System

Abstract

An object of a system according to an embodiment is to efficiently propose and customize a travel plan that meets the needs of a user.SOLUTION: A system according to an embodiment includes a user information input unit a travel plan generation unit and a chat customization unit. The user information input unit inputs information such as a departure place, the number of travel days, and a budget of the user. The travel plan generation unit proposes an optimal travel plan based on the information input by the user information input unit. The chat customization unit customizes the travel plan proposed by the travel plan generation unit in response to a question or a request from the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem of making it difficult to efficiently propose and customize travel plans that meet the user's needs.

[0005] The system according to the embodiment aims to efficiently propose and customize travel plans that meet the needs of users. [Means for solving the problem]

[0006] The system according to the embodiment includes a user information input unit, a travel plan generation unit, and a chat customization unit. The user information input unit inputs information such as the user's departure point, number of travel days, and budget. The travel plan generation unit proposes an optimal travel plan based on the information input by the user information input unit. The chat customization unit customizes the travel plan proposed by the travel plan generation unit in response to the user's questions and requests. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently propose and customize a travel plan that meets the needs of the user. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple 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), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The travel plan proposal system according to an embodiment of the present invention is a system in which a generative AI travel concierge proposes an optimal travel plan based on information such as the user's departure point, number of travel days, budget, etc. This allows the travel plan proposal system to automatically propose a travel plan that meets the user's needs and provide a customized travel plan.

[0029] The travel plan proposal system according to the embodiment includes a user information input unit, a travel plan generation unit, and a chat customization unit. The user information input unit inputs information such as the user's departure point, number of travel days, and budget. For example, the user can input this information through a dedicated app. The user information input unit then transmits the input information to the generation AI travel concierge. The travel plan generation unit proposes an optimal travel plan based on the information input by the user information input unit. For example, if a user inputs "three days from Tokyo, with a budget of 50,000 yen," the generation AI proposes travel destinations, accommodations, and tourist spots that meet those conditions. The generation AI can also customize the travel plan according to the user's needs. The chat customization unit customizes the travel plan proposed by the travel plan generation unit in response to the user's questions and requests. For example, if a user asks, "Is there a cheaper hotel?", the generation AI proposes a cheaper hotel within the budget. If the user wants to add a specific tourist spot, the generation AI re-proposes the plan in response to the request. This allows the system to propose and customize an optimal travel plan based on the user's information.

[0030] The travel plan generation unit can learn the user's past travel history and preferences and propose personalized travel plans. For example, the travel plan generation unit collects data on travel destinations and accommodations visited by the user in the past, and the generation AI proposes the next travel plan based on that data. For example, similar places and facilities are proposed based on ratings of cities and accommodations visited in the past. The travel plan generation unit also learns preferences for specific activities and tourist spots from the user's travel history and customizes the travel plan based on that. For example, for a user who has visited many museums in the past, cities with many museums are proposed. The travel plan generation unit also analyzes the user's past travel history and proposes travel plans tailored to specific seasons and events. For example, for a user who has visited a beach resort in the summer in the past, a summer beach resort is proposed for the next time. This makes it possible to propose more personalized travel plans based on the user's past travel history and preferences.

[0031] The travel plan generation unit can dynamically adjust an optimal travel plan by taking into account the user's real-time health condition and mood. The travel plan generation unit, for example, collects the user's health data (e.g., heart rate and sleep data from a smartwatch) and adjusts the travel plan based on that data. For example, if the user is feeling tired, the travel plan generation unit suggests a plan that allows them to relax. The travel plan generation unit also analyzes the user's mood in real time and suggests a travel plan that matches their mood. For example, if the user is feeling high in stress, the travel plan generation unit suggests a plan that allows them to relax in nature. The travel plan generation unit also dynamically adjusts activities during the trip according to the user's health condition and mood. For example, if the user is not feeling well, the travel plan generation unit suggests lighter activities. This allows the travel plan to be dynamically adjusted based on the user's real-time health condition and mood.

[0032] The travel plan generation unit can reflect the weather and event information of the travel destination in real time based on the information input by the user. For example, when the user inputs a travel destination, the travel plan generation unit obtains weather information for the area in real time and proposes a plan based on the weather. For example, if it rains, indoor activities are proposed. Furthermore, when the user inputs a travel itinerary, the travel plan generation unit obtains information on events being held during that period in real time and proposes a plan that allows the user to participate in events. For example, music festivals and fireworks displays. Furthermore, when the user inputs a travel destination, the travel plan generation unit proposes a plan that reflects the seasonal characteristics and event information of the area. For example, cherry blossom viewing spots are proposed during cherry blossom season. In this way, the weather and event information of the travel destination can be reflected in real time based on the information input by the user.

[0033] The travel plan generation unit can link with the user's social media account and suggest plans that take into account reviews and photos of travel destinations posted by friends and followers. The travel plan generation unit, for example, links with the user's social media account and collects reviews and photos of travel destinations posted by friends and followers. For example, it can suggest restaurants and tourist spots that friends have highly rated. The travel plan generation unit also analyzes information about travel destinations visited by friends and followers from the user's social media feed and suggests travel plans based on that information. For example, it can suggest plans that include places visited by friends. The travel plan generation unit also takes into account travel experiences shared by friends and followers through the user's social media account and suggests plans that allow users to have similar experiences. For example, it can suggest plans that include activities that friends enjoyed. In this way, the travel plan generation unit can link with the user's social media account and suggest plans that take into account reviews and photos of travel destinations posted by friends and followers.

[0034] The chat customization unit can discover the user's hidden needs and desires through chat interactions and propose plans based on them. For example, when a user sends a question or request through chat, the chat customization unit analyzes the content and discovers the user's hidden needs. For example, if a user asks about a specific activity, the chat customization unit proposes a plan that includes that activity. The chat customization unit also grasps the user's wishes and preferences in detail through chat interactions and customizes a travel plan based on them. For example, if a user likes a specific dish, the chat customization unit proposes restaurants that serve that dish. The chat customization unit also analyzes the user's chat history, discovers hidden needs from past questions and requests, and proposes plans based on them. For example, it proposes a plan that includes a topic that the user has asked about frequently in the past. In this way, the chat customization unit can discover the user's hidden needs and desires through chat interactions and propose plans based on them.

[0035] The chat customization unit can analyze the user's chat history and make predictive suggestions based on past questions and requests. The chat customization unit, for example, builds a system that analyzes the user's chat history and makes predictive suggestions based on past questions and requests. For example, it makes next suggestions based on themes that have been frequently asked about in the past. The chat customization unit also learns the user's preferences and needs from the chat history and predictively suggests travel plans based on that. For example, it suggests plans that include activities that have been frequently requested in the past. The chat customization unit also develops a system that predictively suggests next travel plans based on the user's chat history. For example, it suggests the next travel destination and activity based on past requests. This makes it possible to analyze the user's chat history and make predictive suggestions based on past questions and requests.

[0036] The chat customization unit adds a voice recognition function to the chatbot, enabling it to respond to voice questions and requests. For example, the chat customization unit adds a voice recognition function to the chatbot, allowing users to send questions and requests by voice. For example, a user may ask, "Are there any cheaper hotels?" The chat customization unit then uses the voice recognition function to convert the user's voice input into text, and the generation AI analyzes the content and provides an answer. For example, it customizes a travel plan based on the voice request. When a user sends a question or request by voice, the chat customization unit analyzes the voice data and builds a system that provides the optimal answer. For example, it uses voice recognition technology to accurately understand the user's intent. This makes it possible to respond to voice questions and requests.

[0037] The chat customization unit allows users to share chat interactions with other users and receive advice and suggestions on a community basis. The chat customization unit, for example, builds a system for sharing chat interactions with other users and receiving advice and suggestions on a community basis. For example, other users share places they have visited in the past and their experiences. When a user sends a question or request via chat, the chat customization unit publishes the content to the community and receives feedback from other users. For example, other users suggest recommended tourist spots. The chat customization unit also shares chat interactions on a community basis and customizes travel plans based on advice and suggestions from other users. For example, the plan is adjusted based on information provided by other users. This allows users to share chat interactions with other users and receive advice and suggestions on a community basis.

[0038] The multiple travel plan selection and collective booking / payment unit learns the user's selection history and reflects it in future suggestions, thereby providing more accurate plans. The multiple travel plan selection and collective booking / payment unit, for example, collects the history of travel plans selected by the user in the past and makes future suggestions based on that. For example, it refers to accommodations and tourist spots selected in the past. The multiple travel plan selection and collective booking / payment unit also analyzes the user's selection history and builds a system that learns specific patterns and preferences. For example, if a user prefers a particular region or activity, it proposes a plan based on that. The multiple travel plan selection and collective booking / payment unit also customizes future travel plans based on the user's selection history. For example, it proposes plans similar to plans selected in the past. This allows the system to learn the user's selection history and reflect it in future suggestions, thereby providing more accurate plans.

[0039] The multiple travel plan selection and collective booking / payment unit strengthens collaboration with each business operator to provide special offers and discounts tailored to the user's specific needs. For example, the multiple travel plan selection and collective booking / payment unit strengthens collaboration with each business operator (hotels, restaurants, tourist attractions, etc.) to build a system that provides special offers and discounts tailored to the user's specific needs. For example, it may provide a discount at a specific accommodation facility. The multiple travel plan selection and collective booking / payment unit also obtains special offer and discount information from each business operator in real time according to the user's needs and reflects this in the travel plan. For example, it may suggest a discount at a specific restaurant. The multiple travel plan selection and collective booking / payment unit also provides customized offers tailored to the user's specific needs through collaboration with each business operator. For example, it may suggest a special experience at a specific tourist attraction. This strengthens collaboration with each business operator to provide special offers and discounts tailored to the user's specific needs.

[0040] The multiple travel plan selection and collective booking / payment section can display reviews and ratings from other users based on the plan selected by the user, allowing the user to make a selection. The multiple travel plan selection and collective booking / payment section, for example, builds a system that displays reviews and ratings from other users in real time based on the travel plan selected by the user. For example, it displays reviews of accommodations and tourist attractions. The multiple travel plan selection and collective booking / payment section also provides detailed information about the selected plan based on the reviews and ratings of other users. For example, it displays ratings of specific restaurants and activities. The multiple travel plan selection and collective booking / payment section also collects feedback from other users about the plan selected by the user and provides information to help the user make a selection based on that feedback. For example, it displays the opinions of users who have selected the same plan in the past. This allows the user to display reviews and ratings from other users based on the plan selected by the user, allowing the user to make a selection.

[0041] The multiple travel plan selection and bulk booking / payment unit can add a function for automatically accumulating points or miles when making a bulk booking and payment, improving user convenience. A system is being developed that adds a function for automatically accumulating points or miles when a user selects a travel plan and makes a bulk booking and payment. For example, airline miles are automatically accumulated. Furthermore, the multiple travel plan selection and bulk booking / payment unit can automatically accumulate points or miles when a user makes a bulk booking and payment through collaboration with each business. For example, a hotel point program can be automatically applied. Furthermore, a system is being developed that displays the accumulated points or miles in real time when a user makes a bulk booking and payment. For example, the points or miles earned at the time of booking can be displayed. This allows the automatic accumulation of points or miles when making a bulk booking and payment to be added, improving user convenience.

[0042] The information provision unit during travel can provide optimal tourist spot and event information in real time based on the user's current location and schedule. The information provision unit during travel, for example, acquires the user's current location using GPS and builds a system that provides information on nearby tourist spots and events in real time. For example, nearby tourist spots and events are suggested. The information provision unit during travel also provides optimal tourist spot and event information based on the user's travel schedule. For example, additional tourist spots are suggested when there is room in the schedule. The information provision unit during travel also develops a system that provides optimal tourist spot and event information in real time based on the user's current location and schedule. For example, tourist spots within walking distance from the current location are suggested. This makes it possible to provide optimal tourist spot and event information in real time based on the user's current location and schedule.

[0043] The information provision unit for during travel can add a recommendation function according to the user's preferences and personalize the experience during the travel. The information provision unit for during travel, for example, learns the user's preferences and adds a recommendation function that personalizes the experience during the travel based on them. For example, it suggests activities that the user likes. The information provision unit for during travel also builds a system that personalizes the experience during the travel based on the user's past travel history and preferences. For example, it suggests tourist spots similar to places visited in the past. The information provision unit for during travel also adds a recommendation function according to the user's preferences and personalizes the experience during the travel. For example, it suggests restaurants that serve dishes that the user likes. In this way, it is possible to add a recommendation function according to the user's preferences and personalize the experience during the travel.

[0044] The information providing unit during travel can add an AR function to a dedicated app and visually provide information about tourist spots. For example, the information providing unit during travel can add an AR function to a dedicated app and provide visual information through the smartphone camera when the user arrives at a tourist spot. For example, the history and highlights of the tourist spot can be displayed. The information providing unit during travel can also use the AR function to build a system that visually provides information about tourist spots. For example, when the user holds the camera over the tourist spot, detailed information about the tourist spot can be displayed. The information providing unit during travel can also add an AR function to a dedicated app and visually provide information about tourist spots. For example, when the user views a tourist spot through the camera, related information can be displayed. This allows the AR function to be added to the dedicated app and visually provide information about tourist spots.

[0045] The travel information providing unit can add a function to link the travel information provided with the user's social media account and share it with friends and followers. The travel information providing unit, for example, adds an social media linking function to a dedicated app, allowing the user to share travel information with friends and followers in real time. For example, the travel information providing unit posts photos and reviews of tourist spots. The travel information providing unit can also link with the user's social media account to build a system that automatically shares travel information. For example, the travel information providing unit can automatically post places the user has visited and experiences. The travel information providing unit can also link with the social media account to add a function to share travel information with friends and followers. For example, the travel information providing unit can share information about tourist spots the user has visited in real time. This allows the travel information providing unit to link with the user's social media account to add a function to share travel information with friends and followers.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The travel plan generation unit can also propose travel plans along specific themes based on the user's hobbies and interests. For example, a plan touring historical sites can be proposed to a user who loves history. A plan to enjoy local specialties can also be proposed to a user who loves food. Furthermore, it is possible to propose plans including hiking and camping to a user who loves the outdoors. In this way, it is possible to provide themed trips that match the user's hobbies and interests.

[0048] The travel plan generation unit can learn a user's past travel history and preferences and propose personalized travel plans. For example, data on travel destinations and accommodations visited by the user is collected, and the generation AI uses that data to propose the next travel plan. For example, similar places and facilities are proposed based on ratings of cities and accommodations visited in the past. The travel plan generation unit can also learn preferences for specific activities and tourist spots from the user's travel history and customize the travel plan based on that. For example, for a user who has visited many museums in the past, cities with many museums are proposed. The travel plan generation unit can also analyze a user's past travel history and propose travel plans tailored to specific seasons or events. For example, for a user who has visited a beach resort in the summer in the past, a summer beach resort is proposed for the next visit. This allows for more personalized travel plans to be proposed based on the user's past travel history and preferences.

[0049] The travel plan generation unit can dynamically adjust the optimal travel plan by taking into account the user's real-time health condition and mood. For example, it collects the user's health data (e.g., heart rate and sleep data from a smartwatch) and adjusts the travel plan based on that. For example, if the user is tired, it can suggest a plan that allows them to relax. The travel plan generation unit also analyzes the user's mood in real time and suggests a travel plan that suits their mood. For example, if the user is under a lot of stress, it can suggest a plan that allows them to relax in nature. The travel plan generation unit also dynamically adjusts activities during the trip according to the user's health condition and mood. For example, if the user is not feeling well, it can suggest lighter activities. This allows the travel plan to be dynamically adjusted based on the user's real-time health condition and mood.

[0050] The travel plan generation unit can reflect the weather and event information of the travel destination in real time based on the information input by the user. For example, when the user inputs a travel destination, the unit obtains weather information for the area in real time and proposes a plan based on the weather. For example, if it rains, indoor activities are proposed. Furthermore, when the user inputs a travel itinerary, the travel plan generation unit obtains information on events being held during that period in real time and proposes a plan that allows the user to participate in events. For example, music festivals and fireworks displays. Furthermore, when the user inputs a travel destination, the travel plan generation unit proposes a plan that reflects the seasonal characteristics and event information of the area. For example, cherry blossom viewing spots are proposed during cherry blossom season. In this way, the weather and event information of the travel destination can be reflected in real time based on the information input by the user.

[0051] The travel plan generation unit can link with the user's social media account and suggest plans that take into account reviews and photos of travel destinations posted by friends and followers. For example, the unit can link with the user's social media account and collect reviews and photos of travel destinations posted by friends and followers. For example, the unit can suggest restaurants and tourist spots that friends have highly rated. The travel plan generation unit can also analyze information about travel destinations visited by friends and followers from the user's social media feed and suggest travel plans based on that information. For example, the unit can suggest plans that include places visited by friends. The travel plan generation unit can also take into account travel experiences shared by friends and followers through the user's social media account and suggest plans that allow users to have similar experiences. For example, the unit can suggest plans that include activities that friends enjoyed. In this way, the unit can link with the user's social media account and suggest plans that take into account reviews and photos of travel destinations posted by friends and followers.

[0052] The chat customization unit can discover the user's hidden needs and desires through chat interactions and propose plans based on them. For example, when a user sends a question or request through chat, the content is analyzed to discover the user's hidden needs. For example, if the user asks about a specific activity, a plan that includes that activity is proposed. The chat customization unit also obtains a detailed understanding of the user's wishes and preferences through chat interactions and customizes travel plans based on them. For example, if the user likes a specific dish, it proposes restaurants that serve that dish. The chat customization unit also analyzes the user's chat history to discover hidden needs from past questions and requests and propose plans based on them. For example, it proposes plans that include topics that the user has asked about frequently in the past. In this way, the chat customization unit can discover the user's hidden needs and desires through chat interactions and propose plans based on them.

[0053] The chat customization unit can analyze a user's chat history and make predictive suggestions based on past questions and requests. For example, a system can be constructed that analyzes a user's chat history and makes predictive suggestions based on past questions and requests. For example, next suggestions are made based on themes that have been frequently asked about in the past. The chat customization unit can also learn the user's preferences and needs from the chat history and predictively suggest travel plans based on that. For example, it can suggest plans that include activities that have been frequently requested in the past. The chat customization unit can also develop a system that predictively suggests next travel plans based on the user's chat history. For example, it can suggest the next travel destination and activity based on past requests. This makes it possible to analyze a user's chat history and make predictive suggestions based on past questions and requests.

[0054] The chat customization unit adds a voice recognition function to the chatbot, enabling it to respond to voice questions and requests. For example, adding a voice recognition function to the chatbot allows users to send questions and requests by voice. For example, a user may ask, "Are there any cheaper hotels?" The chat customization unit then uses the voice recognition function to convert the user's voice input into text, and the generation AI analyzes the content and provides an answer. For example, it customizes a travel plan based on the voice request. When a user sends a question or request by voice, the chat customization unit analyzes the voice data and builds a system that provides the optimal answer. For example, it uses voice recognition technology to accurately understand the user's intent. This makes it possible to respond to voice questions and requests.

[0055] The chat customization unit allows users to share chat interactions with other users and receive advice and suggestions on a community basis. For example, a system is constructed in which users share chat interactions with other users and receive advice and suggestions on a community basis. For example, other users share places they have visited in the past and their experiences. When a user sends a question or request via chat, the chat customization unit publishes the content to the community and receives feedback from other users. For example, other users suggest recommended tourist spots. The chat customization unit also shares chat interactions on a community basis and customizes travel plans based on advice and suggestions from other users. For example, the plan is adjusted based on information provided by other users. This allows users to share chat interactions with other users and receive advice and suggestions on a community basis.

[0056] The multiple travel plan selection and collective booking / payment unit learns the user's selection history and reflects it in future suggestions, thereby providing more accurate plans. For example, it collects the history of travel plans selected by the user in the past and makes next suggestions based on that. For example, it refers to accommodations and tourist spots selected in the past. The multiple travel plan selection and collective booking / payment unit also analyzes the user's selection history and builds a system that learns specific patterns and preferences. For example, if a user prefers a specific region or activity, it proposes a plan based on that. The multiple travel plan selection and collective booking / payment unit also customizes future travel plans based on the user's selection history. For example, it proposes plans similar to plans selected in the past. In this way, the system can learn the user's selection history and reflect it in future suggestions, thereby providing more accurate plans.

[0057] The multiple travel plan selection and collective booking / payment unit strengthens collaboration with each business to provide special offers and discounts tailored to the user's specific needs. For example, by strengthening collaboration with each business (hotels, restaurants, tourist attractions, etc.), a system can be built to provide special offers and discounts tailored to the user's specific needs. For example, a discount can be offered at a specific accommodation facility. The multiple travel plan selection and collective booking / payment unit also obtains special offer and discount information from each business in real time according to the user's needs and reflects this in the travel plan. For example, a discount can be offered at a specific restaurant. The multiple travel plan selection and collective booking / payment unit also provides customized offers tailored to the user's specific needs through collaboration with each business. For example, a special experience can be offered at a specific tourist attraction. This strengthens collaboration with each business to provide special offers and discounts tailored to the user's specific needs.

[0058] The multiple travel plan selection and collective booking / payment section can display reviews and ratings from other users based on the plan selected by the user, to help with selection. For example, a system can be built that displays reviews and ratings from other users in real time based on the travel plan selected by the user. For example, reviews of accommodations and tourist attractions can be displayed. The multiple travel plan selection and collective booking / payment section can also provide detailed information about the selected plan based on the reviews and ratings of other users. For example, ratings of specific restaurants and activities can be displayed. The multiple travel plan selection and collective booking / payment section can also collect feedback from other users about the plan selected by the user and provide information to help with selection based on that feedback. For example, opinions of users who have selected the same plan in the past can be displayed. This allows reviews and ratings from other users to be displayed based on the plan selected by the user, to help with selection.

[0059] The multiple travel plan selection and bulk booking / payment section can add an automatic points or mileage accrual function when making a bulk booking and payment, improving user convenience. For example, we will build a system that adds an automatic points or mileage accrual function when a user selects a travel plan and makes a bulk booking and payment. For example, airline miles can be automatically accumulated. Furthermore, the multiple travel plan selection and bulk booking / payment section can automatically accumulate points or miles when a user makes a bulk booking and payment through collaboration with each business. For example, hotel points programs can be automatically applied. Furthermore, we will develop a system that displays the accumulated points or mileage status in real time when a user makes a bulk booking and payment. For example, it displays the points or miles earned at the time of booking. This will add an automatic points or mileage accrual function when making a bulk booking and payment, improving user convenience.

[0060] The information provision unit during travel can provide optimal tourist spot and event information in real time based on the user's current location and schedule. For example, a system is constructed that acquires the user's current location using GPS and provides information on tourist spots and events in the surrounding area in real time. For example, nearby tourist spots and events are suggested. The information provision unit during travel also provides optimal tourist spot and event information based on the user's travel schedule. For example, additional tourist spots are suggested if there is room in the schedule. A system is also developed in which the information provision unit during travel provides optimal tourist spot and event information in real time based on the user's current location and schedule. For example, tourist spots within walking distance from the current location are suggested. This makes it possible to provide optimal tourist spot and event information in real time based on the user's current location and schedule.

[0061] The travel information provision unit can add a recommendation function based on the user's preferences and personalize the travel experience. For example, it can learn the user's preferences and add a recommendation function that personalizes the travel experience based on them. For example, it can suggest activities that the user likes. The travel information provision unit can also build a system that personalizes the travel experience based on the user's past travel history and preferences. For example, it can suggest tourist spots similar to places visited in the past. The travel information provision unit can also add a recommendation function based on the user's preferences and personalize the travel experience. For example, it can suggest restaurants that serve dishes that the user likes. In this way, it is possible to add a recommendation function based on the user's preferences and personalize the travel experience.

[0062] The information provision unit during travel can add an AR function to a dedicated app and visually provide information about tourist spots. For example, an AR function can be added to the dedicated app, and when the user arrives at a tourist spot, the information can be provided visually through the smartphone camera. For example, the history and highlights of the tourist spot can be displayed. The information provision unit during travel can also use the AR function to build a system that visually provides information about tourist spots. For example, when the user holds the camera over the tourist spot, detailed information about the tourist spot can be displayed. The information provision unit during travel can also add an AR function to the dedicated app and visually provide information about tourist spots. For example, when the user looks at a tourist spot through the camera, related information can be displayed. This allows the AR function to be added to the dedicated app and visually provide information about tourist spots.

[0063] The travel information providing unit can add a function to link the travel information provided with the user's social media account and share it with friends and followers. For example, an SNS linking function can be added to a dedicated app, allowing the user to share travel information with friends and followers in real time. For example, photos and reviews of tourist spots can be posted. The travel information providing unit can also link with the user's social media account to build a system that automatically shares travel information. For example, the user can automatically post places visited and experiences. The travel information providing unit can also link with the social media account to add a function to share travel information with friends and followers. For example, information about tourist spots visited by the user can be shared in real time. This makes it possible to add a function to link the travel information provided with the user's social media account and share it with friends and followers.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The user information input unit inputs information such as the user's departure point, number of travel days, and budget. For example, the user can input this information through a dedicated app. The user information input unit then sends the input information to the generated AI travel concierge. Step 2: The travel plan generation unit proposes the optimal travel plan based on the information entered by the user information input unit. For example, if a user enters "three days from Tokyo, budget 50,000 yen," the generation AI will propose travel destinations, accommodations, and tourist spots that fit those conditions. The generation AI can also customize the travel plan according to the user's needs. Step 3: The chat customization unit customizes the travel plan proposed by the travel plan generation unit based on the user's questions and requests. For example, if the user asks, "Are there any cheaper hotels?", the generation AI will suggest cheaper hotels within the user's budget. Also, if the user wants to add a specific tourist spot, the AI ​​will re-propose the plan based on that request.

[0066] (Example 2) The travel plan proposal system according to an embodiment of the present invention is a system in which a generative AI travel concierge proposes an optimal travel plan based on information such as the user's departure point, number of travel days, budget, etc. This allows the travel plan proposal system to automatically propose a travel plan that meets the user's needs and provide a customized travel plan.

[0067] The travel plan proposal system according to the embodiment includes a user information input unit, a travel plan generation unit, and a chat customization unit. The user information input unit inputs information such as the user's departure point, number of travel days, and budget. For example, the user can input this information through a dedicated app. The user information input unit then transmits the input information to the generation AI travel concierge. The travel plan generation unit proposes an optimal travel plan based on the information input by the user information input unit. For example, if a user inputs "three days from Tokyo, with a budget of 50,000 yen," the generation AI proposes travel destinations, accommodations, and tourist spots that meet those conditions. The generation AI can also customize the travel plan according to the user's needs. The chat customization unit customizes the travel plan proposed by the travel plan generation unit in response to the user's questions and requests. For example, if a user asks, "Is there a cheaper hotel?", the generation AI proposes a cheaper hotel within the budget. If the user wants to add a specific tourist spot, the generation AI re-proposes the plan in response to the request. This allows the system to propose and customize an optimal travel plan based on the user's information.

[0068] The travel plan generation unit can learn the user's past travel history and preferences and propose personalized travel plans. For example, the travel plan generation unit collects data on travel destinations and accommodations visited by the user in the past, and the generation AI proposes the next travel plan based on that data. For example, similar places and facilities are proposed based on ratings of cities and accommodations visited in the past. The travel plan generation unit also learns preferences for specific activities and tourist spots from the user's travel history and customizes the travel plan based on that. For example, for a user who has visited many museums in the past, cities with many museums are proposed. The travel plan generation unit also analyzes the user's past travel history and proposes travel plans tailored to specific seasons and events. For example, for a user who has visited a beach resort in the summer in the past, a summer beach resort is proposed for the next time. This makes it possible to propose more personalized travel plans based on the user's past travel history and preferences.

[0069] The travel plan generation unit can dynamically adjust an optimal travel plan by taking into account the user's real-time health condition and mood. The travel plan generation unit, for example, collects the user's health data (e.g., heart rate and sleep data from a smartwatch) and adjusts the travel plan based on that data. For example, if the user is feeling tired, the travel plan generation unit suggests a plan that allows them to relax. The travel plan generation unit also analyzes the user's mood in real time and suggests a travel plan that matches their mood. For example, if the user is feeling high in stress, the travel plan generation unit suggests a plan that allows them to relax in nature. The travel plan generation unit also dynamically adjusts activities during the trip according to the user's health condition and mood. For example, if the user is not feeling well, the travel plan generation unit suggests lighter activities. This allows the travel plan to be dynamically adjusted based on the user's real-time health condition and mood.

[0070] The travel plan generation unit uses the emotion estimation function to analyze the emotions felt by the user when entering information and can propose travel plans that elicit positive emotions. For example, the travel plan generation unit analyzes the facial expressions and voice of the user when entering a travel plan and calculates an emotion score. For example, if a smile or an excited voice is detected, the travel plan generation unit proposes a plan that reinforces that emotion. The travel plan generation unit also uses the emotion estimation function to analyze the emotions felt by the user when entering information in real time and makes proposals to elicit positive emotions. For example, if the user appears to be having fun, the travel plan generation unit proposes a plan that maintains that emotion. The travel plan generation unit also proposes a new plan based on the user's emotion data, referring to travel plans that have elicited positive emotions in the past. For example, the travel plan generation unit proposes a plan that includes activities that the user has enjoyed in the past. In this way, the travel plan generation unit can analyze the user's emotions and propose travel plans that elicit positive emotions.

[0071] The travel plan generation unit can reflect the weather and event information of the travel destination in real time based on the information input by the user. For example, when the user inputs a travel destination, the travel plan generation unit obtains weather information for the area in real time and proposes a plan based on the weather. For example, if it rains, indoor activities are proposed. Furthermore, when the user inputs a travel itinerary, the travel plan generation unit obtains information on events being held during that period in real time and proposes a plan that allows the user to participate in events. For example, music festivals and fireworks displays. Furthermore, when the user inputs a travel destination, the travel plan generation unit proposes a plan that reflects the seasonal characteristics and event information of the area. For example, cherry blossom viewing spots are proposed during cherry blossom season. In this way, the weather and event information of the travel destination can be reflected in real time based on the information input by the user.

[0072] The travel plan generation unit can link with the user's social media account and suggest plans that take into account reviews and photos of travel destinations posted by friends and followers. The travel plan generation unit, for example, links with the user's social media account and collects reviews and photos of travel destinations posted by friends and followers. For example, it can suggest restaurants and tourist spots that friends have highly rated. The travel plan generation unit also analyzes information about travel destinations visited by friends and followers from the user's social media feed and suggests travel plans based on that information. For example, it can suggest plans that include places visited by friends. The travel plan generation unit also takes into account travel experiences shared by friends and followers through the user's social media account and suggests plans that allow users to have similar experiences. For example, it can suggest plans that include activities that friends enjoyed. In this way, the travel plan generation unit can link with the user's social media account and suggest plans that take into account reviews and photos of travel destinations posted by friends and followers.

[0073] The travel plan generation unit uses the emotion estimation function to analyze the emotions of the user when entering information in real time and make suggestions to optimize the input content. For example, the travel plan generation unit analyzes the facial expressions and voice of the user when entering a travel plan in real time and calculates an emotion score. For example, if the user appears anxious, the travel plan generation unit makes suggestions to alleviate the anxiety. The travel plan generation unit also uses the emotion estimation function to analyze the emotions of the user when entering information in real time and makes suggestions to bring out positive emotions. For example, if the user appears to be enjoying themselves, the travel plan generation unit suggests a plan to maintain that emotion. The travel plan generation unit also provides feedback to optimize the input content in real time based on the user's emotion data. For example, if the user is unsure, the travel plan generation unit makes specific suggestions. This makes it possible to analyze the user's emotions in real time and make suggestions to optimize the input content.

[0074] The chat customization unit can discover the user's hidden needs and desires through chat interactions and propose plans based on them. For example, when a user sends a question or request through chat, the chat customization unit analyzes the content and discovers the user's hidden needs. For example, if a user asks about a specific activity, the chat customization unit proposes a plan that includes that activity. The chat customization unit also grasps the user's wishes and preferences in detail through chat interactions and customizes a travel plan based on them. For example, if a user likes a specific dish, the chat customization unit proposes restaurants that serve that dish. The chat customization unit also analyzes the user's chat history, discovers hidden needs from past questions and requests, and proposes plans based on them. For example, it proposes a plan that includes a topic that the user has asked about frequently in the past. In this way, the chat customization unit can discover the user's hidden needs and desires through chat interactions and propose plans based on them.

[0075] The chat customization unit can analyze the user's chat history and make predictive suggestions based on past questions and requests. The chat customization unit, for example, builds a system that analyzes the user's chat history and makes predictive suggestions based on past questions and requests. For example, it makes next suggestions based on themes that have been frequently asked about in the past. The chat customization unit also learns the user's preferences and needs from the chat history and predictively suggests travel plans based on that. For example, it suggests plans that include activities that have been frequently requested in the past. The chat customization unit also develops a system that predictively suggests next travel plans based on the user's chat history. For example, it suggests the next travel destination and activity based on past requests. This makes it possible to analyze the user's chat history and make predictive suggestions based on past questions and requests.

[0076] The chat customization unit can use the emotion estimation function to analyze the user's emotions in response to questions and requests and provide the optimal answer. For example, the chat customization unit analyzes the user's facial expressions and voice when sending a question or request via chat and calculates an emotion score. For example, if the user appears anxious, it provides an answer that alleviates that anxiety. The chat customization unit also uses the emotion estimation function to analyze the user's emotions in response to questions and requests in real time and provides an answer that elicits positive emotions. For example, if the user appears happy, it provides an answer that maintains that emotion. The chat customization unit also builds a system that provides the optimal answer to questions and requests based on the user's emotion data. For example, if the user is unsure, it makes specific suggestions. This makes it possible to analyze the user's emotions in response to questions and requests and provide the optimal answer.

[0077] The chat customization unit adds a voice recognition function to the chatbot, enabling it to respond to voice questions and requests. For example, the chat customization unit adds a voice recognition function to the chatbot, allowing users to send questions and requests by voice. For example, a user may ask, "Are there any cheaper hotels?" The chat customization unit then uses the voice recognition function to convert the user's voice input into text, and the generation AI analyzes the content and provides an answer. For example, it customizes a travel plan based on the voice request. When a user sends a question or request by voice, the chat customization unit analyzes the voice data and builds a system that provides the optimal answer. For example, it uses voice recognition technology to accurately understand the user's intent. This makes it possible to respond to voice questions and requests.

[0078] The chat customization unit allows users to share chat interactions with other users and receive advice and suggestions on a community basis. The chat customization unit, for example, builds a system for sharing chat interactions with other users and receiving advice and suggestions on a community basis. For example, other users share places they have visited in the past and their experiences. When a user sends a question or request via chat, the chat customization unit publishes the content to the community and receives feedback from other users. For example, other users suggest recommended tourist spots. The chat customization unit also shares chat interactions on a community basis and customizes travel plans based on advice and suggestions from other users. For example, the plan is adjusted based on information provided by other users. This allows users to share chat interactions with other users and receive advice and suggestions on a community basis.

[0079] The chat customization unit uses the emotion estimation function to analyze the emotions of users during chat in real time and provide appropriate responses. For example, the chat customization unit analyzes facial expressions and voices in real time when a user sends a question or request via chat and calculates an emotion score. For example, if the user appears anxious, it provides an answer that alleviates that anxiety. The chat customization unit also uses the emotion estimation function to analyze the emotions of users during chat in real time and provide responses that draw out positive emotions. For example, if the user appears happy, it provides an answer that maintains that emotion. The chat customization unit also builds a system that optimizes responses during chat based on the user's emotion data. For example, if the user is unsure, it makes specific suggestions. This makes it possible to analyze the emotions of users during chat in real time and provide appropriate responses.

[0080] The multiple travel plan selection and collective booking / payment unit learns the user's selection history and reflects it in future suggestions, thereby providing more accurate plans. The multiple travel plan selection and collective booking / payment unit, for example, collects the history of travel plans selected by the user in the past and makes future suggestions based on that. For example, it refers to accommodations and tourist spots selected in the past. The multiple travel plan selection and collective booking / payment unit also analyzes the user's selection history and builds a system that learns specific patterns and preferences. For example, if a user prefers a particular region or activity, it proposes a plan based on that. The multiple travel plan selection and collective booking / payment unit also customizes future travel plans based on the user's selection history. For example, it proposes plans similar to plans selected in the past. This allows the system to learn the user's selection history and reflect it in future suggestions, thereby providing more accurate plans.

[0081] The multiple travel plan selection and collective booking / payment unit strengthens collaboration with each business operator to provide special offers and discounts tailored to the user's specific needs. For example, the multiple travel plan selection and collective booking / payment unit strengthens collaboration with each business operator (hotels, restaurants, tourist attractions, etc.) to build a system that provides special offers and discounts tailored to the user's specific needs. For example, it may provide a discount at a specific accommodation facility. The multiple travel plan selection and collective booking / payment unit also obtains special offer and discount information from each business operator in real time according to the user's needs and reflects this in the travel plan. For example, it may suggest a discount at a specific restaurant. The multiple travel plan selection and collective booking / payment unit also provides customized offers tailored to the user's specific needs through collaboration with each business operator. For example, it may suggest a special experience at a specific tourist attraction. This strengthens collaboration with each business operator to provide special offers and discounts tailored to the user's specific needs.

[0082] The multiple travel plan selection, collective booking, and payment unit uses an emotion estimation function to analyze the emotions felt by the user when making a selection and can suggest plans that will provide high satisfaction. For example, the multiple travel plan selection, collective booking, and payment unit analyzes facial expressions and voices when the user selects a travel plan and calculates an emotion score. For example, if the user is excited, the unit suggests plans that reinforce that emotion. The multiple travel plan selection, collective booking, and payment unit also uses the emotion estimation function to analyze the emotions felt by the user in real time when making a selection and suggests plans that elicit positive emotions. For example, if the user appears to be enjoying themselves, the unit suggests plans that maintain that emotion. The multiple travel plan selection, collective booking, and payment unit also builds a system that uses the user's emotion data to suggest plans that maximize satisfaction when making a selection. For example, if the user is unsure, the unit makes specific suggestions. This allows the system to analyze the emotions felt by the user when making a selection and suggest plans that will provide high satisfaction.

[0083] The multiple travel plan selection and collective booking / payment section can display reviews and ratings from other users based on the plan selected by the user, allowing the user to make a selection. The multiple travel plan selection and collective booking / payment section, for example, builds a system that displays reviews and ratings from other users in real time based on the travel plan selected by the user. For example, it displays reviews of accommodations and tourist attractions. The multiple travel plan selection and collective booking / payment section also provides detailed information about the selected plan based on the reviews and ratings of other users. For example, it displays ratings of specific restaurants and activities. The multiple travel plan selection and collective booking / payment section also collects feedback from other users about the plan selected by the user and provides information to help the user make a selection based on that feedback. For example, it displays the opinions of users who have selected the same plan in the past. This allows the user to display reviews and ratings from other users based on the plan selected by the user, allowing the user to make a selection.

[0084] The multiple travel plan selection and bulk booking / payment unit can add a function for automatically accumulating points or miles when making a bulk booking and payment, improving user convenience. A system is being developed that adds a function for automatically accumulating points or miles when a user selects a travel plan and makes a bulk booking and payment. For example, airline miles are automatically accumulated. Furthermore, the multiple travel plan selection and bulk booking / payment unit can automatically accumulate points or miles when a user makes a bulk booking and payment through collaboration with each business. For example, a hotel point program can be automatically applied. Furthermore, a system is being developed that displays the accumulated points or miles in real time when a user makes a bulk booking and payment. For example, the points or miles earned at the time of booking can be displayed. This allows the automatic accumulation of points or miles when making a bulk booking and payment to be added, improving user convenience.

[0085] The multiple travel plan selection, collective booking, and payment unit uses emotion estimation to analyze the user's emotions in real time when selecting a travel plan and support optimal selection. For example, the multiple travel plan selection, collective booking, and payment unit analyzes the user's facial expressions and voice in real time when selecting a travel plan and calculates an emotion score. For example, if the user appears anxious, the unit makes suggestions to alleviate that anxiety. The multiple travel plan selection, collective booking, and payment unit also uses emotion estimation to analyze the user's emotions in real time when selecting a plan and support the user in eliciting positive emotions. For example, if the user appears to be enjoying themselves, the unit makes suggestions to maintain that emotion. The multiple travel plan selection, collective booking, and payment unit also builds a system that provides optimal support when selecting a plan based on the user's emotion data. For example, if the user is unsure, the unit makes specific suggestions. This allows the system to analyze the user's emotions in real time when selecting a plan and support the user in making the optimal selection.

[0086] The information provision unit during travel can provide optimal tourist spot and event information in real time based on the user's current location and schedule. The information provision unit during travel, for example, acquires the user's current location using GPS and builds a system that provides information on nearby tourist spots and events in real time. For example, nearby tourist spots and events are suggested. The information provision unit during travel also provides optimal tourist spot and event information based on the user's travel schedule. For example, additional tourist spots are suggested when there is room in the schedule. The information provision unit during travel also develops a system that provides optimal tourist spot and event information in real time based on the user's current location and schedule. For example, tourist spots within walking distance from the current location are suggested. This makes it possible to provide optimal tourist spot and event information in real time based on the user's current location and schedule.

[0087] The information provision unit for during travel can add a recommendation function according to the user's preferences and personalize the experience during the travel. The information provision unit for during travel, for example, learns the user's preferences and adds a recommendation function that personalizes the experience during the travel based on them. For example, it suggests activities that the user likes. The information provision unit for during travel also builds a system that personalizes the experience during the travel based on the user's past travel history and preferences. For example, it suggests tourist spots similar to places visited in the past. The information provision unit for during travel also adds a recommendation function according to the user's preferences and personalizes the experience during the travel. For example, it suggests restaurants that serve dishes that the user likes. In this way, it is possible to add a recommendation function according to the user's preferences and personalize the experience during the travel.

[0088] The information provision unit during travel uses the emotion estimation function to provide information according to the user's current emotional state, thereby improving travel satisfaction. The information provision unit during travel, for example, builds a system that analyzes the user's current emotional state in real time and provides information according to that emotion. For example, if the user is tired, it suggests places where the user can relax. The information provision unit during travel also uses the emotion estimation function to provide information according to the user's current emotional state. For example, if the user is excited, it suggests active activities. The information provision unit during travel also develops a system that provides information to improve travel satisfaction based on the user's emotional data. For example, if the user seems anxious, it provides reassuring information. This makes it possible to provide information according to the user's current emotional state and improve travel satisfaction.

[0089] The information providing unit during travel can add an AR function to a dedicated app and visually provide information about tourist spots. For example, the information providing unit during travel can add an AR function to a dedicated app and provide visual information through the smartphone camera when the user arrives at a tourist spot. For example, the history and highlights of the tourist spot can be displayed. The information providing unit during travel can also use the AR function to build a system that visually provides information about tourist spots. For example, when the user holds the camera over the tourist spot, detailed information about the tourist spot can be displayed. The information providing unit during travel can also add an AR function to a dedicated app and visually provide information about tourist spots. For example, when the user views a tourist spot through the camera, related information can be displayed. This allows the AR function to be added to the dedicated app and visually provide information about tourist spots.

[0090] The travel information providing unit can add a function to link the travel information provided with the user's social media account and share it with friends and followers. The travel information providing unit, for example, adds an social media linking function to a dedicated app, allowing the user to share travel information with friends and followers in real time. For example, the travel information providing unit posts photos and reviews of tourist spots. The travel information providing unit can also link with the user's social media account to build a system that automatically shares travel information. For example, the travel information providing unit can automatically post places the user has visited and experiences. The travel information providing unit can also link with the social media account to add a function to share travel information with friends and followers. For example, the travel information providing unit can share information about tourist spots the user has visited in real time. This allows the travel information providing unit to link with the user's social media account to add a function to share travel information with friends and followers.

[0091] The information provision unit for during travel can use the emotion estimation function to analyze the user's emotions while traveling in real time and provide optimal information. The information provision unit for during travel, for example, builds a system that analyzes the user's emotional state while traveling in real time and provides information according to that emotion. For example, if the user is tired, it suggests places where the user can relax. The information provision unit for during travel also uses the emotion estimation function to analyze the user's emotions while traveling in real time and provides information to elicit positive emotions. For example, if the user seems to be having fun, it provides information to maintain that emotion. The information provision unit for during travel also develops a system that provides optimal information while traveling based on the user's emotion data. For example, if the user seems anxious, it provides reassuring information. In this way, the user's emotions while traveling can be analyzed in real time and optimal information can be provided.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The travel plan generation unit can also propose travel plans along specific themes based on the user's hobbies and interests. For example, a plan touring historical sites can be proposed to a user who loves history. A plan to enjoy local specialties can also be proposed to a user who loves food. Furthermore, it is possible to propose plans including hiking and camping to a user who loves the outdoors. In this way, it is possible to provide themed trips that match the user's hobbies and interests.

[0094] The travel plan generation unit can learn a user's past travel history and preferences and propose personalized travel plans. For example, data on travel destinations and accommodations visited by the user is collected, and the generation AI uses that data to propose the next travel plan. For example, similar places and facilities are proposed based on ratings of cities and accommodations visited in the past. The travel plan generation unit can also learn preferences for specific activities and tourist spots from the user's travel history and customize the travel plan based on that. For example, for a user who has visited many museums in the past, cities with many museums are proposed. The travel plan generation unit can also analyze a user's past travel history and propose travel plans tailored to specific seasons or events. For example, for a user who has visited a beach resort in the summer in the past, a summer beach resort is proposed for the next visit. This allows for more personalized travel plans to be proposed based on the user's past travel history and preferences.

[0095] The travel plan generation unit can dynamically adjust the optimal travel plan by taking into account the user's real-time health condition and mood. For example, it collects the user's health data (e.g., heart rate and sleep data from a smartwatch) and adjusts the travel plan based on that. For example, if the user is tired, it can suggest a plan that allows them to relax. The travel plan generation unit also analyzes the user's mood in real time and suggests a travel plan that suits their mood. For example, if the user is under a lot of stress, it can suggest a plan that allows them to relax in nature. The travel plan generation unit also dynamically adjusts activities during the trip according to the user's health condition and mood. For example, if the user is not feeling well, it can suggest lighter activities. This allows the travel plan to be dynamically adjusted based on the user's real-time health condition and mood.

[0096] The travel plan generation unit uses the emotion estimation function to analyze the emotions felt by the user when entering information and can propose travel plans that elicit positive emotions. For example, it analyzes the facial expressions and voice of the user when entering a travel plan and calculates an emotion score. For example, if a smile or an excited voice is detected, a plan that reinforces that emotion is proposed. The travel plan generation unit also uses the emotion estimation function to analyze the emotions felt by the user when entering information in real time and makes proposals to elicit positive emotions. For example, if the user appears to be having fun, a plan that maintains that emotion is proposed. The travel plan generation unit also proposes new plans based on the user's emotion data, referring to travel plans that have elicited positive emotions in the past. For example, it proposes plans that include activities that the user has enjoyed in the past. In this way, it is possible to analyze the user's emotions and propose travel plans that elicit positive emotions.

[0097] The travel plan generation unit can reflect the weather and event information of the travel destination in real time based on the information input by the user. For example, when the user inputs a travel destination, the unit obtains weather information for the area in real time and proposes a plan based on the weather. For example, if it rains, indoor activities are proposed. Furthermore, when the user inputs a travel itinerary, the travel plan generation unit obtains information on events being held during that period in real time and proposes a plan that allows the user to participate in events. For example, music festivals and fireworks displays. Furthermore, when the user inputs a travel destination, the travel plan generation unit proposes a plan that reflects the seasonal characteristics and event information of the area. For example, cherry blossom viewing spots are proposed during cherry blossom season. In this way, the weather and event information of the travel destination can be reflected in real time based on the information input by the user.

[0098] The travel plan generation unit can link with the user's social media account and suggest plans that take into account reviews and photos of travel destinations posted by friends and followers. For example, the unit can link with the user's social media account and collect reviews and photos of travel destinations posted by friends and followers. For example, the unit can suggest restaurants and tourist spots that friends have highly rated. The travel plan generation unit can also analyze information about travel destinations visited by friends and followers from the user's social media feed and suggest travel plans based on that information. For example, the unit can suggest plans that include places visited by friends. The travel plan generation unit can also take into account travel experiences shared by friends and followers through the user's social media account and suggest plans that allow users to have similar experiences. For example, the unit can suggest plans that include activities that friends enjoyed. In this way, the unit can link with the user's social media account and suggest plans that take into account reviews and photos of travel destinations posted by friends and followers.

[0099] The travel plan generation unit can use the emotion estimation function to analyze the emotions of the user when entering information in real time and make suggestions to optimize the input content. For example, the emotion estimation function can analyze the facial expressions and voice of the user when entering a travel plan in real time and calculate an emotion score. For example, if the user appears anxious, the travel plan generation unit can make suggestions to reduce the anxiety. The travel plan generation unit can also use the emotion estimation function to analyze the emotions of the user when entering information in real time and make suggestions to bring out positive emotions. For example, if the user appears to be enjoying themselves, the travel plan generation unit can suggest a plan to maintain that emotion. The travel plan generation unit can also provide feedback to optimize the input content in real time based on the user's emotion data. For example, if the user is unsure, the travel plan generation unit can make specific suggestions. This makes it possible to analyze the user's emotions in real time and make suggestions to optimize the input content.

[0100] The chat customization unit can discover the user's hidden needs and desires through chat interactions and propose plans based on them. For example, when a user sends a question or request through chat, the content is analyzed to discover the user's hidden needs. For example, if the user asks about a specific activity, a plan that includes that activity is proposed. The chat customization unit also obtains a detailed understanding of the user's wishes and preferences through chat interactions and customizes travel plans based on them. For example, if the user likes a specific dish, it proposes restaurants that serve that dish. The chat customization unit also analyzes the user's chat history to discover hidden needs from past questions and requests and propose plans based on them. For example, it proposes plans that include topics that the user has asked about frequently in the past. In this way, the chat customization unit can discover the user's hidden needs and desires through chat interactions and propose plans based on them.

[0101] The chat customization unit can analyze a user's chat history and make predictive suggestions based on past questions and requests. For example, a system can be constructed that analyzes a user's chat history and makes predictive suggestions based on past questions and requests. For example, next suggestions are made based on themes that have been frequently asked about in the past. The chat customization unit can also learn the user's preferences and needs from the chat history and predictively suggest travel plans based on that. For example, it can suggest plans that include activities that have been frequently requested in the past. The chat customization unit can also develop a system that predictively suggests next travel plans based on the user's chat history. For example, it can suggest the next travel destination and activity based on past requests. This makes it possible to analyze a user's chat history and make predictive suggestions based on past questions and requests.

[0102] The chat customization unit can use the emotion estimation function to analyze the emotions of users in response to questions and requests and provide optimal answers. For example, it can analyze facial expressions and voice when a user sends a question or request via chat and calculate an emotion score. For example, if the user appears anxious, it can provide an answer that alleviates that anxiety. The chat customization unit can also use the emotion estimation function to analyze the emotions of users in response to questions and requests in real time and provide an answer that elicits positive emotions. For example, if the user appears happy, it can provide an answer that maintains that emotion. The chat customization unit can also build a system that provides optimal answers to questions and requests based on the user's emotion data. For example, if the user is unsure, it can make specific suggestions. This makes it possible to analyze the emotions of users in response to questions and requests and provide optimal answers.

[0103] The chat customization unit adds a voice recognition function to the chatbot, enabling it to respond to voice questions and requests. For example, adding a voice recognition function to the chatbot allows users to send questions and requests by voice. For example, a user may ask, "Are there any cheaper hotels?" The chat customization unit then uses the voice recognition function to convert the user's voice input into text, and the generation AI analyzes the content and provides an answer. For example, it customizes a travel plan based on the voice request. When a user sends a question or request by voice, the chat customization unit analyzes the voice data and builds a system that provides the optimal answer. For example, it uses voice recognition technology to accurately understand the user's intent. This makes it possible to respond to voice questions and requests.

[0104] The chat customization unit allows users to share chat interactions with other users and receive advice and suggestions on a community basis. For example, a system is constructed in which users share chat interactions with other users and receive advice and suggestions on a community basis. For example, other users share places they have visited in the past and their experiences. When a user sends a question or request via chat, the chat customization unit publishes the content to the community and receives feedback from other users. For example, other users suggest recommended tourist spots. The chat customization unit also shares chat interactions on a community basis and customizes travel plans based on advice and suggestions from other users. For example, the plan is adjusted based on information provided by other users. This allows users to share chat interactions with other users and receive advice and suggestions on a community basis.

[0105] The chat customization unit uses the emotion estimation function to analyze the emotions of users during chat in real time and respond appropriately. For example, it analyzes facial expressions and voice in real time when a user sends a question or request via chat and calculates an emotion score. For example, if the user appears anxious, it provides an answer that alleviates that anxiety. The chat customization unit also uses the emotion estimation function to analyze the emotions of users during chat in real time and responds to elicit positive emotions. For example, if the user appears happy, it provides an answer that maintains that emotion. The chat customization unit also builds a system that optimizes responses during chat based on the user's emotion data. For example, if the user is unsure, it makes specific suggestions. This makes it possible to analyze the emotions of users during chat in real time and respond appropriately.

[0106] The multiple travel plan selection and collective booking / payment unit learns the user's selection history and reflects it in future suggestions, thereby providing more accurate plans. For example, it collects the history of travel plans selected by the user in the past and makes next suggestions based on that. For example, it refers to accommodations and tourist spots selected in the past. The multiple travel plan selection and collective booking / payment unit also analyzes the user's selection history and builds a system that learns specific patterns and preferences. For example, if a user prefers a specific region or activity, it proposes a plan based on that. The multiple travel plan selection and collective booking / payment unit also customizes future travel plans based on the user's selection history. For example, it proposes plans similar to plans selected in the past. In this way, the system can learn the user's selection history and reflect it in future suggestions, thereby providing more accurate plans.

[0107] The multiple travel plan selection and collective booking / payment unit strengthens collaboration with each business to provide special offers and discounts tailored to the user's specific needs. For example, by strengthening collaboration with each business (hotels, restaurants, tourist attractions, etc.), a system can be built to provide special offers and discounts tailored to the user's specific needs. For example, a discount can be offered at a specific accommodation facility. The multiple travel plan selection and collective booking / payment unit also obtains special offer and discount information from each business in real time according to the user's needs and reflects this in the travel plan. For example, a discount can be offered at a specific restaurant. The multiple travel plan selection and collective booking / payment unit also provides customized offers tailored to the user's specific needs through collaboration with each business. For example, a special experience can be offered at a specific tourist attraction. This strengthens collaboration with each business to provide special offers and discounts tailored to the user's specific needs.

[0108] The multiple travel plan selection, collective booking, and payment unit uses emotion estimation to analyze the emotions felt by the user when selecting a travel plan and can suggest plans that will provide high satisfaction. For example, it can analyze the user's facial expressions and voice when selecting a travel plan and calculate an emotion score. For example, if the user is excited, it can suggest plans that reinforce that emotion. The multiple travel plan selection, collective booking, and payment unit also uses emotion estimation to analyze the emotions felt by the user in real time when selecting a plan and suggest plans that elicit positive emotions. For example, if the user appears to be enjoying themselves, it can suggest plans that maintain that emotion. The multiple travel plan selection, collective booking, and payment unit also builds a system that uses user emotion data to suggest plans that maximize satisfaction when selecting a plan. For example, it can make specific suggestions when the user is unsure. This allows it to analyze the emotions felt by the user when selecting a plan and suggest plans that will provide high satisfaction.

[0109] The multiple travel plan selection and collective booking / payment section can display reviews and ratings from other users based on the plan selected by the user, to help with selection. For example, a system can be built that displays reviews and ratings from other users in real time based on the travel plan selected by the user. For example, reviews of accommodations and tourist attractions can be displayed. The multiple travel plan selection and collective booking / payment section can also provide detailed information about the selected plan based on the reviews and ratings of other users. For example, ratings of specific restaurants and activities can be displayed. The multiple travel plan selection and collective booking / payment section can also collect feedback from other users about the plan selected by the user and provide information to help with selection based on that feedback. For example, opinions of users who have selected the same plan in the past can be displayed. This allows reviews and ratings from other users to be displayed based on the plan selected by the user, to help with selection.

[0110] The multiple travel plan selection and bulk booking / payment section can add an automatic points or mileage accrual function when making a bulk booking and payment, improving user convenience. For example, we will build a system that adds an automatic points or mileage accrual function when a user selects a travel plan and makes a bulk booking and payment. For example, airline miles can be automatically accumulated. Furthermore, the multiple travel plan selection and bulk booking / payment section can automatically accumulate points or miles when a user makes a bulk booking and payment through collaboration with each business. For example, hotel points programs can be automatically applied. Furthermore, we will develop a system that displays the accumulated points or mileage status in real time when a user makes a bulk booking and payment. For example, it displays the points or miles earned at the time of booking. This will add an automatic points or mileage accrual function when making a bulk booking and payment, improving user convenience.

[0111] The multiple travel plan selection, collective booking, and payment unit uses emotion estimation to analyze the user's emotions in real time when selecting a travel plan and support optimal selection. For example, it can analyze the user's facial expressions and voice in real time when selecting a travel plan and calculate an emotion score. For example, if the user appears anxious, it can make suggestions to alleviate that anxiety. The multiple travel plan selection, collective booking, and payment unit also uses emotion estimation to analyze the user's emotions in real time when selecting a plan and support the user in eliciting positive emotions. For example, if the user appears to be enjoying themselves, it can make suggestions to maintain that emotion. The multiple travel plan selection, collective booking, and payment unit also builds a system that provides optimal support when selecting a plan based on the user's emotion data. For example, it can make specific suggestions when the user is unsure. This allows the user's emotions to be analyzed in real time when selecting a plan and support the user in making the optimal selection.

[0112] The information provision unit during travel can provide optimal tourist spot and event information in real time based on the user's current location and schedule. For example, a system is constructed that acquires the user's current location using GPS and provides information on tourist spots and events in the surrounding area in real time. For example, nearby tourist spots and events are suggested. The information provision unit during travel also provides optimal tourist spot and event information based on the user's travel schedule. For example, additional tourist spots are suggested if there is room in the schedule. A system is also developed in which the information provision unit during travel provides optimal tourist spot and event information in real time based on the user's current location and schedule. For example, tourist spots within walking distance from the current location are suggested. This makes it possible to provide optimal tourist spot and event information in real time based on the user's current location and schedule.

[0113] The travel information provision unit can add a recommendation function based on the user's preferences and personalize the travel experience. For example, it can learn the user's preferences and add a recommendation function that personalizes the travel experience based on them. For example, it can suggest activities that the user likes. The travel information provision unit can also build a system that personalizes the travel experience based on the user's past travel history and preferences. For example, it can suggest tourist spots similar to places visited in the past. The travel information provision unit can also add a recommendation function based on the user's preferences and personalize the travel experience. For example, it can suggest restaurants that serve dishes that the user likes. In this way, it is possible to add a recommendation function based on the user's preferences and personalize the travel experience.

[0114] The information provision unit during travel uses the emotion estimation function to provide information according to the user's current emotional state, thereby improving travel satisfaction. For example, a system is constructed that analyzes the user's current emotional state in real time and provides information according to that emotion. For example, if the user is tired, it suggests places where the user can relax. The information provision unit during travel also uses the emotion estimation function to provide information according to the user's current emotional state. For example, if the user is excited, it suggests active activities. The information provision unit during travel also develops a system that provides information to improve travel satisfaction based on the user's emotional data. For example, if the user seems anxious, it provides reassuring information. This makes it possible to provide information according to the user's current emotional state and improve travel satisfaction.

[0115] The information provision unit during travel can add an AR function to a dedicated app and visually provide information about tourist spots. For example, an AR function can be added to the dedicated app, and when the user arrives at a tourist spot, the information can be provided visually through the smartphone camera. For example, the history and highlights of the tourist spot can be displayed. The information provision unit during travel can also use the AR function to build a system that visually provides information about tourist spots. For example, when the user holds the camera over the tourist spot, detailed information about the tourist spot can be displayed. The information provision unit during travel can also add an AR function to the dedicated app and visually provide information about tourist spots. For example, when the user looks at a tourist spot through the camera, related information can be displayed. This allows the AR function to be added to the dedicated app and visually provide information about tourist spots.

[0116] The travel information providing unit can add a function to link the travel information provided with the user's social media account and share it with friends and followers. For example, an SNS linking function can be added to a dedicated app, allowing the user to share travel information with friends and followers in real time. For example, photos and reviews of tourist spots can be posted. The travel information providing unit can also link with the user's social media account to build a system that automatically shares travel information. For example, the user can automatically post places visited and experiences. The travel information providing unit can also link with the social media account to add a function to share travel information with friends and followers. For example, information about tourist spots visited by the user can be shared in real time. This makes it possible to add a function to link the travel information provided with the user's social media account and share it with friends and followers.

[0117] The information provision unit during travel can use the emotion estimation function to analyze the user's emotions while traveling in real time and provide optimal information. For example, a system can be constructed that analyzes the user's emotional state while traveling in real time and provides information according to that emotion. For example, if the user is tired, it can suggest places where the user can relax. The information provision unit during travel can also use the emotion estimation function to analyze the user's emotions while traveling in real time and provide information to elicit positive emotions. For example, if the user seems to be having fun, it can provide information to maintain that emotion. The information provision unit during travel can also develop a system that provides optimal information during travel based on the user's emotion data. For example, if the user seems anxious, it can provide reassuring information. This makes it possible to analyze the user's emotions while traveling in real time and provide optimal information.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The user information input unit inputs information such as the user's departure point, number of travel days, and budget. For example, the user can input this information through a dedicated app. The user information input unit then sends the input information to the generated AI travel concierge. Step 2: The travel plan generation unit proposes the optimal travel plan based on the information entered by the user information input unit. For example, if a user enters "three days from Tokyo, budget 50,000 yen," the generation AI will propose travel destinations, accommodations, and tourist spots that fit those conditions. The generation AI can also customize the travel plan according to the user's needs. Step 3: The chat customization unit customizes the travel plan proposed by the travel plan generation unit based on the user's questions and requests. For example, if the user asks, "Are there any cheaper hotels?", the generation AI will suggest cheaper hotels within the user's budget. Also, if the user wants to add a specific tourist spot, the AI ​​will re-propose the plan based on that request.

[0120] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

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

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] The data processing device 12 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0140] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0161] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0162] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0163] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0165] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0166] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0167] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0169] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0170] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0171] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0172] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0173] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0174] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0175] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0176] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0177] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0178] 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.

[0179] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0180] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0181] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0182] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0183] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0184] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0185] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0186] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

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

Claims

1. a user information input unit for inputting information such as the user's departure point, number of travel days, and budget; a travel plan creation unit that proposes an optimal travel plan based on the information input by the user information input unit; a chat customization unit that customizes the travel plan proposed by the travel plan creation unit in response to questions and requests from the user. A system characterized by:

2. The travel plan generation unit Learn about the user's past travel history and preferences to suggest personalized travel plans 2. The system of claim 1.

3. The travel plan generation unit Dynamically adjusts optimal travel plans based on the user's real-time health and mood.

2. The system of claim 1.

4. The travel plan generation unit Analyze the emotions felt by the user when they input their information and suggest travel plans that elicit positive emotions 2. The system of claim 1.

5. The travel plan generation unit Based on the information entered by the user, weather and event information for the travel destination is reflected in real time.

2. The system of claim 1.

6. The travel plan generation unit Linking with the user's social media account, the app suggests plans based on reviews and photos of travel destinations from friends and followers.

2. The system of claim 1.

Images