system

Abstract

The system according to this embodiment aims to analyze travelers' interests and suggest tourist spots and activities based on those interests. [Solution] The system according to this embodiment comprises an interest analysis unit, a suggestion unit, and an information provision unit. The interest analysis unit analyzes the traveler's interests. The suggestion unit suggests tourist spots and activities based on the interests analyzed by the interest analysis unit. The information provision unit provides information about the tourist spots and activities suggested by the suggestion 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 as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, proposals for tourist spots and activities based on the interests of travelers have not been sufficiently made, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the interests of travelers and propose tourist spots and activities based on them.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an interest analysis unit, a suggestion unit, and an information provision unit. The interest analysis unit analyzes the traveler's interests. The suggestion unit suggests tourist spots and activities based on the interests analyzed by the interest analysis unit. The information provision unit provides information about the tourist spots and activities suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze travelers' interests and suggest tourist spots and activities based on them. [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 numbered 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 applied 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) The travel suggestion system according to an embodiment of the present invention is a system that analyzes a traveler's interests and suggests sightseeing spots and activities based on those interests. This system collects the traveler's past travel history and current interests in order to analyze their interests. Next, the AI ​​analyzes the collected data to identify the traveler's interests. Furthermore, it suggests sightseeing spots and activities based on the identified interests. The AI ​​agent provides information in real time and offers interactive support using a chatbot. It also provides restaurant and activity reservation functions. This system is particularly ideal for travelers who find it difficult to plan their own trips, providing them with detailed information about their destinations and helping them choose places to visit. This allows travelers to have more meaningful experiences, and by quickly addressing problems and questions that arise during their trip, it provides peace of mind and leads to increased satisfaction. The introduction of the AI ​​agent will provide travelers with a groundbreaking experience and support the entire tourism industry. In the future, as AI technology evolves, it is expected that the quality of travel will further improve. For example, the travel suggestion system collects the traveler's past travel history and current interests in order to analyze their interests. Next, the AI ​​analyzes the collected data to identify the traveler's interests. Furthermore, it suggests sightseeing spots and activities based on the identified interests. The AI ​​agent provides real-time information and interactive support using a chatbot. It also offers restaurant and activity booking features. This system is particularly ideal for travelers who find it difficult to plan their own trips, providing detailed information about destinations and helping them choose places to visit. This allows travelers to have more meaningful experiences, and by quickly addressing problems and questions that arise during their trip, it provides peace of mind and leads to increased satisfaction. The introduction of the AI ​​agent will provide travelers with a groundbreaking experience and support the entire tourism industry. In the future, as AI technology evolves, the quality of travel is expected to improve even further. This will enable travel suggestion systems to analyze travelers' interests and suggest sightseeing spots and activities based on those interests.

[0029] The travel suggestion system according to this embodiment comprises an interest analysis unit, a suggestion unit, and an information provision unit. The interest analysis unit analyzes the traveler's interests. The interest analysis unit collects, for example, the traveler's past travel history and current interests. The interest analysis unit uses AI to analyze the collected data and identify the traveler's interests. The interest analysis unit can, for example, consider the traveler's past travel history. The interest analysis unit can also collect the traveler's current interests. The interest analysis unit can identify the traveler's interests using AI. The suggestion unit suggests tourist spots and activities based on the interests analyzed by the interest analysis unit. The suggestion unit suggests tourist spots and activities based on the identified interests. The suggestion unit can suggest tourist spots and activities using AI. The suggestion unit can suggest tourist spots and activities based on the traveler's interests. The information provision unit provides information about the tourist spots and activities suggested by the suggestion unit. The information provision unit provides information about the suggested tourist spots and activities. The information provision unit can provide information using AI. The information provision unit can also provide information in real time. Some or all of the processing described above in the information provision section may be performed using AI, for example, or without using AI. This allows the travel suggestion system according to the embodiment to analyze the traveler's interests, suggest tourist spots and activities based on those interests, and provide information.

[0030] The Interest Analysis Department analyzes travelers' interests. For example, it collects information on travelers' past travel history and current interests. Specifically, it collects data such as places travelers have visited in the past, activities they have participated in, and hotels they have stayed at, and uses this data to understand travelers' preferences. It also collects information on themes and destinations that travelers are currently interested in. For example, this includes tourist destinations that travelers have recently searched for, travel plans they have saved, and travel-related posts they have shared on social media. The Interest Analysis Department uses AI to analyze the collected data and identify travelers' interests. The AI ​​uses machine learning algorithms to analyze travelers' past behavior patterns and current interests and predict the tourist spots and activities that travelers will like. For example, if many travelers have visited beach resorts in the past, the AI ​​will determine that those travelers are likely to be interested in beach resorts again next time. It can also identify that travelers are interested in hiking and mountaineering based on information about mountainous areas that travelers have recently searched for. In this way, the Interest Analysis Department can comprehensively consider travelers' past travel history and current interests to accurately understand travelers' preferences. Furthermore, the interest analysis department can track changes in travelers' interests in real time and re-evaluate those interests based on the latest data. This allows for a quick response and optimal recommendations even if travelers' interests change.

[0031] The Proposal Department suggests tourist spots and activities based on the interests analyzed by the Interest Analysis Department. Specifically, it selects the most suitable tourist spots and activities based on the traveler's preferences identified by the Interest Analysis Department. The Proposal Department can also use AI to suggest tourist spots and activities. The AI ​​refers to a vast tourism database and searches for tourist spots and activities that match the traveler's interests. For example, if a traveler is interested in historical buildings, the AI ​​will suggest historical sites and museums in that area. Also, if a traveler prefers outdoor activities, it can suggest hiking trails and campsites. The Proposal Department can suggest tourist spots and activities based on the traveler's interests. Furthermore, the Proposal Department also considers the traveler's schedule, budget, and purpose of travel to make the best suggestions. For example, for short trips, it will suggest spots that can be visited efficiently, and if the budget is limited, it will suggest cost-effective activities. It can also make suggestions tailored to the purpose of travel, such as family trips or couple trips. In this way, the Proposal Department can provide customized suggestions that meet the individual needs of travelers, thereby increasing traveler satisfaction.

[0032] The Information Provision Department provides information on tourist attractions and activities suggested by the Proposal Department. Specifically, it provides travelers with detailed information on suggested tourist attractions and activities. The Information Provision Department can use AI to provide information. The AI ​​collects the latest information on suggested tourist attractions and activities and provides it to travelers. For example, it can provide information such as opening hours, admission fees, access methods, and information on nearby restaurants and accommodations. It can also provide detailed descriptions of activities, how to participate, and what to bring. The Information Provision Department can also provide information in real time. For example, it can provide real-time information on the crowd situation at tourist attractions, weather information, and traffic information to help travelers visit at the optimal time. Furthermore, the Information Provision Department can collect traveler feedback and continuously improve the accuracy and content of the information it provides. For example, it can collect ratings and reviews of tourist attractions visited and activities participated in by travelers and provide them as reference information to other travelers. In addition, the Information Provision Department can reliably transmit information using multiple communication methods. For example, it can provide information through smartphone apps, websites, email, SMS, etc., so that travelers can get the information they need anytime, anywhere. This allows the information department to provide travelers with timely and accurate information, supporting them in planning and executing their trips.

[0033] The history consideration unit can take into account past travel history. For example, the history consideration unit collects and considers a traveler's past travel history. The history consideration unit uses AI to analyze past travel history and identify the traveler's interests. By taking past travel history into account, the history consideration unit improves the accuracy of suggestions based on the traveler's interests. Some or all of the above processing in the history consideration unit may be performed using AI, for example, or without AI. For example, the history consideration unit can input past travel history into a generating AI and have the generating AI identify the traveler's interests. This improves the accuracy of suggestions based on the traveler's interests by taking past travel history into account.

[0034] The support department can provide interactive support using a chatbot. For example, the support department can provide real-time support to travelers using a chatbot. The support department can provide interactive support to travelers by utilizing a chatbot. The support department can control the chatbot using AI. The support department can answer travelers' questions in real time. Some or all of the above processes in the support department may be performed using AI, or not. For example, the support department can input the chatbot's responses into a generating AI and have the generating AI generate answers to travelers' questions. This allows the support department to provide real-time support to travelers by utilizing a chatbot.

[0035] The reservation department can provide restaurant and activity reservation functions. For example, the reservation department can provide restaurant and activity reservation functions. The reservation department can allow travelers to reserve their desired restaurants and activities. The reservation department can use AI to automate the reservation process. The reservation department can suggest the best reservations based on the traveler's preferences. Some or all of the above processes in the reservation department may be performed using AI, or not. For example, the reservation department can input the traveler's preferences into a generating AI and have the generating AI suggest the best reservations. This allows travelers to smoothly reserve restaurants and activities by providing a reservation function.

[0036] The information provision department can provide weather and event information in real time. For example, the information provision department can provide weather information in real time. The information provision department can also provide event information in real time. The information provision department can use AI to collect and provide weather and event information. The information provision department can provide travelers with the latest information. Some or all of the above processing in the information provision department may be performed using AI, for example, or without AI. For example, the information provision department can input weather and event information into a generating AI and have the generating AI perform real-time information provision. This allows travelers to obtain the latest information by providing weather and event information in real time.

[0037] The interest analysis unit can analyze a traveler's past travel history in detail and identify changes in their interests. For example, the interest analysis unit can analyze trends in tourist spots visited by travelers in the past and identify changes in their interests. The interest analysis unit can also analyze the types of activities travelers have participated in in the past and identify changes in their interests. The interest analysis unit can also analyze the types of accommodations travelers have used in the past and identify changes in their interests. By analyzing past travel history in detail, the interest analysis unit can identify changes in travelers' interests. Some or all of the above processing in the interest analysis unit may be performed using AI, for example, or not using AI. For example, the interest analysis unit can input past travel history data into a generating AI and have the generating AI perform the identification of changes in interests. This makes it possible to identify changes in travelers' interests by analyzing past travel history in detail.

[0038] The interest analysis unit can analyze a traveler's interests based on their current living situation and areas of interest. For example, the interest analysis unit can analyze a traveler's interests based on their current occupation and hobbies. The interest analysis unit can also analyze a traveler's interests based on their current family structure and lifestyle. The interest analysis unit can also analyze a traveler's interests based on their current health status and fitness level. By analyzing interests based on the traveler's current living situation and areas of interest, the interest analysis unit can make more appropriate suggestions. Some or all of the above processing in the interest analysis unit may be performed using AI, for example, or not using AI. For example, the interest analysis unit can input the traveler's current living situation data into a generating AI and have the generating AI perform the interest analysis. This makes it possible to make more appropriate suggestions by analyzing interests based on the current living situation and areas of interest.

[0039] The interest analysis unit can analyze travelers' interests while considering their geographical location. For example, the interest analysis unit can prioritize analyzing tourist attractions in the area where the traveler is currently located. It can also prioritize analyzing tourist attractions in areas the traveler plans to visit. The interest analysis unit can also analyze travelers' interests by referring to tourist attractions in areas the traveler has visited in the past. By considering geographical location when analyzing interests, the interest analysis unit can make more appropriate suggestions. Some or all of the above processes in the interest analysis unit may be performed using AI, for example, or without AI. For example, the interest analysis unit can input the traveler's geographical location into a generating AI and have the generating AI perform the interest analysis. This allows for more appropriate suggestions by considering geographical location when analyzing interests.

[0040] The Interest Analysis Department can analyze travelers' social media activity and identify their interest trends. For example, it can analyze photos and posts that travelers share on social media to identify their interest trends. It can also analyze accounts and groups that travelers follow to identify their interest trends. It can also analyze events and activities that travelers participate in on social media to identify their interest trends. By analyzing social media activity, the Interest Analysis Department can identify travelers' interest trends. Some or all of the above processes in the Interest Analysis Department may be performed using AI, for example, or not using AI. For example, the Interest Analysis Department can input travelers' social media data into a generating AI and have the generating AI perform the identification of interest trends. This allows the traveler's interest trends to be identified by analyzing their social media activity.

[0041] The suggestion function can adjust the level of detail in its suggestions based on the importance of the tourist attractions and activities. For example, the suggestion function can provide detailed information for highly important tourist attractions. For less important tourist attractions, it can provide concise information. For moderately important tourist attractions, it can provide information with a moderate level of detail. The suggestion function adjusts the level of detail in its suggestions based on the importance of the tourist attractions and activities. Some or all of the above processing in the suggestion function may be performed using AI, for example, or without AI. For example, the suggestion function can input importance data for tourist attractions and activities into a generating AI and have the generating AI perform the adjustment of the level of detail in its suggestions. This makes it possible to make more appropriate suggestions by adjusting the level of detail in suggestions based on the importance of tourist attractions and activities.

[0042] The proposal unit can apply different proposal algorithms depending on the category of tourist spot or activity when making proposals. For example, for natural tourist spots, the proposal unit can apply a proposal algorithm that emphasizes the beauty of nature. For cultural tourist spots, the proposal unit can also apply a proposal algorithm that emphasizes the history and cultural background. For activities, the proposal unit can also apply a proposal algorithm that emphasizes the participant's experience. The proposal unit applies different proposal algorithms depending on the category of tourist spot or activity. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input category data of tourist spots and activities into a generating AI and have the generating AI execute the application of proposal algorithms. This makes it possible to make more appropriate proposals by applying different proposal algorithms depending on the category of tourist spot or activity.

[0043] The proposal unit can determine the priority of proposals based on the availability period of tourist attractions and activities. For example, the proposal unit may prioritize proposals for seasonal tourist attractions. The proposal unit may also prioritize proposals for tourist attractions where specific events are held. The proposal unit may also prioritize proposals for activities that are only available for a limited time. The proposal unit determines the priority of proposals based on the availability period of tourist attractions and activities. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input data on the availability period of tourist attractions and activities into a generating AI and have the generating AI perform the determination of proposal priorities. This makes it possible to make more appropriate proposals by determining the priority of proposals based on the availability period of tourist attractions and activities.

[0044] The suggestion unit can adjust the order of suggestions based on the relevance of tourist spots and activities. For example, the suggestion unit may suggest nearby tourist spots consecutively. The suggestion unit may also suggest tourist spots of the same category consecutively. The suggestion unit may also suggest tourist spots related to the traveler's interests consecutively. The suggestion unit adjusts the order of suggestions based on the relevance of tourist spots and activities. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input relevance data of tourist spots and activities into a generating AI and have the generating AI perform the adjustment of the suggestion order. This makes it possible to make more appropriate suggestions by adjusting the order of suggestions based on the relevance of tourist spots and activities.

[0045] The information provision department can provide optimal information by referring to the traveler's past information usage history when providing information. For example, the information provision department can provide optimal information based on the information the traveler has used in the past. The information provision department can also prioritize providing information of interest to the traveler based on their past information usage history. The information provision department can also analyze the traveler's past information usage history and provide the most relevant information. The information provision department provides optimal information to the traveler by referring to their past information usage history. Some or all of the above processing in the information provision department may be performed using AI, for example, or without AI. For example, the information provision department can input the traveler's past information usage history data into a generating AI and have the generating AI perform the task of providing optimal information. This allows the information provision department to provide optimal information to the traveler by referring to their past information usage history.

[0046] The information provision department can customize the content of information based on the traveler's current situation when providing information. For example, the information provision department can prioritize providing information about the area where the traveler is currently located. The information provision department can also provide appropriate information based on the traveler's current weather. The information provision department can also provide optimal information based on the traveler's current time of day. The information provision department customizes the content of information based on the current situation. Some or all of the above processing in the information provision department may be performed using AI, for example, or not using AI. For example, the information provision department can input the traveler's current situation data into a generating AI and have the generating AI perform the information customization. This makes it possible to provide more appropriate information by customizing the content of information based on the traveler's current situation.

[0047] The information provision unit can provide optimal information by considering the traveler's geographical location when providing information. For example, the information provision unit can prioritize providing information on tourist spots in the area where the traveler is currently located. The information provision unit can also prioritize providing information on tourist spots in areas the traveler plans to visit. The information provision unit can also provide information by referring to information on tourist spots in areas the traveler has visited in the past. The information provision unit provides optimal information by considering geographical location. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without AI. For example, the information provision unit can input the traveler's geographical location into a generating AI and have the generating AI perform the task of providing optimal information. This makes it possible to provide useful information to travelers by providing optimal information by considering geographical location.

[0048] The information provision department can provide relevant information by analyzing the traveler's social media activity when providing information. For example, the information provision department can provide relevant information based on photos and posts shared by the traveler on social media. The information provision department can also provide relevant information based on accounts and groups followed by the traveler. The information provision department can also provide relevant information based on events and activities participated in by the traveler on social media. The information provision department analyzes social media activity to provide relevant information. Some or all of the above processing in the information provision department may be performed using AI, for example, or not using AI. For example, the information provision department can input the traveler's social media data into a generating AI and have the generating AI perform the provision of relevant information. This makes it possible to provide useful information to travelers by analyzing social media activity and providing relevant information.

[0049] The history consideration unit can select the optimal history by considering the traveler's geographical location information when considering history. For example, the history consideration unit may prioritize considering the traveler's past travel history in the area where the traveler is currently located. The history consideration unit may also prioritize considering the traveler's past travel history in areas the traveler plans to visit. The history consideration unit may also select the optimal history by referring to the travel history of areas the traveler has visited in the past. The history consideration unit selects the optimal history by considering geographical location information. Some or all of the above processing in the history consideration unit may be performed using AI, for example, or without using AI. For example, the history consideration unit can input the traveler's geographical location information into a generating AI and have the generating AI perform the selection of the optimal history. This makes it possible to perform more appropriate history consideration by selecting the optimal history by considering geographical location information.

[0050] The support department can provide optimal support by referring to the traveler's past support history during support sessions. For example, the support department can provide optimal support based on the content of support the traveler has received in the past. The support department can also prioritize providing support that the traveler is interested in based on their past support history. The support department can also analyze the traveler's past support history and provide the most relevant support. By referring to past support history, the support department can provide optimal support to the traveler. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input the traveler's past support history data into a generating AI and have the generating AI perform the task of providing optimal support. This allows the support department to provide optimal support to the traveler by referring to past support history.

[0051] The support unit can provide optimal support by considering the traveler's device information during support. For example, if the traveler is using a smartphone, the support unit will provide support tailored to the screen size. If the traveler is using a tablet, the support unit can also provide support optimized for a larger screen. If the traveler is using a smartwatch, the support unit can also provide concise and highly visible support. The support unit provides optimal support by considering device information. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the traveler's device information into a generating AI and have the generating AI perform the task of providing optimal support. This makes it possible to provide support that is beneficial to the traveler by considering device information and providing optimal support.

[0052] The booking department can select the optimal booking method by referring to the traveler's past booking history at the time of booking. For example, the booking department can select the optimal booking method based on the booking methods the traveler has used in the past. The booking department can also prioritize providing booking methods of interest based on the traveler's past booking history. The booking department can also analyze the traveler's past booking history and provide the most relevant booking method. By referring to past booking history, the booking department can provide the traveler with the optimal booking method. Some or all of the above processes in the booking department may be performed using AI, for example, or not using AI. For example, the booking department can input the traveler's past booking history data into a generating AI and have the generating AI perform the selection of the optimal booking method. This allows the booking department to provide the traveler with the optimal booking method by referring to past booking history.

[0053] The booking department can select the optimal booking method at the time of booking, taking into account the traveler's geographical location. For example, the booking department may prioritize providing booking methods for the area the traveler is currently in. The booking department may also prioritize providing booking methods for areas the traveler plans to visit. The booking department may also select the optimal booking method by referring to booking methods for areas the traveler has visited in the past. The booking department selects the optimal booking method by taking geographical location into consideration. Some or all of the above processes in the booking department may be performed using AI, for example, or not using AI. For example, the booking department can input the traveler's geographical location into a generating AI and have the generating AI select the optimal booking method. This makes it possible to make bookings that are beneficial to travelers by selecting the optimal booking method while taking geographical location into consideration.

[0054] The information provision unit can provide optimal information by referring to the traveler's past information usage history when providing real-time information. For example, the information provision unit can provide optimal information based on the information the traveler has used in the past. The information provision unit can also prioritize providing information of interest to the traveler based on their past information usage history. The information provision unit can also analyze the traveler's past information usage history and provide the most relevant information. By referring to past information usage history, the information provision unit can provide travelers with the most suitable real-time information. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without AI. For example, the information provision unit can input the traveler's past information usage history data into a generating AI and have the generating AI perform the task of providing optimal information. This allows the information provision unit to provide travelers with the most suitable real-time information by referring to their past information usage history.

[0055] The information provision unit can provide optimal information by considering the traveler's geographical location when providing real-time information. For example, the information provision unit can prioritize providing real-time information for the area the traveler is currently in. The information provision unit can also prioritize providing real-time information for areas the traveler plans to visit. The information provision unit can also provide information by referring to real-time information for areas the traveler has visited in the past. The information provision unit provides optimal real-time information by considering geographical location. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without AI. For example, the information provision unit can input the traveler's geographical location information into a generating AI and have the generating AI perform the task of providing optimal information. This makes it possible to provide useful information to travelers by providing optimal real-time information by considering geographical location.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The travel suggestion system can also make suggestions that take into account the traveler's health condition. For example, it can suggest active activities such as hiking and cycling to travelers in good health. Conversely, it can suggest relaxing options such as hot springs and spas to travelers in less-than-ideal health. Furthermore, it can also suggest meals based on health conditions; for example, it can suggest organic restaurants to health-conscious travelers. This allows for optimal suggestions tailored to the traveler's health needs.

[0058] The travel suggestion system can also take into account the traveler's hobbies and skills. For example, a traveler who enjoys photography can be suggested scenic spots and photogenic locations. A traveler who enjoys cooking can be suggested local cooking classes and food markets. Furthermore, a traveler who loves music can be suggested local music festivals and live music venues. This allows for optimal suggestions tailored to the traveler's interests and skills.

[0059] The travel suggestion system can also make suggestions considering the traveler's budget. For example, it can suggest free or low-cost attractions and activities to travelers with a limited budget, and luxury hotels and fine dining restaurants to travelers with a larger budget. Furthermore, it can suggest transportation options according to the budget; for instance, it can suggest public transport for those with a limited budget, and taxis or rental cars for those with a larger budget. This allows for optimal suggestions tailored to the traveler's budget.

[0060] The travel suggestion system can also make suggestions based on travelers' past reviews and ratings. For example, it can prioritize suggesting tourist spots and activities that travelers have given high ratings to in the past. Conversely, it can exclude places that travelers have given low ratings to. Furthermore, it can analyze the content of reviews written by travelers in the past and make suggestions based on their preferences and interests. This makes it possible to make optimal suggestions based on travelers' past reviews and ratings.

[0061] The travel suggestion system can also provide suggestions that take into account the traveler's mode of transportation. For example, it can suggest driving routes and parking information for travelers traveling by car. For travelers using public transport, it can suggest tourist attractions and activities close to train stations and bus stops. Furthermore, for travelers who prefer walking, it can suggest easy walking routes and sightseeing trails. This allows for optimal suggestions tailored to the traveler's mode of transport.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The Interest Analysis Department analyzes the traveler's interests. The Interest Analysis Department collects the traveler's past travel history and current interests, and identifies the traveler's interests by analyzing them using AI. Step 2: The suggestion unit proposes tourist spots and activities based on the interests analyzed by the interest analysis unit. The suggestion unit can use AI to propose tourist spots and activities based on identified interests. Step 3: The Information Provision Department provides information on the tourist spots and activities suggested by the Proposal Department. The Information Provision Department provides information on the suggested tourist spots and activities, and can also provide information in real time using AI.

[0064] (Example of form 2) The travel suggestion system according to an embodiment of the present invention is a system that analyzes a traveler's interests and suggests sightseeing spots and activities based on those interests. This system collects the traveler's past travel history and current interests in order to analyze their interests. Next, the AI ​​analyzes the collected data to identify the traveler's interests. Furthermore, it suggests sightseeing spots and activities based on the identified interests. The AI ​​agent provides information in real time and offers interactive support using a chatbot. It also provides restaurant and activity reservation functions. This system is particularly ideal for travelers who find it difficult to plan their own trips, providing them with detailed information about their destinations and helping them choose places to visit. This allows travelers to have more meaningful experiences, and by quickly addressing problems and questions that arise during their trip, it provides peace of mind and leads to increased satisfaction. The introduction of the AI ​​agent will provide travelers with a groundbreaking experience and support the entire tourism industry. In the future, as AI technology evolves, it is expected that the quality of travel will further improve. For example, the travel suggestion system collects the traveler's past travel history and current interests in order to analyze their interests. Next, the AI ​​analyzes the collected data to identify the traveler's interests. Furthermore, it suggests sightseeing spots and activities based on the identified interests. The AI ​​agent provides real-time information and interactive support using a chatbot. It also offers restaurant and activity booking features. This system is particularly ideal for travelers who find it difficult to plan their own trips, providing detailed information about destinations and helping them choose places to visit. This allows travelers to have more meaningful experiences, and by quickly addressing problems and questions that arise during their trip, it provides peace of mind and leads to increased satisfaction. The introduction of the AI ​​agent will provide travelers with a groundbreaking experience and support the entire tourism industry. In the future, as AI technology evolves, the quality of travel is expected to improve even further. This will enable travel suggestion systems to analyze travelers' interests and suggest sightseeing spots and activities based on those interests.

[0065] The travel suggestion system according to this embodiment comprises an interest analysis unit, a suggestion unit, and an information provision unit. The interest analysis unit analyzes the traveler's interests. The interest analysis unit collects, for example, the traveler's past travel history and current interests. The interest analysis unit uses AI to analyze the collected data and identify the traveler's interests. The interest analysis unit can, for example, consider the traveler's past travel history. The interest analysis unit can also collect the traveler's current interests. The interest analysis unit can identify the traveler's interests using AI. The suggestion unit suggests tourist spots and activities based on the interests analyzed by the interest analysis unit. The suggestion unit suggests tourist spots and activities based on the identified interests. The suggestion unit can suggest tourist spots and activities using AI. The suggestion unit can suggest tourist spots and activities based on the traveler's interests. The information provision unit provides information about the tourist spots and activities suggested by the suggestion unit. The information provision unit provides information about the suggested tourist spots and activities. The information provision unit can provide information using AI. The information provision unit can also provide information in real time. Some or all of the processing described above in the information provision section may be performed using AI, for example, or without using AI. This allows the travel suggestion system according to the embodiment to analyze the traveler's interests, suggest tourist spots and activities based on those interests, and provide information.

[0066] The Interest Analysis Department analyzes travelers' interests. For example, it collects information on travelers' past travel history and current interests. Specifically, it collects data such as places travelers have visited in the past, activities they have participated in, and hotels they have stayed at, and uses this data to understand travelers' preferences. It also collects information on themes and destinations that travelers are currently interested in. For example, this includes tourist destinations that travelers have recently searched for, travel plans they have saved, and travel-related posts they have shared on social media. The Interest Analysis Department uses AI to analyze the collected data and identify travelers' interests. The AI ​​uses machine learning algorithms to analyze travelers' past behavior patterns and current interests and predict the tourist spots and activities that travelers will like. For example, if many travelers have visited beach resorts in the past, the AI ​​will determine that those travelers are likely to be interested in beach resorts again next time. It can also identify that travelers are interested in hiking and mountaineering based on information about mountainous areas that travelers have recently searched for. In this way, the Interest Analysis Department can comprehensively consider travelers' past travel history and current interests to accurately understand travelers' preferences. Furthermore, the interest analysis department can track changes in travelers' interests in real time and re-evaluate those interests based on the latest data. This allows for a quick response and optimal recommendations even if travelers' interests change.

[0067] The Proposal Department suggests tourist spots and activities based on the interests analyzed by the Interest Analysis Department. Specifically, it selects the most suitable tourist spots and activities based on the traveler's preferences identified by the Interest Analysis Department. The Proposal Department can also use AI to suggest tourist spots and activities. The AI ​​refers to a vast tourism database and searches for tourist spots and activities that match the traveler's interests. For example, if a traveler is interested in historical buildings, the AI ​​will suggest historical sites and museums in that area. Also, if a traveler prefers outdoor activities, it can suggest hiking trails and campsites. The Proposal Department can suggest tourist spots and activities based on the traveler's interests. Furthermore, the Proposal Department also considers the traveler's schedule, budget, and purpose of travel to make the best suggestions. For example, for short trips, it will suggest spots that can be visited efficiently, and if the budget is limited, it will suggest cost-effective activities. It can also make suggestions tailored to the purpose of travel, such as family trips or couple trips. In this way, the Proposal Department can provide customized suggestions that meet the individual needs of travelers, thereby increasing traveler satisfaction.

[0068] The Information Provision Department provides information on tourist attractions and activities suggested by the Proposal Department. Specifically, it provides travelers with detailed information on suggested tourist attractions and activities. The Information Provision Department can use AI to provide information. The AI ​​collects the latest information on suggested tourist attractions and activities and provides it to travelers. For example, it can provide information such as opening hours, admission fees, access methods, and information on nearby restaurants and accommodations. It can also provide detailed descriptions of activities, how to participate, and what to bring. The Information Provision Department can also provide information in real time. For example, it can provide real-time information on the crowd situation at tourist attractions, weather information, and traffic information to help travelers visit at the optimal time. Furthermore, the Information Provision Department can collect traveler feedback and continuously improve the accuracy and content of the information it provides. For example, it can collect ratings and reviews of tourist attractions visited and activities participated in by travelers and provide them as reference information to other travelers. In addition, the Information Provision Department can reliably transmit information using multiple communication methods. For example, it can provide information through smartphone apps, websites, email, SMS, etc., so that travelers can get the information they need anytime, anywhere. This allows the information department to provide travelers with timely and accurate information, supporting them in planning and executing their trips.

[0069] The history consideration unit can take into account past travel history. For example, the history consideration unit collects and considers a traveler's past travel history. The history consideration unit uses AI to analyze past travel history and identify the traveler's interests. By taking past travel history into account, the history consideration unit improves the accuracy of suggestions based on the traveler's interests. Some or all of the above processing in the history consideration unit may be performed using AI, for example, or without AI. For example, the history consideration unit can input past travel history into a generating AI and have the generating AI identify the traveler's interests. This improves the accuracy of suggestions based on the traveler's interests by taking past travel history into account.

[0070] The support department can provide interactive support using a chatbot. For example, the support department can provide real-time support to travelers using a chatbot. The support department can provide interactive support to travelers by utilizing a chatbot. The support department can control the chatbot using AI. The support department can answer travelers' questions in real time. Some or all of the above processes in the support department may be performed using AI, or not. For example, the support department can input the chatbot's responses into a generating AI and have the generating AI generate answers to travelers' questions. This allows the support department to provide real-time support to travelers by utilizing a chatbot.

[0071] The reservation department can provide restaurant and activity reservation functions. For example, the reservation department can provide restaurant and activity reservation functions. The reservation department can allow travelers to reserve their desired restaurants and activities. The reservation department can use AI to automate the reservation process. The reservation department can suggest the best reservations based on the traveler's preferences. Some or all of the above processes in the reservation department may be performed using AI, or not. For example, the reservation department can input the traveler's preferences into a generating AI and have the generating AI suggest the best reservations. This allows travelers to smoothly reserve restaurants and activities by providing a reservation function.

[0072] The information provision department can provide weather and event information in real time. For example, the information provision department can provide weather information in real time. The information provision department can also provide event information in real time. The information provision department can use AI to collect and provide weather and event information. The information provision department can provide travelers with the latest information. Some or all of the above processing in the information provision department may be performed using AI, for example, or without AI. For example, the information provision department can input weather and event information into a generating AI and have the generating AI perform real-time information provision. This allows travelers to obtain the latest information by providing weather and event information in real time.

[0073] The interest analysis unit can estimate the traveler's emotions and adjust the method of analyzing interests based on the estimated emotions. For example, the interest analysis unit estimates the traveler's emotions. The interest analysis unit adjusts the method of analyzing interests based on the estimated emotions. If the traveler is feeling stressed, the interest analysis unit can prioritize analyzing relaxing tourist spots. If the traveler is excited, the interest analysis unit can also prioritize analyzing active activities. If the traveler is tired, the interest analysis unit can also prioritize analyzing relaxing activities. By adjusting the method of analyzing interests based on the traveler's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interest analysis unit may be performed using AI, for example, or not using AI. For example, the interest analysis unit can input traveler emotion data into a generative AI and have the generative AI perform emotion estimation.

[0074] The interest analysis unit can analyze a traveler's past travel history in detail and identify changes in their interests. For example, the interest analysis unit can analyze trends in tourist spots visited by travelers in the past and identify changes in their interests. The interest analysis unit can also analyze the types of activities travelers have participated in in the past and identify changes in their interests. The interest analysis unit can also analyze the types of accommodations travelers have used in the past and identify changes in their interests. By analyzing past travel history in detail, the interest analysis unit can identify changes in travelers' interests. Some or all of the above processing in the interest analysis unit may be performed using AI, for example, or not using AI. For example, the interest analysis unit can input past travel history data into a generating AI and have the generating AI perform the identification of changes in interests. This makes it possible to identify changes in travelers' interests by analyzing past travel history in detail.

[0075] The interest analysis unit can analyze a traveler's interests based on their current living situation and areas of interest. For example, the interest analysis unit can analyze a traveler's interests based on their current occupation and hobbies. The interest analysis unit can also analyze a traveler's interests based on their current family structure and lifestyle. The interest analysis unit can also analyze a traveler's interests based on their current health status and fitness level. By analyzing interests based on the traveler's current living situation and areas of interest, the interest analysis unit can make more appropriate suggestions. Some or all of the above processing in the interest analysis unit may be performed using AI, for example, or not using AI. For example, the interest analysis unit can input the traveler's current living situation data into a generating AI and have the generating AI perform the interest analysis. This makes it possible to make more appropriate suggestions by analyzing interests based on the current living situation and areas of interest.

[0076] The interest analysis unit can estimate the traveler's emotions and determine the priority of interests based on those estimated emotions. For example, if the traveler wants to relax, the interest analysis unit will prioritize suggesting relaxing tourist spots. If the traveler wants to be active, the interest analysis unit can also prioritize suggesting active activities. If the traveler is seeking a cultural experience, the interest analysis unit can also prioritize suggesting cultural tourist spots. This allows for more appropriate suggestions by prioritizing interests based on the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interest analysis unit may be performed using AI or not using AI. For example, the interest analysis unit can input traveler emotion data into a generative AI and have the generative AI perform emotion estimation.

[0077] The interest analysis unit can analyze travelers' interests while considering their geographical location. For example, the interest analysis unit can prioritize analyzing tourist attractions in the area where the traveler is currently located. It can also prioritize analyzing tourist attractions in areas the traveler plans to visit. The interest analysis unit can also analyze travelers' interests by referring to tourist attractions in areas the traveler has visited in the past. By considering geographical location when analyzing interests, the interest analysis unit can make more appropriate suggestions. Some or all of the above processes in the interest analysis unit may be performed using AI, for example, or without AI. For example, the interest analysis unit can input the traveler's geographical location into a generating AI and have the generating AI perform the interest analysis. This allows for more appropriate suggestions by considering geographical location when analyzing interests.

[0078] The Interest Analysis Department can analyze travelers' social media activity and identify their interest trends. For example, it can analyze photos and posts that travelers share on social media to identify their interest trends. It can also analyze accounts and groups that travelers follow to identify their interest trends. It can also analyze events and activities that travelers participate in on social media to identify their interest trends. By analyzing social media activity, the Interest Analysis Department can identify travelers' interest trends. Some or all of the above processes in the Interest Analysis Department may be performed using AI, for example, or not using AI. For example, the Interest Analysis Department can input travelers' social media data into a generating AI and have the generating AI perform the identification of interest trends. This allows the traveler's interest trends to be identified by analyzing their social media activity.

[0079] The suggestion unit can estimate the traveler's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the traveler is relaxed, the suggestion unit will present suggestions in a calm manner. If the traveler is excited, the suggestion unit may present suggestions in an energetic manner. If the traveler is feeling anxious, the suggestion unit may present suggestions in a reassuring manner. By adjusting the way suggestions are presented based on the traveler's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input traveler emotion data into a generative AI and have the generative AI perform emotion estimation.

[0080] The suggestion function can adjust the level of detail in its suggestions based on the importance of the tourist attractions and activities. For example, the suggestion function can provide detailed information for highly important tourist attractions. For less important tourist attractions, it can provide concise information. For moderately important tourist attractions, it can provide information with a moderate level of detail. The suggestion function adjusts the level of detail in its suggestions based on the importance of the tourist attractions and activities. Some or all of the above processing in the suggestion function may be performed using AI, for example, or without AI. For example, the suggestion function can input importance data for tourist attractions and activities into a generating AI and have the generating AI perform the adjustment of the level of detail in its suggestions. This makes it possible to make more appropriate suggestions by adjusting the level of detail in suggestions based on the importance of tourist attractions and activities.

[0081] The proposal unit can apply different proposal algorithms depending on the category of tourist spot or activity when making proposals. For example, for natural tourist spots, the proposal unit can apply a proposal algorithm that emphasizes the beauty of nature. For cultural tourist spots, the proposal unit can also apply a proposal algorithm that emphasizes the history and cultural background. For activities, the proposal unit can also apply a proposal algorithm that emphasizes the participant's experience. The proposal unit applies different proposal algorithms depending on the category of tourist spot or activity. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input category data of tourist spots and activities into a generating AI and have the generating AI execute the application of proposal algorithms. This makes it possible to make more appropriate proposals by applying different proposal algorithms depending on the category of tourist spot or activity.

[0082] The suggestion unit can estimate the traveler's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the traveler is in a hurry, the suggestion unit will make short, concise suggestions. If the traveler is relaxed, the suggestion unit can make detailed suggestions. If the traveler is excited, the suggestion unit can make energetic suggestions. By adjusting the length of suggestions based on the traveler's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input traveler emotion data into a generative AI and have the generative AI perform emotion estimation.

[0083] The proposal unit can determine the priority of proposals based on the availability period of tourist attractions and activities. For example, the proposal unit may prioritize proposals for seasonal tourist attractions. The proposal unit may also prioritize proposals for tourist attractions where specific events are held. The proposal unit may also prioritize proposals for activities that are only available for a limited time. The proposal unit determines the priority of proposals based on the availability period of tourist attractions and activities. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input data on the availability period of tourist attractions and activities into a generating AI and have the generating AI perform the determination of proposal priorities. This makes it possible to make more appropriate proposals by determining the priority of proposals based on the availability period of tourist attractions and activities.

[0084] The suggestion unit can adjust the order of suggestions based on the relevance of tourist spots and activities. For example, the suggestion unit may suggest nearby tourist spots consecutively. The suggestion unit may also suggest tourist spots of the same category consecutively. The suggestion unit may also suggest tourist spots related to the traveler's interests consecutively. The suggestion unit adjusts the order of suggestions based on the relevance of tourist spots and activities. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input relevance data of tourist spots and activities into a generating AI and have the generating AI perform the adjustment of the suggestion order. This makes it possible to make more appropriate suggestions by adjusting the order of suggestions based on the relevance of tourist spots and activities.

[0085] The information provider can estimate the traveler's emotions and adjust how information is displayed based on the estimated emotions. For example, if the traveler is relaxed, the information provider can provide a calm display method. If the traveler is excited, the information provider can also provide an energetic display method. If the traveler is feeling anxious, the information provider can also provide a reassuring display method. By adjusting how information is displayed based on the traveler's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input traveler emotion data into a generative AI and have the generative AI perform emotion estimation.

[0086] The information provision department can provide optimal information by referring to the traveler's past information usage history when providing information. For example, the information provision department can provide optimal information based on the information the traveler has used in the past. The information provision department can also prioritize providing information of interest to the traveler based on their past information usage history. The information provision department can also analyze the traveler's past information usage history and provide the most relevant information. The information provision department provides optimal information to the traveler by referring to their past information usage history. Some or all of the above processing in the information provision department may be performed using AI, for example, or without AI. For example, the information provision department can input the traveler's past information usage history data into a generating AI and have the generating AI perform the task of providing optimal information. This allows the information provision department to provide optimal information to the traveler by referring to their past information usage history.

[0087] The information provision department can customize the content of information based on the traveler's current situation when providing information. For example, the information provision department can prioritize providing information about the area where the traveler is currently located. The information provision department can also provide appropriate information based on the traveler's current weather. The information provision department can also provide optimal information based on the traveler's current time of day. The information provision department customizes the content of information based on the current situation. Some or all of the above processing in the information provision department may be performed using AI, for example, or not using AI. For example, the information provision department can input the traveler's current situation data into a generating AI and have the generating AI perform the information customization. This makes it possible to provide more appropriate information by customizing the content of information based on the traveler's current situation.

[0088] The information provision unit can estimate the traveler's emotions and prioritize information based on those estimated emotions. For example, if the traveler is relaxed, the information provision unit will prioritize providing relaxing information. If the traveler is excited, the information provision unit may also prioritize providing energetic information. If the traveler is feeling anxious, the information provision unit may also prioritize providing reassuring information. By prioritizing information based on the traveler's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provision unit may be performed using AI, or not using AI. For example, the information provision unit can input traveler emotion data into a generative AI and have the generative AI perform emotion estimation.

[0089] The information provision unit can provide optimal information by considering the traveler's geographical location when providing information. For example, the information provision unit can prioritize providing information on tourist spots in the area where the traveler is currently located. The information provision unit can also prioritize providing information on tourist spots in areas the traveler plans to visit. The information provision unit can also provide information by referring to information on tourist spots in areas the traveler has visited in the past. The information provision unit provides optimal information by considering geographical location. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without AI. For example, the information provision unit can input the traveler's geographical location into a generating AI and have the generating AI perform the task of providing optimal information. This makes it possible to provide useful information to travelers by providing optimal information by considering geographical location.

[0090] The information provision department can provide relevant information by analyzing the traveler's social media activity when providing information. For example, the information provision department can provide relevant information based on photos and posts shared by the traveler on social media. The information provision department can also provide relevant information based on accounts and groups followed by the traveler. The information provision department can also provide relevant information based on events and activities participated in by the traveler on social media. The information provision department analyzes social media activity to provide relevant information. Some or all of the above processing in the information provision department may be performed using AI, for example, or not using AI. For example, the information provision department can input the traveler's social media data into a generating AI and have the generating AI perform the provision of relevant information. This makes it possible to provide useful information to travelers by analyzing social media activity and providing relevant information.

[0091] The history consideration unit can estimate the traveler's emotions and adjust the history consideration method based on the estimated emotions. For example, if the traveler is relaxed, the history consideration unit may prioritize past travel history that promotes relaxation. If the traveler is excited, the history consideration unit may also prioritize past travel history that promotes activity. If the traveler is feeling anxious, the history consideration unit may also prioritize past travel history that provides a sense of security. This allows for more appropriate history consideration by adjusting the history consideration method based on the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the history consideration unit may be performed using AI or not using AI. For example, the history consideration unit can input the traveler's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0092] The history consideration unit can estimate the traveler's emotions and determine the priority of travel history based on the estimated emotions. For example, if the traveler is relaxed, the history consideration unit will prioritize past travel history that promotes relaxation. If the traveler is excited, the history consideration unit may also prioritize past travel history that promotes activity. If the traveler is feeling anxious, the history consideration unit may also prioritize past travel history that provides a sense of security. This allows for more appropriate history consideration by determining the priority of travel history based on the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the history consideration unit may be performed using AI or not using AI. For example, the history consideration unit can input traveler emotion data into a generative AI and have the generative AI perform emotion estimation.

[0093] The history consideration unit can select the optimal history by considering the traveler's geographical location information when considering history. For example, the history consideration unit may prioritize considering the traveler's past travel history in the area where the traveler is currently located. The history consideration unit may also prioritize considering the traveler's past travel history in areas the traveler plans to visit. The history consideration unit may also select the optimal history by referring to the travel history of areas the traveler has visited in the past. The history consideration unit selects the optimal history by considering geographical location information. Some or all of the above processing in the history consideration unit may be performed using AI, for example, or without using AI. For example, the history consideration unit can input the traveler's geographical location information into a generating AI and have the generating AI perform the selection of the optimal history. This makes it possible to perform more appropriate history consideration by selecting the optimal history by considering geographical location information.

[0094] The support unit can estimate the traveler's emotions and adjust its support methods based on the estimated emotions. For example, if the traveler is relaxed, the support unit can provide gentle support. If the traveler is excited, the support unit can also provide energetic support. If the traveler is feeling anxious, the support unit can also provide reassuring support. By adjusting the support methods based on the traveler's emotions, more appropriate support becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the traveler's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0095] The support department can provide optimal support by referring to the traveler's past support history during support sessions. For example, the support department can provide optimal support based on the content of support the traveler has received in the past. The support department can also prioritize providing support that the traveler is interested in based on their past support history. The support department can also analyze the traveler's past support history and provide the most relevant support. By referring to past support history, the support department can provide optimal support to the traveler. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input the traveler's past support history data into a generating AI and have the generating AI perform the task of providing optimal support. This allows the support department to provide optimal support to the traveler by referring to past support history.

[0096] The support unit can estimate the traveler's emotions and prioritize support based on those emotions. For example, if the traveler is relaxed, the support unit will prioritize providing relaxing support. If the traveler is excited, the support unit may also prioritize providing energetic support. If the traveler is feeling anxious, the support unit may also prioritize providing reassuring support. By prioritizing support based on the traveler's emotions, more appropriate support becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, or not using AI. For example, the support unit can input traveler emotion data into a generative AI and have the generative AI perform emotion estimation.

[0097] The support unit can provide optimal support by considering the traveler's device information during support. For example, if the traveler is using a smartphone, the support unit will provide support tailored to the screen size. If the traveler is using a tablet, the support unit can also provide support optimized for a larger screen. If the traveler is using a smartwatch, the support unit can also provide concise and highly visible support. The support unit provides optimal support by considering device information. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the traveler's device information into a generating AI and have the generating AI perform the task of providing optimal support. This makes it possible to provide support that is beneficial to the traveler by considering device information and providing optimal support.

[0098] The booking system can estimate the traveler's emotions and adjust the booking method based on the estimated emotions. For example, if the traveler is relaxed, the booking system can provide a calm booking method. If the traveler is excited, the booking system can also provide an energetic booking method. If the traveler is feeling anxious, the booking system can also provide a reassuring booking method. By adjusting the booking method based on the traveler's emotions, more appropriate bookings can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the booking system may be performed using AI or not using AI. For example, the booking system can input traveler emotion data into a generative AI and have the generative AI perform emotion estimation.

[0099] The booking department can select the optimal booking method by referring to the traveler's past booking history at the time of booking. For example, the booking department can select the optimal booking method based on the booking methods the traveler has used in the past. The booking department can also prioritize providing booking methods of interest based on the traveler's past booking history. The booking department can also analyze the traveler's past booking history and provide the most relevant booking method. By referring to past booking history, the booking department can provide the traveler with the optimal booking method. Some or all of the above processes in the booking department may be performed using AI, for example, or not using AI. For example, the booking department can input the traveler's past booking history data into a generating AI and have the generating AI perform the selection of the optimal booking method. This allows the booking department to provide the traveler with the optimal booking method by referring to past booking history.

[0100] The booking system can estimate a traveler's emotions and prioritize bookings based on those emotions. For example, if a traveler is relaxed, the system will prioritize bookings that promote relaxation. If a traveler is excited, the system may also prioritize bookings that promote energy. If a traveler is feeling anxious, the system may also prioritize bookings that provide a sense of security. This allows for more appropriate bookings by prioritizing bookings based on the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the booking system may be performed using AI or not. For example, the booking system can input traveler emotion data into a generative AI and have the generative AI perform emotion estimation.

[0101] The booking department can select the optimal booking method at the time of booking, taking into account the traveler's geographical location. For example, the booking department may prioritize providing booking methods for the area the traveler is currently in. The booking department may also prioritize providing booking methods for areas the traveler plans to visit. The booking department may also select the optimal booking method by referring to booking methods for areas the traveler has visited in the past. The booking department selects the optimal booking method by taking geographical location into consideration. Some or all of the above processes in the booking department may be performed using AI, for example, or not using AI. For example, the booking department can input the traveler's geographical location into a generating AI and have the generating AI select the optimal booking method. This makes it possible to make bookings that are beneficial to travelers by selecting the optimal booking method while taking geographical location into consideration.

[0102] The information provision unit can estimate the traveler's emotions and adjust the display method of real-time information based on the estimated emotions. For example, if the traveler is relaxed, the information provision unit can provide a calm display method. If the traveler is excited, the information provision unit can also provide an energetic display method. If the traveler is feeling anxious, the information provision unit can also provide a reassuring display method. By adjusting the display method of real-time information based on the traveler's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provision unit may be performed using AI, for example, or not using AI. For example, the information provision unit can input traveler emotion data into a generative AI and have the generative AI perform emotion estimation.

[0103] The information provision unit can provide optimal information by referring to the traveler's past information usage history when providing real-time information. For example, the information provision unit can provide optimal information based on the information the traveler has used in the past. The information provision unit can also prioritize providing information of interest to the traveler based on their past information usage history. The information provision unit can also analyze the traveler's past information usage history and provide the most relevant information. By referring to past information usage history, the information provision unit can provide travelers with the most suitable real-time information. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without AI. For example, the information provision unit can input the traveler's past information usage history data into a generating AI and have the generating AI perform the task of providing optimal information. This allows the information provision unit to provide travelers with the most suitable real-time information by referring to their past information usage history.

[0104] The information provision unit can estimate the traveler's emotions and prioritize real-time information based on the estimated emotions. For example, if the traveler is relaxed, the information provision unit will prioritize providing relaxing information. If the traveler is excited, the information provision unit can also prioritize providing energetic information. If the traveler is feeling anxious, the information provision unit can also prioritize providing reassuring information. This allows for more appropriate information provision by prioritizing real-time information based on the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provision unit may be performed using AI, for example, or not using AI. For example, the information provision unit can input traveler emotion data into a generative AI and have the generative AI perform emotion estimation.

[0105] The information provision unit can provide optimal information by considering the traveler's geographical location when providing real-time information. For example, the information provision unit can prioritize providing real-time information for the area the traveler is currently in. The information provision unit can also prioritize providing real-time information for areas the traveler plans to visit. The information provision unit can also provide information by referring to real-time information for areas the traveler has visited in the past. The information provision unit provides optimal real-time information by considering geographical location. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without AI. For example, the information provision unit can input the traveler's geographical location information into a generating AI and have the generating AI perform the task of providing optimal information. This makes it possible to provide useful information to travelers by providing optimal real-time information by considering geographical location.

[0106] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0107] The travel suggestion system can also make suggestions that take into account the traveler's health condition. For example, it can suggest active activities such as hiking and cycling to travelers in good health. Conversely, it can suggest relaxing options such as hot springs and spas to travelers in less-than-ideal health. Furthermore, it can also suggest meals based on health conditions; for example, it can suggest organic restaurants to health-conscious travelers. This allows for optimal suggestions tailored to the traveler's health needs.

[0108] The travel suggestion system can also take into account the traveler's hobbies and skills. For example, a traveler who enjoys photography can be suggested scenic spots and photogenic locations. A traveler who enjoys cooking can be suggested local cooking classes and food markets. Furthermore, a traveler who loves music can be suggested local music festivals and live music venues. This allows for optimal suggestions tailored to the traveler's interests and skills.

[0109] The travel suggestion system can also make suggestions considering the traveler's budget. For example, it can suggest free or low-cost attractions and activities to travelers with a limited budget, and luxury hotels and fine dining restaurants to travelers with a larger budget. Furthermore, it can suggest transportation options according to the budget; for instance, it can suggest public transport for those with a limited budget, and taxis or rental cars for those with a larger budget. This allows for optimal suggestions tailored to the traveler's budget.

[0110] The travel suggestion system can also make suggestions based on travelers' past reviews and ratings. For example, it can prioritize suggesting tourist spots and activities that travelers have given high ratings to in the past. Conversely, it can exclude places that travelers have given low ratings to. Furthermore, it can analyze the content of reviews written by travelers in the past and make suggestions based on their preferences and interests. This makes it possible to make optimal suggestions based on travelers' past reviews and ratings.

[0111] The travel suggestion system can also provide suggestions that take into account the traveler's mode of transportation. For example, it can suggest driving routes and parking information for travelers traveling by car. For travelers using public transport, it can suggest tourist attractions and activities close to train stations and bus stops. Furthermore, for travelers who prefer walking, it can suggest easy walking routes and sightseeing trails. This allows for optimal suggestions tailored to the traveler's mode of transport.

[0112] The travel suggestion system can estimate the traveler's emotions and adjust the timing of suggestions based on those emotions. For example, if the traveler is relaxed, suggestions can be made at a slow pace. If the traveler is excited, suggestions can be made quickly. Furthermore, if the traveler is feeling anxious, suggestions can be made at a time that provides reassurance. By adjusting the timing of suggestions based on the traveler's emotions, more appropriate suggestions can be made.

[0113] The travel suggestion system can estimate the traveler's emotions and customize the suggestions based on those emotions. For example, if the traveler is relaxed, it can offer calming suggestions. If the traveler is excited, it can offer energetic suggestions. Furthermore, if the traveler is feeling anxious, it can offer reassuring suggestions. By customizing suggestions based on the traveler's emotions, the system can provide more appropriate recommendations.

[0114] The travel suggestion system can estimate the traveler's emotions and adjust the order of suggestions based on those emotions. For example, if the traveler is relaxed, relaxing attractions can be suggested first. If the traveler is excited, active activities can be suggested first. Furthermore, if the traveler is feeling anxious, attractions that provide a sense of security can be suggested first. By adjusting the order of suggestions based on the traveler's emotions, more appropriate suggestions can be made.

[0115] The travel suggestion system can estimate the traveler's emotions and adjust the frequency of suggestions based on those emotions. For example, if the traveler is relaxed, the frequency of suggestions can be reduced. Conversely, if the traveler is excited, the frequency of suggestions can be increased. Furthermore, if the traveler is feeling anxious, suggestions can be made at a frequency that provides reassurance. By adjusting the frequency of suggestions based on the traveler's emotions, more appropriate suggestions can be made.

[0116] The travel suggestion system can estimate the traveler's emotions and adjust the format of the suggestions based on those emotions. For example, if the traveler is relaxed, the suggestions can be presented in a calm format. If the traveler is excited, the suggestions can be presented in an energetic format. Furthermore, if the traveler is feeling anxious, the suggestions can be presented in a reassuring format. By adjusting the format of suggestions based on the traveler's emotions, more appropriate suggestions can be made.

[0117] The following briefly describes the processing flow for example form 2.

[0118] Step 1: The Interest Analysis Department analyzes the traveler's interests. The Interest Analysis Department collects the traveler's past travel history and current interests, and identifies the traveler's interests by analyzing them using AI. Step 2: The suggestion unit proposes tourist spots and activities based on the interests analyzed by the interest analysis unit. The suggestion unit can use AI to propose tourist spots and activities based on identified interests. Step 3: The Information Provision Department provides information on the tourist spots and activities suggested by the Proposal Department. The Information Provision Department provides information on the suggested tourist spots and activities, and can also provide information in real time using AI.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] Each of the multiple elements described above, including the interest analysis unit, suggestion unit, information provision unit, history consideration unit, support unit, and reservation unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the interest analysis unit is implemented by the control unit 46A of the smart device 14, which collects the traveler's past travel history and current interests and analyzes them using AI. The suggestion unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which suggests tourist spots and activities based on the interests identified by the interest analysis unit. The information provision unit is implemented, for example, by the control unit 46A of the smart device 14, which provides information on suggested tourist spots and activities in real time. The history consideration unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which improves the accuracy of suggestions by considering past travel history. The support unit is implemented, for example, by the control unit 46A of the smart device 14, which provides interactive support to travelers using a chatbot. The reservation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which provides reservation functions for restaurants and activities. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0123] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the interest analysis unit, suggestion unit, information provision unit, history consideration unit, support unit, and reservation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the interest analysis unit is implemented by the control unit 46A of the smart glasses 214, which collects the traveler's past travel history and current interests and analyzes them using AI. The suggestion unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which suggests tourist spots and activities based on the interests identified by the interest analysis unit. The information provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, which provides information on suggested tourist spots and activities in real time. The history consideration unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which improves the accuracy of suggestions by considering past travel history. The support unit is implemented, for example, by the control unit 46A of the smart glasses 214, which provides interactive support to travelers using a chatbot. The reservation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which provides reservation functions for restaurants and activities. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0139] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the interest analysis unit, suggestion unit, information provision unit, history consideration unit, support unit, and reservation unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the interest analysis unit is implemented by the control unit 46A of the headset terminal 314, which collects the traveler's past travel history and current interests and analyzes them using AI. The suggestion unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which suggests tourist spots and activities based on the interests identified by the interest analysis unit. The information provision unit is implemented, for example, by the control unit 46A of the headset terminal 314, which provides information on suggested tourist spots and activities in real time. The history consideration unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which improves the accuracy of suggestions by considering past travel history. The support unit is implemented, for example, by the control unit 46A of the headset terminal 314, which provides interactive support to travelers using a chatbot. The reservation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which provides reservation functions for restaurants and activities. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0155] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] Each of the multiple elements described above, including the interest analysis unit, suggestion unit, information provision unit, history consideration unit, support unit, and reservation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the interest analysis unit is implemented by the control unit 46A of the robot 414, which collects the traveler's past travel history and current interests and analyzes them using AI. The suggestion unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which suggests tourist spots and activities based on the interests identified by the interest analysis unit. The information provision unit is implemented by, for example, the control unit 46A of the robot 414, which provides information on suggested tourist spots and activities in real time. The history consideration unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which improves the accuracy of suggestions by considering past travel history. The support unit is implemented by, for example, the control unit 46A of the robot 414, which provides interactive support to the traveler using a chatbot. The reservation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which provides reservation functions for restaurants and activities. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] (Note 1) The Interest Analysis Department analyzes the interests of travelers, Based on the interests analyzed by the aforementioned interest analysis department, the proposal department suggests tourist spots and activities, The system includes an information provision unit that provides information on tourist spots and activities proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) Further equipped with a history-based consideration section that takes past travel history into account. The system described in Appendix 1, characterized by the features described herein. (Note 3) We will further enhance our support department by providing interactive support using chatbots. The system described in Appendix 1, characterized by the features described herein. (Note 4) It also includes a reservation department that provides restaurant and activity booking functions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned information provision unit, Provides real-time weather and event information. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned interest analysis department, We estimate travelers' emotions and adjust our interest analysis methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned interest analysis department, We analyze travelers' past travel history in detail to identify changes in their interests. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned interest analysis department, We analyze travelers' interests based on their current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned interest analysis department, It estimates the traveler's emotions and determines the priority of their interests based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned interest analysis department, Analyze travelers' interests while considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned interest analysis department, Analyze travelers' social media activity to identify their interests. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned proposal section is, We estimate the traveler's emotions and adjust the way the proposal is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the tourist attractions and activities. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the category of tourist attractions or activities. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, Estimate the traveler's sentiment and adjust the length of the suggestion based on the estimated traveler's sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When submitting proposals, prioritize them based on the availability of tourist attractions and activities. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of tourist spots and activities. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned information provision unit, We estimate the traveler's sentiment and adjust how information is displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned information provision unit, When providing information, we refer to the traveler's past information usage history to provide the most relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned information provision unit, When providing information, customize the content of the information based on the traveler's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned information provision unit, It estimates the sentiment of travelers and prioritizes information based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned information provision unit, When providing information, we will consider the traveler's geographical location to provide the most relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned information provision unit, When providing information, we analyze travelers' social media activity to provide relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The history consideration unit is, We estimate the traveler's sentiment and adjust how history is considered based on the estimated traveler's sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 25) The history consideration unit is, It estimates the traveler's sentiment and prioritizes history based on the estimated traveler's sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 26) The history consideration unit is, When considering travel history, the system selects the most suitable history based on the traveler's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned support unit is We estimate the traveler's emotions and adjust the support methods based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 28) The aforementioned support unit is When providing support, we refer to the traveler's past support history to provide the most suitable support. The system described in Appendix 3, characterized by the features described herein. (Note 29) The aforementioned support unit is The system estimates the traveler's emotions and determines support priorities based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 30) The aforementioned support unit is When providing support, we take into account the traveler's device information to offer the best possible support. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned reservation section is, We estimate travelers' sentiments and adjust booking methods based on those estimated sentiments. The system described in Appendix 4, characterized by the features described herein. (Note 32) The aforementioned reservation section is, When making a reservation, the system will refer to the traveler's past booking history to select the most suitable booking method. The system described in Appendix 4, characterized by the features described herein. (Note 33) The aforementioned reservation section is, It estimates the sentiment of travelers and determines booking priorities based on the estimated sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 34) The aforementioned reservation section is, When making a reservation, the system will select the most suitable booking method, taking into account the traveler's geographical location. The system described in Appendix 4, characterized by the features described herein. (Note 35) The aforementioned information provision unit, It estimates the sentiment of travelers and adjusts how real-time information is displayed based on the estimated sentiment. The system described in Appendix 5, characterized by the features described herein. (Note 36) The aforementioned information provision unit, When providing real-time information, we refer to the traveler's past information usage history to provide the most relevant information. The system described in Appendix 5, characterized by the features described herein. (Note 37) The aforementioned information provision unit, It estimates travelers' sentiments and prioritizes real-time information based on those estimated sentiments. The system described in Appendix 5, characterized by the features described herein. (Note 38) The aforementioned information provision unit, When providing real-time information, we will provide the most relevant information by taking into account the traveler's geographical location. The system described in Appendix 5, characterized by the features described herein. [Explanation of Symbols]

[0191] 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. The Interest Analysis Department analyzes the interests of travelers, Based on the interests analyzed by the aforementioned interest analysis department, the proposal department suggests tourist spots and activities, The system includes an information provision unit that provides information on tourist spots and activities proposed by the aforementioned proposal unit. A system characterized by the following features.

2. Further equipped with a history-based consideration section that takes past travel history into account. The system according to feature 1.

3. We will further enhance our support department by providing interactive support using chatbots. The system according to feature 1.

4. It also includes a reservation department that provides restaurant and activity booking functions. The system according to feature 1.

5. The aforementioned information provision unit, Provides real-time weather and event information. The system according to feature 1.

6. The aforementioned interest analysis department, We estimate travelers' emotions and adjust our interest analysis methods based on those estimated emotions. The system according to feature 1.

7. The aforementioned interest analysis department, We analyze travelers' past travel history in detail to identify changes in their interests. The system according to feature 1.

8. The aforementioned interest analysis department, We analyze travelers' interests based on their current living situation and areas of interest. The system according to feature 1.

Images