system

Abstract

The system according to this embodiment aims to generate travel plans tailored to the user's wishes and budget, and to provide support during the trip. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a proposal unit, and a support unit. The reception unit receives input from the user regarding their travel preferences and budget. The generation unit generates a travel plan based on the information received by the reception unit. The proposal unit proposes the travel plan generated by the generation unit to the user. The support unit suggests new experiences during the trip according to the local conditions and the user's mood.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that a travel plan according to a user's wishes and budget has not been sufficiently generated efficiently, and support during travel has not been provided.

[0005] The system according to the embodiment aims to generate a travel plan according to a user's wishes and budget and provide support during travel.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a proposal unit, and a support unit. The reception unit receives input from the user regarding their travel preferences and budget. The generation unit generates a travel plan based on the information received by the reception unit. The proposal unit proposes the travel plan generated by the generation unit to the user. The support unit suggests new experiences during the trip according to local conditions and the user's mood. [Effects of the Invention]

[0007] The system according to this embodiment can generate travel plans tailored to the user's wishes and budget, and provide support during the trip. [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, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a reception 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 reception 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 proposes travel plans using generative AI and provides real-time support during travel. This travel planning system allows users to input their travel preferences and budget, and the generative AI then proposes themed travel plans (e.g., "nature-focused," "luxury travel," "cultural exploration," etc.) based on the user's input information. Users can enjoy their trip without the hassle of planning, simply by selecting from the proposed plans. Furthermore, during the trip, real-time AI support flexibly proposes new experiences according to local conditions and the user's mood. This allows for responses to unexpected changes during the trip and local troubles. First, the user inputs their travel preferences and budget. At this time, the user only needs to input their preferences, travel purpose, budget, etc. For example, they might input information such as "I want a nature-focused trip" or "My budget is under 100,000 yen." This information is input into the generative AI. Next, the generative AI analyzes the input information and proposes a travel plan that matches the user's preferences. The generative AI generates themed travel plans based on the user's input information. For example, if the user selects the "nature-focused" theme, a plan including nature-rich tourist destinations and activities will be proposed. Furthermore, if the user selects the "luxury travel" theme, plans including high-end hotels and resorts will be suggested. Users can enjoy their trip without the hassle of planning, simply by choosing from the suggested plans. For example, if a user selects a "nature-focused" plan, the travel booking will be automatically made based on that plan. This allows users to easily enjoy their trip without spending time planning. In addition, during the trip, real-time AI support flexibly suggests new experiences according to local conditions and the user's mood. For example, if the weather deteriorates during the trip, the real-time AI support will suggest indoor activities. It can also suggest new tourist destinations and restaurants according to the user's mood. This allows for flexible responses to unexpected changes during the trip and problems encountered locally. This system makes it easy for even busy users to enjoy a trip that suits them. Moreover, the real-time support during the trip expands the possibilities of travel and allows users to enjoy new experiences.For example, users can discover new tourist destinations or enjoy local culture and cuisine while traveling. This allows users to discover new values ​​and possibilities through travel, and design a richer life. In this way, the travel planning system generates travel plans based on the user's travel wishes and budget, and provides support during the trip, making it easy for users to enjoy traveling.

[0029] The travel planning system according to this embodiment comprises a reception unit, a generation unit, a proposal unit, and a support unit. The reception unit receives input from the user regarding their travel preferences and budget. The user's travel preferences include, but are not limited to, a destination, purpose of travel, and desired activities. The reception unit provides, for example, an interface for the user to input their travel preferences. The user can input their travel preferences and budget through the interface. The generation unit generates a travel plan based on the information received by the reception unit, using a generation AI. The generation AI generates a travel plan that meets the user's preferences, for example, using a machine learning model or a neural network. The generation unit generates, for example, nature-oriented travel plans, luxury travel plans, cultural exploration travel plans, etc., based on the user's preferences. The proposal unit proposes the travel plan generated by the generation unit to the user. The proposal unit presents the generated travel plan to the user, for example, allowing the user to select one. The proposal unit can also automatically make travel reservations based on the travel plan selected by the user. The support unit proposes new experiences during the trip according to local conditions and the user's mood. The support unit, for example, can suggest indoor activities if the weather deteriorates during a trip. It can also suggest new tourist destinations or restaurants based on the user's mood. Thus, the travel planning system according to this embodiment generates travel plans based on the user's travel preferences and budget, and provides support during the trip, allowing users to easily enjoy their travels.

[0030] The reception desk accepts user input regarding their travel preferences and budget. These preferences may include, but are not limited to, destination, purpose of travel, and desired activities. The reception desk provides an interface for users to input their travel preferences. Through this interface, users can enter their travel preferences and budget. Specifically, the reception desk provides users with an intuitive and user-friendly input form via a website or mobile application. This form includes dropdown menus, checkboxes, and text input fields, allowing users to easily enter information. For example, destination options include a list of major cities and tourist destinations both domestically and internationally, allowing users to select their desired destination. For purpose of travel, options include relaxation, adventure, cultural experiences, and business, allowing users to choose the one best suited to their needs. Furthermore, desired activities include hiking, shopping, museum visits, and gourmet tours, allowing users to select according to their interests. Regarding budget input, users can enter their budget range, enabling the system to generate an optimal plan based on the user's budget. The reception desk saves the information entered by the user in real time, making it available for subsequent processing. This allows users to easily create travel plans based on their preferences.

[0031] The generation unit uses a generation AI to generate travel plans based on information received by the reception unit. The generation AI uses, for example, machine learning models and neural networks to generate travel plans tailored to the user's preferences. For instance, based on the user's preferences, the generation unit can generate travel plans such as nature-focused trips, luxury trips, or cultural exploration trips. Specifically, the generation AI learns from past travel data and user preference data to propose the most suitable travel plan based on the user's input. For example, a nature-focused trip might include activities such as nature parks, hiking trails, and eco-tours. A luxury trip might include luxurious experiences like high-end hotels, resorts, gourmet restaurants, and spas. A cultural exploration trip might include historical sites, museums, art galleries, and local cultural events. The generation AI combines these elements to generate the most suitable travel plan for the user's preferences. Furthermore, the generation AI can incorporate the latest tourist spots and event information based on real-time updates. This ensures that users always receive travel plans based on the most up-to-date information. The generation unit internally verifies the generated travel plan before presenting it to the user, confirming its consistency and feasibility. This ensures that users receive highly reliable travel plans.

[0032] The suggestion unit proposes travel plans generated by the generation unit to the user. For example, the suggestion unit presents the generated travel plans to the user, allowing the user to select one. The suggestion unit can also automatically make travel reservations based on the travel plan selected by the user. Specifically, the suggestion unit provides an interface to visually present multiple travel plans to the user in an easy-to-understand manner. This interface displays detailed information, photos, maps, and reviews for each travel plan, allowing the user to compare and consider each plan. Once the user selects a plan they like, the suggestion unit automatically makes reservations for accommodation, transportation, and activities based on that plan. For example, it checks the availability of hotels included in the plan the user selected and confirms the reservation. It can also make reservations for flights and trains, as well as local tours and activities, all in one go. The suggestion unit notifies the user of the reservation progress in real time and provides necessary information. Furthermore, the suggestion unit can collect user feedback and use it to generate future travel plans. In this way, the suggestion unit can propose the optimal travel plan to the user and provide consistent support from travel planning to booking.

[0033] The support team suggests new experiences during the trip based on local conditions and the user's mood. For example, if the weather deteriorates during the trip, the support team will suggest indoor activities. They can also suggest new tourist destinations and restaurants based on the user's mood. Specifically, the support team analyzes the user's current location, weather information, and past activity history in real time to provide optimal suggestions. For instance, if the weather worsens, they might suggest nearby museums, art galleries, or shopping malls for indoor activities. They can also suggest restaurants that serve a particular type of cuisine if the user prefers it. The support team provides real-time information through the user's smartphone or wearable device, supporting the user to have a comfortable trip. Furthermore, the support team includes customer support functions to handle user inquiries and problems. For example, if a user gets lost or encounters booking issues, the support team will respond quickly and provide solutions. This allows the support team to ensure users can travel with peace of mind and improve their overall travel satisfaction.

[0034] The generation unit can generate travel plans tailored to the user's preferences using a generation AI. For example, the generation unit uses the generation AI to generate travel plans based on the user's preferences. For example, when generating a nature-oriented travel plan, the generation unit can generate a plan that includes nature-rich tourist destinations and activities. The generation unit can also generate a luxury travel plan that includes high-end hotels and resorts. When generating a cultural travel plan, the generation unit can also generate a plan that includes historical tourist destinations and cultural events. Thus, by using the generation AI, travel plans tailored to the user's preferences can be generated. Some or all of the above-described processes in the generation unit may be performed using the generation AI, or they may be performed without the generation AI. For example, the generation unit can input a travel plan generated based on the user's preferences into the generation AI, which can then generate the travel plan.

[0035] The suggestion unit can propose generated travel plans to the user. For example, the suggestion unit can present the generated travel plans to the user and allow the user to select one. The suggestion unit can also automatically make travel reservations based on the travel plan selected by the user. The suggestion unit can also send the generated travel plans to the user via email. Furthermore, the suggestion unit can notify the user of the generated travel plans on their smartphone. This allows the user to enjoy traveling without the hassle of planning by suggesting generated travel plans to them. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the generated travel plans into AI, which can then propose them to the user.

[0036] The support department can suggest new experiences during a trip based on local conditions and the user's mood. For example, if the weather deteriorates during the trip, the support department can suggest indoor activities. For example, the support department can suggest indoor activities such as museums, art galleries, and shopping malls. The support department can also suggest new tourist destinations and restaurants based on the user's mood. For example, if the user wants to relax, the support department can suggest quiet tourist destinations and relaxation facilities. The support department can also suggest activities and sports facilities if the user wants to be active. In this way, the possibilities of travel are expanded by suggesting new experiences based on local conditions and the user's mood during the trip. Some or all of the above processing in the support department may be performed using AI, for example, or not using AI. For example, the support department can input new experiences based on local conditions and the user's mood into the AI, and the AI ​​can suggest new experiences.

[0037] The support department can suggest indoor activities that can be enjoyed in response to changes in weather. For example, if the weather deteriorates, the support department can suggest indoor activities such as museums, art galleries, and shopping malls. The support department can also suggest indoor sports and entertainment facilities that can be enjoyed in rainy weather. Furthermore, the support department can prepare a list of indoor activities in advance in case of bad weather. This allows for unexpected changes during travel by suggesting indoor activities that can be enjoyed in response to changes in weather. Some or all of the above processes in the support department may be performed using AI, for example, or not. For example, the support department can input indoor activities based on changes in weather into the AI, and the AI ​​can suggest indoor activities.

[0038] The support unit can suggest new tourist destinations and restaurants according to the user's mood. For example, if the user wants to relax, the support unit can suggest quiet tourist destinations and relaxation facilities. If the user wants to be active, the support unit can suggest activities and sports facilities. The support unit can also suggest new restaurants according to the user's mood. For example, if the user wants to enjoy local cuisine, the support unit can suggest restaurants serving local food. If the user wants to enjoy a high-end restaurant, the support unit can suggest Michelin-starred restaurants. In this way, suggesting new tourist destinations and restaurants according to the user's mood enriches the travel experience. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input new tourist destinations and restaurants based on the user's mood into the AI, and the AI ​​can suggest new tourist destinations and restaurants.

[0039] The reception desk can analyze the user's past travel history and suggest the most suitable input format. For example, the reception desk can automatically display as suggestions the user's frequently entered travel preferences and budget in the past. The reception desk can also prioritize suggesting input methods the user has used in the past (voice, text, etc.). Furthermore, the reception desk can suggest input formats related to specific seasons or events based on the user's past travel history. In this way, the reception desk can suggest the most suitable input format by analyzing the user's past travel history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past travel history data into a generating AI, which can then suggest the most suitable input format.

[0040] The reception desk can customize input fields based on the user's current lifestyle and areas of interest when they enter their travel preferences and budget. For example, if the user is planning a family trip, the reception desk will prioritize displaying family-friendly activities and accommodations. If the user is planning a business trip, the reception desk can also prioritize displaying business-oriented hotels and conference facilities. Furthermore, if the user is planning a hobby trip, the reception desk can prioritize displaying tourist destinations and events related to that hobby. This allows users to input more relevant information by customizing input fields based on their current 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 current lifestyle and areas of interest into a generating AI, which can then customize the input fields.

[0041] The reception desk can prioritize displaying highly relevant input fields when users input their travel preferences and budget, taking into account their geographical location. For example, the reception desk can prioritize displaying tourist attractions and accommodations close to the user's current location. If the user is traveling to a specific region, the reception desk can also prioritize displaying tourist attractions and activities related to that region. Furthermore, if the user is traveling to a specific city, the reception desk can prioritize displaying events and restaurants related to that city. In this way, highly relevant input fields can be prioritized by considering 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 information into a generating AI, which can then suggest highly relevant input fields.

[0042] The reception desk can analyze the user's social media activity when they input their travel preferences and budget, and suggest relevant input fields. For example, the reception desk can suggest relevant input fields based on travel destinations and activities the user has shared on social media. The reception desk can also suggest relevant input fields based on travel-related accounts the user follows on social media. Furthermore, the reception desk can suggest relevant input fields based on travel destinations and activities the user has "liked" on social media. In this way, relevant input fields can be suggested by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI, and the generating AI can suggest relevant input fields.

[0043] The generation unit can generate the optimal travel plan by referring to the user's past travel history. For example, the generation unit can generate the optimal travel plan based on places the user has visited in the past. For example, the generation unit can also suggest routes that avoid crowds based on the user's past travel history. Furthermore, the generation unit can analyze the user's past travel history and generate the most efficient travel plan. In this way, the optimal travel plan can be generated by referring to the user's past travel history. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's past travel history data into a generation AI, and the generation AI can generate the optimal travel plan.

[0044] The generation unit can customize travel plans based on the user's current lifestyle and areas of interest when generating them. For example, if the user is planning a family trip, the generation unit will generate a plan that includes family-friendly activities and accommodations. If the user is planning a business trip, the generation unit can also generate a plan that includes business-friendly hotels and conference facilities. Furthermore, if the user is planning a hobby trip, the generation unit can generate a plan that includes tourist destinations and events related to that hobby. This allows for the generation of more appropriate travel plans by customizing them based on the user's current lifestyle and areas of interest. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input data on the user's current lifestyle and areas of interest into the generation AI, which can then customize the plan.

[0045] The generation unit can prioritize generating highly relevant travel plans by considering the user's geographical location information when generating travel plans. For example, the generation unit can generate plans that include tourist attractions and accommodations close to the user's current location. For example, if the user is traveling to a specific region, the generation unit can also generate plans that include tourist attractions and activities related to that region. Furthermore, if the user is traveling to a specific city, the generation unit can generate plans that include events and restaurants related to that city. In this way, highly relevant travel plans can be generated by considering the user's geographical location information. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI, which can then generate highly relevant plans.

[0046] The generation unit can analyze a user's social media activity and generate relevant travel plans when creating them. For example, the generation unit can generate relevant plans based on travel destinations and activities shared by the user on social media. The generation unit can also generate relevant plans based on travel-related accounts followed by the user on social media. Furthermore, the generation unit can generate relevant plans based on travel destinations and activities liked by the user on social media. In this way, relevant travel plans can be generated by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's social media activity data into a generation AI, which can then generate relevant plans.

[0047] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the travel plan. For example, for important travel plans, the suggestion unit will provide suggestions with detailed information. For short-term travel plans, the suggestion unit may provide suggestions with concise information. Furthermore, for travel plans of particular interest to the user, the suggestion unit may provide suggestions with relevant detailed information. This allows the user to receive the most relevant information by adjusting the level of detail in the suggestions based on the importance of the travel plan. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input travel plan importance data into a generating AI, which can then adjust the level of detail in the suggestions.

[0048] The suggestion unit can apply different suggestion algorithms depending on the category of the travel plan when making suggestions. For example, in the case of a nature-oriented travel plan, the suggestion unit will prioritize suggesting information on natural landscapes and outdoor activities. For example, in the case of a luxury travel plan, the suggestion unit can prioritize suggesting information on high-end hotels and resorts. Furthermore, in the case of a cultural exploration travel plan, the suggestion unit can prioritize suggesting information on historical tourist spots and cultural events. By applying different suggestion algorithms depending on the category of the travel plan, it becomes possible to make suggestions that are optimal for the user. 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 category data into a generating AI, and the generating AI can apply different suggestion algorithms.

[0049] The proposal department can prioritize proposals based on when the travel plan is submitted. For example, if a plan is submitted shortly before the trip, the proposal department will provide a proposal quickly. If a plan is submitted several months before the trip, the proposal department may also provide a proposal with more detailed information. Furthermore, if a plan is submitted during the trip, the proposal department may provide a proposal tailored to the local situation. By prioritizing proposals based on when the travel plan is submitted, it becomes possible to provide the most suitable proposal for the user. 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 a generating AI, which can then determine the priority of the proposals.

[0050] The suggestion unit can adjust the order of suggestions based on the relevance of the travel plans. For example, the suggestion unit may first suggest the plan most relevant to the user's preferences. It may also prioritize suggesting the plan that best fits the user's budget. Furthermore, it may prioritize suggesting the plan that best matches the user's travel purpose. By adjusting the order of suggestions based on the relevance of the travel plans, it becomes possible to provide the user with the most suitable suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input travel plan relevance data into a generating AI, which can then adjust the order of suggestions.

[0051] The support unit can suggest the optimal experience during travel by referring to the user's past travel history. For example, the support unit can suggest relevant experiences based on activities the user has enjoyed in the past. For example, the support unit can also suggest experiences that avoid crowds based on the user's past travel history. Furthermore, the support unit can analyze the user's past travel history and suggest the most efficient experience. In this way, the optimal experience can be suggested by referring to the user's past travel history. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's past travel history data into a generating AI, and the generating AI can suggest the optimal experience.

[0052] The support unit can customize the user's experience during travel based on their current lifestyle and areas of interest. For example, if the user is traveling with family, the support unit can suggest family-friendly activities. If the user is traveling for business, the support unit can also suggest business-related events or meetings. Furthermore, if the user is traveling for leisure, the support unit can suggest experiences related to their hobby. By customizing the experience based on the user's current lifestyle and areas of interest, a more appropriate experience can be suggested. Some or all of the above processing in the support unit may be performed using AI, for example, or not. For example, the support unit can input data on the user's current lifestyle and areas of interest into a generating AI, which can then customize the experience.

[0053] The support unit can suggest the most suitable experience for a user during their trip, taking into account their geographical location. For example, the support unit can suggest tourist attractions or activities close to the user's current location. If the user is staying in a specific region, the support unit can also suggest experiences related to that region. Furthermore, if the user is staying in a specific city, the support unit can suggest events or restaurants related to that city. In this way, the support unit can suggest the most suitable experience by taking into account the user's geographical location. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's geographical location information into a generating AI, which can then suggest the most suitable experience.

[0054] The support department can analyze a user's social media activity and suggest relevant experiences during travel support. For example, the support department can suggest relevant experiences based on travel destinations and activities shared by the user on social media. The support department can also suggest relevant experiences based on travel-related accounts followed by the user on social media. Furthermore, the support department can suggest relevant experiences based on travel destinations and activities liked by the user on social media. In this way, relevant experiences can be suggested by analyzing the user's social media activity. Some or all of the above processing in the support department may be performed using AI, for example, or not. For example, the support department can input the user's social media activity data into a generating AI, which can then suggest relevant experiences.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The reception desk can suggest the most suitable input format when receiving user input regarding travel preferences and budget, by referring to the user's past travel history. For example, it can automatically display suggestions for travel preferences and budgets that the user has frequently entered in the past. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can suggest input formats related to specific seasons or events based on the user's past travel history. In this way, the system can suggest the most suitable input format by analyzing the user's past travel history.

[0057] The generation unit can customize travel plans based on the user's current lifestyle and areas of interest. For example, if a user is planning a family trip, it can generate a plan that includes family-friendly activities and accommodations. If a user is planning a business trip, it can also generate a plan that includes business-oriented hotels and conference facilities. Furthermore, if a user is planning a hobby trip, it can generate a plan that includes tourist destinations and events related to that hobby. By customizing the plan based on the user's current lifestyle and areas of interest, it can generate a more appropriate travel plan.

[0058] The proposal function can adjust the level of detail in a proposal based on the importance of the travel plan. For example, for important travel plans, it will provide a proposal with detailed information. For short-term travel plans, it can provide a proposal with concise information. Furthermore, for travel plans of particular interest to the user, it can provide a proposal with relevant and detailed information. By adjusting the level of detail in the proposal based on the importance of the travel plan, the system can provide the user with the most relevant information.

[0059] The support team can suggest the most suitable experience for users during their trip by referring to their past travel history. For example, they can suggest relevant experiences based on activities the user has enjoyed in the past. They can also suggest experiences that avoid crowds based on the user's past travel history. Furthermore, they can analyze the user's past travel history to suggest the most efficient experience. In this way, they can suggest the most suitable experience by referring to the user's past travel history.

[0060] The support team can customize the user's travel experience based on their current lifestyle and interests. For example, if a user is traveling with family, they can suggest family-friendly activities. If a user is traveling for business, they can suggest business-related events and meetings. Furthermore, if a user is traveling for leisure, they can suggest experiences related to their hobby. By customizing the experience based on the user's current lifestyle and interests, they can offer a more appropriate experience.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk receives the user's travel preferences and budget. The user's travel preferences include destination, purpose of travel, and desired activities. The reception desk provides an interface for the user to enter their travel preferences, and the user can enter their travel preferences and budget through this interface. Step 2: The generation unit generates travel plans based on the information received by the reception unit. The generation unit uses generation AI, machine learning models, and neural networks to generate travel plans that meet the user's preferences. For example, it can generate nature-focused travel plans, luxury travel plans, cultural exploration travel plans, and more. Step 3: The suggestion unit proposes the travel plan generated by the generation unit to the user. The suggestion unit presents the generated travel plan to the user and allows the user to select one. It can also automatically make a travel reservation based on the travel plan selected by the user. Step 4: The support team will suggest new experiences during the trip based on local conditions and the user's mood. For example, if the weather deteriorates during the trip, they will suggest indoor activities. They can also suggest new tourist destinations or restaurants based on the user's mood.

[0063] (Example of form 2) The travel planning system according to an embodiment of the present invention is a system that proposes travel plans using generative AI and provides real-time support during travel. This travel planning system allows users to input their travel preferences and budget, and the generative AI then proposes themed travel plans (e.g., "nature-focused," "luxury travel," "cultural exploration," etc.) based on the user's input information. Users can enjoy their trip without the hassle of planning, simply by selecting from the proposed plans. Furthermore, during the trip, real-time AI support flexibly proposes new experiences according to local conditions and the user's mood. This allows for responses to unexpected changes during the trip and local troubles. First, the user inputs their travel preferences and budget. At this time, the user only needs to input their preferences, travel purpose, budget, etc. For example, they might input information such as "I want a nature-focused trip" or "My budget is under 100,000 yen." This information is input into the generative AI. Next, the generative AI analyzes the input information and proposes a travel plan that matches the user's preferences. The generative AI generates themed travel plans based on the user's input information. For example, if the user selects the "nature-focused" theme, a plan including nature-rich tourist destinations and activities will be proposed. Furthermore, if the user selects the "luxury travel" theme, plans including high-end hotels and resorts will be suggested. Users can enjoy their trip without the hassle of planning, simply by choosing from the suggested plans. For example, if a user selects a "nature-focused" plan, the travel booking will be automatically made based on that plan. This allows users to easily enjoy their trip without spending time planning. In addition, during the trip, real-time AI support flexibly suggests new experiences according to local conditions and the user's mood. For example, if the weather deteriorates during the trip, the real-time AI support will suggest indoor activities. It can also suggest new tourist destinations and restaurants according to the user's mood. This allows for flexible responses to unexpected changes during the trip and problems encountered locally. This system makes it easy for even busy users to enjoy a trip that suits them. Moreover, the real-time support during the trip expands the possibilities of travel and allows users to enjoy new experiences.For example, users can discover new tourist destinations or enjoy local culture and cuisine while traveling. This allows users to discover new values ​​and possibilities through travel, and design a richer life. In this way, the travel planning system generates travel plans based on the user's travel wishes and budget, and provides support during the trip, making it easy for users to enjoy traveling.

[0064] The travel planning system according to this embodiment comprises a reception unit, a generation unit, a proposal unit, and a support unit. The reception unit receives input from the user regarding their travel preferences and budget. The user's travel preferences include, but are not limited to, a destination, purpose of travel, and desired activities. The reception unit provides, for example, an interface for the user to input their travel preferences. The user can input their travel preferences and budget through the interface. The generation unit generates a travel plan based on the information received by the reception unit, using a generation AI. The generation AI generates a travel plan that meets the user's preferences, for example, using a machine learning model or a neural network. The generation unit generates, for example, nature-oriented travel plans, luxury travel plans, cultural exploration travel plans, etc., based on the user's preferences. The proposal unit proposes the travel plan generated by the generation unit to the user. The proposal unit presents the generated travel plan to the user, for example, allowing the user to select one. The proposal unit can also automatically make travel reservations based on the travel plan selected by the user. The support unit proposes new experiences during the trip according to local conditions and the user's mood. The support unit, for example, can suggest indoor activities if the weather deteriorates during a trip. It can also suggest new tourist destinations or restaurants based on the user's mood. Thus, the travel planning system according to this embodiment generates travel plans based on the user's travel preferences and budget, and provides support during the trip, allowing users to easily enjoy their travels.

[0065] The reception desk accepts user input regarding their travel preferences and budget. These preferences may include, but are not limited to, destination, purpose of travel, and desired activities. The reception desk provides an interface for users to input their travel preferences. Through this interface, users can enter their travel preferences and budget. Specifically, the reception desk provides users with an intuitive and user-friendly input form via a website or mobile application. This form includes dropdown menus, checkboxes, and text input fields, allowing users to easily enter information. For example, destination options include a list of major cities and tourist destinations both domestically and internationally, allowing users to select their desired destination. For purpose of travel, options include relaxation, adventure, cultural experiences, and business, allowing users to choose the one best suited to their needs. Furthermore, desired activities include hiking, shopping, museum visits, and gourmet tours, allowing users to select according to their interests. Regarding budget input, users can enter their budget range, enabling the system to generate an optimal plan based on the user's budget. The reception desk saves the information entered by the user in real time, making it available for subsequent processing. This allows users to easily create travel plans based on their preferences.

[0066] The generation unit uses a generation AI to generate travel plans based on information received by the reception unit. The generation AI uses, for example, machine learning models and neural networks to generate travel plans tailored to the user's preferences. For instance, based on the user's preferences, the generation unit can generate travel plans such as nature-focused trips, luxury trips, or cultural exploration trips. Specifically, the generation AI learns from past travel data and user preference data to propose the most suitable travel plan based on the user's input. For example, a nature-focused trip might include activities such as nature parks, hiking trails, and eco-tours. A luxury trip might include luxurious experiences like high-end hotels, resorts, gourmet restaurants, and spas. A cultural exploration trip might include historical sites, museums, art galleries, and local cultural events. The generation AI combines these elements to generate the most suitable travel plan for the user's preferences. Furthermore, the generation AI can incorporate the latest tourist spots and event information based on real-time updates. This ensures that users always receive travel plans based on the most up-to-date information. The generation unit internally verifies the generated travel plan before presenting it to the user, confirming its consistency and feasibility. This ensures that users receive highly reliable travel plans.

[0067] The suggestion unit proposes travel plans generated by the generation unit to the user. For example, the suggestion unit presents the generated travel plans to the user, allowing the user to select one. The suggestion unit can also automatically make travel reservations based on the travel plan selected by the user. Specifically, the suggestion unit provides an interface to visually present multiple travel plans to the user in an easy-to-understand manner. This interface displays detailed information, photos, maps, and reviews for each travel plan, allowing the user to compare and consider each plan. Once the user selects a plan they like, the suggestion unit automatically makes reservations for accommodation, transportation, and activities based on that plan. For example, it checks the availability of hotels included in the plan the user selected and confirms the reservation. It can also make reservations for flights and trains, as well as local tours and activities, all in one go. The suggestion unit notifies the user of the reservation progress in real time and provides necessary information. Furthermore, the suggestion unit can collect user feedback and use it to generate future travel plans. In this way, the suggestion unit can propose the optimal travel plan to the user and provide consistent support from travel planning to booking.

[0068] The support team suggests new experiences during the trip based on local conditions and the user's mood. For example, if the weather deteriorates during the trip, the support team will suggest indoor activities. They can also suggest new tourist destinations and restaurants based on the user's mood. Specifically, the support team analyzes the user's current location, weather information, and past activity history in real time to provide optimal suggestions. For instance, if the weather worsens, they might suggest nearby museums, art galleries, or shopping malls for indoor activities. They can also suggest restaurants that serve a particular type of cuisine if the user prefers it. The support team provides real-time information through the user's smartphone or wearable device, supporting the user to have a comfortable trip. Furthermore, the support team includes customer support functions to handle user inquiries and problems. For example, if a user gets lost or encounters booking issues, the support team will respond quickly and provide solutions. This allows the support team to ensure users can travel with peace of mind and improve their overall travel satisfaction.

[0069] The generation unit can generate travel plans tailored to the user's preferences using a generation AI. For example, the generation unit uses the generation AI to generate travel plans based on the user's preferences. For example, when generating a nature-oriented travel plan, the generation unit can generate a plan that includes nature-rich tourist destinations and activities. The generation unit can also generate a luxury travel plan that includes high-end hotels and resorts. When generating a cultural travel plan, the generation unit can also generate a plan that includes historical tourist destinations and cultural events. Thus, by using the generation AI, travel plans tailored to the user's preferences can be generated. Some or all of the above-described processes in the generation unit may be performed using the generation AI, or they may be performed without the generation AI. For example, the generation unit can input a travel plan generated based on the user's preferences into the generation AI, which can then generate the travel plan.

[0070] The suggestion unit can propose generated travel plans to the user. For example, the suggestion unit can present the generated travel plans to the user and allow the user to select one. The suggestion unit can also automatically make travel reservations based on the travel plan selected by the user. The suggestion unit can also send the generated travel plans to the user via email. Furthermore, the suggestion unit can notify the user of the generated travel plans on their smartphone. This allows the user to enjoy traveling without the hassle of planning by suggesting generated travel plans to them. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the generated travel plans into AI, which can then propose them to the user.

[0071] The support department can suggest new experiences during a trip based on local conditions and the user's mood. For example, if the weather deteriorates during the trip, the support department can suggest indoor activities. For example, the support department can suggest indoor activities such as museums, art galleries, and shopping malls. The support department can also suggest new tourist destinations and restaurants based on the user's mood. For example, if the user wants to relax, the support department can suggest quiet tourist destinations and relaxation facilities. The support department can also suggest activities and sports facilities if the user wants to be active. In this way, the possibilities of travel are expanded by suggesting new experiences based on local conditions and the user's mood during the trip. Some or all of the above processing in the support department may be performed using AI, for example, or not using AI. For example, the support department can input new experiences based on local conditions and the user's mood into the AI, and the AI ​​can suggest new experiences.

[0072] The support department can suggest indoor activities that can be enjoyed in response to changes in weather. For example, if the weather deteriorates, the support department can suggest indoor activities such as museums, art galleries, and shopping malls. The support department can also suggest indoor sports and entertainment facilities that can be enjoyed in rainy weather. Furthermore, the support department can prepare a list of indoor activities in advance in case of bad weather. This allows for unexpected changes during travel by suggesting indoor activities that can be enjoyed in response to changes in weather. Some or all of the above processes in the support department may be performed using AI, for example, or not. For example, the support department can input indoor activities based on changes in weather into the AI, and the AI ​​can suggest indoor activities.

[0073] The support unit can suggest new tourist destinations and restaurants according to the user's mood. For example, if the user wants to relax, the support unit can suggest quiet tourist destinations and relaxation facilities. If the user wants to be active, the support unit can suggest activities and sports facilities. The support unit can also suggest new restaurants according to the user's mood. For example, if the user wants to enjoy local cuisine, the support unit can suggest restaurants serving local food. If the user wants to enjoy a high-end restaurant, the support unit can suggest Michelin-starred restaurants. In this way, suggesting new tourist destinations and restaurants according to the user's mood enriches the travel experience. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input new tourist destinations and restaurants based on the user's mood into the AI, and the AI ​​can suggest new tourist destinations and restaurants.

[0074] The reception desk can estimate the user's emotions and adjust the input method for travel preferences and budget based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of travel preferences and budget. This allows users to comfortably input information by adjusting the input method based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's emotion data into a generative AI, which can estimate the emotions and adjust the input method.

[0075] The reception desk can analyze the user's past travel history and suggest the most suitable input format. For example, the reception desk can automatically display as suggestions the user's frequently entered travel preferences and budget in the past. The reception desk can also prioritize suggesting input methods the user has used in the past (voice, text, etc.). Furthermore, the reception desk can suggest input formats related to specific seasons or events based on the user's past travel history. In this way, the reception desk can suggest the most suitable input format by analyzing the user's past travel history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past travel history data into a generating AI, which can then suggest the most suitable input format.

[0076] The reception desk can customize input fields based on the user's current lifestyle and areas of interest when they enter their travel preferences and budget. For example, if the user is planning a family trip, the reception desk will prioritize displaying family-friendly activities and accommodations. If the user is planning a business trip, the reception desk can also prioritize displaying business-oriented hotels and conference facilities. Furthermore, if the user is planning a hobby trip, the reception desk can prioritize displaying tourist destinations and events related to that hobby. This allows users to input more relevant information by customizing input fields based on their current 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 current lifestyle and areas of interest into a generating AI, which can then customize the input fields.

[0077] The reception desk can estimate the user's emotions and prioritize input fields based on those emotions. For example, if the user is tired, the reception desk may prioritize displaying the most important input fields and simplify the input process. If the user is excited, the reception desk may also provide detailed input fields and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk may allow the most important input fields to be entered quickly via voice input. This allows the user to efficiently input information by prioritizing input fields based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of input fields.

[0078] The reception desk can prioritize displaying highly relevant input fields when users input their travel preferences and budget, taking into account their geographical location. For example, the reception desk can prioritize displaying tourist attractions and accommodations close to the user's current location. If the user is traveling to a specific region, the reception desk can also prioritize displaying tourist attractions and activities related to that region. Furthermore, if the user is traveling to a specific city, the reception desk can prioritize displaying events and restaurants related to that city. In this way, highly relevant input fields can be prioritized by considering 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 information into a generating AI, which can then suggest highly relevant input fields.

[0079] The reception desk can analyze the user's social media activity when they input their travel preferences and budget, and suggest relevant input fields. For example, the reception desk can suggest relevant input fields based on travel destinations and activities the user has shared on social media. The reception desk can also suggest relevant input fields based on travel-related accounts the user follows on social media. Furthermore, the reception desk can suggest relevant input fields based on travel destinations and activities the user has "liked" on social media. In this way, relevant input fields can be suggested by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI, and the generating AI can suggest relevant input fields.

[0080] The generation unit can estimate the user's emotions and adjust the travel plan generation method based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a travel plan that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can also generate a travel plan that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate a travel plan with visually stimulating effects. In this way, a more appropriate travel plan can be generated by adjusting the travel plan generation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not using a generation AI. For example, the generation unit can input user emotion data into a generation AI, which can estimate the emotions and adjust the travel plan generation method.

[0081] The generation unit can generate the optimal travel plan by referring to the user's past travel history. For example, the generation unit can generate the optimal travel plan based on places the user has visited in the past. For example, the generation unit can also suggest routes that avoid crowds based on the user's past travel history. Furthermore, the generation unit can analyze the user's past travel history and generate the most efficient travel plan. In this way, the optimal travel plan can be generated by referring to the user's past travel history. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's past travel history data into a generation AI, and the generation AI can generate the optimal travel plan.

[0082] The generation unit can customize travel plans based on the user's current lifestyle and areas of interest when generating them. For example, if the user is planning a family trip, the generation unit will generate a plan that includes family-friendly activities and accommodations. If the user is planning a business trip, the generation unit can also generate a plan that includes business-friendly hotels and conference facilities. Furthermore, if the user is planning a hobby trip, the generation unit can generate a plan that includes tourist destinations and events related to that hobby. This allows for the generation of more appropriate travel plans by customizing them based on the user's current lifestyle and areas of interest. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input data on the user's current lifestyle and areas of interest into the generation AI, which can then customize the plan.

[0083] The generation unit can estimate the user's emotions and determine the priority of the travel plan to generate based on the estimated emotions. For example, if the user is tired, the generation unit will generate a plan that prioritizes relaxing activities. If the user is excited, the generation unit can also generate a plan that prioritizes active activities. Furthermore, if the user is in a hurry, the generation unit can generate a plan that prioritizes efficient travel routes. This allows for the generation of more appropriate travel plans by prioritizing them based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into a generation AI, which can estimate the emotions and determine the priority of the travel plan.

[0084] The generation unit can prioritize generating highly relevant travel plans by considering the user's geographical location information when generating travel plans. For example, the generation unit can generate plans that include tourist attractions and accommodations close to the user's current location. For example, if the user is traveling to a specific region, the generation unit can also generate plans that include tourist attractions and activities related to that region. Furthermore, if the user is traveling to a specific city, the generation unit can generate plans that include events and restaurants related to that city. In this way, highly relevant travel plans can be generated by considering the user's geographical location information. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI, which can then generate highly relevant plans.

[0085] The generation unit can analyze a user's social media activity and generate relevant travel plans when creating them. For example, the generation unit can generate relevant plans based on travel destinations and activities shared by the user on social media. The generation unit can also generate relevant plans based on travel-related accounts followed by the user on social media. Furthermore, the generation unit can generate relevant plans based on travel destinations and activities liked by the user on social media. In this way, relevant travel plans can be generated by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's social media activity data into a generation AI, which can then generate relevant plans.

[0086] 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 nervous, the suggestion unit can provide a simple and easily understandable suggestion. If the user is relaxed, the suggestion unit can also provide a suggestion that includes detailed information. Furthermore, if the user is in a hurry, the suggestion unit can provide a concise suggestion. By adjusting the way suggestions are presented based on the user's emotions, more appropriate suggestions become possible. 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can estimate the emotion and adjust the way suggestions are presented.

[0087] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the travel plan. For example, for important travel plans, the suggestion unit will provide suggestions with detailed information. For short-term travel plans, the suggestion unit may provide suggestions with concise information. Furthermore, for travel plans of particular interest to the user, the suggestion unit may provide suggestions with relevant detailed information. This allows the user to receive the most relevant information by adjusting the level of detail in the suggestions based on the importance of the travel plan. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input travel plan importance data into a generating AI, which can then adjust the level of detail in the suggestions.

[0088] The suggestion unit can apply different suggestion algorithms depending on the category of the travel plan when making suggestions. For example, in the case of a nature-oriented travel plan, the suggestion unit will prioritize suggesting information on natural landscapes and outdoor activities. For example, in the case of a luxury travel plan, the suggestion unit can prioritize suggesting information on high-end hotels and resorts. Furthermore, in the case of a cultural exploration travel plan, the suggestion unit can prioritize suggesting information on historical tourist spots and cultural events. By applying different suggestion algorithms depending on the category of the travel plan, it becomes possible to make suggestions that are optimal for the user. 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 category data into a generating AI, and the generating AI can apply different suggestion algorithms.

[0089] 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 detailed explanations. If the user is excited, the suggestion unit can also provide suggestions with visually stimulating effects. By adjusting the length of suggestions based on the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the length of the suggestions.

[0090] The proposal department can prioritize proposals based on when the travel plan is submitted. For example, if a plan is submitted shortly before the trip, the proposal department will provide a proposal quickly. If a plan is submitted several months before the trip, the proposal department may also provide a proposal with more detailed information. Furthermore, if a plan is submitted during the trip, the proposal department may provide a proposal tailored to the local situation. By prioritizing proposals based on when the travel plan is submitted, it becomes possible to provide the most suitable proposal for the user. 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 a generating AI, which can then determine the priority of the proposals.

[0091] The suggestion unit can adjust the order of suggestions based on the relevance of the travel plans. For example, the suggestion unit may first suggest the plan most relevant to the user's preferences. It may also prioritize suggesting the plan that best fits the user's budget. Furthermore, it may prioritize suggesting the plan that best matches the user's travel purpose. By adjusting the order of suggestions based on the relevance of the travel plans, it becomes possible to provide the user with the most suitable suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input travel plan relevance data into a generating AI, which can then adjust the order of suggestions.

[0092] The support unit can estimate the user's emotions and adjust how new experiences are suggested based on those emotions. For example, if the user is relaxed, the support unit can suggest experiences that can be enjoyed at a relaxed pace. If the user is excited, the support unit can also suggest active activities. If the user is tired, the support unit can also suggest relaxing experiences. In this way, by adjusting how new experiences are suggested based on the user's emotions, more appropriate experiences can be suggested. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI or not using AI. For example, the support unit can input user emotion data into a generative AI, which can estimate the emotions and adjust how new experiences are suggested.

[0093] The support unit can suggest the optimal experience during travel by referring to the user's past travel history. For example, the support unit can suggest relevant experiences based on activities the user has enjoyed in the past. For example, the support unit can also suggest experiences that avoid crowds based on the user's past travel history. Furthermore, the support unit can analyze the user's past travel history and suggest the most efficient experience. In this way, the optimal experience can be suggested by referring to the user's past travel history. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's past travel history data into a generating AI, and the generating AI can suggest the optimal experience.

[0094] The support unit can customize the user's experience during travel based on their current lifestyle and areas of interest. For example, if the user is traveling with family, the support unit can suggest family-friendly activities. If the user is traveling for business, the support unit can also suggest business-related events or meetings. Furthermore, if the user is traveling for leisure, the support unit can suggest experiences related to their hobby. By customizing the experience based on the user's current lifestyle and areas of interest, a more appropriate experience can be suggested. Some or all of the above processing in the support unit may be performed using AI, for example, or not. For example, the support unit can input data on the user's current lifestyle and areas of interest into a generating AI, which can then customize the experience.

[0095] The support unit can estimate the user's emotions and prioritize new experiences based on those emotions. For example, if the user is tired, the support unit will prioritize suggesting relaxing experiences. If the user is excited, the support unit may also prioritize suggesting active experiences. Furthermore, if the user is in a hurry, the support unit may prioritize suggesting efficient experiences. This allows for the suggestion of more appropriate experiences by prioritizing new experiences based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI or not. For example, the support unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of new experiences.

[0096] The support unit can suggest the most suitable experience for a user during their trip, taking into account their geographical location. For example, the support unit can suggest tourist attractions or activities close to the user's current location. If the user is staying in a specific region, the support unit can also suggest experiences related to that region. Furthermore, if the user is staying in a specific city, the support unit can suggest events or restaurants related to that city. In this way, the support unit can suggest the most suitable experience by taking into account the user's geographical location. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's geographical location information into a generating AI, which can then suggest the most suitable experience.

[0097] The support department can analyze a user's social media activity and suggest relevant experiences during travel support. For example, the support department can suggest relevant experiences based on travel destinations and activities shared by the user on social media. The support department can also suggest relevant experiences based on travel-related accounts followed by the user on social media. Furthermore, the support department can suggest relevant experiences based on travel destinations and activities liked by the user on social media. In this way, relevant experiences can be suggested by analyzing the user's social media activity. Some or all of the above processing in the support department may be performed using AI, for example, or not. For example, the support department can input the user's social media activity data into a generating AI, which can then suggest relevant experiences.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The reception desk can suggest the most suitable input format when receiving user input regarding travel preferences and budget, by referring to the user's past travel history. For example, it can automatically display suggestions for travel preferences and budgets that the user has frequently entered in the past. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can suggest input formats related to specific seasons or events based on the user's past travel history. In this way, the system can suggest the most suitable input format by analyzing the user's past travel history.

[0100] The generation unit can customize travel plans based on the user's current lifestyle and areas of interest. For example, if a user is planning a family trip, it can generate a plan that includes family-friendly activities and accommodations. If a user is planning a business trip, it can also generate a plan that includes business-oriented hotels and conference facilities. Furthermore, if a user is planning a hobby trip, it can generate a plan that includes tourist destinations and events related to that hobby. By customizing the plan based on the user's current lifestyle and areas of interest, it can generate a more appropriate travel plan.

[0101] The proposal function can adjust the level of detail in a proposal based on the importance of the travel plan. For example, for important travel plans, it will provide a proposal with detailed information. For short-term travel plans, it can provide a proposal with concise information. Furthermore, for travel plans of particular interest to the user, it can provide a proposal with relevant and detailed information. By adjusting the level of detail in the proposal based on the importance of the travel plan, the system can provide the user with the most relevant information.

[0102] The support team can suggest the most suitable experience for users during their trip by referring to their past travel history. For example, they can suggest relevant experiences based on activities the user has enjoyed in the past. They can also suggest experiences that avoid crowds based on the user's past travel history. Furthermore, they can analyze the user's past travel history to suggest the most efficient experience. In this way, they can suggest the most suitable experience by referring to the user's past travel history.

[0103] The support team can customize the user's travel experience based on their current lifestyle and interests. For example, if a user is traveling with family, they can suggest family-friendly activities. If a user is traveling for business, they can suggest business-related events and meetings. Furthermore, if a user is traveling for leisure, they can suggest experiences related to their hobby. By customizing the experience based on the user's current lifestyle and interests, they can offer a more appropriate experience.

[0104] The reception desk can estimate the user's emotions and adjust how they input their travel preferences and budget based on that estimation. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can offer detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input to allow them to quickly input their travel preferences and budget. In this way, by adjusting the input method based on the user's emotions, the system ensures that users can input information comfortably.

[0105] The generation unit can estimate the user's emotions and adjust the travel plan generation method based on the estimated emotions. For example, if the user is relaxed, it can generate a travel plan that proceeds at a leisurely pace. If the user is in a hurry, it can generate a travel plan that emphasizes the shortest route. Furthermore, if the user is excited, it can generate a travel plan with visually stimulating effects. In this way, by adjusting the travel plan generation method based on the user's emotions, a more appropriate travel plan can be generated.

[0106] The proposal function can estimate the user's emotions and adjust the presentation of the proposal based on those emotions. For example, if the user is nervous, it can provide a simple and highly visible proposal. If the user is relaxed, it can provide a proposal that includes detailed information. Furthermore, if the user is in a hurry, it can provide a proposal that gets straight to the point. By adjusting the presentation of the proposal based on the user's emotions, it becomes possible to provide more appropriate proposals.

[0107] The support team can estimate the user's emotions and adjust how new experiences are suggested based on those estimates. For example, if the user is relaxed, it can suggest experiences that can be enjoyed at a leisurely pace. If the user is excited, it can suggest active activities. Furthermore, if the user is tired, it can suggest relaxing experiences. In this way, by adjusting how new experiences are suggested based on the user's emotions, more appropriate experiences can be suggested.

[0108] The support team can estimate the user's emotions and prioritize new experiences based on those emotions. For example, if the user is tired, it can prioritize suggesting relaxing experiences. If the user is excited, it can prioritize suggesting active experiences. Furthermore, if the user is in a hurry, it can prioritize suggesting efficient experiences. In this way, by prioritizing new experiences based on the user's emotions, it can suggest more appropriate experiences.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The reception desk receives the user's travel preferences and budget. The user's travel preferences include destination, purpose of travel, and desired activities. The reception desk provides an interface for the user to enter their travel preferences, and the user can enter their travel preferences and budget through this interface. Step 2: The generation unit generates travel plans based on the information received by the reception unit. The generation unit uses generation AI, machine learning models, and neural networks to generate travel plans that meet the user's preferences. For example, it can generate nature-focused travel plans, luxury travel plans, cultural exploration travel plans, and more. Step 3: The suggestion unit proposes the travel plan generated by the generation unit to the user. The suggestion unit presents the generated travel plan to the user and allows the user to select one. It can also automatically make a travel reservation based on the travel plan selected by the user. Step 4: The support team will suggest new experiences during the trip based on local conditions and the user's mood. For example, if the weather deteriorates during the trip, they will suggest indoor activities. They can also suggest new tourist destinations or restaurants based on the user's mood.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the reception unit, generation unit, proposal unit, and support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives input from the user regarding their travel preferences and budget. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a travel plan using generation AI. The proposal unit is implemented by the output device 40 of the smart device 14 and presents the generated travel plan to the user. The support unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides real-time support during the trip. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the reception unit, generation unit, proposal unit, and support unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives input of the user's travel preferences and budget. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates a travel plan using generation AI. The proposal unit is implemented, for example, by the speaker 240 of the smart glasses 214 and presents the generated travel plan to the user. The support unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides real-time support during the trip. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the reception unit, generation unit, proposal unit, and support unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives input from the user regarding their travel preferences and budget. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates a travel plan using generation AI. The proposal unit is implemented by, for example, the display 343 of the headset terminal 314 and presents the generated travel plan to the user. The support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides real-time support during the trip. 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.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the reception unit, generation unit, proposal unit, and support unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives input of the user's travel preferences and budget. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates a travel plan using generation AI. The proposal unit is implemented by, for example, the speaker 240 of the robot 414 and presents the generated travel plan to the user. The support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides real-time support during the trip. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A reception desk that accepts users' travel preferences and budgets, A generation unit that generates a travel plan based on the information received by the reception unit, A proposal unit that proposes a travel plan generated by the generation unit to the user, It includes a support department that suggests new experiences during travel based on local conditions and the user's mood. A system characterized by the following features. (Note 2) The generating unit is The AI ​​generates travel plans tailored to the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, Propose the generated travel plan to the user. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned support unit is During your trip, we will suggest new experiences based on the local situation and the user's mood. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned support unit is We suggest indoor activities that can be enjoyed depending on the weather. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned support unit is Suggests new tourist destinations and restaurants based on the user's mood. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts how they input their travel preferences and budget based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past travel history and suggests the optimal input format. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users enter their travel preferences and budget, the input fields are customized based on their current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the user's emotions and prioritizes input fields based on those emotions. 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 and budget, the system prioritizes displaying the most relevant input fields, taking into account their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When users enter their travel preferences and budget, the system analyzes their social media activity and suggests relevant input fields. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is We estimate the user's emotions and adjust the travel plan generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating a travel plan, the system references the user's past travel history to create the most suitable plan. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating travel plans, the plan is customized based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and determines the priority of travel plans generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating travel plans, the system prioritizes generating highly relevant plans by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating travel plans, the system analyzes the user's social media activity and generates relevant plans. The system described in Appendix 1, characterized by the features described herein. (Note 19) 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 20) 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 21) 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 22) 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 23) 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 24) 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 25) The aforementioned support unit is It estimates the user's emotions and adjusts how new experiences are suggested based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned support unit is When providing support during a trip, we refer to the user's past travel history to suggest the best possible experience. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned support unit is During travel support, the experience is customized based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned support unit is It estimates the user's emotions and determines the priority of new experiences based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned support unit is When providing support during travel, we suggest the optimal experience by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned support unit is During travel support, we analyze the user's social media activity and suggest relevant experiences. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 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 that accepts users' travel preferences and budgets, A generation unit that generates a travel plan based on the information received by the reception unit, A proposal unit that proposes a travel plan generated by the generation unit to the user, It includes a support department that suggests new experiences during travel based on local conditions and the user's mood. A system characterized by the following features.

2. The generating unit is The AI ​​generates travel plans tailored to the user's preferences. The system according to feature 1.

3. The aforementioned proposal section is, Propose the generated travel plan to the user. The system according to feature 1.

4. The aforementioned support unit is During your trip, we will suggest new experiences based on the local situation and the user's mood. The system according to feature 1.

5. The aforementioned support unit is We suggest indoor activities that can be enjoyed depending on the weather. The system according to feature 1.

6. The aforementioned support unit is Suggests new tourist destinations and restaurants based on the user's mood. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts how they input their travel preferences and budget based on those emotions. The system according to feature 1.

8. The aforementioned reception unit is It analyzes the user's past travel history and suggests the optimal input format. The system according to feature 1.

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