checks into on social media. It can also analyze the content of posts from accounts the user follows on social media and suggest travel genres that might interest them. Furthermore, it can suggest relevant travel genres based on travel photos the user has shared on social media. In this way, by analyzing the user's social media activity, it is possible to suggest the most suitable travel genres for the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI suggest relevant travel genres.

[0039] The reception desk can automatically complete relevant dates, times, and numbers of people by referring to the user's past travel history during input. For example, the reception desk can predict and automatically complete the date and time of the next trip based on the dates and times of places the user has visited in the past. It can also predict and automatically complete the number of people on the next trip based on the number of people the user has traveled with in the past. Furthermore, the reception desk can analyze the user's past travel history and suggest dates and times that are suitable for specific seasons or events. This allows the reception desk to automatically complete relevant dates, times, and numbers of people by referring to the user's past travel history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past travel history data into a generating AI and have the generating AI complete the relevant dates and numbers of people.

[0040] The suggestion unit can analyze the user's past travel history and propose the optimal travel plan. For example, the suggestion unit can propose relevant travel plans based on data of places the user has visited in the past. It can also propose travel plans that avoid crowds based on the user's past travel history. Furthermore, the suggestion unit can analyze the user's past travel history and propose the most efficient travel plan. In this way, by analyzing the user's past travel history, it is possible to propose the optimal travel plan. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's past travel history data into a generating AI and have the generating AI execute the proposal of the optimal travel plan.

[0041] The suggestion unit can provide a customized travel plan based on the user's current lifestyle and areas of interest. For example, the suggestion unit can propose an optimal travel plan considering the user's current lifestyle (work, family, etc.). It can also propose a customized travel plan based on the user's areas of interest (history, nature, art, etc.). Furthermore, it can propose a travel plan that is manageable considering the user's current health and physical condition. This allows the suggestion unit to propose the optimal travel plan for the user by providing a customized travel plan based on the user's current lifestyle and areas of interest. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the task of providing a customized travel plan.

[0042] The suggestion unit can prioritize suggesting highly relevant travel plans by considering the user's geographical location. For example, the suggestion unit can prioritize suggesting tourist spots close to the user's current location. Furthermore, if the user is staying in a specific area, the suggestion unit can prioritize suggesting tourist spots within that area. Additionally, if the user is on the move, the suggestion unit can prioritize suggesting tourist spots along their travel route. This allows the suggestion unit to provide optimal suggestions by prioritizing highly relevant travel plans based on the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI, or without AI. For example, the suggestion unit can input the user's geographical location data into a generating AI and have the AI ​​generate highly relevant travel plan suggestions.

[0043] The suggestion unit can analyze the user's social media activity and propose relevant travel plans when making suggestions. For example, the suggestion unit can suggest relevant travel plans based on places the user frequently checks into on social media. It can also analyze the content of posts from accounts the user follows on social media and propose travel plans that might interest them. Furthermore, the suggestion unit can suggest relevant travel plans based on travel photos the user has shared on social media. In this way, by analyzing the user's social media activity, it is possible to propose the most suitable travel plan for the user. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's social media data into a generating AI and have the generating AI generate suggestions for relevant travel plans.

[0044] The generation unit can analyze the user's past travel history when generating bookmarks and provide an optimal travel schedule. For example, the generation unit can suggest a relevant travel schedule based on data of places the user has visited in the past. The generation unit can also suggest a travel schedule that avoids crowds based on the user's past travel history. Furthermore, the generation unit can analyze the user's past travel history and suggest the most efficient travel schedule. In this way, by analyzing the user's past travel history, it is possible to provide an optimal travel schedule. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past travel history data into a generation AI and have the generation AI perform the task of providing an optimal travel schedule.

[0045] The generation unit can provide a customized travel schedule based on the user's current lifestyle and areas of interest when generating bookmarks. For example, the generation unit can suggest an optimal travel schedule considering the user's current lifestyle (work, family, etc.). The generation unit can also suggest a customized travel schedule based on the user's areas of interest (history, nature, art, etc.). Furthermore, the generation unit can suggest a reasonable travel schedule considering the user's current health and physical condition. In this way, by providing a customized travel schedule based on the user's current lifestyle and areas of interest, the generation unit can suggest the optimal travel schedule for the user. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the user's lifestyle and areas of interest into a generation AI and have the generation AI perform the task of providing a customized travel schedule.

[0046] The generation unit can prioritize listing highly relevant places to visit when generating bookmarks, taking into account the user's geographical location. For example, the generation unit can prioritize listing tourist spots close to the user's current location. Furthermore, if the user is staying in a specific area, the generation unit can prioritize listing tourist spots within that area. Additionally, if the user is on the move, the generation unit can prioritize listing tourist spots along their travel route. This allows the generation unit to provide the user with the most relevant bookmarks by prioritizing highly relevant places to visit while considering the user's geographical location. Some or all of the above processing in the generation unit may be performed using AI, or without AI. For example, the generation unit can input the user's geographical location data into a generation AI and have the generation AI list highly relevant places to visit.

[0047] The generation unit can analyze the user's social media activity and include relevant places to visit when generating bookmarks. For example, the generation unit can include relevant places to visit in the bookmarks based on places the user frequently checks in to on social media. The generation unit can also analyze the content of posts from accounts the user follows on social media and include places that might be of interest to the user. Furthermore, the generation unit can include relevant places to visit in the bookmarks based on travel photos the user has shared on social media. In this way, by analyzing the user's social media activity, the generation unit can include the most suitable places to visit in the bookmarks for the user. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the user's social media data into a generation AI and have the generation AI perform the task of including relevant places to visit.

[0048] The guide unit can analyze the user's past travel history when providing a guide and provide the most suitable guide content. For example, the guide unit can provide relevant guide content based on data of places the user has visited in the past. The guide unit can also provide guide content that avoids crowds based on the user's past travel history. Furthermore, the guide unit can analyze the user's past travel history and provide the most efficient guide content. In this way, the guide unit can provide the most suitable guide content by analyzing the user's past travel history. Some or all of the above processing in the guide unit may be performed using AI, for example, or without AI. For example, the guide unit can input the user's past travel history data into a generating AI and have the generating AI perform the task of providing the most suitable guide content.

[0049] The guide unit can provide customized guide content based on the user's current living situation and areas of interest when providing a guide. For example, the guide unit can provide optimal guide content considering the user's current living situation (work, family, etc.). The guide unit can also provide customized guide content based on the user's areas of interest (history, nature, art, etc.). Furthermore, the guide unit can provide guide content that is not strenuous, considering the user's current health condition and physical strength. In this way, by providing customized guide content based on the user's current living situation and areas of interest, the guide unit can provide optimal guide content for the user. Some or all of the above processing in the guide unit may be performed using AI, for example, or without AI. For example, the guide unit can input data on the user's living situation and areas of interest into a generating AI and have the generating AI perform the provision of customized guide content.

[0050] The guide unit can prioritize providing highly relevant guide content by considering the user's geographical location when providing a guide. For example, the guide unit can prioritize including tourist spots close to the user's current location in the guide content. Furthermore, if the user is staying in a specific area, the guide unit can prioritize including tourist spots within that area in the guide content. Additionally, if the user is on the move, the guide unit can prioritize including tourist spots along their travel route in the guide content. This allows the guide unit to provide optimal guide content by prioritizing highly relevant information based on the user's geographical location. Some or all of the above processing in the guide unit may be performed using AI, or without AI. For example, the guide unit can input the user's geographical location data into a generating AI and have the generating AI provide highly relevant guide content.

[0051] The guide unit can analyze the user's social media activity when providing a guide and provide relevant guide content. For example, the guide unit can provide relevant guide content based on places the user frequently checks in to on social media. It can also analyze the posts of accounts the user follows on social media and provide guide content that might be of interest to the user. Furthermore, the guide unit can provide relevant guide content based on travel photos the user has shared on social media. This allows the guide unit to provide optimal guide content by analyzing the user's social media activity. Some or all of the above processing in the guide unit may be performed using AI, for example, or without AI. For example, the guide unit can input the user's social media data into a generating AI and have the generating AI provide relevant guide content.

[0052] The navigation unit can analyze the user's past travel history during navigation and provide the optimal navigation method. For example, the navigation unit can suggest the optimal navigation method based on routes the user has used in the past. It can also suggest a navigation method that avoids congestion based on the user's past travel history. Furthermore, the navigation unit can analyze the user's past travel history and suggest the most efficient navigation method. This allows the navigation unit to provide the optimal navigation method by analyzing the user's past travel history. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's past travel history data into a generating AI and have the generating AI perform the task of providing the optimal navigation method.

[0053] The navigation unit can provide a customized navigation method based on the user's current lifestyle and areas of interest during navigation. For example, the navigation unit can provide an optimal navigation method considering the user's current lifestyle (work, family, etc.). It can also provide a customized navigation method based on the user's areas of interest (history, nature, art, etc.). Furthermore, the navigation unit can provide a manageable navigation method considering the user's current health and physical condition. By providing a customized navigation method based on the user's current lifestyle and areas of interest, the navigation unit can provide the optimal navigation method for the user. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the task of providing a customized navigation method.

[0054] The navigation unit can prioritize providing highly relevant navigation methods by considering the user's geographical location information during navigation. For example, the navigation unit can prioritize including routes close to the user's current location in the navigation content. Furthermore, if the user is staying in a specific area, the navigation unit can prioritize including routes within that area in the navigation content. Additionally, if the user is on the move, the navigation unit can prioritize providing navigation methods along the travel route. This allows the system to provide the optimal navigation method for the user by prioritizing highly relevant navigation methods while considering the user's geographical location information. Some or all of the above processing in the navigation unit may be performed using AI, or without AI. For example, the navigation unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing highly relevant navigation methods.

[0055] The navigation unit can analyze the user's social media activity during navigation and provide relevant navigation methods. For example, the navigation unit can provide relevant navigation methods based on places the user frequently checks in to on social media. It can also analyze the content of posts from accounts the user follows on social media and provide navigation methods that might be of interest to the user. Furthermore, the navigation unit can provide relevant navigation methods based on travel photos the user has shared on social media. In this way, by analyzing the user's social media activity, the system can provide the most suitable navigation method for the user. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's social media data into a generating AI and have the generating AI perform the task of providing relevant navigation methods.

[0056] The sharing function can analyze a user's past sharing history and provide the most suitable content for sharing. For example, the sharing function can provide relevant content based on what the user has shared in the past. It can also provide content that the user might be interested in based on their past sharing history. Furthermore, the sharing function can analyze the user's past sharing history and provide the most efficient content for sharing. In this way, by analyzing the user's past sharing history, it is possible to provide the most suitable content for sharing. Some or all of the above processing in the sharing function may be performed using AI, for example, or without AI. For example, the sharing function can input the user's past sharing history data into a generating AI and have the generating AI perform the task of providing the most suitable content for sharing.

[0057] The sharing function can prioritize providing highly relevant content when sharing, taking into account the user's geographical location. For example, the sharing function can prioritize including information about tourist spots near the user's current location in the shared content. Furthermore, if the user is staying in a specific area, the sharing function can prioritize including information about tourist spots within that area. Additionally, if the user is on the move, the sharing function can prioritize including information about tourist spots along their travel route. This allows the sharing function to provide optimal content for the user by prioritizing highly relevant content while considering their geographical location. Some or all of the above processing in the sharing function may be performed using AI, or without AI. For example, the sharing function can input the user's geographical location data into a generating AI and have the generating AI provide highly relevant content.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The reception desk can analyze the user's past input history and provide input assistance functions. For example, the reception desk can automatically display areas and tourist genres that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict specific dates, times, and numbers of people based on the user's past input history and provide input assistance functions accordingly. In this way, by analyzing the user's past input history, the reception desk can provide optimal input assistance functions. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI perform the provision of input assistance functions.

[0060] The reception desk can automatically suggest relevant areas and tourist genres by considering the user's current location information during input. For example, when a user opens the app, the reception desk can automatically acquire the user's current location and suggest relevant tourist spots. Furthermore, when a user enters a destination, the reception desk can suggest the most suitable tourist genre by considering the distance from the user's current location. Additionally, if the user uses the app while on the move, the reception desk can update the user's current location in real time and suggest relevant areas and tourist genres. This allows the system to provide optimal suggestions to the user by automatically suggesting relevant areas and tourist genres based on the user's current location information. 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 location data into a generating AI and have the generating AI suggest relevant areas and tourist genres.

[0061] The suggestion unit can analyze the user's past travel history and propose the optimal travel plan. For example, the suggestion unit can propose relevant travel plans based on data of places the user has visited in the past. It can also propose travel plans that avoid crowds based on the user's past travel history. Furthermore, the suggestion unit can analyze the user's past travel history and propose the most efficient travel plan. In this way, by analyzing the user's past travel history, it is possible to propose the optimal travel plan. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's past travel history data into a generating AI and have the generating AI execute the proposal of the optimal travel plan.

[0062] The generation unit can analyze the user's past travel history when generating bookmarks and provide an optimal travel schedule. For example, the generation unit can suggest a relevant travel schedule based on data of places the user has visited in the past. The generation unit can also suggest a travel schedule that avoids crowds based on the user's past travel history. Furthermore, the generation unit can analyze the user's past travel history and suggest the most efficient travel schedule. In this way, by analyzing the user's past travel history, it is possible to provide an optimal travel schedule. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past travel history data into a generation AI and have the generation AI perform the task of providing an optimal travel schedule.

[0063] The guide unit can analyze the user's past travel history when providing a guide and provide the most suitable guide content. For example, the guide unit can provide relevant guide content based on data of places the user has visited in the past. The guide unit can also provide guide content that avoids crowds based on the user's past travel history. Furthermore, the guide unit can analyze the user's past travel history and provide the most efficient guide content. In this way, the guide unit can provide the most suitable guide content by analyzing the user's past travel history. Some or all of the above processing in the guide unit may be performed using AI, for example, or without AI. For example, the guide unit can input the user's past travel history data into a generating AI and have the generating AI perform the task of providing the most suitable guide content.

[0064] The navigation unit can analyze the user's past travel history during navigation and provide the optimal navigation method. For example, the navigation unit can suggest the optimal navigation method based on routes the user has used in the past. It can also suggest a navigation method that avoids congestion based on the user's past travel history. Furthermore, the navigation unit can analyze the user's past travel history and suggest the most efficient navigation method. This allows the navigation unit to provide the optimal navigation method by analyzing the user's past travel history. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's past travel history data into a generating AI and have the generating AI perform the task of providing the optimal navigation method.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The reception desk accepts input from the user regarding their desired area or type of tourism, date and time, or number of people. For example, users can enter their desired area, type of tourism, date and time, and number of people using free text. Step 2: The proposal department analyzes the information received by the reception department and proposes a travel itinerary tailored to the individual's preferences. For example, it uses a generation AI to analyze the input information, cross-reference it with data from various travel agencies, and propose a personalized travel itinerary tailored to the individual's wishes. Step 3: The generation unit generates a travel itinerary based on the itinerary proposed by the proposal unit. For example, it uses a generation AI to generate an original travel itinerary that summarizes the travel schedule. This itinerary includes the travel schedule, places to visit, routes, dates and times, etc. Step 4: The guide unit provides a tour guide tailored to the age and knowledge level of the target audience, based on the bookmarks generated by the generation unit. For example, AI can be used to provide a unique tour guide tailored to the age and knowledge level of the target audience. For instance, simple explanations can be provided for children, while detailed historical and cultural explanations can be provided for adults. Step 5: The navigation unit works in conjunction with a map application to provide navigation based on the information provided by the guide unit. For example, it uses AI to provide optimal navigation in conjunction with a map application.

[0067] (Example of form 2) The information platform according to an embodiment of the present invention is an information platform that utilizes generative AI to propose a personalized itinerary to travelers, generates an original travel guide summarizing the travel schedule, and further provides a unique tour guide tailored to the age and knowledge level of the target person. This information platform allows users to input their desired area, sightseeing genre, date and time, number of people, etc., using free text. The generative AI analyzes the input information, matches it with data from various travel agencies, and proposes a personalized itinerary tailored to the individual's preferences. Furthermore, the generative AI generates an original travel guide summarizing the travel schedule. This guide includes the travel schedule, places to visit, routes, dates, etc. The generative AI also provides a unique tour guide tailored to the age and knowledge level of the target person. For example, it can provide simple explanations for children and detailed historical and cultural explanations for adults. Furthermore, it integrates with a map application to provide optimal navigation using an agent. This allows users to easily understand transportation options to their destination and enjoy their trip with peace of mind. By sharing schedules and information on the information platform, it is also possible to provide better reference information to future visitors. This allows the information platform to propose personalized itineraries tailored to the user's preferences, generate original travel guides summarizing the travel schedule, provide unique tour guides suited to the age and knowledge level of the target audience, and integrate with map applications for optimal navigation.

[0068] The information platform according to this embodiment comprises a reception unit, a proposal unit, a generation unit, a guide unit, and a navigation unit. The reception unit accepts input of the desired area or tourism genre, date and time, or number of people. For example, the reception unit allows users to input their desired area or tourism genre, date and time, number of people, etc., using free text. The proposal unit analyzes the information received by the reception unit and proposes a travel itinerary tailored to the individual's preferences. For example, the proposal unit uses a generation AI to analyze the input information, compare it with data from various travel agencies, and propose a personalized travel itinerary tailored to the individual's preferences. The generation unit generates a travel guide summarizing the travel schedule based on the itinerary proposed by the proposal unit. For example, the generation unit uses a generation AI to generate an original travel guide summarizing the travel schedule. This guide includes the travel schedule, places to visit, routes, dates and times, etc. The guide unit provides a tour guide tailored to the age and knowledge level of the target person based on the guide generated by the generation unit. For example, the guide unit uses AI to provide a unique tour guide tailored to the age and knowledge level of the target person. For example, it can provide simple explanations for children and detailed historical and cultural explanations for adults. The navigation unit works in conjunction with a map application to provide navigation based on the information provided by the guide unit. The navigation unit can use AI, for example, to provide optimal navigation in conjunction with the map application. As a result, the information platform according to the embodiment can propose a personalized itinerary tailored to the user's wishes, generate an original travel guide summarizing the travel schedule, provide a unique tour guide tailored to the age and knowledge level of the target person, and provide optimal navigation in conjunction with a map application.

[0069] The reception desk accepts input from users regarding their desired area or type of tourism, date and time, and number of people. For example, users can freely input their desired area, type of tourism, date and time, and number of people using keywords. Specifically, when planning a trip, users can freely input their desired tourist destinations, places they want to visit, purpose of travel, travel dates, and number of companions. This allows users to easily input a travel plan that suits their preferences. Furthermore, the reception desk has a function to automatically analyze the information entered by the user and extract necessary information. For example, if a user enters "Family trip to Tokyo, 3 days, 4 people," the reception desk will extract the keywords "Tokyo," "Family trip," "3 days," and "4 people," and send this information to the next step, the suggestion desk. The reception desk also has a function to automatically display suggestions for related tourist destinations, events, and activities based on the information entered by the user. This allows users to easily find a travel plan that suits their preferences. Additionally, the reception desk has a function to save the information entered by the user and reuse it later. This allows users to create new travel plans by referring to past travel plans.

[0070] The Proposal Department analyzes the information received by the Reception Department and proposes itineraries tailored to individual preferences. For example, using a generative AI, the Proposal Department analyzes the input information, matches it with data from various travel agencies, and proposes a personalized itinerary tailored to the individual's wishes. Specifically, the generative AI analyzes past travel data and plans provided by travel agencies based on information such as the desired area, type of sightseeing, date, and number of people entered by the user, and generates the optimal itinerary. For example, if a user enters "Family trip to Tokyo, 3 days, 4 people," the generative AI will propose a family-friendly travel plan based on information such as tourist attractions, events, and activities in Tokyo. Furthermore, the generative AI can make even more personalized suggestions by considering the user's past travel history and preferences. For example, based on data of places the user has visited and activities they have participated in in the past, it will suggest new tourist destinations and events that the user might be interested in. In addition, the Proposal Department can also propose detailed plans such as travel budget, transportation, and accommodation based on the information entered by the user. This makes it easy for users to find a travel plan that suits their preferences. Furthermore, the Proposal Department also has a function to provide feedback to users on the proposed plans. This allows users to customize the proposed plan and create one that better suits their needs.

[0071] The generation unit generates a travel itinerary based on the itinerary proposed by the suggestion unit. For example, the generation unit uses a generation AI to create an original travel itinerary. This itinerary includes the travel schedule, places to visit, routes, and dates. Specifically, the generation AI creates a detailed travel schedule based on the itinerary proposed by the suggestion unit. For example, if a user selects a plan such as "Family trip to Tokyo, 3 days, 4 people," the generation AI creates a specific schedule, such as visiting Senso-ji Temple in the morning of the first day and sightseeing at Tokyo Skytree in the afternoon. The generation AI also calculates the optimal mode of transportation and travel time based on information such as places to visit, routes, and dates, creating an efficient travel plan. Furthermore, the generation unit can customize the itinerary based on information entered by the user and feedback from the suggestion unit. For example, the user can add specific tourist destinations or change visit times. This allows users to create an original travel itinerary tailored to their preferences. Additionally, the generation unit has the functionality to provide the itinerary content in digital format. This allows users to check their bookmarks on devices such as smartphones and tablets and use them during their trip.

[0072] The guide unit provides tour guides tailored to the age and knowledge level of the target audience, based on bookmarks generated by the generation unit. The guide unit, for example, uses AI to provide unique tour guides tailored to the age and knowledge level of the target audience. Specifically, the AI ​​generates optimal guide content based on information such as the user's age, knowledge level, and interests. For example, it can provide simple explanations and quiz-style guides for children, and detailed historical and cultural explanations for adults. Furthermore, the AI ​​can adjust the guide content based on the user's real-time reactions and feedback. For example, if a user shows interest in a particular place, it can provide detailed information about that place. In addition, the guide unit can provide guides in various formats, such as audio guides, text guides, and video guides. This allows users to choose the guide format that suits their preferences. Moreover, the guide unit has a function to update the guide content in real time based on the places and routes the user visits. This ensures that users always receive guides based on the latest information.

[0073] The navigation unit provides navigation in conjunction with a map application based on information provided by the guide unit. For example, the navigation unit uses AI to provide optimal navigation in conjunction with the map application. Specifically, the AI ​​calculates the optimal route based on the user's current location, destination, and places to visit, and displays it on the map application. For example, if a user is traveling from Senso-ji Temple to Tokyo Skytree, the AI ​​calculates the optimal mode of transport and travel time and displays it on the map application. The navigation unit also has a function to acquire real-time traffic and weather information and optimize the route. This ensures that users always receive navigation based on the latest information. Furthermore, the navigation unit has a function to update the navigation content in real time based on the places and routes the user is visiting. This ensures that users always receive navigation based on the latest information. In addition, the navigation unit can provide various forms of navigation, such as voice guidance and vibration notifications. This allows users to choose the navigation format that suits their preferences.

[0074] The information platform includes a sharing section for sharing schedules or information on the platform. The sharing section allows users to share information such as travel schedules, places to visit, routes, and dates with other users. Users can share travel schedules and information through, for example, a website or mobile app. This allows for the provision of better reference information to future visitors by sharing schedules and information. Some or all of the above processing in the sharing section may be performed using, for example, AI, or not. For example, the sharing section may use AI to analyze information shared by users and automatically extract and provide useful information to other users.

[0075] The reception desk can estimate the user's emotions and dynamically change the design of the input interface based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple and intuitive 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. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input, allowing them to quickly enter their desired area or tourist genre. This allows for a user-friendly interface by dynamically changing the design of the input interface according to 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 reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expressions and voice data into the generative AI and have the generative AI perform emotion estimation.

[0076] The reception desk can analyze the user's past input history and provide input assistance functions. For example, the reception desk can automatically display areas and tourist genres that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict specific dates, times, and numbers of people based on the user's past input history and provide input assistance functions accordingly. In this way, by analyzing the user's past input history, the reception desk can provide optimal input assistance functions. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI perform the provision of input assistance functions.

[0077] The reception desk can automatically suggest relevant areas and tourist genres by considering the user's current location information during input. For example, when a user opens the app, the reception desk can automatically acquire the user's current location and suggest relevant tourist spots. Furthermore, when a user enters a destination, the reception desk can suggest the most suitable tourist genre by considering the distance from the user's current location. Additionally, if the user uses the app while on the move, the reception desk can update the user's current location in real time and suggest relevant areas and tourist genres. This allows the system to provide optimal suggestions to the user by automatically suggesting relevant areas and tourist genres based on the user's current location information. 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 location data into a generating AI and have the generating AI suggest relevant areas and tourist genres.

[0078] The reception desk can estimate the user's emotions and prioritize input based on those emotions. For example, if the user is stressed, the reception desk can prioritize displaying important input items and simplify the input process. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input, allowing them to quickly enter their desired area or tourist genre. This allows the system to provide the user with the most optimal input method by prioritizing input according to 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 can input the user's facial expressions and voice data into a generative AI and have the generative AI perform emotion estimation.

[0079] The reception desk can analyze the user's social media activity during input and suggest relevant travel genres. For example, it can suggest relevant travel genres based on places the user frequently checks into on social media. It can also analyze the content of posts from accounts the user follows on social media and suggest travel genres that might interest them. Furthermore, it can suggest relevant travel genres based on travel photos the user has shared on social media. In this way, by analyzing the user's social media activity, it is possible to suggest the most suitable travel genres for the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI suggest relevant travel genres.

[0080] The reception desk can automatically complete relevant dates, times, and numbers of people by referring to the user's past travel history during input. For example, the reception desk can predict and automatically complete the date and time of the next trip based on the dates and times of places the user has visited in the past. It can also predict and automatically complete the number of people on the next trip based on the number of people the user has traveled with in the past. Furthermore, the reception desk can analyze the user's past travel history and suggest dates and times that are suitable for specific seasons or events. This allows the reception desk to automatically complete relevant dates, times, and numbers of people by referring to the user's past travel history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past travel history data into a generating AI and have the generating AI complete the relevant dates and numbers of people.

[0081] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are presented based on those emotions. For example, if the user is relaxed, the suggestion unit's generating AI can provide suggestions that proceed at a relaxed pace. If the user is in a hurry, the suggestion unit's generating AI can provide suggestions that emphasize the shortest route. Furthermore, if the user is excited, the suggestion unit's generating AI can provide suggestions with visually stimulating effects. By adjusting the way the suggestions are presented according to the user's emotions, the system can provide the most suitable suggestions for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's facial expressions and voice data into the generating AI and have the generating AI perform emotion estimation.

[0082] The suggestion unit can analyze the user's past travel history and propose the optimal travel plan. For example, the suggestion unit can propose relevant travel plans based on data of places the user has visited in the past. It can also propose travel plans that avoid crowds based on the user's past travel history. Furthermore, the suggestion unit can analyze the user's past travel history and propose the most efficient travel plan. In this way, by analyzing the user's past travel history, it is possible to propose the optimal travel plan. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's past travel history data into a generating AI and have the generating AI execute the proposal of the optimal travel plan.

[0083] The suggestion unit can provide a customized travel plan based on the user's current lifestyle and areas of interest. For example, the suggestion unit can propose an optimal travel plan considering the user's current lifestyle (work, family, etc.). It can also propose a customized travel plan based on the user's areas of interest (history, nature, art, etc.). Furthermore, it can propose a travel plan that is manageable considering the user's current health and physical condition. This allows the suggestion unit to propose the optimal travel plan for the user by providing a customized travel plan based on the user's current lifestyle and areas of interest. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the task of providing a customized travel plan.

[0084] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will prioritize suggesting relaxing tourist spots. If the user is relaxed, the suggestion unit can also prioritize suggesting active tourist spots. Furthermore, if the user is in a hurry, the suggestion unit can prioritize suggesting tourist spots that can be enjoyed in a short amount of time. In this way, by prioritizing suggestions according to the user's emotions, the system can provide the most suitable suggestions for the user. 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 suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input the user's facial expressions or voice data into the generative AI and have the generative AI perform emotion estimation.

[0085] The suggestion unit can prioritize suggesting highly relevant travel plans by considering the user's geographical location. For example, the suggestion unit can prioritize suggesting tourist spots close to the user's current location. Furthermore, if the user is staying in a specific area, the suggestion unit can prioritize suggesting tourist spots within that area. Additionally, if the user is on the move, the suggestion unit can prioritize suggesting tourist spots along their travel route. This allows the suggestion unit to provide optimal suggestions by prioritizing highly relevant travel plans based on the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI, or without AI. For example, the suggestion unit can input the user's geographical location data into a generating AI and have the AI ​​generate highly relevant travel plan suggestions.

[0086] The suggestion unit can analyze the user's social media activity and propose relevant travel plans when making suggestions. For example, the suggestion unit can suggest relevant travel plans based on places the user frequently checks into on social media. It can also analyze the content of posts from accounts the user follows on social media and propose travel plans that might interest them. Furthermore, the suggestion unit can suggest relevant travel plans based on travel photos the user has shared on social media. In this way, by analyzing the user's social media activity, it is possible to propose the most suitable travel plan for the user. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's social media data into a generating AI and have the generating AI generate suggestions for relevant travel plans.

[0087] The generation unit can estimate the user's emotions and dynamically change the bookmark design based on the estimated emotions. For example, if the user is relaxed, the generation unit can provide a relaxed-looking bookmark design using the generation AI. If the user is in a hurry, the generation unit can also provide a simple, to-the-point bookmark design using the generation AI. Furthermore, if the user is excited, the generation unit can provide a visually stimulating bookmark design using the generation AI. This allows the system to dynamically change the bookmark design according to the user's emotions, thereby providing the user with the most suitable bookmark. Emotion estimation is achieved using an emotion estimation function, for example, 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 may be performed using AI, or not using AI. For example, the generation unit can input the user's facial expressions or voice data into the generation AI and have the generation AI perform emotion estimation.

[0088] The generation unit can analyze the user's past travel history when generating bookmarks and provide an optimal travel schedule. For example, the generation unit can suggest a relevant travel schedule based on data of places the user has visited in the past. The generation unit can also suggest a travel schedule that avoids crowds based on the user's past travel history. Furthermore, the generation unit can analyze the user's past travel history and suggest the most efficient travel schedule. In this way, by analyzing the user's past travel history, it is possible to provide an optimal travel schedule. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past travel history data into a generation AI and have the generation AI perform the task of providing an optimal travel schedule.

[0089] The generation unit can provide a customized travel schedule based on the user's current lifestyle and areas of interest when generating bookmarks. For example, the generation unit can suggest an optimal travel schedule considering the user's current lifestyle (work, family, etc.). The generation unit can also suggest a customized travel schedule based on the user's areas of interest (history, nature, art, etc.). Furthermore, the generation unit can suggest a reasonable travel schedule considering the user's current health and physical condition. In this way, by providing a customized travel schedule based on the user's current lifestyle and areas of interest, the generation unit can suggest the optimal travel schedule for the user. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the user's lifestyle and areas of interest into a generation AI and have the generation AI perform the task of providing a customized travel schedule.

[0090] The generation unit can estimate the user's emotions and prioritize the bookmark content based on the estimated emotions. For example, if the user is stressed, the generation unit will prioritize relaxing tourist spots in the bookmarks. If the user is relaxed, the generation unit can also prioritize active tourist spots. Furthermore, if the user is in a hurry, the generation unit can prioritize tourist spots that can be enjoyed in a short time. This allows the system to provide the user with the most suitable bookmarks by prioritizing the bookmark content according to 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 may be performed using AI, or not. For example, the generation unit can input the user's facial expressions or voice data into the generation AI and have the generation AI perform emotion estimation.

[0091] The generation unit can prioritize listing highly relevant places to visit when generating bookmarks, taking into account the user's geographical location. For example, the generation unit can prioritize listing tourist spots close to the user's current location. Furthermore, if the user is staying in a specific area, the generation unit can prioritize listing tourist spots within that area. Additionally, if the user is on the move, the generation unit can prioritize listing tourist spots along their travel route. This allows the generation unit to provide the user with the most relevant bookmarks by prioritizing highly relevant places to visit while considering the user's geographical location. Some or all of the above processing in the generation unit may be performed using AI, or without AI. For example, the generation unit can input the user's geographical location data into a generation AI and have the generation AI list highly relevant places to visit.

[0092] The generation unit can analyze the user's social media activity and include relevant places to visit when generating bookmarks. For example, the generation unit can include relevant places to visit in the bookmarks based on places the user frequently checks in to on social media. The generation unit can also analyze the content of posts from accounts the user follows on social media and include places that might be of interest to the user. Furthermore, the generation unit can include relevant places to visit in the bookmarks based on travel photos the user has shared on social media. In this way, by analyzing the user's social media activity, the generation unit can include the most suitable places to visit in the bookmarks for the user. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the user's social media data into a generation AI and have the generation AI perform the task of including relevant places to visit.

[0093] The guide unit can estimate the user's emotions and adjust the way the guide content is presented based on the estimated emotions. For example, if the user is relaxed, the guide unit can provide guide content that proceeds at a leisurely pace. If the user is in a hurry, the guide unit can also provide concise guide content that gets straight to the point. Furthermore, if the user is excited, the guide unit can provide guide content with visually stimulating effects. In this way, by adjusting the way the guide content is presented according to the user's emotions, the guide unit can provide the most suitable guide content for the user. 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 guide unit may be performed using AI, or not using AI. For example, the guide unit can input the user's facial expressions and voice data into the generative AI and have the generative AI perform emotion estimation.

[0094] The guide unit can analyze the user's past travel history when providing a guide and provide the most suitable guide content. For example, the guide unit can provide relevant guide content based on data of places the user has visited in the past. The guide unit can also provide guide content that avoids crowds based on the user's past travel history. Furthermore, the guide unit can analyze the user's past travel history and provide the most efficient guide content. In this way, the guide unit can provide the most suitable guide content by analyzing the user's past travel history. Some or all of the above processing in the guide unit may be performed using AI, for example, or without AI. For example, the guide unit can input the user's past travel history data into a generating AI and have the generating AI perform the task of providing the most suitable guide content.

[0095] The guide unit can provide customized guide content based on the user's current living situation and areas of interest when providing a guide. For example, the guide unit can provide optimal guide content considering the user's current living situation (work, family, etc.). The guide unit can also provide customized guide content based on the user's areas of interest (history, nature, art, etc.). Furthermore, the guide unit can provide guide content that is not strenuous, considering the user's current health condition and physical strength. In this way, by providing customized guide content based on the user's current living situation and areas of interest, the guide unit can provide optimal guide content for the user. Some or all of the above processing in the guide unit may be performed using AI, for example, or without AI. For example, the guide unit can input data on the user's living situation and areas of interest into a generating AI and have the generating AI perform the provision of customized guide content.

[0096] The guide unit can estimate the user's emotions and prioritize the guide content based on the estimated emotions. For example, if the user is stressed, the guide unit can prioritize including relaxing tourist spots in the guide content. If the user is relaxed, the guide unit can also prioritize including active tourist spots. Furthermore, if the user is in a hurry, the guide unit can prioritize including tourist spots that can be enjoyed in a short time. This allows the guide unit to provide the user with the most suitable guide content by prioritizing the guide content according to 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 guide unit may be performed using AI, or not. For example, the guide unit can input the user's facial expressions and voice data into the generative AI and have the generative AI perform emotion estimation.

[0097] The guide unit can prioritize providing highly relevant guide content by considering the user's geographical location when providing a guide. For example, the guide unit can prioritize including tourist spots close to the user's current location in the guide content. Furthermore, if the user is staying in a specific area, the guide unit can prioritize including tourist spots within that area in the guide content. Additionally, if the user is on the move, the guide unit can prioritize including tourist spots along their travel route in the guide content. This allows the guide unit to provide optimal guide content by prioritizing highly relevant information based on the user's geographical location. Some or all of the above processing in the guide unit may be performed using AI, or without AI. For example, the guide unit can input the user's geographical location data into a generating AI and have the generating AI provide highly relevant guide content.

[0098] The guide unit can analyze the user's social media activity when providing a guide and provide relevant guide content. For example, the guide unit can provide relevant guide content based on places the user frequently checks in to on social media. It can also analyze the posts of accounts the user follows on social media and provide guide content that might be of interest to the user. Furthermore, the guide unit can provide relevant guide content based on travel photos the user has shared on social media. This allows the guide unit to provide optimal guide content by analyzing the user's social media activity. Some or all of the above processing in the guide unit may be performed using AI, for example, or without AI. For example, the guide unit can input the user's social media data into a generating AI and have the generating AI provide relevant guide content.

[0099] 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. Furthermore, if the user is in a hurry, the navigation unit can provide a concise navigation method. In this way, by adjusting the navigation method according to the user's emotions, the system can provide the user with the most optimal navigation method. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's facial expressions and voice data into the generative AI and have the generative AI perform emotion estimation.

[0100] The navigation unit can analyze the user's past travel history during navigation and provide the optimal navigation method. For example, the navigation unit can suggest the optimal navigation method based on routes the user has used in the past. It can also suggest a navigation method that avoids congestion based on the user's past travel history. Furthermore, the navigation unit can analyze the user's past travel history and suggest the most efficient navigation method. This allows the navigation unit to provide the optimal navigation method by analyzing the user's past travel history. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's past travel history data into a generating AI and have the generating AI perform the task of providing the optimal navigation method.

[0101] The navigation unit can provide a customized navigation method based on the user's current lifestyle and areas of interest during navigation. For example, the navigation unit can provide an optimal navigation method considering the user's current lifestyle (work, family, etc.). It can also provide a customized navigation method based on the user's areas of interest (history, nature, art, etc.). Furthermore, the navigation unit can provide a manageable navigation method considering the user's current health and physical condition. By providing a customized navigation method based on the user's current lifestyle and areas of interest, the navigation unit can provide the optimal navigation method for the user. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the task of providing a customized navigation method.

[0102] The navigation unit can estimate the user's emotions and prioritize navigation content based on the estimated emotions. For example, if the user is stressed, the navigation unit can prioritize relaxing routes in its navigation content. If the user is relaxed, the navigation unit can also prioritize active routes. Furthermore, if the user is in a hurry, the navigation unit can prioritize routes that allow for quick travel. This allows the system to provide the user with optimal navigation content by prioritizing navigation content according to 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 navigation unit may be performed using AI, or not. For example, the navigation unit can input the user's facial expressions and voice data into the generative AI and have the generative AI perform emotion estimation.

[0103] The navigation unit can prioritize providing highly relevant navigation methods by considering the user's geographical location information during navigation. For example, the navigation unit can prioritize including routes close to the user's current location in the navigation content. Furthermore, if the user is staying in a specific area, the navigation unit can prioritize including routes within that area in the navigation content. Additionally, if the user is on the move, the navigation unit can prioritize providing navigation methods along the travel route. This allows the system to provide the optimal navigation method for the user by prioritizing highly relevant navigation methods while considering the user's geographical location information. Some or all of the above processing in the navigation unit may be performed using AI, or without AI. For example, the navigation unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing highly relevant navigation methods.

[0104] The navigation unit can analyze the user's social media activity during navigation and provide relevant navigation methods. For example, the navigation unit can provide relevant navigation methods based on places the user frequently checks in to on social media. It can also analyze the content of posts from accounts the user follows on social media and provide navigation methods that might be of interest to the user. Furthermore, the navigation unit can provide relevant navigation methods based on travel photos the user has shared on social media. In this way, by analyzing the user's social media activity, the system can provide the most suitable navigation method for the user. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's social media data into a generating AI and have the generating AI perform the task of providing relevant navigation methods.

[0105] The sharing function can estimate the user's emotions and adjust the way it presents the shared content based on those emotions. For example, if the user is relaxed, the sharing function can provide content that proceeds at a leisurely pace. If the user is in a hurry, the sharing function can also provide concise content that gets straight to the point. Furthermore, if the user is excited, the sharing function can provide content that includes visually stimulating effects. By adjusting the way the shared content is presented according to the user's emotions, the sharing function can provide the most suitable content for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing function may be performed using AI, or not using AI. For example, the sharing function can input the user's facial expressions and voice data into the generative AI and have the generative AI perform emotion estimation.

[0106] The sharing function can analyze a user's past sharing history and provide the most suitable content for sharing. For example, the sharing function can provide relevant content based on what the user has shared in the past. It can also provide content that the user might be interested in based on their past sharing history. Furthermore, the sharing function can analyze the user's past sharing history and provide the most efficient content for sharing. In this way, by analyzing the user's past sharing history, it is possible to provide the most suitable content for sharing. Some or all of the above processing in the sharing function may be performed using AI, for example, or without AI. For example, the sharing function can input the user's past sharing history data into a generating AI and have the generating AI perform the task of providing the most suitable content for sharing.

[0107] The sharing unit can estimate the user's emotions and prioritize the content to share based on those emotions. For example, if the user is stressed, the sharing unit will prioritize providing relaxing content. If the user is relaxed, the sharing unit can also prioritize providing active content. Furthermore, if the user is in a hurry, the sharing unit can prioritize providing content that can be shared quickly. In this way, by prioritizing content according to the user's emotions, the sharing unit can provide the user with the most suitable content. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing unit may be performed using AI, or not using AI. For example, the sharing unit can input the user's facial expressions and voice data into the generative AI and have the generative AI perform emotion estimation.

[0108] The sharing function can prioritize providing highly relevant content when sharing, taking into account the user's geographical location. For example, the sharing function can prioritize including information about tourist spots near the user's current location in the shared content. Furthermore, if the user is staying in a specific area, the sharing function can prioritize including information about tourist spots within that area. Additionally, if the user is on the move, the sharing function can prioritize including information about tourist spots along their travel route. This allows the sharing function to provide optimal content for the user by prioritizing highly relevant content while considering their geographical location. Some or all of the above processing in the sharing function may be performed using AI, or without AI. For example, the sharing function can input the user's geographical location data into a generating AI and have the generating AI provide highly relevant content.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are presented based on those emotions. For example, if the user is relaxed, the suggestion unit's generating AI can provide suggestions that proceed at a relaxed pace. If the user is in a hurry, the suggestion unit's generating AI can provide suggestions that emphasize the shortest route. Furthermore, if the user is excited, the suggestion unit's generating AI can provide suggestions with visually stimulating effects. By adjusting the way the suggestions are presented according to the user's emotions, the system can provide the most suitable suggestions for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's facial expressions and voice data into the generating AI and have the generating AI perform emotion estimation.

[0111] The reception desk can analyze the user's past input history and provide input assistance functions. For example, the reception desk can automatically display areas and tourist genres that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict specific dates, times, and numbers of people based on the user's past input history and provide input assistance functions accordingly. In this way, by analyzing the user's past input history, the reception desk can provide optimal input assistance functions. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI perform the provision of input assistance functions.

[0112] The guide unit can estimate the user's emotions and adjust the way the guide content is presented based on the estimated emotions. For example, if the user is relaxed, the guide unit can provide guide content that proceeds at a leisurely pace. If the user is in a hurry, the guide unit can also provide concise guide content that gets straight to the point. Furthermore, if the user is excited, the guide unit can provide guide content with visually stimulating effects. In this way, by adjusting the way the guide content is presented according to the user's emotions, the guide unit can provide the most suitable guide content for the user. 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 guide unit may be performed using AI, or not using AI. For example, the guide unit can input the user's facial expressions and voice data into the generative AI and have the generative AI perform emotion estimation.

[0113] 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. Furthermore, if the user is in a hurry, the navigation unit can provide a concise navigation method. In this way, by adjusting the navigation method according to the user's emotions, the system can provide the user with the most optimal navigation method. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's facial expressions and voice data into the generative AI and have the generative AI perform emotion estimation.

[0114] The sharing function can estimate the user's emotions and adjust the way it presents the shared content based on those emotions. For example, if the user is relaxed, the sharing function can provide content that proceeds at a leisurely pace. If the user is in a hurry, the sharing function can also provide concise content that gets straight to the point. Furthermore, if the user is excited, the sharing function can provide content that includes visually stimulating effects. By adjusting the way the shared content is presented according to the user's emotions, the sharing function can provide the most suitable content for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing function may be performed using AI, or not using AI. For example, the sharing function can input the user's facial expressions and voice data into the generative AI and have the generative AI perform emotion estimation.

[0115] The reception desk can automatically suggest relevant areas and tourist genres by considering the user's current location information during input. For example, when a user opens the app, the reception desk can automatically acquire the user's current location and suggest relevant tourist spots. Furthermore, when a user enters a destination, the reception desk can suggest the most suitable tourist genre by considering the distance from the user's current location. Additionally, if the user uses the app while on the move, the reception desk can update the user's current location in real time and suggest relevant areas and tourist genres. This allows the system to provide optimal suggestions to the user by automatically suggesting relevant areas and tourist genres based on the user's current location information. 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 location data into a generating AI and have the generating AI suggest relevant areas and tourist genres.

[0116] The suggestion unit can analyze the user's past travel history and propose the optimal travel plan. For example, the suggestion unit can propose relevant travel plans based on data of places the user has visited in the past. It can also propose travel plans that avoid crowds based on the user's past travel history. Furthermore, the suggestion unit can analyze the user's past travel history and propose the most efficient travel plan. In this way, by analyzing the user's past travel history, it is possible to propose the optimal travel plan. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's past travel history data into a generating AI and have the generating AI execute the proposal of the optimal travel plan.

[0117] The generation unit can analyze the user's past travel history when generating bookmarks and provide an optimal travel schedule. For example, the generation unit can suggest a relevant travel schedule based on data of places the user has visited in the past. The generation unit can also suggest a travel schedule that avoids crowds based on the user's past travel history. Furthermore, the generation unit can analyze the user's past travel history and suggest the most efficient travel schedule. In this way, by analyzing the user's past travel history, it is possible to provide an optimal travel schedule. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past travel history data into a generation AI and have the generation AI perform the task of providing an optimal travel schedule.

[0118] The guide unit can analyze the user's past travel history when providing a guide and provide the most suitable guide content. For example, the guide unit can provide relevant guide content based on data of places the user has visited in the past. The guide unit can also provide guide content that avoids crowds based on the user's past travel history. Furthermore, the guide unit can analyze the user's past travel history and provide the most efficient guide content. In this way, the guide unit can provide the most suitable guide content by analyzing the user's past travel history. Some or all of the above processing in the guide unit may be performed using AI, for example, or without AI. For example, the guide unit can input the user's past travel history data into a generating AI and have the generating AI perform the task of providing the most suitable guide content.

[0119] The navigation unit can analyze the user's past travel history during navigation and provide the optimal navigation method. For example, the navigation unit can suggest the optimal navigation method based on routes the user has used in the past. It can also suggest a navigation method that avoids congestion based on the user's past travel history. Furthermore, the navigation unit can analyze the user's past travel history and suggest the most efficient navigation method. This allows the navigation unit to provide the optimal navigation method by analyzing the user's past travel history. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's past travel history data into a generating AI and have the generating AI perform the task of providing the optimal navigation method.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The reception desk accepts input from the user regarding their desired area or type of tourism, date and time, or number of people. For example, users can enter their desired area, type of tourism, date and time, and number of people using free text. Step 2: The proposal department analyzes the information received by the reception department and proposes a travel itinerary tailored to the individual's preferences. For example, it uses a generation AI to analyze the input information, cross-reference it with data from various travel agencies, and propose a personalized travel itinerary tailored to the individual's wishes. Step 3: The generation unit generates a travel itinerary based on the itinerary proposed by the proposal unit. For example, it uses a generation AI to generate an original travel itinerary that summarizes the travel schedule. This itinerary includes the travel schedule, places to visit, routes, dates and times, etc. Step 4: The guide unit provides a tour guide tailored to the age and knowledge level of the target audience, based on the bookmarks generated by the generation unit. For example, AI can be used to provide a unique tour guide tailored to the age and knowledge level of the target audience. For instance, simple explanations can be provided for children, while detailed historical and cultural explanations can be provided for adults. Step 5: The navigation unit works in conjunction with a map application to provide navigation based on the information provided by the guide unit. For example, it uses AI to provide optimal navigation in conjunction with a map application.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] Each of the multiple elements described above, including the reception unit, proposal unit, generation unit, guide unit, navigation unit, and sharing unit, is implemented by 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, allowing the user to input their desired area, type of sightseeing, date and time, number of people, etc. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the input information using a generation AI and proposes a personalized itinerary tailored to the individual's preferences. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates an original travel guide summarizing the travel schedule based on the proposed itinerary. The guide unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides a unique tour guide tailored to the age and knowledge level of the target person. The navigation unit is implemented by the control unit 46A of the smart device 14, which performs optimal navigation in cooperation with a map application. The sharing function is implemented, for example, by the control unit 46A of the smart device 14, allowing users to share travel schedules and information with other users. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the reception unit, proposal unit, generation unit, guide unit, navigation unit, and sharing unit, is implemented by 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, allowing the user to input their desired area, type of sightseeing, date and time, number of people, etc. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the input information using a generation AI and proposes a personalized itinerary tailored to the individual's preferences. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates an original travel guide summarizing the travel schedule based on the proposed itinerary. The guide unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides a unique tour guide tailored to the age and knowledge level of the target person. The navigation unit is implemented by the control unit 46A of the smart glasses 214, which performs optimal navigation in cooperation with a map application. The sharing function is implemented, for example, by the control unit 46A of the smart glasses 214, allowing users to share travel schedules and information with other users. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] 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.

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

[0145] The 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.

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

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

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

[0149] Figure 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.

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

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

[0152] In the 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.

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

[0154] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0156] The data processing system 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.

[0157] Each of the multiple elements described above, including the reception unit, proposal unit, generation unit, guide unit, navigation unit, and sharing unit, is implemented by 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, allowing the user to input their desired area, type of sightseeing, date and time, number of people, etc. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the input information using a generation AI and proposes a personalized itinerary tailored to the individual's preferences. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates an original travel guide summarizing the travel schedule based on the proposed itinerary. The guide unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides a unique tour guide tailored to the age and knowledge level of the target person. The navigation unit is implemented by the control unit 46A of the headset terminal 314, which performs optimal navigation in cooperation with a map application. The sharing function is implemented, for example, by the control unit 46A of the headset terminal 314, allowing users to share travel schedules and information with other users. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] 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.

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

[0161] The 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.

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

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS 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).

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

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] Each of the multiple elements described above, including the reception unit, proposal unit, generation unit, guide unit, navigation unit, and sharing 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, allowing the user to input their desired area, type of sightseeing, date and time, number of people, etc. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the input information using a generation AI and proposes a personalized itinerary tailored to the individual's wishes. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates an original travel guide summarizing the travel schedule based on the proposed itinerary. The guide unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which provides a unique tour guide tailored to the age and knowledge level of the target person. The navigation unit is implemented by, for example, the control unit 46A of the robot 414, which performs optimal navigation in cooperation with a map application. The sharing function is implemented, for example, by the control unit 46A of the robot 414, allowing users to share travel schedules and information with other users. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] (Note 1) A system characterized by comprising: a reception unit that accepts input of desired area or tourist genre, date and time or number of people; a proposal unit that analyzes the information received by the reception unit and proposes a travel itinerary tailored to the individual's preferences; a generation unit that generates a travel guide summarizing the travel schedule based on the itinerary proposed by the proposal unit; a guide unit that provides a tour guide tailored to the age and knowledge level of the target person based on the travel guide generated by the generation unit; and a navigation unit that provides navigation in conjunction with a map application based on the information provided by the guide unit. (Note 2) The system according to Appendix 1, characterized by having a sharing section for sharing schedules or information on an information platform. (Note 3) The aforementioned reception unit is It estimates the user's emotions and dynamically changes the design of the input interface based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The system described in Appendix 1, wherein the reception unit analyzes the user's past input history and provides an input assistance function. (Note 5) The system described in Appendix 1 is characterized in that the reception unit automatically suggests relevant areas or tourist genres based on the user's current location information when inputting data. (Note 6) The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is During input, the system analyzes the user's social media activity and suggests relevant tourism genres. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When users enter information, the system automatically completes relevant dates, times, and the number of people by referencing their past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way the suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The system described in Appendix 1 is characterized in that the proposal unit analyzes the user's past travel history and proposes a travel plan at the time of proposal. (Note 11) The aforementioned proposal section is, When making a proposal, we provide a customized travel plan based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned proposal section is, It estimates the user's emotions and prioritizes suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, When making suggestions, the system prioritizes suggesting highly relevant travel plans, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making a proposal, we analyze the user's social media activity and suggest relevant travel plans. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is It estimates the user's emotions and dynamically changes the bookmark design based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The system according to Appendix 1, characterized in that the generation unit analyzes the user's past travel history and provides a travel schedule when generating bookmarks. (Note 17) The generating unit is When generating bookmarks, the system provides a customized travel schedule based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is It estimates the user's emotions and prioritizes the bookmark content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating bookmarks, the system takes the user's geographical location into consideration and prioritizes listing highly relevant places visited. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating bookmarks, the system analyzes the user's social media activity and includes relevant places they have visited. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned guide section is It estimates the user's emotions and adjusts the way the guide content is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The system described in Appendix 1, wherein the guide unit analyzes the user's past travel history when providing a guide and provides the guide content accordingly. (Note 23) The aforementioned guide section is When providing a guide, we will provide customized guide content based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned guide section is The system estimates the user's emotions and prioritizes the guide content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned guide section is When providing guides, we take the user's geographical location into consideration and prioritize providing guides with the most relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned guide section is When providing guides, we analyze users' social media activity and provide guide content relevant to that activity. The system described in Appendix 1, characterized by the features described herein. (Note 27) 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 28) The system as described in Appendix 1, characterized in that the navigation unit analyzes the user's past movement history during navigation and provides a navigation method. (Note 29) The aforementioned navigation unit is, During navigation, the system provides a customized navigation method based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned navigation unit is, It estimates the user's emotions and prioritizes navigation content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned navigation unit is, When navigating, the system prioritizes providing the most relevant navigation methods by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned navigation unit is, During navigation, the system analyzes the user's social media activity and provides relevant navigation methods. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned share section is, It estimates the user's emotions and adjusts the way shared content is expressed based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The sharing unit is characterized in that, at the time of sharing, it analyzes the user's past sharing history and provides the content of the shared item, as described in Appendix 2. (Note 35) The aforementioned share section is, It estimates the user's emotions and prioritizes the content to be shared based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned share section is, When sharing, the system prioritizes providing highly relevant content by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. [Explanation of Symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk accepts inputs regarding the desired area or type of sightseeing, date and time, or number of people. The information received by the aforementioned reception department is analyzed, and a proposal department proposes itineraries tailored to individual preferences, A generation unit generates a travel itinerary that summarizes the travel schedule based on the proposed itinerary by the aforementioned proposal unit, A guide unit provides a tour guide tailored to the age and knowledge level of the target person based on the bookmark generated by the aforementioned generation unit, The system includes a navigation unit that performs navigation in conjunction with a map application based on information provided by the aforementioned guide unit. A system characterized by the following features.

2. It includes a sharing section for sharing schedules or information on an information platform. The system according to feature 1.

3. The aforementioned reception unit is It estimates the user's emotions and dynamically changes the design of the input interface based on the estimated user emotions. The system according to feature 1.

4. The system according to claim 1, characterized in that the reception unit analyzes the user's past input history and provides an input assistance function.

5. The system according to claim 1, characterized in that the reception unit automatically suggests relevant areas or tourist genres based on the user's current location information when inputting data.

6. The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is During input, the system analyzes the user's social media activity and suggests relevant tourism genres. The system according to feature 1.

8. The aforementioned reception unit is When users enter information, the system automatically completes relevant dates, times, and the number of people by referencing their past travel history. The system according to feature 1.

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