system

Abstract

The system according to this embodiment aims to improve the overall travel experience by suggesting an optimal itinerary based on the user's preferences. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a proposal unit, a bookmark generation unit, a guide unit, and a navigation unit. The reception unit receives the user's requests. The analysis unit analyzes the information entered by the reception unit. The proposal unit proposes the optimal itinerary based on the information analyzed by the analysis unit. The bookmark generation unit compiles the travel itinerary proposed by the proposal unit. The guide unit provides a tour guide based on the bookmark generated by the bookmark generation unit. The navigation unit provides navigation in cooperation 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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is room for improvement in efficiently proposing a personalized itinerary based on the user's wishes and improving the overall travel experience.

[0005] The system according to an embodiment aims to propose an optimal itinerary based on the user's wishes and improve the overall travel experience.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a proposal unit, a bookmark generation unit, a guide unit, and a navigation unit. The reception unit receives the user's requests. The analysis unit analyzes the information entered by the reception unit. The proposal unit proposes the optimal itinerary based on the information analyzed by the analysis unit. The bookmark generation unit compiles the travel itinerary proposed by the proposal unit. The guide unit provides a tour guide based on the bookmark generated by the bookmark generation unit. The navigation unit provides 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 suggest an optimal itinerary based on the user's preferences and improve the overall travel experience. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An information platform according to an embodiment of the present invention is a system that uses a generating AI to propose a personalized itinerary tailored to an individual's preferences. This information platform allows users to input their desired area, type of sightseeing, date, time, number of people, etc., using free text. The generating 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, it generates an original travel guide summarizing the itinerary and provides a unique tour guide tailored to the age and knowledge level of the target person. It also integrates with a map application to provide optimal navigation. By sharing schedules and information on the information platform, it becomes possible to provide better reference information to future visitors. For example, a user inputs their desired area, type of sightseeing, date, time, number of people, etc., using free text. At this time, the user can freely input specific preferences. For example, they might input information such as "Family trip to Tokyo, 3 days, 5 people." This information is input into the generating AI. Next, the generating AI analyzes the input information. Based on the user's preferences, the generating AI matches data from various travel agencies and proposes the optimal itinerary. For example, if a user enters "Family trip to Tokyo, 3 days, 5 people," the generating AI analyzes information on Tokyo's tourist spots, accommodations, and transportation options to suggest the optimal itinerary. Furthermore, the generating AI creates an original travel guide summarizing the trip. For instance, based on the information entered by the user, it might suggest a specific itinerary such as Tokyo Tower and Senso-ji Temple on day 1, Disneyland on day 2, and Odaiba on day 3. This guide can be used by the user as a reference during their trip. The generating AI also provides a unique tour guide tailored to the age and knowledge level of the target audience. For example, it can provide easy-to-understand explanations for children and detailed historical and cultural explanations for adults. This allows users to gain a deeper understanding of their travel destination. In addition, it integrates with map applications to provide optimal navigation. For example, if a user searches for a route from their current location to a tourist spot, the map application can display the optimal route and provide navigation. This allows users to reach their destination without getting lost.Finally, by sharing schedules and information on the information platform, better reference information can be provided to future visitors. For example, users can post photos and impressions taken during their trip on the platform, which other users can use as reference. This is expected to promote information sharing and make travel planning smoother. As a result, the information platform can propose personalized itineraries tailored to the user's wishes, generate original travel guides summarizing travel dates, provide unique tour guides, and offer optimal navigation.

[0029] The information platform according to this embodiment comprises a reception unit, an analysis unit, a proposal unit, a bookmark generation unit, a guide unit, and a navigation unit. The reception unit receives the user's preferences. The user can enter, for example, their desired area, type of sightseeing, date and time, and number of people using free text. For example, the user can enter information such as "Family trip to Tokyo, 3 days, 5 people." The analysis unit analyzes the information entered by the reception unit. The analysis unit uses a generation AI to analyze the information entered based on the user's preferences. For example, the analysis unit compares data from various travel agencies based on the information entered by the user and proposes the optimal itinerary. The proposal unit proposes the optimal itinerary based on the information analyzed by the analysis unit. The proposal unit uses a generation AI to propose a personalized itinerary tailored to the user's preferences. For example, if the user enters "Family trip to Tokyo, 3 days, 5 people," the proposal unit analyzes information such as tourist spots, accommodations, and transportation in Tokyo and proposes the optimal itinerary. The bookmark generation unit compiles the travel itinerary proposed by the proposal unit. The bookmark generation unit uses generation AI to generate an original travel itinerary. For example, based on the information entered by the user, the bookmark generation unit suggests a specific itinerary such as Tokyo Tower and Senso-ji Temple on day 1, Disneyland on day 2, and Odaiba on day 3. The guide unit provides a tour guide based on the bookmark generated by the bookmark generation unit. The guide unit uses generation AI to provide a unique tour guide tailored to the age and knowledge level of the target audience. For example, the guide unit can provide easy-to-understand explanations for children and detailed explanations of history and culture 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 uses AI to display the optimal route from the user's current location to the tourist spot. For example, when the user searches for a route from their current location to a tourist spot, the navigation unit can display the optimal route using a map application and provide navigation. As a result, the information platform according to this embodiment can propose a personalized itinerary tailored to the user's wishes, generate an original travel itinerary summarizing the travel schedule, provide a unique tour guide, and achieve optimal navigation.

[0030] The reception desk receives user requests. Users can enter details such as their desired area, type of sightseeing, date, time, and number of people using free text. Specifically, a user might enter information like "Family trip to Tokyo, 3 days, 5 people." The reception desk receives the information entered by the user and provides an interface for processing it appropriately within the system. Since the information entered by the user is in text format, it is analyzed using natural language processing technology. For example, if a user enters "Family trip to Tokyo, 3 days, 5 people," the reception desk extracts keywords such as "Tokyo," "Family trip," "3 days," and "5 people," and sends each element to the analysis department. Furthermore, the reception desk saves the information entered by the user so that it can be referenced later. This saves the user the trouble of entering the same information again. The reception desk can also suggest related options and additional information based on the information entered by the user. For example, if a user enters "Family trip to Tokyo," the reception desk will suggest popular tourist spots such as "Disneyland," "Senso-ji Temple," and "Odaiba." This makes it easier for users to enter more specific requests. The reception desk provides a user-friendly interface to facilitate smooth user input and plays a role in accurately conveying user requests to the system.

[0031] The analysis unit analyzes the information entered by the reception unit. The analysis unit uses generative AI to analyze the information entered based on the user's preferences. Specifically, it compares data from various travel agencies based on the information entered by the user and proposes the optimal itinerary. The generative AI uses natural language processing technology to understand the user's input and extracts appropriate information from relevant databases. For example, if a user enters "Family trip to Tokyo, 3 days, 5 people," the analysis unit will use keywords such as "Tokyo," "family trip," "3 days," and "5 people" to collect information such as Tokyo tourist spots, accommodations, and transportation from travel agency databases. Furthermore, the analysis unit generates the itinerary that best suits the user's preferences based on the collected information. The generative AI uses algorithms to propose the best options based on past data and user ratings. For example, it prioritizes suggesting the highest-rated tourist spots and accommodations by referring to ratings from users who have traveled under similar conditions in the past. In addition, the analysis unit can make suggestions that respond to the latest situation based on information that is updated in real time. For example, it can flexibly adjust the itinerary in response to changes in weather or traffic conditions. This allows the analysis unit to quickly and accurately propose the optimal itinerary based on the user's preferences.

[0032] The suggestion department proposes the optimal itinerary based on the information analyzed by the analysis department. The suggestion department uses generative AI to propose a personalized itinerary tailored to the user's preferences. Specifically, if a user inputs "family trip to Tokyo, 3 days, 5 people," the suggestion department analyzes information such as Tokyo's tourist spots, accommodations, and transportation options to propose the optimal itinerary. Based on the user's input information, the generative AI proposes the most suitable options by referring to past data and evaluations. For example, in the case of a family trip, it prioritizes suggesting activities for children and family-friendly accommodations. Furthermore, the suggestion department can present multiple options according to the user's preferences. For example, if a user inputs "family trip to Tokyo, 3 days, 5 people," the suggestion department will not only propose a specific schedule such as "Day 1: Tokyo Tower and Senso-ji Temple, Day 2: Disneyland, Day 3: Odaiba," but will also present other options such as "Day 1: Ueno Zoo and Ameyoko, Day 2: Odaiba, Day 3: Senso-ji Temple and Tokyo Skytree." This allows the user to choose the most suitable itinerary from multiple options. Furthermore, the suggestion system can continuously improve its suggestions based on user feedback. For example, users can provide ratings and comments on suggested itineraries, allowing the generating AI to learn from this information and incorporate it into future suggestions. This enables the suggestion system to provide personalized itineraries that best suit the user's preferences, thereby improving user satisfaction.

[0033] The Itinerary Generation Unit compiles the travel itineraries proposed by the Proposal Unit. Using a generation AI, the Itinerary Generation Unit generates an original travel itinerary summarizing the travel schedule. Specifically, based on the information entered by the user, it proposes a specific itinerary such as Tokyo Tower and Senso-ji Temple on day 1, Disneyland on day 2, and Odaiba on day 3. The generation AI automatically generates the optimal travel itinerary based on the user's input information and provides it in itinerary format. The itinerary includes detailed schedules for each day, information on sightseeing spots, transportation, and places to eat. For example, the itinerary for day 1 might include a specific schedule such as, "Visit Tokyo Tower at 10am, move to Senso-ji Temple at 1pm, and have lunch at a restaurant near Senso-ji Temple." Furthermore, the Itinerary Generation Unit can customize the content of the itinerary according to the user's wishes. For example, if the user wishes to include many activities suitable for children, the Itinerary Generation Unit will prioritize including sightseeing spots and activities suitable for children according to that wish. In addition, the Itinerary Generation Unit provides the generated itinerary in digital format, making it easy for users to view on their smartphones and tablets. This makes it easier for users to refer to their itineraries during their trip, enabling a smoother travel experience. The itinerary generation unit provides original travel itineraries based on the user's preferences, making travel planning more convenient and enjoyable.

[0034] The guide unit provides tour guides based on bookmarks generated by the bookmark generation unit. The guide unit uses a generation AI to provide unique tour guides tailored to the age and knowledge level of the target audience. Specifically, it can provide easy-to-understand explanations for children and detailed historical and cultural explanations for adults. The generation AI generates optimal guide content based on the user's profile information. For example, for a family trip, it can provide fun explanations using characters for children and detailed explanations of historical background and cultural significance for adults. The guide unit can also provide audio and text guides. Users can listen to audio guides or read text guides provided by the guide unit using their smartphones or tablets. Furthermore, the guide unit can provide the latest guide content based on real-time updated information. For example, if the opening hours or event information of a tourist spot changes, the guide unit immediately reflects this information and provides users with the latest information. This ensures that users can always enjoy sightseeing based on the most up-to-date information. The guide unit provides unique tour guides tailored to the user's age and knowledge level, maximizing the enjoyment and learning of their trip.

[0035] The navigation unit works in conjunction with a map application to provide navigation based on information provided by the guide unit. The navigation unit uses AI to display the optimal route from the user's current location to the tourist spot. Specifically, when a user searches for a route from their current location to a tourist spot, the map application displays the optimal route and provides navigation. The navigation unit calculates the optimal route based on real-time updated traffic and weather information. For example, if there is traffic congestion or road construction, the navigation unit takes this information into consideration and suggests an alternative route. Furthermore, the navigation unit can suggest routes tailored to the user's mode of transportation. For example, it calculates and displays the optimal route for walking, cycling, driving, public transport, etc. In addition, the navigation unit can track the user's current location in real time and monitor their progress towards their destination. This allows the user to reach their destination without getting lost. The navigation unit provides the optimal route from the user's current location to the tourist spot, supporting smooth travel. This allows users to enjoy sightseeing without stress during their trip.

[0036] The navigation unit can display the optimal route from the user's current location to a tourist spot. For example, when a user searches for a route from their current location to a tourist spot, the navigation unit can display the optimal route using a map application and provide navigation. The navigation unit can, for example, display a route that takes into account the shortest distance and traffic conditions. This allows the user to reach their destination without getting lost. Some or all of the above processing in the navigation unit may be performed using AI or not. For example, the navigation unit can display the optimal route using an AI model that takes the user's current location and destination as input and outputs the optimal route.

[0037] The guide unit can provide unique tour guides tailored to the age and knowledge level of the target audience. For example, the guide unit can provide easy-to-understand explanations for children and detailed historical and cultural explanations for adults. The guide unit can also provide guides based on specific themes or interactive guides. This allows users to gain a deeper understanding of the travel destination. Some or all of the above processing in the guide unit may be performed using generative AI or not. For example, the guide unit can provide unique tour guides using a generative AI model that takes the user's age and knowledge level as input and outputs optimal guide content.

[0038] The itinerary generation unit can generate an original travel itinerary based on the user's preferences. For example, based on the information entered by the user, the itinerary generation unit can suggest a specific itinerary such as Tokyo Tower and Senso-ji Temple on day 1, Disneyland on day 2, and Odaiba on day 3. The itinerary generation unit can also customize the itinerary based on the user's individual preferences. This allows the user to refer to it during their trip. Some or all of the above-described processes in the itinerary generation unit may be performed using a generation AI, or they may not. For example, the itinerary generation unit can generate an itinerary using a generation AI model that takes the user's preferences as input and outputs an original travel itinerary.

[0039] The suggestion unit can propose the optimal itinerary by cross-referencing data from various travel agencies. For example, if a user inputs "family trip to Tokyo, 3 days, 5 people," the suggestion unit can analyze information such as tourist spots, accommodations, and transportation options in Tokyo and propose the optimal itinerary. The suggestion unit can also propose the optimal itinerary considering factors such as time efficiency and cost performance. This allows for the proposal of a personalized itinerary tailored to the user's preferences. Some or all of the above-described processes in the suggestion unit may be performed using generative AI, or they may not. For example, the suggestion unit can propose an itinerary using a generative AI model that takes the user's preferences as input and outputs the optimal itinerary.

[0040] The analysis unit can analyze information entered based on the user's preferences. For example, if the user enters "Family trip to Tokyo, 3 days, 5 people," the analysis unit can analyze information such as tourist spots, accommodations, and transportation options in Tokyo. The analysis unit can perform information analysis based on the user's preferences using, for example, data analysis methods and algorithms. This makes it possible to perform information analysis based on the user's preferences. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or they may be performed without using generative AI. For example, the analysis unit can analyze information using a generative AI model that takes the user's preferences as input and outputs analysis results.

[0041] The reception desk allows users to input their desired area, type of tourism, date, time, number of people, etc., using free text. For example, a user could input information such as "Family trip to Tokyo, 3 days, 5 people." The reception desk can also allow users to freely input their specific preferences using, for example, keyword search or natural language input. This allows users to freely input their specific preferences. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can assist with input using an AI model that analyzes the user's input and provides appropriate input assistance.

[0042] The reception desk can analyze the user's past input history and provide optimal 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 and suggest areas and tourist genres to be used at a specific time of day based on the user's past input history. This improves input efficiency based on the user's past input history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can assist with input using an AI model that analyzes the user's past input history and provides optimal input assistance.

[0043] The reception desk can display relevant tourist information and event information in real time based on the user's input. For example, when a user enters a specific area, the reception desk can display event information held in that area in real time. It can also display information on tourist spots related to a specific tourist genre when the user enters a genre. Furthermore, when a user enters a date and time, the reception desk can display tourist information tailored to that date and time in real time. This allows the reception desk to provide relevant information in real time based on the user's input. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can display information using an AI model that analyzes the user's input and provides relevant tourist information and event information.

[0044] The reception desk can provide local information relevant to the input content, taking into account the user's geographical location. For example, when the user enters a specific area, the reception desk can provide local information for that area. It can also provide local information related to a tourism genre when the user enters a tourism genre. Furthermore, when the user enters a date and time, the reception desk can provide local information tailored to that date and time. This allows the reception desk to provide relevant local information based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can display information using an AI model that analyzes the user's geographical location and provides relevant local information.

[0045] The reception desk can analyze the user's social media activity and suggest relevant input content. For example, the reception desk can suggest relevant areas and tourist genres based on past travel information shared by the user on social media. It can also suggest relevant tourist information based on information about accounts the user follows on social media. Furthermore, the reception desk can suggest relevant tourist information based on posts the user has "liked" on social media. In this way, relevant input content can be suggested based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can assist with input using an AI model that analyzes the user's social media activity and suggests relevant input content.

[0046] The analysis unit can analyze the user's past travel history and improve the accuracy of the analysis results. For example, the analysis unit can provide relevant tourist information based on places the user has visited in the past. It can also identify the user's preferred tourist genres from their past travel history and provide relevant information. Furthermore, the analysis unit can analyze the user's past travel history and propose an optimal travel plan. This improves the accuracy of the analysis results based on the user's past travel history. Some or all of the above processing in the analysis unit may be performed using generative AI, or without generative AI. For example, the analysis unit can analyze information using a generative AI model that takes the user's past travel history as input and outputs analysis results.

[0047] The analysis unit can supplement the analysis results by referring to relevant historical data based on the user's input. For example, when the user inputs a specific area, the analysis unit can refer to historical data related to that area and supplement the analysis results. Similarly, when the user inputs a tourism genre, the analysis unit can refer to historical data related to that genre and supplement the analysis results. Furthermore, when the user inputs a date and time, the analysis unit can refer to historical data related to that date and time and supplement the analysis results. In this way, the analysis results are supplemented by referring to relevant historical data based on the user's input. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can analyze information using a generative AI model that inputs the user's input into a generative AI and supplements the analysis results by referring to relevant historical data.

[0048] The analysis unit can add local information to the analysis results, taking into account the user's geographical location. For example, when the user inputs a specific area, the analysis unit can add local information for that area to the analysis results. It can also add local information related to a tourism genre when the user inputs a tourism genre. Furthermore, when the user inputs a date and time, the analysis unit can add local information related to that date and time to the analysis results. This allows the analysis results to be updated based on the user's geographical location. Some or all of the above processing in the analysis unit may be performed using a generative AI, or without one. For example, the analysis unit can input the user's geographical location into a generative AI and analyze the information using a generative AI model that adds local information.

[0049] The analysis unit can analyze a user's social media activity and add relevant information to the analysis results. For example, the analysis unit can add relevant information to the analysis results based on past travel information shared by the user on social media. It can also add relevant information to the analysis results based on information about accounts the user follows on social media. Furthermore, the analysis unit can add relevant information to the analysis results based on posts the user has "liked" on social media. This allows for the addition of relevant information to the analysis results based on the user's social media activity. Some or all of the above processing in the analysis unit may be performed using generative AI, or without generative AI. For example, the analysis unit can input the user's social media activity into a generative AI and analyze the information using a generative AI model that adds relevant information.

[0050] The suggestion unit can analyze the user's past travel history to improve the accuracy of its suggestions. For example, the suggestion unit can provide relevant suggestions based on places the user has visited in the past. It can also identify the user's preferred travel genres from their past travel history and provide relevant suggestions. Furthermore, the suggestion unit can analyze the user's past travel history to provide optimal suggestions. This improves the accuracy of suggestions based on the user's past travel history. Some or all of the above processing in the suggestion unit may be performed using generative AI, or not. For example, the suggestion unit can input the user's past travel history into a generative AI and analyze the information using a generative AI model that improves the accuracy of its suggestions.

[0051] The suggestion unit can update relevant suggestions in real time based on user input. For example, when a user enters a specific area, the suggestion unit can update suggestions related to that area in real time. It can also update suggestions related to a tourism genre when a user enters a tourism genre. Furthermore, when a user enters a date and time, the suggestion unit can update suggestions related to that date and time in real time. This allows the system to provide relevant suggestions in real time based on user input. Some or all of the above processing in the suggestion unit may be performed using generative AI, or without generative AI. For example, the suggestion unit can analyze information using a generative AI model that inputs user input and updates relevant suggestions in real time.

[0052] The suggestion unit can add local information to the suggestions, taking into account the user's geographical location. For example, when the user enters a specific area, the suggestion unit can add local information for that area to the suggestions. It can also add local information related to a tourism genre when the user enters a genre. Furthermore, when the user enters a date and time, the suggestion unit can add local information related to that date and time. This allows the suggestion unit to add local information to the suggestions based on the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or without one. For example, the suggestion unit can input the user's geographical location into a generative AI and analyze the information using a generative AI model that adds local information.

[0053] The suggestion unit can analyze the user's social media activity and add information relevant to the suggestions. For example, the suggestion unit can provide relevant suggestions based on past travel information shared by the user on social media. It can also provide relevant suggestions based on information about accounts the user follows on social media. Furthermore, it can provide relevant suggestions based on posts the user has "liked" on social media. This allows information relevant to the suggestions to be added based on the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using generative AI, or not. For example, the suggestion unit can analyze the information using a generative AI model that inputs the user's social media activity and adds relevant information.

[0054] The bookmark generation unit can analyze the user's past travel history and customize the bookmark content. For example, the bookmark generation unit can add relevant sightseeing information to the bookmark based on places the user has visited in the past. It can also identify the user's preferred sightseeing genres from their past travel history and add relevant information to the bookmark. Furthermore, the bookmark generation unit can analyze the user's past travel history and reflect the optimal travel plan in the bookmark. As a result, the bookmark content is customized based on the user's past travel history. Some or all of the above processing in the bookmark generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the bookmark generation unit can input the user's past travel history into a generation AI and analyze the information using a generation AI model that customizes the bookmark content.

[0055] The bookmark generation unit can add relevant information to bookmarks based on user input. For example, when a user enters a specific area, the bookmark generation unit can add tourist information related to that area to the bookmark. It can also add information about tourist spots related to a specific tourist genre when a user enters a genre. Furthermore, when a user enters a date and time, the bookmark generation unit can add event information related to that date and time to the bookmark. In this way, relevant information is added to bookmarks based on user input. Some or all of the above processing in the bookmark generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the bookmark generation unit can input user input into a generation AI and analyze the information using a generation AI model that adds relevant information.

[0056] The bookmark generation unit can add local information to bookmarks, taking into account the user's geographical location. For example, when the user enters a specific area, the bookmark generation unit can add local information for that area to the bookmark. It can also add local information related to a travel genre when the user enters a travel genre. Furthermore, when the user enters a date and time, the bookmark generation unit can add local information related to that date and time to the bookmark. This ensures that local information is added to bookmarks based on the user's geographical location. Some or all of the above processing in the bookmark generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the bookmark generation unit can input the user's geographical location information into a generation AI and analyze the information using a generation AI model that adds local information.

[0057] The bookmark generation unit can analyze the user's social media activity and add information relevant to the bookmark. For example, the bookmark generation unit can add relevant information to the bookmark based on past travel information shared by the user on social media. It can also add relevant information to the bookmark based on information about accounts the user follows on social media. Furthermore, the bookmark generation unit can add relevant information to the bookmark based on posts the user has "liked" on social media. In this way, information relevant to the bookmark is added based on the user's social media activity. Some or all of the above processing in the bookmark generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the bookmark generation unit can input the user's social media activity into a generation AI and analyze the information using a generation AI model that adds relevant information.

[0058] The guide unit can analyze the user's past travel history and customize the guide content. For example, the guide unit can provide relevant guide content based on places the user has visited in the past. It can also identify the user's preferred travel genres from their past travel history and provide relevant guide content. Furthermore, the guide unit can analyze the user's past travel history and provide optimal guide content. This ensures that the guide content is customized based on the user's past travel history. Some or all of the above processing in the guide unit may be performed using generative AI, or without generative AI. For example, the guide unit can input the user's past travel history into a generative AI and analyze the information using a generative AI model that customizes the guide content.

[0059] The guide unit can update relevant guide information in real time based on user input. For example, when a user enters a specific area, the guide unit can update guide information related to that area in real time. It can also update guide information related to a tourism genre when a user enters a tourism genre. Furthermore, when a user enters a date and time, the guide unit can update guide information related to that date and time in real time. This ensures that relevant guide information is provided in real time based on user input. Some or all of the above processing in the guide unit may be performed using a generative AI, or without one. For example, the guide unit can analyze information using a generative AI model that inputs user input and updates relevant guide information in real time.

[0060] The guide unit can add local information to the guide content, taking into account the user's geographical location. For example, when the user enters a specific area, the guide unit can add local information for that area to the guide content. It can also add local information related to a tourism genre when the user enters a genre. Furthermore, when the user enters a date and time, the guide unit can add local information related to that date and time to the guide content. This ensures that local information is added to the guide content based on the user's geographical location. Some or all of the above processing in the guide unit may be performed using a generative AI, or without one. For example, the guide unit can input the user's geographical location into a generative AI and analyze the information using a generative AI model that adds local information.

[0061] The guide unit can analyze the user's social media activity and add information relevant to the guide content. For example, the guide unit can provide relevant guide content based on past travel information shared by the user on social media. It can also provide relevant guide content based on information about accounts the user follows on social media. Furthermore, the guide unit can provide relevant guide content based on posts the user has "liked" on social media. In this way, information relevant to the guide content is added based on the user's social media activity. Some or all of the above processing in the guide unit may be performed using generative AI, or not. For example, the guide unit can input the user's social media activity into a generative AI and analyze the information using a generative AI model that adds relevant information.

[0062] The navigation unit can analyze the user's past travel history to improve navigation accuracy. For example, the navigation unit can provide the optimal navigation method based on routes previously used by the user. It can also provide 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 provide the most efficient navigation method. This improves navigation accuracy based on the user's past travel history. Some or all of the above processing in the navigation unit may be performed using generative AI, or it may be performed without using generative AI. For example, the navigation unit can input the user's past travel history into a generative AI and analyze the information using a generative AI model that improves navigation accuracy.

[0063] The navigation unit can update relevant navigation information in real time based on user input. For example, when a user enters a specific area, the navigation unit can update navigation information related to that area in real time. It can also update navigation information related to a tourism genre when a user enters a tourism genre. Furthermore, when a user enters a date and time, the navigation unit can update navigation information related to that date and time in real time. This ensures that relevant navigation information is provided in real time based on user input. Some or all of the above processing in the navigation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the navigation unit can analyze information using a generative AI model that inputs user input into a generative AI and updates relevant navigation information in real time.

[0064] The navigation unit can add local information to the navigation content, taking into account the user's geographical location. For example, when the user enters a specific area, the navigation unit can add local information for that area to the navigation content. It can also add local information related to a tourism genre when the user enters a genre. Furthermore, when the user enters a date and time, the navigation unit can add local information related to that date and time to the navigation content. This ensures that local information is added to the navigation content based on the user's geographical location. Some or all of the above processing in the navigation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the navigation unit can input the user's geographical location into a generative AI and analyze the information using a generative AI model that adds local information.

[0065] The navigation unit can analyze the user's social media activity and add information relevant to the navigation content. For example, the navigation unit can provide relevant navigation content based on past travel information shared by the user on social media. It can also provide relevant navigation content based on information about accounts the user follows on social media. Furthermore, the navigation unit can provide relevant navigation content based on posts the user has "liked" on social media. In this way, information relevant to the navigation content is added based on the user's social media activity. Some or all of the above processing in the navigation unit may be performed using generative AI, or it may be performed without generative AI. For example, the navigation unit can analyze the information using a generative AI model that inputs the user's social media activity and adds relevant information.

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

[0067] The reception desk can display relevant past travel reviews based on the user's input. For example, when a user enters a specific area, it can display past traveler reviews related to that area. It can also display past traveler reviews related to a travel genre when the user enters a travel genre. Furthermore, when a user enters a date and time, it can display past traveler reviews related to that date and time. This allows users to refer to the experiences of other travelers and make better travel plans. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can display information using an AI model that analyzes the user's input and provides relevant past travel reviews.

[0068] The analysis unit can add relevant weather information to the analysis results based on the user's input. For example, when a user enters a specific area, the weather information for that area can be added to the analysis results. Similarly, when a user enters a tourism genre, weather information related to that genre can be added to the analysis results. Furthermore, when a user enters a date and time, weather information related to that date and time can be added to the analysis results. This allows users to consider weather information when planning their trips. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can analyze information using a generative AI model that inputs the user's input into a generative AI and adds weather information.

[0069] The suggestion unit can add relevant health information to the suggestions based on the user's input. For example, when a user enters a specific area, health risk information for that area can be added to the suggestions. Similarly, when a user enters a travel genre, health information related to that genre can be added to the suggestions. Furthermore, when a user enters a date and time, health information related to that date and time can be added to the suggestions. This allows users to consider health risks when planning their trips. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the suggestion unit can input the user's input into a generative AI and analyze the information using a generative AI model that adds health information.

[0070] The bookmark generation unit can add relevant cultural information to bookmarks based on user input. For example, when a user enters a specific area, cultural information for that area can be added to the bookmark. Similarly, when a user enters a travel genre, cultural information related to that genre can be added. Furthermore, when a user enters a date and time, cultural information related to that date and time can be added to the bookmark. This allows users to gain a deeper understanding of the culture of their travel destination. Some or all of the above-described processes in the bookmark generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the bookmark generation unit can input user input into a generation AI and analyze the information using a generation AI model that adds cultural information.

[0071] The guide unit can add relevant historical information to the guide content based on the user's input. For example, when a user enters a specific area, the guide unit can add historical information about that area. Similarly, when a user enters a travel genre, the guide unit can add historical information related to that genre. Furthermore, when a user enters a date and time, the guide unit can add historical information related to that date and time. This allows the user to gain a deeper understanding of the history of their travel destination. Some or all of the above processing in the guide unit may be performed using generative AI, or without generative AI. For example, the guide unit can input the user's input into a generative AI and analyze the information using a generative AI model that adds historical information.

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

[0073] Step 1: The reception desk enters the user's preferences. Users can enter their desired area, type of sightseeing, date and time, number of people, etc., using free text. For example, a user might enter information such as "Family trip to Tokyo, 3 days, 5 people." Step 2: The analysis unit analyzes the information entered by the reception unit. The analysis unit uses a generation AI to analyze the information entered based on the user's preferences. For example, the analysis unit compares data from various travel agencies based on the information entered by the user and proposes the optimal itinerary. Step 3: The suggestion unit proposes the optimal itinerary based on the information analyzed by the analysis unit. The suggestion unit uses generative AI to propose a personalized itinerary tailored to the user's preferences. For example, if the user enters "Family trip to Tokyo, 3 days, 5 people," the suggestion unit will analyze information such as tourist spots, accommodations, and transportation in Tokyo and propose the optimal itinerary. Step 4: The itinerary generation unit compiles the travel itinerary proposed by the proposal unit. The itinerary generation unit uses a generation AI to generate an original travel itinerary that compiles the travel itinerary. For example, based on the information entered by the user, the itinerary generation unit proposes a specific itinerary such as Tokyo Tower and Senso-ji Temple on day 1, Disneyland on day 2, and Odaiba on day 3. Step 5: The guide unit provides a tour guide based on the bookmarks generated by the bookmark generation unit. The guide unit uses a generation AI to provide a unique tour guide tailored to the age and knowledge level of the target audience. For example, the guide unit can provide easy-to-understand explanations for children and detailed explanations of history and culture for adults. Step 6: 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 uses AI to display the optimal route from the user's current location to the tourist spot. For example, when the user searches for a route from their current location to a tourist spot, the navigation unit can display the optimal route using a map application and provide navigation.

[0074] (Example of form 2) An information platform according to an embodiment of the present invention is a system that uses a generating AI to propose a personalized itinerary tailored to an individual's preferences. This information platform allows users to input their desired area, type of sightseeing, date, time, number of people, etc., using free text. The generating 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, it generates an original travel guide summarizing the itinerary and provides a unique tour guide tailored to the age and knowledge level of the target person. It also integrates with a map application to provide optimal navigation. By sharing schedules and information on the information platform, it becomes possible to provide better reference information to future visitors. For example, a user inputs their desired area, type of sightseeing, date, time, number of people, etc., using free text. At this time, the user can freely input specific preferences. For example, they might input information such as "Family trip to Tokyo, 3 days, 5 people." This information is input into the generating AI. Next, the generating AI analyzes the input information. Based on the user's preferences, the generating AI matches data from various travel agencies and proposes the optimal itinerary. For example, if a user enters "Family trip to Tokyo, 3 days, 5 people," the generating AI analyzes information on Tokyo's tourist spots, accommodations, and transportation options to suggest the optimal itinerary. Furthermore, the generating AI creates an original travel guide summarizing the trip. For instance, based on the information entered by the user, it might suggest a specific itinerary such as Tokyo Tower and Senso-ji Temple on day 1, Disneyland on day 2, and Odaiba on day 3. This guide can be used by the user as a reference during their trip. The generating AI also provides a unique tour guide tailored to the age and knowledge level of the target audience. For example, it can provide easy-to-understand explanations for children and detailed historical and cultural explanations for adults. This allows users to gain a deeper understanding of their travel destination. In addition, it integrates with map applications to provide optimal navigation. For example, if a user searches for a route from their current location to a tourist spot, the map application can display the optimal route and provide navigation. This allows users to reach their destination without getting lost.Finally, by sharing schedules and information on the information platform, better reference information can be provided to future visitors. For example, users can post photos and impressions taken during their trip on the platform, which other users can use as reference. This is expected to promote information sharing and make travel planning smoother. As a result, the information platform can propose personalized itineraries tailored to the user's wishes, generate original travel guides summarizing travel dates, provide unique tour guides, and offer optimal navigation.

[0075] The information platform according to this embodiment comprises a reception unit, an analysis unit, a proposal unit, a bookmark generation unit, a guide unit, and a navigation unit. The reception unit receives the user's preferences. The user can enter, for example, their desired area, type of sightseeing, date and time, and number of people using free text. For example, the user can enter information such as "Family trip to Tokyo, 3 days, 5 people." The analysis unit analyzes the information entered by the reception unit. The analysis unit uses a generation AI to analyze the information entered based on the user's preferences. For example, the analysis unit compares data from various travel agencies based on the information entered by the user and proposes the optimal itinerary. The proposal unit proposes the optimal itinerary based on the information analyzed by the analysis unit. The proposal unit uses a generation AI to propose a personalized itinerary tailored to the user's preferences. For example, if the user enters "Family trip to Tokyo, 3 days, 5 people," the proposal unit analyzes information such as tourist spots, accommodations, and transportation in Tokyo and proposes the optimal itinerary. The bookmark generation unit compiles the travel itinerary proposed by the proposal unit. The bookmark generation unit uses generation AI to generate an original travel itinerary. For example, based on the information entered by the user, the bookmark generation unit suggests a specific itinerary such as Tokyo Tower and Senso-ji Temple on day 1, Disneyland on day 2, and Odaiba on day 3. The guide unit provides a tour guide based on the bookmark generated by the bookmark generation unit. The guide unit uses generation AI to provide a unique tour guide tailored to the age and knowledge level of the target audience. For example, the guide unit can provide easy-to-understand explanations for children and detailed explanations of history and culture 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 uses AI to display the optimal route from the user's current location to the tourist spot. For example, when the user searches for a route from their current location to a tourist spot, the navigation unit can display the optimal route using a map application and provide navigation. As a result, the information platform according to this embodiment can propose a personalized itinerary tailored to the user's wishes, generate an original travel itinerary summarizing the travel schedule, provide a unique tour guide, and achieve optimal navigation.

[0076] The reception desk receives user requests. Users can enter details such as their desired area, type of sightseeing, date, time, and number of people using free text. Specifically, a user might enter information like "Family trip to Tokyo, 3 days, 5 people." The reception desk receives the information entered by the user and provides an interface for processing it appropriately within the system. Since the information entered by the user is in text format, it is analyzed using natural language processing technology. For example, if a user enters "Family trip to Tokyo, 3 days, 5 people," the reception desk extracts keywords such as "Tokyo," "Family trip," "3 days," and "5 people," and sends each element to the analysis department. Furthermore, the reception desk saves the information entered by the user so that it can be referenced later. This saves the user the trouble of entering the same information again. The reception desk can also suggest related options and additional information based on the information entered by the user. For example, if a user enters "Family trip to Tokyo," the reception desk will suggest popular tourist spots such as "Disneyland," "Senso-ji Temple," and "Odaiba." This makes it easier for users to enter more specific requests. The reception desk provides a user-friendly interface to facilitate smooth user input and plays a role in accurately conveying user requests to the system.

[0077] The analysis unit analyzes the information entered by the reception unit. The analysis unit uses generative AI to analyze the information entered based on the user's preferences. Specifically, it compares data from various travel agencies based on the information entered by the user and proposes the optimal itinerary. The generative AI uses natural language processing technology to understand the user's input and extracts appropriate information from relevant databases. For example, if a user enters "Family trip to Tokyo, 3 days, 5 people," the analysis unit will use keywords such as "Tokyo," "family trip," "3 days," and "5 people" to collect information such as Tokyo tourist spots, accommodations, and transportation from travel agency databases. Furthermore, the analysis unit generates the itinerary that best suits the user's preferences based on the collected information. The generative AI uses algorithms to propose the best options based on past data and user ratings. For example, it prioritizes suggesting the highest-rated tourist spots and accommodations by referring to ratings from users who have traveled under similar conditions in the past. In addition, the analysis unit can make suggestions that respond to the latest situation based on information that is updated in real time. For example, it can flexibly adjust the itinerary in response to changes in weather or traffic conditions. This allows the analysis unit to quickly and accurately propose the optimal itinerary based on the user's preferences.

[0078] The suggestion department proposes the optimal itinerary based on the information analyzed by the analysis department. The suggestion department uses generative AI to propose a personalized itinerary tailored to the user's preferences. Specifically, if a user inputs "family trip to Tokyo, 3 days, 5 people," the suggestion department analyzes information such as Tokyo's tourist spots, accommodations, and transportation options to propose the optimal itinerary. Based on the user's input information, the generative AI proposes the most suitable options by referring to past data and evaluations. For example, in the case of a family trip, it prioritizes suggesting activities for children and family-friendly accommodations. Furthermore, the suggestion department can present multiple options according to the user's preferences. For example, if a user inputs "family trip to Tokyo, 3 days, 5 people," the suggestion department will not only propose a specific schedule such as "Day 1: Tokyo Tower and Senso-ji Temple, Day 2: Disneyland, Day 3: Odaiba," but will also present other options such as "Day 1: Ueno Zoo and Ameyoko, Day 2: Odaiba, Day 3: Senso-ji Temple and Tokyo Skytree." This allows the user to choose the most suitable itinerary from multiple options. Furthermore, the suggestion system can continuously improve its suggestions based on user feedback. For example, users can provide ratings and comments on suggested itineraries, allowing the generating AI to learn from this information and incorporate it into future suggestions. This enables the suggestion system to provide personalized itineraries that best suit the user's preferences, thereby improving user satisfaction.

[0079] The Itinerary Generation Unit compiles the travel itineraries proposed by the Proposal Unit. Using a generation AI, the Itinerary Generation Unit generates an original travel itinerary summarizing the travel schedule. Specifically, based on the information entered by the user, it proposes a specific itinerary such as Tokyo Tower and Senso-ji Temple on day 1, Disneyland on day 2, and Odaiba on day 3. The generation AI automatically generates the optimal travel itinerary based on the user's input information and provides it in itinerary format. The itinerary includes detailed schedules for each day, information on sightseeing spots, transportation, and places to eat. For example, the itinerary for day 1 might include a specific schedule such as, "Visit Tokyo Tower at 10am, move to Senso-ji Temple at 1pm, and have lunch at a restaurant near Senso-ji Temple." Furthermore, the Itinerary Generation Unit can customize the content of the itinerary according to the user's wishes. For example, if the user wishes to include many activities suitable for children, the Itinerary Generation Unit will prioritize including sightseeing spots and activities suitable for children according to that wish. In addition, the Itinerary Generation Unit provides the generated itinerary in digital format, making it easy for users to view on their smartphones and tablets. This makes it easier for users to refer to their itineraries during their trip, enabling a smoother travel experience. The itinerary generation unit provides original travel itineraries based on the user's preferences, making travel planning more convenient and enjoyable.

[0080] The guide unit provides tour guides based on bookmarks generated by the bookmark generation unit. The guide unit uses a generation AI to provide unique tour guides tailored to the age and knowledge level of the target audience. Specifically, it can provide easy-to-understand explanations for children and detailed historical and cultural explanations for adults. The generation AI generates optimal guide content based on the user's profile information. For example, for a family trip, it can provide fun explanations using characters for children and detailed explanations of historical background and cultural significance for adults. The guide unit can also provide audio and text guides. Users can listen to audio guides or read text guides provided by the guide unit using their smartphones or tablets. Furthermore, the guide unit can provide the latest guide content based on real-time updated information. For example, if the opening hours or event information of a tourist spot changes, the guide unit immediately reflects this information and provides users with the latest information. This ensures that users can always enjoy sightseeing based on the most up-to-date information. The guide unit provides unique tour guides tailored to the user's age and knowledge level, maximizing the enjoyment and learning of their trip.

[0081] The navigation unit works in conjunction with a map application to provide navigation based on information provided by the guide unit. The navigation unit uses AI to display the optimal route from the user's current location to the tourist spot. Specifically, when a user searches for a route from their current location to a tourist spot, the map application displays the optimal route and provides navigation. The navigation unit calculates the optimal route based on real-time updated traffic and weather information. For example, if there is traffic congestion or road construction, the navigation unit takes this information into consideration and suggests an alternative route. Furthermore, the navigation unit can suggest routes tailored to the user's mode of transportation. For example, it calculates and displays the optimal route for walking, cycling, driving, public transport, etc. In addition, the navigation unit can track the user's current location in real time and monitor their progress towards their destination. This allows the user to reach their destination without getting lost. The navigation unit provides the optimal route from the user's current location to the tourist spot, supporting smooth travel. This allows users to enjoy sightseeing without stress during their trip.

[0082] The navigation unit can display the optimal route from the user's current location to a tourist spot. For example, when a user searches for a route from their current location to a tourist spot, the navigation unit can display the optimal route using a map application and provide navigation. The navigation unit can, for example, display a route that takes into account the shortest distance and traffic conditions. This allows the user to reach their destination without getting lost. Some or all of the above processing in the navigation unit may be performed using AI or not. For example, the navigation unit can display the optimal route using an AI model that takes the user's current location and destination as input and outputs the optimal route.

[0083] The guide unit can provide unique tour guides tailored to the age and knowledge level of the target audience. For example, the guide unit can provide easy-to-understand explanations for children and detailed historical and cultural explanations for adults. The guide unit can also provide guides based on specific themes or interactive guides. This allows users to gain a deeper understanding of the travel destination. Some or all of the above processing in the guide unit may be performed using generative AI or not. For example, the guide unit can provide unique tour guides using a generative AI model that takes the user's age and knowledge level as input and outputs optimal guide content.

[0084] The itinerary generation unit can generate an original travel itinerary based on the user's preferences. For example, based on the information entered by the user, the itinerary generation unit can suggest a specific itinerary such as Tokyo Tower and Senso-ji Temple on day 1, Disneyland on day 2, and Odaiba on day 3. The itinerary generation unit can also customize the itinerary based on the user's individual preferences. This allows the user to refer to it during their trip. Some or all of the above-described processes in the itinerary generation unit may be performed using a generation AI, or they may not. For example, the itinerary generation unit can generate an itinerary using a generation AI model that takes the user's preferences as input and outputs an original travel itinerary.

[0085] The suggestion unit can propose the optimal itinerary by cross-referencing data from various travel agencies. For example, if a user inputs "family trip to Tokyo, 3 days, 5 people," the suggestion unit can analyze information such as tourist spots, accommodations, and transportation options in Tokyo and propose the optimal itinerary. The suggestion unit can also propose the optimal itinerary considering factors such as time efficiency and cost performance. This allows for the proposal of a personalized itinerary tailored to the user's preferences. Some or all of the above-described processes in the suggestion unit may be performed using generative AI, or they may not. For example, the suggestion unit can propose an itinerary using a generative AI model that takes the user's preferences as input and outputs the optimal itinerary.

[0086] The analysis unit can analyze information entered based on the user's preferences. For example, if the user enters "Family trip to Tokyo, 3 days, 5 people," the analysis unit can analyze information such as tourist spots, accommodations, and transportation options in Tokyo. The analysis unit can perform information analysis based on the user's preferences using, for example, data analysis methods and algorithms. This makes it possible to perform information analysis based on the user's preferences. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or they may be performed without using generative AI. For example, the analysis unit can analyze information using a generative AI model that takes the user's preferences as input and outputs analysis results.

[0087] The reception desk allows users to input their desired area, type of tourism, date, time, number of people, etc., using free text. For example, a user could input information such as "Family trip to Tokyo, 3 days, 5 people." The reception desk can also allow users to freely input their specific preferences using, for example, keyword search or natural language input. This allows users to freely input their specific preferences. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can assist with input using an AI model that analyzes the user's input and provides appropriate input assistance.

[0088] 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 improves the ease of input by providing an interface that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0089] The reception desk can analyze the user's past input history and provide optimal 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 and suggest areas and tourist genres to be used at a specific time of day based on the user's past input history. This improves input efficiency based on the user's past input history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can assist with input using an AI model that analyzes the user's past input history and provides optimal input assistance.

[0090] The reception desk can display relevant tourist information and event information in real time based on the user's input. For example, when a user enters a specific area, the reception desk can display event information held in that area in real time. It can also display information on tourist spots related to a specific tourist genre when the user enters a genre. Furthermore, when a user enters a date and time, the reception desk can display tourist information tailored to that date and time in real time. This allows the reception desk to provide relevant information in real time based on the user's input. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can display information using an AI model that analyzes the user's input and provides relevant tourist information and event information.

[0091] The reception desk can estimate the user's emotions and automatically adjust the priority of input content based on the estimated 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 displaying the most important input items to enable quick input. This improves input efficiency by adjusting the priority of input content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0092] The reception desk can provide local information relevant to the input content, taking into account the user's geographical location. For example, when the user enters a specific area, the reception desk can provide local information for that area. It can also provide local information related to a tourism genre when the user enters a tourism genre. Furthermore, when the user enters a date and time, the reception desk can provide local information tailored to that date and time. This allows the reception desk to provide relevant local information based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can display information using an AI model that analyzes the user's geographical location and provides relevant local information.

[0093] The reception desk can analyze the user's social media activity and suggest relevant input content. For example, the reception desk can suggest relevant areas and tourist genres based on past travel information shared by the user on social media. It can also suggest relevant tourist information based on information about accounts the user follows on social media. Furthermore, the reception desk can suggest relevant tourist information based on posts the user has "liked" on social media. In this way, relevant input content can be suggested based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can assist with input using an AI model that analyzes the user's social media activity and suggests relevant input content.

[0094] The analysis unit can estimate the user's emotions and dynamically adjust the analysis algorithm based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide more information. If the user is in a hurry, the analysis unit can perform a rapid analysis and prioritize providing the most important information. Furthermore, if the user is stressed, the analysis unit can perform a simple analysis and provide information that is intuitively understandable. By adjusting the analysis algorithm according to the user's emotions, the accuracy of the analysis results is improved. 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-described processes in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0095] The analysis unit can analyze the user's past travel history and improve the accuracy of the analysis results. For example, the analysis unit can provide relevant tourist information based on places the user has visited in the past. It can also identify the user's preferred tourist genres from their past travel history and provide relevant information. Furthermore, the analysis unit can analyze the user's past travel history and propose an optimal travel plan. This improves the accuracy of the analysis results based on the user's past travel history. Some or all of the above processing in the analysis unit may be performed using generative AI, or without generative AI. For example, the analysis unit can analyze information using a generative AI model that takes the user's past travel history as input and outputs analysis results.

[0096] The analysis unit can supplement the analysis results by referring to relevant historical data based on the user's input. For example, when the user inputs a specific area, the analysis unit can refer to historical data related to that area and supplement the analysis results. Similarly, when the user inputs a tourism genre, the analysis unit can refer to historical data related to that genre and supplement the analysis results. Furthermore, when the user inputs a date and time, the analysis unit can refer to historical data related to that date and time and supplement the analysis results. In this way, the analysis results are supplemented by referring to relevant historical data based on the user's input. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can analyze information using a generative AI model that inputs the user's input into a generative AI and supplements the analysis results by referring to relevant historical data.

[0097] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, visibility is improved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using the generative AI or not. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0098] The analysis unit can add local information to the analysis results, taking into account the user's geographical location. For example, when the user inputs a specific area, the analysis unit can add local information for that area to the analysis results. It can also add local information related to a tourism genre when the user inputs a tourism genre. Furthermore, when the user inputs a date and time, the analysis unit can add local information related to that date and time to the analysis results. This allows the analysis results to be updated based on the user's geographical location. Some or all of the above processing in the analysis unit may be performed using a generative AI, or without one. For example, the analysis unit can input the user's geographical location into a generative AI and analyze the information using a generative AI model that adds local information.

[0099] The analysis unit can analyze a user's social media activity and add relevant information to the analysis results. For example, the analysis unit can add relevant information to the analysis results based on past travel information shared by the user on social media. It can also add relevant information to the analysis results based on information about accounts the user follows on social media. Furthermore, the analysis unit can add relevant information to the analysis results based on posts the user has "liked" on social media. This allows for the addition of relevant information to the analysis results based on the user's social media activity. Some or all of the above processing in the analysis unit may be performed using generative AI, or without generative AI. For example, the analysis unit can input the user's social media activity into a generative AI and analyze the information using a generative AI model that adds relevant information.

[0100] 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 can provide detailed suggestions. If the user is in a hurry, it can provide concise suggestions. Furthermore, if the user is stressed, it can provide simple and intuitive suggestions. By adjusting the way suggestions are presented according to the user's emotions, the likelihood of the suggestions being accepted is increased. 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-described processes in the suggestion unit may be performed using or without a generative AI. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0101] The suggestion unit can analyze the user's past travel history to improve the accuracy of its suggestions. For example, the suggestion unit can provide relevant suggestions based on places the user has visited in the past. It can also identify the user's preferred travel genres from their past travel history and provide relevant suggestions. Furthermore, the suggestion unit can analyze the user's past travel history to provide optimal suggestions. This improves the accuracy of suggestions based on the user's past travel history. Some or all of the above processing in the suggestion unit may be performed using generative AI, or not. For example, the suggestion unit can input the user's past travel history into a generative AI and analyze the information using a generative AI model that improves the accuracy of its suggestions.

[0102] The suggestion unit can update relevant suggestions in real time based on user input. For example, when a user enters a specific area, the suggestion unit can update suggestions related to that area in real time. It can also update suggestions related to a tourism genre when a user enters a tourism genre. Furthermore, when a user enters a date and time, the suggestion unit can update suggestions related to that date and time in real time. This allows the system to provide relevant suggestions in real time based on user input. Some or all of the above processing in the suggestion unit may be performed using generative AI, or without generative AI. For example, the suggestion unit can analyze information using a generative AI model that inputs user input and updates relevant suggestions in real time.

[0103] The suggestion unit can estimate the user's emotions and adjust the priority of suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can prioritize displaying important suggestions. If the user is relaxed, the suggestion unit can also provide detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can prioritize displaying the most important suggestions. This improves the likelihood of suggestions being accepted by adjusting the priority of suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using or without a generative AI. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0104] The suggestion unit can add local information to the suggestions, taking into account the user's geographical location. For example, when the user enters a specific area, the suggestion unit can add local information for that area to the suggestions. It can also add local information related to a tourism genre when the user enters a genre. Furthermore, when the user enters a date and time, the suggestion unit can add local information related to that date and time. This allows the suggestion unit to add local information to the suggestions based on the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or without one. For example, the suggestion unit can input the user's geographical location into a generative AI and analyze the information using a generative AI model that adds local information.

[0105] The suggestion unit can analyze the user's social media activity and add information relevant to the suggestions. For example, the suggestion unit can provide relevant suggestions based on past travel information shared by the user on social media. It can also provide relevant suggestions based on information about accounts the user follows on social media. Furthermore, it can provide relevant suggestions based on posts the user has "liked" on social media. This allows information relevant to the suggestions to be added based on the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using generative AI, or not. For example, the suggestion unit can analyze the information using a generative AI model that inputs the user's social media activity and adds relevant information.

[0106] The bookmark 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 bookmark generation unit can provide a colorful and cheerful bookmark design. If the user is in a hurry, the bookmark generation unit can provide a simple and highly visible bookmark design. Furthermore, if the user is stressed, the bookmark generation unit can provide a bookmark with calming colors. This improves the visibility of the bookmarks by providing bookmarks with designs that match the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the bookmark generation unit may be performed using a generative AI or not. For example, the bookmark generation unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0107] The bookmark generation unit can analyze the user's past travel history and customize the bookmark content. For example, the bookmark generation unit can add relevant sightseeing information to the bookmark based on places the user has visited in the past. It can also identify the user's preferred sightseeing genres from their past travel history and add relevant information to the bookmark. Furthermore, the bookmark generation unit can analyze the user's past travel history and reflect the optimal travel plan in the bookmark. As a result, the bookmark content is customized based on the user's past travel history. Some or all of the above processing in the bookmark generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the bookmark generation unit can input the user's past travel history into a generation AI and analyze the information using a generation AI model that customizes the bookmark content.

[0108] The bookmark generation unit can add relevant information to bookmarks based on user input. For example, when a user enters a specific area, the bookmark generation unit can add tourist information related to that area to the bookmark. It can also add information about tourist spots related to a specific tourist genre when a user enters a genre. Furthermore, when a user enters a date and time, the bookmark generation unit can add event information related to that date and time to the bookmark. In this way, relevant information is added to bookmarks based on user input. Some or all of the above processing in the bookmark generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the bookmark generation unit can input user input into a generation AI and analyze the information using a generation AI model that adds relevant information.

[0109] The bookmark generation unit can estimate the user's emotions and adjust the priority of bookmarks based on the estimated emotions. For example, if the user is stressed, the bookmark generation unit can prioritize displaying important information and simplify the bookmark content. Conversely, if the user is relaxed, the bookmark generation unit can provide bookmarks containing detailed information. Furthermore, if the user is in a hurry, the bookmark generation unit can prioritize displaying the most important information for quick review. This improves the visibility of bookmarks by adjusting their priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the bookmark generation unit may be performed using or without a generative AI. For example, the bookmark generation unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0110] The bookmark generation unit can add local information to bookmarks, taking into account the user's geographical location. For example, when the user enters a specific area, the bookmark generation unit can add local information for that area to the bookmark. It can also add local information related to a travel genre when the user enters a travel genre. Furthermore, when the user enters a date and time, the bookmark generation unit can add local information related to that date and time to the bookmark. This ensures that local information is added to bookmarks based on the user's geographical location. Some or all of the above processing in the bookmark generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the bookmark generation unit can input the user's geographical location information into a generation AI and analyze the information using a generation AI model that adds local information.

[0111] The bookmark generation unit can analyze the user's social media activity and add information relevant to the bookmark. For example, the bookmark generation unit can add relevant information to the bookmark based on past travel information shared by the user on social media. It can also add relevant information to the bookmark based on information about accounts the user follows on social media. Furthermore, the bookmark generation unit can add relevant information to the bookmark based on posts the user has "liked" on social media. In this way, information relevant to the bookmark is added based on the user's social media activity. Some or all of the above processing in the bookmark generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the bookmark generation unit can input the user's social media activity into a generation AI and analyze the information using a generation AI model that adds relevant information.

[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 detailed guidance. If the user is in a hurry, the guide unit can provide concise guidance. Furthermore, if the user is stressed, the guide unit can provide simple and intuitive guidance. By adjusting the way the guide content is presented according to the user's emotions, the readability of the guide is improved. 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 or without a generative AI. For example, the guide unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0113] The guide unit can analyze the user's past travel history and customize the guide content. For example, the guide unit can provide relevant guide content based on places the user has visited in the past. It can also identify the user's preferred travel genres from their past travel history and provide relevant guide content. Furthermore, the guide unit can analyze the user's past travel history and provide optimal guide content. This ensures that the guide content is customized based on the user's past travel history. Some or all of the above processing in the guide unit may be performed using generative AI, or without generative AI. For example, the guide unit can input the user's past travel history into a generative AI and analyze the information using a generative AI model that customizes the guide content.

[0114] The guide unit can update relevant guide information in real time based on user input. For example, when a user enters a specific area, the guide unit can update guide information related to that area in real time. It can also update guide information related to a tourism genre when a user enters a tourism genre. Furthermore, when a user enters a date and time, the guide unit can update guide information related to that date and time in real time. This ensures that relevant guide information is provided in real time based on user input. Some or all of the above processing in the guide unit may be performed using a generative AI, or without one. For example, the guide unit can analyze information using a generative AI model that inputs user input and updates relevant guide information in real time.

[0115] The guide unit can estimate the user's emotions and adjust the priority of the guide content based on the estimated emotions. For example, if the user is feeling stressed, the guide unit can prioritize displaying important guide content. If the user is relaxed, the guide unit can also provide detailed guide content. Furthermore, if the user is in a hurry, the guide unit can prioritize displaying the most important guide content. This improves the readability of the guide by adjusting the priority of the guide content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guide unit may be performed using or without a generative AI. For example, the guide unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0116] The guide unit can add local information to the guide content, taking into account the user's geographical location. For example, when the user enters a specific area, the guide unit can add local information for that area to the guide content. It can also add local information related to a tourism genre when the user enters a genre. Furthermore, when the user enters a date and time, the guide unit can add local information related to that date and time to the guide content. This ensures that local information is added to the guide content based on the user's geographical location. Some or all of the above processing in the guide unit may be performed using a generative AI, or without one. For example, the guide unit can input the user's geographical location into a generative AI and analyze the information using a generative AI model that adds local information.

[0117] The guide unit can analyze the user's social media activity and add information relevant to the guide content. For example, the guide unit can provide relevant guide content based on past travel information shared by the user on social media. It can also provide relevant guide content based on information about accounts the user follows on social media. Furthermore, the guide unit can provide relevant guide content based on posts the user has "liked" on social media. In this way, information relevant to the guide content is added based on the user's social media activity. Some or all of the above processing in the guide unit may be performed using generative AI, or not. For example, the guide unit can input the user's social media activity into a generative AI and analyze the information using a generative AI model that adds relevant information.

[0118] 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 provide a more detailed navigation method. Furthermore, if the user is in a hurry, the navigation unit can provide a concise navigation method. By adjusting the navigation method according to the user's emotions, the acceptability of the navigation is improved. 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 or without a generative AI. For example, the navigation unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0119] The navigation unit can analyze the user's past travel history to improve navigation accuracy. For example, the navigation unit can provide the optimal navigation method based on routes previously used by the user. It can also provide 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 provide the most efficient navigation method. This improves navigation accuracy based on the user's past travel history. Some or all of the above processing in the navigation unit may be performed using generative AI, or it may be performed without using generative AI. For example, the navigation unit can input the user's past travel history into a generative AI and analyze the information using a generative AI model that improves navigation accuracy.

[0120] The navigation unit can update relevant navigation information in real time based on user input. For example, when a user enters a specific area, the navigation unit can update navigation information related to that area in real time. It can also update navigation information related to a tourism genre when a user enters a tourism genre. Furthermore, when a user enters a date and time, the navigation unit can update navigation information related to that date and time in real time. This ensures that relevant navigation information is provided in real time based on user input. Some or all of the above processing in the navigation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the navigation unit can analyze information using a generative AI model that inputs user input into a generative AI and updates relevant navigation information in real time.

[0121] The navigation unit can estimate the user's emotions and adjust the navigation priority based on the estimated emotions. For example, if the user is stressed, the navigation unit can prioritize displaying important navigation information. If the user is relaxed, the navigation unit can also provide detailed navigation information. Furthermore, if the user is in a hurry, the navigation unit can prioritize displaying the most important navigation information. This improves the readability of the navigation by adjusting the navigation priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the navigation unit may be performed using or without a generative AI. For example, the navigation unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0122] The navigation unit can add local information to the navigation content, taking into account the user's geographical location. For example, when the user enters a specific area, the navigation unit can add local information for that area to the navigation content. It can also add local information related to a tourism genre when the user enters a genre. Furthermore, when the user enters a date and time, the navigation unit can add local information related to that date and time to the navigation content. This ensures that local information is added to the navigation content based on the user's geographical location. Some or all of the above processing in the navigation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the navigation unit can input the user's geographical location into a generative AI and analyze the information using a generative AI model that adds local information.

[0123] The navigation unit can analyze the user's social media activity and add information relevant to the navigation content. For example, the navigation unit can provide relevant navigation content based on past travel information shared by the user on social media. It can also provide relevant navigation content based on information about accounts the user follows on social media. Furthermore, the navigation unit can provide relevant navigation content based on posts the user has "liked" on social media. In this way, information relevant to the navigation content is added based on the user's social media activity. Some or all of the above processing in the navigation unit may be performed using generative AI, or it may be performed without generative AI. For example, the navigation unit can analyze the information using a generative AI model that inputs the user's social media activity and adds relevant information.

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

[0125] The reception desk can display relevant past travel reviews based on the user's input. For example, when a user enters a specific area, it can display past traveler reviews related to that area. It can also display past traveler reviews related to a travel genre when the user enters a travel genre. Furthermore, when a user enters a date and time, it can display past traveler reviews related to that date and time. This allows users to refer to the experiences of other travelers and make better travel plans. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can display information using an AI model that analyzes the user's input and provides relevant past travel reviews.

[0126] The analysis unit can add relevant weather information to the analysis results based on the user's input. For example, when a user enters a specific area, the weather information for that area can be added to the analysis results. Similarly, when a user enters a tourism genre, weather information related to that genre can be added to the analysis results. Furthermore, when a user enters a date and time, weather information related to that date and time can be added to the analysis results. This allows users to consider weather information when planning their trips. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can analyze information using a generative AI model that inputs the user's input into a generative AI and adds weather information.

[0127] The suggestion unit can add relevant health information to the suggestions based on the user's input. For example, when a user enters a specific area, health risk information for that area can be added to the suggestions. Similarly, when a user enters a travel genre, health information related to that genre can be added to the suggestions. Furthermore, when a user enters a date and time, health information related to that date and time can be added to the suggestions. This allows users to consider health risks when planning their trips. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the suggestion unit can input the user's input into a generative AI and analyze the information using a generative AI model that adds health information.

[0128] The bookmark generation unit can add relevant cultural information to bookmarks based on user input. For example, when a user enters a specific area, cultural information for that area can be added to the bookmark. Similarly, when a user enters a travel genre, cultural information related to that genre can be added. Furthermore, when a user enters a date and time, cultural information related to that date and time can be added to the bookmark. This allows users to gain a deeper understanding of the culture of their travel destination. Some or all of the above-described processes in the bookmark generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the bookmark generation unit can input user input into a generation AI and analyze the information using a generation AI model that adds cultural information.

[0129] The guide unit can add relevant historical information to the guide content based on the user's input. For example, when a user enters a specific area, the guide unit can add historical information about that area. Similarly, when a user enters a travel genre, the guide unit can add historical information related to that genre. Furthermore, when a user enters a date and time, the guide unit can add historical information related to that date and time. This allows the user to gain a deeper understanding of the history of their travel destination. Some or all of the above processing in the guide unit may be performed using generative AI, or without generative AI. For example, the guide unit can input the user's input into a generative AI and analyze the information using a generative AI model that adds historical information.

[0130] The reception desk can estimate the user's emotions and provide feedback on the input based on the estimated emotions. For example, if the user is stressed, the reception desk can provide positive feedback on the input to alleviate the user's feelings. If the user is relaxed, the reception desk can provide detailed feedback to further enrich the user's input. Furthermore, if the user is in a hurry, the reception desk can provide quick feedback to efficiently review the user's input. This improves the ease of input by providing feedback tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0131] The analysis unit can estimate the user's emotions and adjust the level of detail in the analysis results based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed results, allowing the user to obtain more information. If the user is in a hurry, the analysis unit can provide concise results that get straight to the point. Furthermore, if the user is stressed, the analysis unit can provide simple and intuitive results. By adjusting the level of detail in the analysis results according to the user's emotions, the readability of the results is improved. 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-described processes in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0132] The suggestion unit can estimate the user's emotions and customize the suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions, allowing the user to consider more options. If the user is in a hurry, the suggestion unit can provide concise suggestions that get straight to the point. Furthermore, if the user is stressed, the suggestion unit can provide simple and intuitive suggestions. By customizing the suggestions according to the user's emotions, the likelihood of the suggestions being accepted is increased. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using or without generative AI. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0133] The bookmark generation unit can estimate the user's emotions and dynamically change the bookmark content based on the estimated emotions. For example, if the user is relaxed, the bookmark generation unit can provide a bookmark with detailed information, allowing the user to better understand the travel destination. If the user is in a hurry, the bookmark generation unit can provide a concise bookmark that gets straight to the point. Furthermore, if the user is stressed, the bookmark generation unit can provide a simple and intuitive bookmark. By dynamically changing the bookmark content according to the user's emotions, the bookmark's readability is improved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the bookmark generation unit may be performed using a generative AI or not. For example, the bookmark generation unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0134] The navigation unit can estimate the user's emotions and dynamically change the navigation method based on the estimated emotions. For example, if the user is relaxed, the navigation unit can provide detailed navigation instructions, allowing the user to enjoy the process of reaching their destination. If the user is in a hurry, the navigation unit can provide concise and to-the-point navigation instructions. Furthermore, if the user is stressed, the navigation unit can provide simple and intuitive navigation instructions. This dynamically changes the navigation method according to the user's emotions, improving the readability of the navigation. 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-described processes in the navigation unit may be performed using or without a generative AI. For example, the navigation unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

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

[0136] Step 1: The reception desk enters the user's preferences. Users can enter their desired area, type of sightseeing, date and time, number of people, etc., using free text. For example, a user might enter information such as "Family trip to Tokyo, 3 days, 5 people." Step 2: The analysis unit analyzes the information entered by the reception unit. The analysis unit uses a generation AI to analyze the information entered based on the user's preferences. For example, the analysis unit compares data from various travel agencies based on the information entered by the user and proposes the optimal itinerary. Step 3: The suggestion unit proposes the optimal itinerary based on the information analyzed by the analysis unit. The suggestion unit uses generative AI to propose a personalized itinerary tailored to the user's preferences. For example, if the user enters "Family trip to Tokyo, 3 days, 5 people," the suggestion unit will analyze information such as tourist spots, accommodations, and transportation in Tokyo and propose the optimal itinerary. Step 4: The itinerary generation unit compiles the travel itinerary proposed by the proposal unit. The itinerary generation unit uses a generation AI to generate an original travel itinerary that compiles the travel itinerary. For example, based on the information entered by the user, the itinerary generation unit proposes a specific itinerary such as Tokyo Tower and Senso-ji Temple on day 1, Disneyland on day 2, and Odaiba on day 3. Step 5: The guide unit provides a tour guide based on the bookmarks generated by the bookmark generation unit. The guide unit uses a generation AI to provide a unique tour guide tailored to the age and knowledge level of the target audience. For example, the guide unit can provide easy-to-understand explanations for children and detailed explanations of history and culture for adults. Step 6: 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 uses AI to display the optimal route from the user's current location to the tourist spot. For example, when the user searches for a route from their current location to a tourist spot, the navigation unit can display the optimal route using a map application and provide navigation.

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0139] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, bookmark generation unit, guide unit, and navigation 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 and takes the user's wishes as input. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the input information. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal itinerary. The bookmark generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an original travel bookmark. The guide unit is implemented by the control unit 46A of the smart device 14 and provides a tour guide. The navigation unit is implemented by the control unit 46A of the smart device 14 and provides navigation in cooperation with a map application. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0142] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, bookmark generation unit, guide unit, and navigation 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, where the user's wishes are input. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, where the input information is analyzed. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, where the optimal itinerary is proposed. The bookmark generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where an original travel bookmark is generated. The guide unit is implemented by the control unit 46A of the smart glasses 214, where a tour guide is provided. The navigation unit is implemented by the control unit 46A of the smart glasses 214, where navigation is performed in cooperation with a map application. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0158] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0164] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0165] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0166] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0167] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0168] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0169] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0170] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0171] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0172] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, bookmark generation unit, guide unit, and navigation 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, which inputs the user's wishes. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the input information. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes the optimal itinerary. The bookmark generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates an original travel bookmark. The guide unit is implemented by the control unit 46A of the headset terminal 314, which provides a tour guide. The navigation unit is implemented by the control unit 46A of the headset terminal 314, which provides navigation in cooperation with a map application. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0174] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0175] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0176] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0177] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0178] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0179] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0180] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0181] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0182] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0183] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0184] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0185] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0186] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0187] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0188] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0189] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, bookmark generation unit, guide unit, and navigation unit, is implemented by at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and takes the user's wishes as input. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the input information. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal itinerary. The bookmark generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an original travel bookmark. The guide unit is implemented by the control unit 46A of the robot 414 and provides a tour guide. The navigation unit is implemented by the control unit 46A of the robot 414 and performs navigation in cooperation with a map application. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0190] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0191] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0192] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0193] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0194] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0195] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0196] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0197] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0198] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0199] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0200] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0201] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0202] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0203] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0204] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0205] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0206] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0207] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0208] (Note 1) A reception area where users can input their preferences, An analysis unit analyzes the information input by the reception unit, A proposal unit proposes the optimal itinerary based on the information analyzed by the aforementioned analysis unit, A bookmark generation unit that compiles the travel itinerary proposed by the aforementioned proposal unit, A guide unit provides a tour guide based on the bookmark generated by the bookmark 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. (Note 2) The aforementioned navigation unit is, Displays the optimal route from the user's current location to tourist attractions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned guide portion is We provide unique tour guides tailored to the age and knowledge level of the target audience. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned bookmark generating unit is Generate a personalized travel itinerary based on the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We compare data from various travel agencies to propose the optimal itinerary. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, Analyze the information entered based on the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Users enter their desired area, type of sightseeing, date and time, number of people, etc., using free text input. The system described in Appendix 1, characterized by the features described herein. (Note 8) 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 9) The aforementioned reception unit is It analyzes the user's past input history and provides optimal input assistance features. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is Based on user input, relevant tourist information and event information are displayed in real time. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is It estimates the user's emotions and automatically adjusts the priority of input content based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is Provide local information relevant to the input content, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is Analyzes users' social media activity and suggests relevant inputs. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and dynamically adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, Analyze the user's past travel history to improve the accuracy of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, Based on user input, the system references relevant historical data to supplement the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, Local information is added to the analysis results, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, Analyze users' social media activity and add information relevant to the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 20) 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 21) The aforementioned proposal section is, Analyzing users' past travel history improves the accuracy of recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, Based on user input, relevant suggestions are updated in real time. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, It estimates the user's emotions and adjusts the priority of suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, Consider the user's geographical location and add local information to the suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, Analyze users' social media activity and add information relevant to the suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned bookmark 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 27) The aforementioned bookmark generating unit is Analyze the user's past travel history and customize the contents of the bookmarks. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned bookmark generating unit is Based on user input, relevant information is added to bookmarks. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned bookmark generating unit is It estimates the user's emotions and adjusts the priority of bookmarks based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned bookmark generating unit is Add local information to bookmarks, taking the user's geographical location into account. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned bookmark generating unit is Analyze users' social media activity and add information relevant to bookmarks. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned guide portion 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 33) The aforementioned guide portion is Analyze the user's past travel history to customize the guide content. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned guide portion is Based on user input, relevant guide information is updated in real time. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned guide portion is It estimates the user's emotions and adjusts the priority of the guide content based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned guide portion is Take the user's geographical location into account and add local information to the guide content. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned guide portion is Analyze users' social media activity and add information relevant to the guide content. The system described in Appendix 1, characterized by the features described herein. (Note 38) 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 39) The aforementioned navigation unit is, Analyze the user's past movement history to improve navigation accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned navigation unit is, Based on user input, relevant navigation information is updated in real time. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned navigation unit is, It estimates the user's emotions and adjusts navigation priorities based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned navigation unit is, Add local information to navigation content, taking the user's geographical location into account. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned navigation unit is, Analyze users' social media activity and add information relevant to their navigation. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0209] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where users can input their preferences, An analysis unit analyzes the information input by the reception unit, A proposal unit proposes the optimal itinerary based on the information analyzed by the aforementioned analysis unit, A bookmark generation unit that compiles the travel itinerary proposed by the aforementioned proposal unit, A guide unit provides a tour guide based on the bookmark generated by the bookmark 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. The aforementioned navigation unit is, Displays the optimal route from the user's current location to tourist attractions. The system according to feature 1.

3. The aforementioned guide portion is We provide unique tour guides tailored to the age and knowledge level of the target audience. The system according to feature 1.

4. The aforementioned bookmark generating unit is Generate a personalized travel itinerary based on the user's preferences. The system according to feature 1.

5. The aforementioned proposal section is, We compare data from various travel agencies to propose the optimal itinerary. The system according to feature 1.

6. The aforementioned analysis unit, Analyze the information entered based on the user's preferences. The system according to feature 1.

7. The aforementioned reception unit is Users enter their desired area, type of sightseeing, date and time, number of people, etc., using free text input. The system according to feature 1.

8. 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.

9. The aforementioned reception unit is It analyzes the user's past input history and provides optimal input assistance features. The system according to feature 1.

10. The aforementioned reception unit is Based on user input, relevant tourist information and event information are displayed in real time. The system according to feature 1.

Images