system

Abstract

The system according to this embodiment aims to integrate travel plans tailored to individual preferences and facilitate easy booking procedures. [Solution] The system according to the embodiment comprises a reception unit, a proposal unit, an arrangement unit, and a navigation unit. The reception unit receives the user's travel preferences. The proposal unit proposes the optimal travel plan based on the travel preferences entered by the reception unit. The arrangement unit makes all the necessary arrangements for transportation, accommodation, and destination reservations based on the travel plan proposed by the proposal unit. The navigation unit provides VR guidance for station transfers and walking routes based on the travel plan arranged by the arrangement unit.

Description

Technical Field

[0005] ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to provide an integrated service for a travel plan according to personal preferences, and the reservation procedure is complicated.

[0005] The system according to the embodiment aims to integrate a travel plan according to personal preferences and perform a reservation procedure simply.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a proposal unit, an arrangement unit, and a navigation unit. The reception unit receives the user's travel preferences. The proposal unit proposes the most suitable travel plan based on the travel preferences entered by the reception unit. The arrangement unit makes all necessary arrangements for transportation, accommodation, and destination reservations based on the travel plan proposed by the proposal unit. The navigation unit provides VR guidance for station transfers and walking routes based on the travel plan arranged by the arrangement unit. [Effects of the Invention]

[0007] The system according to this embodiment integrates travel plans tailored to individual preferences and allows for easy booking procedures. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards 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 planning system according to an embodiment of the present invention is a system that provides an integrated service from travel planning and arrangement to guidance. This travel planning system solves the problem that conventional tours offered by travel agencies are expensive, and that it is difficult to provide an integrated service tailored to individual preferences because transportation, accommodation, and travel destinations must all be booked separately. The travel planning system solves these problems and is expected to increase sales by expanding the range of services it can handle while being cheaper than conventional travel agency products. It also contributes to solving social issues such as the dispersion of travel dates and destinations depending on the booking situation. Specifically, it consists of the following steps. First, the user inputs their travel preferences. Next, the generating AI proposes the optimal travel plan based on the user's preferences and arranges transportation, accommodation, and travel destination reservations all at once. Furthermore, it provides integrated navigation functions such as VR guidance for station transfers and walking routes, offering a service experience that includes transportation. For example, if the user inputs "I would like to travel from Tokyo to Kyoto," the generating AI will propose the optimal means of transportation (such as Shinkansen or airplane) and make reservations for accommodation and tourist spots all at once. Furthermore, the system uses VR to guide users through train transfers and walking routes during their trip, ensuring they reach their destination without getting lost. This system allows users to plan, arrange, and receive guidance for their trip all in one place without any hassle. In addition, by promoting the distribution of travel dates and destinations based on booking availability, it helps alleviate congestion at tourist spots and contributes to solving social issues. As a result, the travel planning system can efficiently input, suggest, arrange, and guide users based on their travel preferences.

[0029] The travel planning system according to this embodiment comprises a reception unit, a proposal unit, an arrangement unit, and a navigation unit. The reception unit receives the user's travel preferences. These preferences include, but are not limited to, a destination, travel duration, and budget. For example, the user enters their travel preferences into a web form. The reception unit can also accept the user's travel preferences using voice input. For example, it can use speech recognition technology to convert the user's voice into text and input it as the travel preference. The reception unit can also automatically complete the travel preferences by referring to the user's past travel history. For example, it can suggest the next travel destination based on the destinations the user has visited in the past. The proposal unit uses a generation AI to propose the optimal travel plan based on the travel preferences entered by the reception unit. For example, the proposal unit uses a generation AI to analyze the user's travel preferences and generate the optimal travel plan. The proposal unit can also use a generation AI to suggest the optimal mode of transportation based on the user's travel preferences. For example, the generation AI compares modes of transportation such as bullet trains and airplanes and selects the best one. The proposal unit can also use generative AI to suggest the most suitable accommodation based on the user's travel preferences. For example, the generative AI compares accommodations such as hotels and inns and selects the best one. The booking unit makes all the necessary arrangements for transportation, accommodation, and destinations based on the travel plan proposed by the proposal unit. For example, the booking unit makes reservations for the proposed modes of transportation. The booking unit can also make reservations for the proposed accommodations. Furthermore, the booking unit can also make reservations for the proposed tourist destinations. For example, the booking unit accesses the transportation reservation system and makes reservations for the proposed modes of transportation. It accesses the accommodation reservation system and makes reservations for the proposed accommodations. It accesses the tourist destination reservation system and makes reservations for the proposed tourist destinations. The navigation unit provides VR guidance for station transfers and walking routes based on the travel plan arranged by the booking unit. For example, when the user is transferring at a station, the navigation unit uses VR technology to guide them on the transfer route. The navigation unit can also use VR technology to guide the user on walking routes when they are traveling on foot.For example, the navigation unit displays VR guidance on the user's smartphone, enabling the user to reach their destination without getting lost. This allows the travel planning system according to the embodiment to efficiently input, suggest, arrange, and guide the user based on their travel preferences.

[0030] The reception desk inputs the user's travel preferences. These preferences may include, but are not limited to, destination, duration, and budget. For example, the user may input their preferences into a web form. The reception desk can also accept user travel preferences using voice input. For example, speech recognition technology can be used to convert the user's speech into text and input it as travel preferences. Specifically, speech recognition technology analyzes the user's utterance in real time and converts it into text data using natural language processing technology. This text data is further analyzed within the system and extracted as specific preference information such as destination, duration, and budget. The reception desk can also automatically complete travel preferences by referring to the user's past travel history. For example, it can suggest the next destination based on the user's past travel destinations. In this case, the system refers to the user's past travel database and analyzes similar travel patterns and preferences. Furthermore, the reception desk can collect data from the user's social media accounts and other online activities to understand the user's interests and preferences. This allows the system to suggest travel plans that match the user's preferences without the user having to explicitly input them. For example, the system suggests future travel destinations and accommodations based on reviews of tourist spots and accommodations the user has visited in the past. Furthermore, the reception desk encrypts and stores user input data, implementing security measures to protect privacy. This allows users to use the system with peace of mind.

[0031] The Proposal Department uses a generative AI to propose the optimal travel plan based on the travel preferences entered by the Reception Department. For example, the generative AI analyzes the user's travel preferences and generates the optimal travel plan. Specifically, the generative AI analyzes the user's input data and generates the optimal plan by combining candidate destinations, transportation methods, accommodations, and sightseeing spots. The generative AI refers to past travel data and reviews and ratings from other users to propose a plan that suits the user's preferences. The Proposal Department can also use the generative AI to propose the optimal transportation method based on the user's travel preferences. For example, the generative AI compares transportation methods such as bullet trains and airplanes and selects the best one. The generative AI comprehensively evaluates the cost, travel time, convenience, etc., of the transportation method and proposes the most suitable transportation method for the user. The Proposal Department can also use the generative AI to propose the optimal accommodation based on the user's travel preferences. For example, the generative AI compares accommodations such as hotels and inns and selects the best one. The generative AI considers the accommodation's price, location, facilities, review ratings, etc., and proposes the accommodation that best matches the user's preferences. Furthermore, the suggestion department can use generative AI to propose optimal tourist spots and activities based on the user's travel preferences. For example, the generative AI can select the most suitable tourist spots based on their popularity, accessibility, and the user's interests. This allows the suggestion department to comprehensively propose the optimal travel plan based on the user's travel preferences.

[0032] The booking department handles the booking of transportation, accommodation, and destinations based on the travel plan proposed by the proposal department. For example, the booking department books the proposed transportation. Specifically, the booking department accesses the booking system of the transportation company and books the proposed transportation. For example, it connects to the online booking system of an airline or railway company, selects the user's desired date and time and seat, and completes the booking. The booking department can also book the proposed accommodation. The booking department accesses the booking system of the accommodation company and books the proposed accommodation. For example, it connects to the online booking system of a hotel, selects the user's desired room type and dates, and completes the booking. Furthermore, the booking department can also book the proposed tourist destinations. The booking department accesses the booking system of the tourist destination and books the proposed tourist destinations. For example, it connects to the online booking system of a tourist destination, selects the user's desired date and time and activity, and completes the booking. The booking department centrally manages this booking information and provides the user with booking confirmations and tickets. The booking department also handles changes and cancellations of bookings, and can flexibly respond to user requests. For example, if a user wants to change their travel plans, the booking department will quickly update the reservation details and provide the new reservation information. This allows the booking department to efficiently and reliably arrange the user's travel plans, reducing the burden on the user.

[0033] The navigation unit provides VR guidance for station transfers and walking routes based on the travel plan arranged by the booking unit. For example, when a user is transferring at a station, the navigation unit uses VR technology to guide them to the transfer route. Specifically, the navigation unit displays VR guidance on the user's smartphone or tablet, enabling the user to transfer without getting lost within the station. For example, it displays real-time video of the station through the smartphone's camera and overlays the transfer route, allowing the user to intuitively understand the route. The navigation unit can also use VR technology to guide users on walking routes. For example, the navigation unit identifies the user's current location using GPS and displays the optimal walking route to the destination using VR guidance. The user can then follow the route guidance displayed overlaid on the real-world scenery while looking at their smartphone screen. Furthermore, the navigation unit can update the route guidance in real time while the user is moving, providing the optimal route according to changes in traffic conditions and weather. For example, if traffic congestion or bad weather occurs, the navigation unit immediately calculates a new route and notifies the user. Furthermore, the navigation system can provide information on tourist attractions, restaurants, and other destinations according to the user's preferences. This allows the navigation system to support users in reaching their destinations without getting lost during their trip, providing a comfortable travel experience.

[0034] The suggestion unit can propose the optimal travel plan based on the user's travel preferences using a generative AI. For example, the suggestion unit can use the generative AI to analyze the user's travel preferences and generate the optimal travel plan. The suggestion unit can also use the generative AI to propose the optimal mode of transportation based on the user's travel preferences. For example, the generative AI can compare modes of transportation such as bullet trains and airplanes and select the best one. The suggestion unit can also use the generative AI to propose the optimal accommodation based on the user's travel preferences. For example, the generative AI can compare accommodations such as hotels and inns and select the best one. As a result, the accuracy of travel plan proposals is improved by using the generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the suggestion unit may be performed using the generative AI, or they may not be performed using the generative AI. For example, the suggestion unit can input the user's travel preferences into the generative AI and have the generative AI generate the optimal travel plan.

[0035] The booking unit can make reservations for transportation, accommodation, and travel destinations all at once. For example, the booking unit can make reservations for suggested transportation. It can also make reservations for suggested accommodations. Furthermore, it can also make reservations for suggested tourist destinations. For example, the booking unit can access the transportation reservation system and make reservations for suggested transportation. It can access the accommodation reservation system and make reservations for suggested accommodations. It can access the tourist destination reservation system and make reservations for suggested tourist destinations. This allows for bulk booking, saving the user time and effort. Some or all of the above processing in the booking unit may be performed using AI, for example, or not. For example, the booking unit can input information on suggested transportation, accommodations, and tourist destinations into the AI ​​and have the AI ​​execute the reservation arrangements.

[0036] The navigation unit can provide VR guidance for train transfers and walking routes. For example, when a user is transferring at a station, the navigation unit uses VR technology to guide them on the transfer route. The navigation unit can also use VR technology to guide a user on a walking route. For example, the navigation unit can display VR guidance on the user's smartphone, enabling the user to reach their destination without getting lost. This allows the user to reach their destination without getting lost through VR guidance. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's location information into the AI ​​and have the AI ​​perform the optimal transfer route or walking route guidance.

[0037] The suggestion unit can use a generative AI to propose the most suitable mode of transportation based on the user's travel preferences. For example, the suggestion unit can use the generative AI to analyze the user's travel preferences and propose the most suitable mode of transportation. Alternatively, the suggestion unit can use the generative AI to propose the most suitable mode of transportation based on the user's travel preferences. For example, the generative AI can compare modes of transportation such as bullet trains and airplanes and select the most suitable one. This improves the accuracy of the transportation suggestion by using the generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the suggestion unit may be performed using the generative AI, or they may not be performed using the generative AI. For example, the suggestion unit can input the user's travel preferences into the generative AI and have the generative AI propose the most suitable mode of transportation.

[0038] The suggestion unit can use a generative AI to propose the most suitable accommodation based on the user's travel preferences. For example, the suggestion unit can use the generative AI to analyze the user's travel preferences and propose the most suitable accommodation. Alternatively, the suggestion unit can use the generative AI to propose the most suitable accommodation based on the user's travel preferences. For example, the generative AI can compare accommodations such as hotels and inns and select the best one. This improves the accuracy of accommodation suggestions by using the generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the suggestion unit may be performed using the generative AI, or they may not be performed using the generative AI. For example, the suggestion unit can input the user's travel preferences into the generative AI and have the generative AI propose the most suitable accommodation.

[0039] The suggestion unit can propose the most suitable tourist destination based on the user's travel preferences using a generative AI. For example, the suggestion unit can use the generative AI to analyze the user's travel preferences and propose the most suitable tourist destination. Alternatively, the suggestion unit can use the generative AI to propose the most suitable tourist destination based on the user's travel preferences. For example, the generative AI can select the most suitable tourist destination by considering factors such as its popularity and accessibility. This improves the accuracy of the suggested tourist destinations. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the suggestion unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, the suggestion unit can input the user's travel preferences into the generative AI and have the generative AI propose the most suitable tourist destination.

[0040] The navigation unit can help users reach their destination without getting lost. For example, when a user is transferring trains at a station, the navigation unit can use VR technology to guide them along a transfer route. The navigation unit can also use VR technology to guide users along a walking route when they are traveling on foot. For example, the navigation unit can display VR guidance on the user's smartphone, enabling the user to reach their destination without getting lost. This allows the user to reach their destination without getting lost. Some or all of the above-described processes in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's location information into the AI ​​and have the AI ​​provide guidance on the optimal transfer route or walking route.

[0041] The reception desk can analyze the user's past travel history and select the optimal input method. For example, the reception desk can automatically display destinations that the user has frequently visited in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict places visited during specific seasons based on the user's past travel history and suggest them as suggestions. This allows the reception desk to provide the optimal input method based on past travel history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past travel history data into AI and have the AI ​​select the optimal input method.

[0042] The reception desk can filter travel preferences based on the user's current lifestyle and areas of interest when they enter their travel requests. For example, if the user is busy with work, the reception desk can suggest travel destinations that can be enjoyed in a short period of time. It can also prioritize displaying travel destinations rich in nature if the user is interested in nature. Furthermore, if the reception desk wants to travel with family, it can suggest family-friendly travel destinations. This allows the reception desk to provide travel preferences tailored to the user's lifestyle and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input data on the user's lifestyle and areas of interest into an AI and have the AI ​​perform the optimal filtering of travel preferences.

[0043] The reception desk can prioritize highly relevant travel preferences when users input their travel requests, taking into account their geographical location. For example, the reception desk can prioritize suggesting travel destinations close to the user's current location. It can also suggest travel destinations related to a specific region if the user is in that region. Furthermore, if the user is overseas, it can prioritize suggesting local tourist attractions. This allows the reception desk to provide optimal travel preferences based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's geographical location into an AI and have the AI ​​select the optimal travel preferences.

[0044] The reception desk can analyze the user's social media activity when they enter their travel preferences and input relevant preferences. For example, the reception desk can automatically display travel destinations that the user has shared on social media as suggestions. The reception desk can also prioritize suggesting travel destinations that the user has "liked" on social media. Furthermore, the reception desk can predict and suggest travel destinations that the user may be interested in based on the content of their social media posts. This allows the reception desk to provide the most suitable travel preferences based on social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity data into an AI and have the AI ​​select the most suitable travel preferences.

[0045] The proposal unit can adjust the level of detail in its proposals based on the importance of the travel plan. For example, for important travel plans, the proposal unit will provide a proposal with detailed information. For short travel plans, the proposal unit can provide a proposal with concise information. Furthermore, for family trips, the proposal unit can provide a proposal with detailed information tailored to the family. This allows the proposal unit to provide the optimal level of detail in its proposals according to the importance of the travel plan. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input travel plan importance data into the AI ​​and have the AI ​​perform the adjustment of the optimal level of detail in its proposals.

[0046] The suggestion unit can apply different suggestion algorithms depending on the category of the travel plan when making suggestions. For example, for an adventure trip, the suggestion unit will apply an adventurous suggestion algorithm. It can also apply a relaxing suggestion algorithm for a relaxation trip. Furthermore, it can apply a cultural suggestion algorithm for a cultural trip. This allows the suggestion unit to provide the optimal suggestion algorithm according to the category of the travel plan. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input travel plan category data into an AI and have the AI ​​apply the optimal suggestion algorithm.

[0047] The proposal department can determine the priority of proposals based on the timing of travel plan submissions. For example, it may prioritize proposals for recent travel plans. It may also postpone proposals for longer-term travel plans. Furthermore, if a travel plan is related to a specific event, it may prioritize proposals according to the timing of the event. This allows for the provision of optimal proposal priorities based on the timing of travel plan submissions. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input travel plan submission timing data into an AI and have the AI ​​determine the optimal proposal priority.

[0048] The suggestion unit can adjust the order of suggestions based on the relevance of the travel plans when making suggestions. For example, the suggestion unit can prioritize suggesting travel plans that are most relevant to the user's interests. It can also suggest highly relevant travel plans based on the user's past travel history. Furthermore, it can suggest highly relevant travel plans based on the user's current living situation. This allows for the provision of an optimal order of suggestions according to the relevance of the travel plans. 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 travel plan relevance data into AI and have the AI ​​perform the adjustment of the optimal order of suggestions.

[0049] The booking unit can analyze the user's past travel behavior during the booking process to select the optimal booking method. For example, the booking unit can propose the optimal booking method based on the booking methods the user has used in the past. Furthermore, the booking unit can also propose booking methods that avoid congestion based on the user's past travel behavior. In addition, the booking unit can analyze the user's past travel behavior and propose the most efficient booking method. This allows the system to provide the optimal booking method based on past travel behavior. Some or all of the above processing in the booking unit may be performed using AI, or not. For example, the booking unit can input the user's past travel behavior data into an AI and have the AI ​​select the optimal booking method.

[0050] The booking unit can customize the booking method based on the user's current living situation when booking. For example, if the user is busy with work, the booking unit can suggest a booking method that can be arranged in a short period of time. The booking unit can also suggest a family-friendly booking method if the user wishes to take a family trip. Furthermore, if the booking unit is interested in nature, it can suggest a booking method that offers nature-rich experiences. This allows the booking unit to provide the optimal booking method tailored to the user's living situation. Some or all of the above processing in the booking unit may be performed using AI, for example, or not. For example, the booking unit can input the user's living situation data into the AI ​​and have the AI ​​customize the optimal booking method.

[0051] The booking unit can select the optimal booking method when booking, taking into account the user's geographical location information. For example, the booking unit can prioritize suggesting booking methods close to the user's current location. Furthermore, if the user is in a specific region, the booking unit can suggest booking methods relevant to that region. Additionally, if the user is overseas, the booking unit can prioritize suggesting local booking methods. This allows the system to provide the optimal booking method based on the user's geographical location information. Some or all of the above processing in the booking unit may be performed using AI, or not. For example, the booking unit can input the user's geographical location information into an AI and have the AI ​​select the optimal booking method.

[0052] The arrangement unit can analyze the user's social media activity and suggest arrangement methods when making arrangements. For example, the arrangement unit can automatically display arrangement methods that the user has shared on social media as candidates. The arrangement unit can also prioritize suggesting arrangement methods that the user has "liked" on social media. Furthermore, the arrangement unit can predict and suggest arrangement methods that the user may be interested in based on the content of their social media posts. This allows the arrangement unit to provide the most suitable arrangement method based on social media activity. Some or all of the above processing in the arrangement unit may be performed using AI, for example, or not using AI. For example, the arrangement unit can input the user's social media activity data into AI and have the AI ​​select the most suitable arrangement method.

[0053] The navigation unit can select the optimal display method by referring to the user's past travel history when displaying navigation information. For example, the navigation unit can suggest the optimal display method based on routes previously used by the user. Furthermore, the navigation unit can suggest a display method that avoids congestion based on the user's past travel history. In addition, the navigation unit can analyze the user's past travel history and suggest the most efficient display method. This allows the system to provide the optimal navigation display method based on past travel history. Some or all of the above-described processes in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's past travel history data into AI and have the AI ​​select the optimal navigation display method.

[0054] The navigation unit can provide the optimal route based on the user's current location information when displaying navigation. For example, the navigation unit can provide the shortest route from the user's current location to their destination. It can also provide a route that avoids congestion from the user's current location. Furthermore, the navigation unit can provide a route with good scenery from the user's current location. This allows the navigation unit to provide the optimal route based on the current location information. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's current location information into the AI ​​and have the AI ​​perform the task of providing the optimal route.

[0055] The navigation unit can select the optimal display method when displaying navigation information, taking into account the user's device information. For example, if the user is using a smartphone, the navigation unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the navigation unit can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the navigation unit can provide a concise and highly visible display method. This allows the navigation unit to provide the optimal display method based on device information. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's device information into the AI ​​and have the AI ​​select the optimal display method.

[0056] The navigation unit can analyze the user's social media activity and provide the optimal route when displaying navigation. For example, the navigation unit can provide the optimal route based on places the user has shared on social media. It can also provide the optimal route based on places the user has "liked" on social media. Furthermore, the navigation unit can provide routes to places of interest based on the content of the user's social media posts. This allows the navigation unit to provide the optimal route based on social media activity. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's social media activity data into AI and have the AI ​​perform the task of providing the optimal route.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The reception desk can automatically make suggestions based on the user's past travel history and preferences when they input their travel requests. For example, it can suggest the next destination and accommodation based on the user's ratings of previously visited destinations and accommodations. The reception desk can also suggest the most suitable transportation and travel plan based on the user's past use of transportation and travel style (e.g., backpacking, luxury travel). Furthermore, the reception desk can suggest destinations related to specific seasons or events based on the user's past travel history. This allows for more personalized travel requests based on the user's past travel history.

[0059] The booking department can consider the user's current lifestyle and schedule when arranging the optimal travel plan based on the user's travel preferences. For example, if the user is busy with work, it can propose and arrange a short-term travel plan. If the user wants to travel with family, it can also arrange family-friendly accommodations and activities. Furthermore, if the user is interested in nature, it can arrange travel destinations and activities rich in nature. This makes it possible to arrange the optimal travel plan according to the user's lifestyle and areas of interest. Some or all of the above processing in the booking department may be performed using AI, or it may be performed without using AI.

[0060] The suggestion function can analyze the user's social media activity to suggest relevant destinations and activities when proposing the optimal travel plan based on the user's travel preferences. For example, it can suggest the next destination or activity based on the destination or activity the user has shared on social media. It can also prioritize suggesting destinations and activities the user has "liked" on social media. Furthermore, it can predict and suggest destinations and activities of interest based on the user's social media posts. This enables the suggestion of more personalized travel plans based on social media activity. Some or all of the above processing in the suggestion function may be performed using AI or not.

[0061] The navigation unit can select the optimal display method by referring to the user's past travel history. For example, it can suggest the optimal display method based on routes the user has used in the past. The navigation unit can also suggest a display method that avoids congestion based on the user's past travel history. Furthermore, it can analyze the user's past travel history and suggest the most efficient display method. This allows the system to provide the optimal navigation display method based on past travel history. Some or all of the above processing in the navigation unit may be performed using AI, or it may be performed without using AI.

[0062] The booking unit can analyze the user's past travel behavior to select the optimal booking method. For example, it can suggest the optimal booking method based on the booking methods the user has used in the past. It can also suggest booking methods that avoid congestion based on the user's past travel behavior. Furthermore, it can analyze the user's past travel behavior and suggest the most efficient booking method. This allows the system to provide the optimal booking method based on past travel behavior. Some or all of the above processing in the booking unit may be performed using AI, or it may be performed without using AI.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception desk inputs the user's travel preferences. These preferences include destination, duration, budget, etc. The reception desk provides methods for users to input their travel preferences via a web form or by using voice input. The reception desk can also automatically complete travel preferences by referring to the user's past travel history. Step 2: The proposal department uses a generation AI to suggest the optimal travel plan based on the travel preferences entered by the reception department. The proposal department analyzes the user's travel preferences and suggests the optimal travel plan, transportation, and accommodation. Step 3: The Arrangement Department makes all the necessary reservations for transportation, accommodation, and destinations based on the travel plan proposed by the Proposal Department. The Arrangement Department accesses the reservation systems for transportation, accommodation, and tourist attractions and makes the proposed reservations. Step 4: The navigation unit provides VR guidance for station transfers and walking routes based on the travel plan arranged by the arrangement unit. The navigation unit uses VR technology to guide the user when they transfer at stations or travel on foot.

[0065] (Example of form 2) The travel planning system according to an embodiment of the present invention is a system that provides an integrated service from travel planning and arrangement to guidance. This travel planning system solves the problem that conventional tours offered by travel agencies are expensive, and that it is difficult to provide an integrated service tailored to individual preferences because transportation, accommodation, and travel destinations must all be booked separately. The travel planning system solves these problems and is expected to increase sales by expanding the range of services it can handle while being cheaper than conventional travel agency products. It also contributes to solving social issues such as the dispersion of travel dates and destinations depending on the booking situation. Specifically, it consists of the following steps. First, the user inputs their travel preferences. Next, the generating AI proposes the optimal travel plan based on the user's preferences and arranges transportation, accommodation, and travel destination reservations all at once. Furthermore, it provides integrated navigation functions such as VR guidance for station transfers and walking routes, offering a service experience that includes transportation. For example, if the user inputs "I would like to travel from Tokyo to Kyoto," the generating AI will propose the optimal means of transportation (such as Shinkansen or airplane) and make reservations for accommodation and tourist spots all at once. Furthermore, the system uses VR to guide users through train transfers and walking routes during their trip, ensuring they reach their destination without getting lost. This system allows users to plan, arrange, and receive guidance for their trip all in one place without any hassle. In addition, by promoting the distribution of travel dates and destinations based on booking availability, it helps alleviate congestion at tourist spots and contributes to solving social issues. As a result, the travel planning system can efficiently input, suggest, arrange, and guide users based on their travel preferences.

[0066] The travel planning system according to this embodiment comprises a reception unit, a proposal unit, an arrangement unit, and a navigation unit. The reception unit receives the user's travel preferences. These preferences include, but are not limited to, a destination, travel duration, and budget. For example, the user enters their travel preferences into a web form. The reception unit can also accept the user's travel preferences using voice input. For example, it can use speech recognition technology to convert the user's voice into text and input it as the travel preference. The reception unit can also automatically complete the travel preferences by referring to the user's past travel history. For example, it can suggest the next travel destination based on the destinations the user has visited in the past. The proposal unit uses a generation AI to propose the optimal travel plan based on the travel preferences entered by the reception unit. For example, the proposal unit uses a generation AI to analyze the user's travel preferences and generate the optimal travel plan. The proposal unit can also use a generation AI to suggest the optimal mode of transportation based on the user's travel preferences. For example, the generation AI compares modes of transportation such as bullet trains and airplanes and selects the best one. The proposal unit can also use generative AI to suggest the most suitable accommodation based on the user's travel preferences. For example, the generative AI compares accommodations such as hotels and inns and selects the best one. The booking unit makes all the necessary arrangements for transportation, accommodation, and destinations based on the travel plan proposed by the proposal unit. For example, the booking unit makes reservations for the proposed modes of transportation. The booking unit can also make reservations for the proposed accommodations. Furthermore, the booking unit can also make reservations for the proposed tourist destinations. For example, the booking unit accesses the transportation reservation system and makes reservations for the proposed modes of transportation. It accesses the accommodation reservation system and makes reservations for the proposed accommodations. It accesses the tourist destination reservation system and makes reservations for the proposed tourist destinations. The navigation unit provides VR guidance for station transfers and walking routes based on the travel plan arranged by the booking unit. For example, when the user is transferring at a station, the navigation unit uses VR technology to guide them on the transfer route. The navigation unit can also use VR technology to guide the user on walking routes when they are traveling on foot.For example, the navigation unit displays VR guidance on the user's smartphone, enabling the user to reach their destination without getting lost. This allows the travel planning system according to the embodiment to efficiently input, suggest, arrange, and guide the user based on their travel preferences.

[0067] The reception desk inputs the user's travel preferences. These preferences may include, but are not limited to, destination, duration, and budget. For example, the user may input their preferences into a web form. The reception desk can also accept user travel preferences using voice input. For example, speech recognition technology can be used to convert the user's speech into text and input it as travel preferences. Specifically, speech recognition technology analyzes the user's utterance in real time and converts it into text data using natural language processing technology. This text data is further analyzed within the system and extracted as specific preference information such as destination, duration, and budget. The reception desk can also automatically complete travel preferences by referring to the user's past travel history. For example, it can suggest the next destination based on the user's past travel destinations. In this case, the system refers to the user's past travel database and analyzes similar travel patterns and preferences. Furthermore, the reception desk can collect data from the user's social media accounts and other online activities to understand the user's interests and preferences. This allows the system to suggest travel plans that match the user's preferences without the user having to explicitly input them. For example, the system suggests future travel destinations and accommodations based on reviews of tourist spots and accommodations the user has visited in the past. Furthermore, the reception desk encrypts and stores user input data, implementing security measures to protect privacy. This allows users to use the system with peace of mind.

[0068] The Proposal Department uses a generative AI to propose the optimal travel plan based on the travel preferences entered by the Reception Department. For example, the generative AI analyzes the user's travel preferences and generates the optimal travel plan. Specifically, the generative AI analyzes the user's input data and generates the optimal plan by combining candidate destinations, transportation methods, accommodations, and sightseeing spots. The generative AI refers to past travel data and reviews and ratings from other users to propose a plan that suits the user's preferences. The Proposal Department can also use the generative AI to propose the optimal transportation method based on the user's travel preferences. For example, the generative AI compares transportation methods such as bullet trains and airplanes and selects the best one. The generative AI comprehensively evaluates the cost, travel time, convenience, etc., of the transportation method and proposes the most suitable transportation method for the user. The Proposal Department can also use the generative AI to propose the optimal accommodation based on the user's travel preferences. For example, the generative AI compares accommodations such as hotels and inns and selects the best one. The generative AI considers the accommodation's price, location, facilities, review ratings, etc., and proposes the accommodation that best matches the user's preferences. Furthermore, the suggestion department can use generative AI to propose optimal tourist spots and activities based on the user's travel preferences. For example, the generative AI can select the most suitable tourist spots based on their popularity, accessibility, and the user's interests. This allows the suggestion department to comprehensively propose the optimal travel plan based on the user's travel preferences.

[0069] The booking department handles the booking of transportation, accommodation, and destinations based on the travel plan proposed by the proposal department. For example, the booking department books the proposed transportation. Specifically, the booking department accesses the booking system of the transportation company and books the proposed transportation. For example, it connects to the online booking system of an airline or railway company, selects the user's desired date and time and seat, and completes the booking. The booking department can also book the proposed accommodation. The booking department accesses the booking system of the accommodation company and books the proposed accommodation. For example, it connects to the online booking system of a hotel, selects the user's desired room type and dates, and completes the booking. Furthermore, the booking department can also book the proposed tourist destinations. The booking department accesses the booking system of the tourist destination and books the proposed tourist destinations. For example, it connects to the online booking system of a tourist destination, selects the user's desired date and time and activity, and completes the booking. The booking department centrally manages this booking information and provides the user with booking confirmations and tickets. The booking department also handles changes and cancellations of bookings, and can flexibly respond to user requests. For example, if a user wants to change their travel plans, the booking department will quickly update the reservation details and provide the new reservation information. This allows the booking department to efficiently and reliably arrange the user's travel plans, reducing the burden on the user.

[0070] The navigation unit provides VR guidance for station transfers and walking routes based on the travel plan arranged by the booking unit. For example, when a user is transferring at a station, the navigation unit uses VR technology to guide them to the transfer route. Specifically, the navigation unit displays VR guidance on the user's smartphone or tablet, enabling the user to transfer without getting lost within the station. For example, it displays real-time video of the station through the smartphone's camera and overlays the transfer route, allowing the user to intuitively understand the route. The navigation unit can also use VR technology to guide users on walking routes. For example, the navigation unit identifies the user's current location using GPS and displays the optimal walking route to the destination using VR guidance. The user can then follow the route guidance displayed overlaid on the real-world scenery while looking at their smartphone screen. Furthermore, the navigation unit can update the route guidance in real time while the user is moving, providing the optimal route according to changes in traffic conditions and weather. For example, if traffic congestion or bad weather occurs, the navigation unit immediately calculates a new route and notifies the user. Furthermore, the navigation system can provide information on tourist attractions, restaurants, and other destinations according to the user's preferences. This allows the navigation system to support users in reaching their destinations without getting lost during their trip, providing a comfortable travel experience.

[0071] The suggestion unit can propose the optimal travel plan based on the user's travel preferences using a generative AI. For example, the suggestion unit can use the generative AI to analyze the user's travel preferences and generate the optimal travel plan. The suggestion unit can also use the generative AI to propose the optimal mode of transportation based on the user's travel preferences. For example, the generative AI can compare modes of transportation such as bullet trains and airplanes and select the best one. The suggestion unit can also use the generative AI to propose the optimal accommodation based on the user's travel preferences. For example, the generative AI can compare accommodations such as hotels and inns and select the best one. As a result, the accuracy of travel plan proposals is improved by using the generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the suggestion unit may be performed using the generative AI, or they may not be performed using the generative AI. For example, the suggestion unit can input the user's travel preferences into the generative AI and have the generative AI generate the optimal travel plan.

[0072] The booking unit can make reservations for transportation, accommodation, and travel destinations all at once. For example, the booking unit can make reservations for suggested transportation. It can also make reservations for suggested accommodations. Furthermore, it can also make reservations for suggested tourist destinations. For example, the booking unit can access the transportation reservation system and make reservations for suggested transportation. It can access the accommodation reservation system and make reservations for suggested accommodations. It can access the tourist destination reservation system and make reservations for suggested tourist destinations. This allows for bulk booking, saving the user time and effort. Some or all of the above processing in the booking unit may be performed using AI, for example, or not. For example, the booking unit can input information on suggested transportation, accommodations, and tourist destinations into the AI ​​and have the AI ​​execute the reservation arrangements.

[0073] The navigation unit can provide VR guidance for train transfers and walking routes. For example, when a user is transferring at a station, the navigation unit uses VR technology to guide them on the transfer route. The navigation unit can also use VR technology to guide a user on a walking route. For example, the navigation unit can display VR guidance on the user's smartphone, enabling the user to reach their destination without getting lost. This allows the user to reach their destination without getting lost through VR guidance. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's location information into the AI ​​and have the AI ​​perform the optimal transfer route or walking route guidance.

[0074] The suggestion unit can use a generative AI to propose the most suitable mode of transportation based on the user's travel preferences. For example, the suggestion unit can use the generative AI to analyze the user's travel preferences and propose the most suitable mode of transportation. Alternatively, the suggestion unit can use the generative AI to propose the most suitable mode of transportation based on the user's travel preferences. For example, the generative AI can compare modes of transportation such as bullet trains and airplanes and select the most suitable one. This improves the accuracy of the transportation suggestion by using the generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the suggestion unit may be performed using the generative AI, or they may not be performed using the generative AI. For example, the suggestion unit can input the user's travel preferences into the generative AI and have the generative AI propose the most suitable mode of transportation.

[0075] The suggestion unit can use a generative AI to propose the most suitable accommodation based on the user's travel preferences. For example, the suggestion unit can use the generative AI to analyze the user's travel preferences and propose the most suitable accommodation. Alternatively, the suggestion unit can use the generative AI to propose the most suitable accommodation based on the user's travel preferences. For example, the generative AI can compare accommodations such as hotels and inns and select the best one. This improves the accuracy of accommodation suggestions by using the generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the suggestion unit may be performed using the generative AI, or they may not be performed using the generative AI. For example, the suggestion unit can input the user's travel preferences into the generative AI and have the generative AI propose the most suitable accommodation.

[0076] The suggestion unit can propose the most suitable tourist destination based on the user's travel preferences using a generative AI. For example, the suggestion unit can use the generative AI to analyze the user's travel preferences and propose the most suitable tourist destination. Alternatively, the suggestion unit can use the generative AI to propose the most suitable tourist destination based on the user's travel preferences. For example, the generative AI can select the most suitable tourist destination by considering factors such as its popularity and accessibility. This improves the accuracy of the suggested tourist destinations. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the suggestion unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, the suggestion unit can input the user's travel preferences into the generative AI and have the generative AI propose the most suitable tourist destination.

[0077] The navigation unit can help users reach their destination without getting lost. For example, when a user is transferring trains at a station, the navigation unit can use VR technology to guide them along a transfer route. The navigation unit can also use VR technology to guide users along a walking route when they are traveling on foot. For example, the navigation unit can display VR guidance on the user's smartphone, enabling the user to reach their destination without getting lost. This allows the user to reach their destination without getting lost. Some or all of the above-described processes in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's location information into the AI ​​and have the AI ​​provide guidance on the optimal transfer route or walking route.

[0078] The reception desk can estimate the user's emotions and adjust the timing of travel preference input based on the estimated emotions. For example, if the user is feeling stressed, the reception desk may prompt them to input their travel preference during a time when they can relax. The reception desk can also display a pop-up notification to prompt the user to input their travel preference immediately if they are feeling agitated. Furthermore, if the user is tired, the reception desk can send a reminder to input their travel preference the following day. This allows for the provision of optimal input timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into an AI and have the AI ​​adjust the optimal input timing.

[0079] The reception desk can analyze the user's past travel history and select the optimal input method. For example, the reception desk can automatically display destinations that the user has frequently visited in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict places visited during specific seasons based on the user's past travel history and suggest them as suggestions. This allows the reception desk to provide the optimal input method based on past travel history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past travel history data into AI and have the AI ​​select the optimal input method.

[0080] The reception desk can filter travel preferences based on the user's current lifestyle and areas of interest when they enter their travel requests. For example, if the user is busy with work, the reception desk can suggest travel destinations that can be enjoyed in a short period of time. It can also prioritize displaying travel destinations rich in nature if the user is interested in nature. Furthermore, if the reception desk wants to travel with family, it can suggest family-friendly travel destinations. This allows the reception desk to provide travel preferences tailored to the user's lifestyle and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input data on the user's lifestyle and areas of interest into an AI and have the AI ​​perform the optimal filtering of travel preferences.

[0081] The reception desk can estimate the user's emotions and determine the priority of travel preferences to be entered based on the estimated emotions. For example, if the user is relaxed, the reception desk may prioritize long-term travel preferences. If the user is in a hurry, the reception desk may also prioritize short-term travel preferences. Furthermore, if the user is excited, the reception desk may also prioritize adventurous travel preferences. This allows the system to provide the optimal priority of travel preferences according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk may input the user's emotion data into an AI and have the AI ​​determine the optimal priority of travel preferences.

[0082] The reception desk can prioritize highly relevant travel preferences when users input their travel requests, taking into account their geographical location. For example, the reception desk can prioritize suggesting travel destinations close to the user's current location. It can also suggest travel destinations related to a specific region if the user is in that region. Furthermore, if the user is overseas, it can prioritize suggesting local tourist attractions. This allows the reception desk to provide optimal travel preferences based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's geographical location into an AI and have the AI ​​select the optimal travel preferences.

[0083] The reception desk can analyze the user's social media activity when they enter their travel preferences and input relevant preferences. For example, the reception desk can automatically display travel destinations that the user has shared on social media as suggestions. The reception desk can also prioritize suggesting travel destinations that the user has "liked" on social media. Furthermore, the reception desk can predict and suggest travel destinations that the user may be interested in based on the content of their social media posts. This allows the reception desk to provide the most suitable travel preferences based on social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity data into an AI and have the AI ​​select the most suitable travel preferences.

[0084] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is relaxed, the suggestion unit will present suggestions in a relaxed manner. If the user is in a hurry, the suggestion unit can present suggestions in a concise and to-the-point manner. Furthermore, if the user is excited, the suggestion unit can present suggestions in a visually stimulating manner. This allows the system to provide the most appropriate way of presenting suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI and have the AI ​​adjust the optimal way of presenting suggestions.

[0085] The proposal unit can adjust the level of detail in its proposals based on the importance of the travel plan. For example, for important travel plans, the proposal unit will provide a proposal with detailed information. For short travel plans, the proposal unit can provide a proposal with concise information. Furthermore, for family trips, the proposal unit can provide a proposal with detailed information tailored to the family. This allows the proposal unit to provide the optimal level of detail in its proposals according to the importance of the travel plan. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input travel plan importance data into the AI ​​and have the AI ​​perform the adjustment of the optimal level of detail in its proposals.

[0086] The suggestion unit can apply different suggestion algorithms depending on the category of the travel plan when making suggestions. For example, for an adventure trip, the suggestion unit will apply an adventurous suggestion algorithm. It can also apply a relaxing suggestion algorithm for a relaxation trip. Furthermore, it can apply a cultural suggestion algorithm for a cultural trip. This allows the suggestion unit to provide the optimal suggestion algorithm according to the category of the travel plan. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input travel plan category data into an AI and have the AI ​​apply the optimal suggestion algorithm.

[0087] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide longer suggestions with more detailed explanations. Furthermore, if the user is excited, the suggestion unit can provide visually stimulating suggestions. This allows the system to provide the optimal suggestion length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI and have the AI ​​adjust the optimal suggestion length.

[0088] The proposal department can determine the priority of proposals based on the timing of travel plan submissions. For example, it may prioritize proposals for recent travel plans. It may also postpone proposals for longer-term travel plans. Furthermore, if a travel plan is related to a specific event, it may prioritize proposals according to the timing of the event. This allows for the provision of optimal proposal priorities based on the timing of travel plan submissions. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input travel plan submission timing data into an AI and have the AI ​​determine the optimal proposal priority.

[0089] The suggestion unit can adjust the order of suggestions based on the relevance of the travel plans when making suggestions. For example, the suggestion unit can prioritize suggesting travel plans that are most relevant to the user's interests. It can also suggest highly relevant travel plans based on the user's past travel history. Furthermore, it can suggest highly relevant travel plans based on the user's current living situation. This allows for the provision of an optimal order of suggestions according to the relevance of the travel plans. 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 travel plan relevance data into AI and have the AI ​​perform the adjustment of the optimal order of suggestions.

[0090] The ordering unit can estimate the user's emotions and adjust the ordering method based on the estimated emotions. For example, if the user is relaxed, the ordering unit can provide a relaxed ordering method. If the user is in a hurry, the ordering unit can also provide a fast ordering method. Furthermore, if the user is excited, the ordering unit can provide a visually stimulating ordering method. This allows the system to provide the optimal ordering method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the ordering unit may be performed using AI, for example, or not using AI. For example, the ordering unit can input user emotion data into an AI and have the AI ​​adjust the optimal ordering method.

[0091] The booking unit can analyze the user's past travel behavior during the booking process to select the optimal booking method. For example, the booking unit can propose the optimal booking method based on the booking methods the user has used in the past. Furthermore, the booking unit can also propose booking methods that avoid congestion based on the user's past travel behavior. In addition, the booking unit can analyze the user's past travel behavior and propose the most efficient booking method. This allows the system to provide the optimal booking method based on past travel behavior. Some or all of the above processing in the booking unit may be performed using AI, or not. For example, the booking unit can input the user's past travel behavior data into an AI and have the AI ​​select the optimal booking method.

[0092] The booking unit can customize the booking method based on the user's current living situation when booking. For example, if the user is busy with work, the booking unit can suggest a booking method that can be arranged in a short period of time. The booking unit can also suggest a family-friendly booking method if the user wishes to take a family trip. Furthermore, if the booking unit is interested in nature, it can suggest a booking method that offers nature-rich experiences. This allows the booking unit to provide the optimal booking method tailored to the user's living situation. Some or all of the above processing in the booking unit may be performed using AI, for example, or not. For example, the booking unit can input the user's living situation data into the AI ​​and have the AI ​​customize the optimal booking method.

[0093] The booking unit can estimate the user's emotions and determine booking priorities based on those emotions. For example, if the user is relaxed, the booking unit may prioritize long-term bookings. If the user is in a hurry, the booking unit may prioritize short-term bookings. Furthermore, if the user is excited, the booking unit may prioritize adventurous bookings. This allows the system to provide optimal booking priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the booking unit may be performed using AI or not. For example, the booking unit can input user emotion data into an AI and have the AI ​​determine the optimal booking priorities.

[0094] The booking unit can select the optimal booking method when booking, taking into account the user's geographical location information. For example, the booking unit can prioritize suggesting booking methods close to the user's current location. Furthermore, if the user is in a specific region, the booking unit can suggest booking methods relevant to that region. Additionally, if the user is overseas, the booking unit can prioritize suggesting local booking methods. This allows the system to provide the optimal booking method based on the user's geographical location information. Some or all of the above processing in the booking unit may be performed using AI, or not. For example, the booking unit can input the user's geographical location information into an AI and have the AI ​​select the optimal booking method.

[0095] The arrangement unit can analyze the user's social media activity and suggest arrangement methods when making arrangements. For example, the arrangement unit can automatically display arrangement methods that the user has shared on social media as candidates. The arrangement unit can also prioritize suggesting arrangement methods that the user has "liked" on social media. Furthermore, the arrangement unit can predict and suggest arrangement methods that the user may be interested in based on the content of their social media posts. This allows the arrangement unit to provide the most suitable arrangement method based on social media activity. Some or all of the above processing in the arrangement unit may be performed using AI, for example, or not using AI. For example, the arrangement unit can input the user's social media activity data into AI and have the AI ​​select the most suitable arrangement method.

[0096] The navigation unit can estimate the user's emotions and adjust the navigation display method based on the estimated emotions. For example, if the user is tense, the navigation unit can provide a simple and highly visible display method. If the user is relaxed, the navigation unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the navigation unit can provide a concise display method. This allows the system to provide the optimal navigation display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input user emotion data into AI and have the AI ​​adjust the optimal navigation display method.

[0097] The navigation unit can select the optimal display method by referring to the user's past travel history when displaying navigation information. For example, the navigation unit can suggest the optimal display method based on routes previously used by the user. Furthermore, the navigation unit can suggest a display method that avoids congestion based on the user's past travel history. In addition, the navigation unit can analyze the user's past travel history and suggest the most efficient display method. This allows the system to provide the optimal navigation display method based on past travel history. Some or all of the above-described processes in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's past travel history data into AI and have the AI ​​select the optimal navigation display method.

[0098] The navigation unit can provide the optimal route based on the user's current location information when displaying navigation. For example, the navigation unit can provide the shortest route from the user's current location to their destination. It can also provide a route that avoids congestion from the user's current location. Furthermore, the navigation unit can provide a route with good scenery from the user's current location. This allows the navigation unit to provide the optimal route based on the current location information. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's current location information into the AI ​​and have the AI ​​perform the task of providing the optimal route.

[0099] The navigation unit can estimate the user's emotions and adjust the navigation procedure based on the estimated emotions. For example, if the user is tense, the navigation unit can provide simple and intuitive instructions. If the user is relaxed, the navigation unit can also provide detailed instructions. Furthermore, if the user is in a hurry, the navigation unit can provide instructions that allow for quick operation. This allows the system to provide the optimal navigation procedure according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the navigation unit may be performed using AI, for example, or not using AI. For example, the navigation unit can input user emotion data into an AI and have the AI ​​adjust the optimal navigation procedure.

[0100] The navigation unit can select the optimal display method when displaying navigation information, taking into account the user's device information. For example, if the user is using a smartphone, the navigation unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the navigation unit can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the navigation unit can provide a concise and highly visible display method. This allows the navigation unit to provide the optimal display method based on device information. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's device information into the AI ​​and have the AI ​​select the optimal display method.

[0101] The navigation unit can analyze the user's social media activity and provide the optimal route when displaying navigation. For example, the navigation unit can provide the optimal route based on places the user has shared on social media. It can also provide the optimal route based on places the user has "liked" on social media. Furthermore, the navigation unit can provide routes to places of interest based on the content of the user's social media posts. This allows the navigation unit to provide the optimal route based on social media activity. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's social media activity data into AI and have the AI ​​perform the task of providing the optimal route.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The reception desk can automatically make suggestions based on the user's past travel history and preferences when they input their travel requests. For example, it can suggest the next destination and accommodation based on the user's ratings of previously visited destinations and accommodations. The reception desk can also suggest the most suitable transportation and travel plan based on the user's past use of transportation and travel style (e.g., backpacking, luxury travel). Furthermore, the reception desk can suggest destinations related to specific seasons or events based on the user's past travel history. This allows for more personalized travel requests based on the user's past travel history.

[0104] The suggestion unit can estimate the user's emotions and adjust the content of the suggestions based on the estimated emotions. For example, if the user is feeling stressed, it can suggest relaxing travel destinations and activities. If the user is excited, it can suggest adventurous travel destinations and activities. Furthermore, if the user is tired, it can suggest relaxing accommodations and relaxation facilities such as spas. This makes it possible to suggest the optimal travel plan according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not.

[0105] The booking department can consider the user's current lifestyle and schedule when arranging the optimal travel plan based on the user's travel preferences. For example, if the user is busy with work, it can propose and arrange a short-term travel plan. If the user wants to travel with family, it can also arrange family-friendly accommodations and activities. Furthermore, if the user is interested in nature, it can arrange travel destinations and activities rich in nature. This makes it possible to arrange the optimal travel plan according to the user's lifestyle and areas of interest. Some or all of the above processing in the booking department may be performed using AI, or it may be performed without using AI.

[0106] The navigation unit can estimate the user's emotions and adjust the navigation display method based on the estimated emotions. For example, if the user is tense, it can provide a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display method that gets straight to the point. This allows the system to provide the optimal navigation display method according to the user's emotions. Emotion estimation is achieved, for example, using an emotion engine or generative AI. Some or all of the above processing in the navigation unit may be performed using AI or not.

[0107] The suggestion function can analyze the user's social media activity to suggest relevant destinations and activities when proposing the optimal travel plan based on the user's travel preferences. For example, it can suggest the next destination or activity based on the destination or activity the user has shared on social media. It can also prioritize suggesting destinations and activities the user has "liked" on social media. Furthermore, it can predict and suggest destinations and activities of interest based on the user's social media posts. This enables the suggestion of more personalized travel plans based on social media activity. Some or all of the above processing in the suggestion function may be performed using AI or not.

[0108] The ordering unit can estimate the user's emotions and adjust the ordering method based on the estimated emotions. For example, if the user is relaxed, it can provide a relaxed ordering method. If the user is in a hurry, it can provide a quick ordering method. Furthermore, if the user is excited, it can provide a visually stimulating ordering method. This allows the system to provide the optimal ordering method according to the user's emotions. Emotion estimation is achieved, for example, using an emotion engine or generative AI. Some or all of the processing described above in the ordering unit may be performed using AI or not.

[0109] The navigation unit can select the optimal display method by referring to the user's past travel history. For example, it can suggest the optimal display method based on routes the user has used in the past. The navigation unit can also suggest a display method that avoids congestion based on the user's past travel history. Furthermore, it can analyze the user's past travel history and suggest the most efficient display method. This allows the system to provide the optimal navigation display method based on past travel history. Some or all of the above processing in the navigation unit may be performed using AI, or it may be performed without using AI.

[0110] The suggestion section can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is relaxed, suggestions can be presented in a relaxed manner. If the user is in a hurry, suggestions can be presented in a concise and to-the-point manner. Furthermore, if the user is excited, suggestions can be presented in a visually stimulating manner. This allows the system to provide the most appropriate way to present suggestions according to the user's emotions. Emotion estimation can be achieved, for example, using an emotion engine or generative AI. Some or all of the processing described above in the suggestion section may be performed using AI or not.

[0111] The booking unit can analyze the user's past travel behavior to select the optimal booking method. For example, it can suggest the optimal booking method based on the booking methods the user has used in the past. It can also suggest booking methods that avoid congestion based on the user's past travel behavior. Furthermore, it can analyze the user's past travel behavior and suggest the most efficient booking method. This allows the system to provide the optimal booking method based on past travel behavior. Some or all of the above processing in the booking unit may be performed using AI, or it may be performed without using AI.

[0112] The navigation unit can estimate the user's emotions and adjust the navigation procedure based on the estimated emotions. For example, if the user is tense, it can provide a simple and intuitive procedure. If the user is relaxed, it can provide a detailed procedure. Furthermore, if the user is in a hurry, it can provide a procedure that allows for quick operation. This allows the system to provide the optimal navigation procedure according to the user's emotions. Emotion estimation is achieved, for example, using an emotion engine or generative AI. Some or all of the processing described above in the navigation unit may be performed using AI or not.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The reception desk inputs the user's travel preferences. These preferences include destination, duration, budget, etc. The reception desk provides methods for users to input their travel preferences via a web form or by using voice input. The reception desk can also automatically complete travel preferences by referring to the user's past travel history. Step 2: The proposal department uses a generation AI to suggest the optimal travel plan based on the travel preferences entered by the reception department. The proposal department analyzes the user's travel preferences and suggests the optimal travel plan, transportation, and accommodation. Step 3: The Arrangement Department makes all the necessary reservations for transportation, accommodation, and destinations based on the travel plan proposed by the Proposal Department. The Arrangement Department accesses the reservation systems for transportation, accommodation, and tourist attractions and makes the proposed reservations. Step 4: The navigation unit provides VR guidance for station transfers and walking routes based on the travel plan arranged by the arrangement unit. The navigation unit uses VR technology to guide the user when they transfer at stations or travel on foot.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the reception unit, proposal unit, arrangement unit, and navigation unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit receives the user's travel preferences via the control unit 46A of the smart device 14 and estimates the user's emotions via the identification processing unit 290 of the data processing unit 12. The proposal unit proposes an optimal travel plan using generated AI via the identification processing unit 290 of the data processing unit 12. The arrangement unit makes reservations for transportation, accommodation, and travel destinations all at once via the identification processing unit 290 of the data processing unit 12. The navigation unit provides VR guidance for station transfers and walking routes via the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the reception unit, proposal unit, arrangement unit, and navigation unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit receives the user's travel preferences via the control unit 46A of the smart glasses 214 and estimates the user's emotions via the identification processing unit 290 of the data processing unit 12. The proposal unit proposes an optimal travel plan using generated AI via the identification processing unit 290 of the data processing unit 12. The arrangement unit makes reservations for transportation, accommodation, and travel destinations all at once via the identification processing unit 290 of the data processing unit 12. The navigation unit provides VR guidance for station transfers and walking routes via the control unit 46A of the smart glasses 214. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the reception unit, proposal unit, arrangement unit, and navigation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit receives the user's travel preferences via the control unit 46A of the headset terminal 314 and estimates the user's emotions via the identification processing unit 290 of the data processing unit 12. The proposal unit proposes an optimal travel plan using generated AI via the identification processing unit 290 of the data processing unit 12. The arrangement unit makes reservations for transportation, accommodation, and travel destinations all at once via the identification processing unit 290 of the data processing unit 12. The navigation unit provides VR guidance for station transfers and walking routes via the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] Each of the multiple elements described above, including the reception unit, proposal unit, arrangement unit, and navigation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit receives the user's travel preferences via the control unit 46A of the robot 414 and estimates the user's emotions via the identification processing unit 290 of the data processing unit 12. The proposal unit proposes an optimal travel plan using generated AI via the identification processing unit 290 of the data processing unit 12. The arrangement unit makes reservations for transportation, accommodation, and travel destinations all at once via the identification processing unit 290 of the data processing unit 12. The navigation unit provides VR guidance for station transfers and walking routes via the control unit 46A of the robot 414. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] (Note 1) A reception desk where users enter their travel preferences, A proposal unit proposes the most suitable travel plan based on the travel preferences entered by the reception unit, The Arrangement Department is responsible for making all necessary arrangements for transportation, accommodation, and travel destination reservations based on the travel plan proposed by the aforementioned Proposal Department. The system includes a navigation unit that provides VR guidance for station transfers and walking routes based on the travel plan arranged by the aforementioned arrangement unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, The AI ​​generates and proposes the optimal travel plan based on the user's travel preferences. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned procurement unit, We arrange transportation, accommodation, and travel destination reservations all in one place. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned navigation unit is Provides VR guidance for train transfers and walking routes. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, The AI ​​generates suggestions for the most suitable mode of transportation based on the user's travel preferences. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, The AI ​​generates suggestions for the best accommodations based on the user's travel preferences. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned proposal section is, The AI ​​generates suggestions for the best tourist destinations based on the user's travel preferences. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned navigation unit is To help users reach their destination without getting lost. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of travel preference input based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is Analyze the user's past travel history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users enter their travel preferences, the system filters them based on their current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is It estimates the user's emotions and determines the priority of their travel preferences based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When users enter their travel preferences, the system prioritizes highly relevant preferences by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is When users enter their travel preferences, the system analyzes their social media activity and inputs relevant preferences. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the travel plan. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the category of the travel plan. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When submitting proposals, we will prioritize them based on when the travel plan is submitted. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making proposals, adjust the order of suggestions based on the relevance of the travel plan. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned procurement unit, It estimates the user's emotions and adjusts the arrangement method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned procurement unit, During the booking process, the system analyzes the user's past travel behavior to select the most suitable booking method. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned procurement unit, When making arrangements, the method of arrangement is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned procurement unit, It estimates the user's emotions and determines the priority of arrangements based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned procurement unit, When making arrangements, the optimal arrangement method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned ordering unit, When making arrangements, we analyze the user's social media activity and suggest arrangement methods. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned navigation unit is It estimates the user's emotions and adjusts how navigation is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned navigation unit is When displaying navigation, the system selects the optimal display method by referring to the user's past movement history. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned navigation unit is When displaying navigation, the system provides the optimal route based on the user's current location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned navigation unit is It estimates the user's emotions and adjusts the navigation steps based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned navigation unit is When displaying navigation, the system selects the optimal display method considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned navigation unit is When displaying navigation, the system analyzes the user's social media activity to provide the optimal route. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk where users enter their travel preferences, A proposal unit proposes the most suitable travel plan based on the travel preferences entered by the reception unit, The Arrangement Department is responsible for making all necessary arrangements for transportation, accommodation, and travel destination reservations based on the travel plan proposed by the aforementioned Proposal Department. The system includes a navigation unit that provides VR guidance for station transfers and walking routes based on the travel plan arranged by the aforementioned arrangement unit. A system characterized by the following features.

2. The aforementioned proposal section is, The AI ​​generates optimal travel plans based on the user's travel preferences. The system according to feature 1.

3. The aforementioned procurement unit, We arrange transportation, accommodation, and travel destination reservations all in one place. The system according to feature 1.

4. The aforementioned navigation unit is Provides VR guidance for train transfers and walking routes. The system according to feature 1.

5. The aforementioned proposal section is, The AI ​​generates suggestions for the most suitable mode of transportation based on the user's travel preferences. The system according to feature 1.

6. The aforementioned proposal section is, The AI ​​generates suggestions for the best accommodations based on the user's travel preferences. The system according to feature 1.

7. The aforementioned proposal section is, The AI ​​generates suggestions for the most suitable tourist destinations based on the user's travel preferences. The system according to feature 1.

8. The aforementioned navigation unit is To help users reach their destination without getting lost. The system according to feature 1.

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