System

Abstract

An object of a system according to an embodiment is to propose an optimal travel plan based on a vague travel image of a user.SOLUTION: A system includes a travel image acquisition part, a plan generation part, a customization part, a detailed information provision part, and a reservation support part. The travel image acquisition unit acquires a travel image of a user. A plan generation part generates an optimum travel plan on the basis of the travel image acquired by the travel image acquisition part. The customization unit customizes the travel plan generated by the plan generation unit based on the feedback of the user. The detailed information provision section provides detailed information on the travel plan customized by the customization section. The reservation support unit supports a reservation procedure based on the travel plan provided by the detailed information providing unit.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 technology has the problem that travel plans can only be searched based on limited elements, making it difficult to propose optimal travel plans based on the user's vague wishes.

[0005] The system according to the embodiment aims to propose an optimal travel plan based on a user's vague travel image. [Means for solving the problem]

[0006] The system according to the embodiment includes a travel image acquisition unit, a plan creation unit, a customization unit, a detailed information provision unit, and a reservation support unit. The travel image acquisition unit acquires a user's travel image. The plan creation unit creates an optimal travel plan based on the travel image acquired by the travel image acquisition unit. The customization unit customizes the travel plan created by the plan creation unit based on user feedback. The detailed information provision unit provides detailed information about the travel plan customized by the customization unit. The reservation support unit supports the reservation procedure based on the travel plan provided by the detailed information provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose an optimal travel plan based on the user's vague travel image. [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 the embodiment of the present invention is a system that reads a user's vague travel wishes and images and proposes the most suitable travel plan. As a result, the travel plan proposal system can provide a travel plan based on the user's wishes, making travel planning easy.

[0029] A travel plan proposal system according to an embodiment includes a travel image acquisition unit, a plan generation unit, a customization unit, a detailed information provision unit, and a reservation support unit. The travel image acquisition unit acquires a user's travel image. For example, it analyzes spoken language or free-word prompts entered by the user to understand the user's travel image and wishes. The generation AI analyzes vague wishes, such as "I want to relax in a place full of nature" or "I want to go somewhere where I can enjoy delicious food within a budget of 100,000 yen." The plan generation unit generates an optimal travel plan based on the travel image acquired by the travel image acquisition unit. For example, the generation AI suggests tourist spots rich in nature and relaxing accommodations. It also suggests travel destinations and restaurants where you can enjoy gourmet food within your budget. The customization unit customizes the travel plan generated by the plan generation unit based on user feedback. For example, if the user inputs requests such as "I want to keep the budget a little lower" or "I want to visit more tourist spots," the generation AI regenerates the plan according to the requests. The detailed information provision unit provides detailed information about the travel plan customized by the customization unit. For example, the detailed information may include photos and reviews of accommodations, detailed information about tourist spots, and transportation information. The reservation support unit supports the reservation procedure based on the travel plan provided by the detailed information providing unit. For example, it checks the availability of accommodations and handles the reservation procedure on behalf of the user. It also supports the reservation of transportation means. As a result, the travel plan proposal system according to the embodiment can read the user's vague travel wishes and image, propose and customize the optimal travel plan, provide detailed information, and support the reservation procedure.

[0030] The travel image acquisition unit can analyze a user's past travel history and social media posts to understand travel preferences and trends. For example, the generation AI in the travel image acquisition unit analyzes the user's past travel history and understands travel preferences based on data on visited places and accommodations. For example, it analyzes ratings of tourist spots and accommodations visited in the past to identify the user's preferences. It also analyzes social media posts to understand travel trends from travel photos and comments shared by the user. For example, it analyzes the locations of posted photos and tagged activities. It also analyzes the user's past travel reviews and ratings to identify travel preferences and trends. For example, it analyzes rating comments on accommodations and tourist spots to understand the user's preferences. In this way, by analyzing the user's past travel history and social media posts, it is possible to understand travel preferences and trends and propose more appropriate travel plans.

[0031] The travel image acquisition unit can automatically search for related images and videos for free words entered by the user to complement the visual image. For example, the travel image acquisition unit uses a generation AI to automatically search for images of related tourist spots and activities based on free words entered by the user, and provide a visual image. For example, if "beach resort" is entered, images of beaches are displayed. Also, videos related to the free words are automatically searched for to provide the user with a visual image. For example, if "mountain climbing" is entered, videos of mountain climbing are displayed. Also, the generation AI searches for images of related tourist spots and accommodations based on the free words, and provides the user with a visual image. For example, if "hot spring" is entered, images of hot springs are displayed. In this way, by automatically searching for related images and videos for the free words entered by the user and complementing the visual image, more specific travel plans can be proposed.

[0032] The travel image acquisition unit can suggest related music and podcasts for free words entered by the user, thereby broadening the user's travel image. For example, the travel image acquisition unit has a generation AI suggest related music based on free words entered by the user, broadening the user's travel image. For example, if the user enters "beach resort," music that suits the beach will be suggested. The unit also automatically searches for podcasts related to the free words and suggests them to the user. For example, if the user enters "mountain climbing," podcasts related to mountain climbing will be suggested. The generation AI also suggests related music and podcasts based on the free words, broadening the user's travel image. For example, if the user enters "hot spring," music and podcasts that suit hot springs will be suggested. In this way, the unit can broaden the user's travel image by suggesting related music and podcasts for the free words entered by the user.

[0033] The travel image acquisition unit can automatically translate free words entered in different languages ​​and generate a multilingual travel itinerary. The travel image acquisition unit, for example, automatically translates free words entered by a user in different languages, and the generation AI generates a multilingual travel itinerary. For example, if "beach resort" is entered, beach resort information in each language is provided. The free words are also automatically translated, and the generation AI proposes multilingual travel itineraries. For example, if "mountain climbing" is entered, mountain climbing information in each language is provided. The generation AI also automatically translates free words entered in different languages ​​and generates a multilingual travel itinerary. For example, if "hot spring" is entered, hot spring information in each language is provided. In this way, by automatically translating free words entered in different languages ​​and generating a multilingual travel itinerary, it is possible to accommodate international users.

[0034] The plan generation unit can propose optimal travel plans by taking into account real-time weather information and event information. In the plan generation unit, for example, the generation AI analyzes real-time weather information and proposes optimal travel plans according to the weather. For example, if it rains, indoor activities are proposed. The generation AI also considers real-time event information and proposes optimal travel plans. For example, a travel plan tailored to an event being held locally is proposed. The generation AI also integrates weather information and event information and proposes optimal travel plans. For example, outdoor activities are proposed on sunny days, and a plan tailored to the event date is provided. In this way, more appropriate travel plans can be proposed by taking into account real-time weather information and event information.

[0035] The plan generation unit can propose health-conscious travel plans by taking into account the user's health condition and allergy information. In the plan generation unit, for example, the generation AI analyzes the user's health condition and proposes health-conscious travel plans. For example, for a user with a chronic illness, it proposes accommodations that are close to medical facilities. In addition, the generation AI considers the user's allergy information and proposes allergy-friendly travel plans. For example, it proposes restaurants and accommodations that are allergy-friendly. In addition, the generation AI integrates the health condition and allergy information and proposes health-conscious travel plans. For example, it proposes accommodations and activities that provide healthy meals. In this way, it is possible to propose health-conscious travel plans by taking into account the user's health condition and allergy information.

[0036] The plan generation unit can incorporate experiences that allow users to experience local culture and history into the proposed travel plans. For example, the plan generation unit may incorporate experiences that allow users to experience local culture and history into the travel plans proposed by the generation AI. For example, it may suggest experiences with local traditional crafts or tours of historical buildings. The generation AI may also collect information about local culture and history and incorporate it into the travel plans. For example, it may suggest plans to participate in local festivals and events. The generation AI may also suggest experiences that allow users to experience local culture and history. For example, it may provide a travel plan that incorporates local guided tours and visits to museums. In this way, the value of the trip can be increased by incorporating experiences that allow users to experience local culture and history.

[0037] The plan generation unit can suggest pet-friendly accommodations and tourist spots when a user is traveling with a pet. For example, the generation AI of the plan generation unit suggests pet-friendly accommodations. For example, it suggests hotels and pensions that allow pets. In addition, it suggests pet-friendly tourist spots for users traveling with pets. For example, it suggests parks and beaches that allow pets. In addition, the generation AI suggests pet-friendly travel plans. For example, it provides plans that include restaurants and cafes that allow pets. In this way, by suggesting pet-friendly accommodations and tourist spots, it is possible to accommodate travelers with pets.

[0038] The customization unit can analyze user feedback in real time and instantly regenerate a plan. In the customization unit, for example, the generation AI analyzes user feedback in real time and instantly regenerates a travel plan. For example, in response to feedback such as "I want to keep the budget down," the generation AI re-proposes the optimal plan within that budget. The generation AI also regenerates a travel plan based on user feedback. For example, in response to a request such as "I want to add more tourist attractions," the generation AI proposes a plan that adds additional tourist attractions. The generation AI also analyzes user feedback in real time and regenerates the optimal travel plan. For example, in response to a request such as "I want to change accommodation," the generation AI proposes new accommodation. In this way, by analyzing user feedback in real time and instantly regenerating a plan, it is possible to quickly respond to user wishes.

[0039] The customization unit can instantly propose a new plan if the user requests a change during the trip. For example, if the user requests a change during the trip, the customization unit allows the generation AI to instantly propose a new travel plan. For example, in response to a request such as "I want to change my plans and go to another tourist spot," the generation AI will propose a new tourist spot. The generation AI also generates a new plan in real time in response to requests for changes during the trip. For example, in response to a request such as "I want to change my accommodation," the generation AI will propose a new accommodation. The generation AI also analyzes the user's desired changes and instantly proposes a new travel plan. For example, in response to a request such as "I want to change my plans and add another activity," the generation AI will propose a new activity. This allows for flexible responses by instantly proposing a new plan if the user requests a change during the trip.

[0040] The customization unit can incorporate the opinions of the user's friends and family to propose the best plan for a group trip. For example, the customization unit allows the generation AI to incorporate the opinions of the user's friends and family to propose the best plan for a group trip. For example, it can propose tourist spots and accommodations that take everyone's preferences into consideration. The generation AI can also collect the opinions of all members of a group trip to propose the best travel plan. For example, it can propose a plan that includes activities that everyone can enjoy. The generation AI can also incorporate the opinions of the user's friends and family to propose the best plan for a group trip. For example, it can provide a travel plan that takes everyone's budget and preferences into consideration. In this way, the generation AI can propose the best plan for a group trip by incorporating the opinions of the user's friends and family.

[0041] The customization unit can propose customized travel plans based on the user's specific hobbies and interests. In the customization unit, for example, the generation AI proposes customized travel plans based on the user's specific hobbies and interests. For example, it proposes photogenic spots to a user whose hobby is photography. The generation AI also analyzes the user's hobbies and interests and proposes customized travel plans. For example, it proposes a gourmet tour to a user who enjoys eating out. The generation AI also proposes customized travel plans based on the user's specific hobbies and interests. For example, it proposes historical tourist spots to a user who loves history. In this way, a more personalized travel experience can be provided by proposing customized travel plans based on the user's specific hobbies and interests.

[0042] The detailed information providing unit can customize the detailed information of suggested accommodations and tourist attractions to suit the user's preferences. For example, the detailed information providing unit customizes the detailed information of accommodations suggested by the generation AI to suit the user's preferences. For example, it highlights and displays facilities and services that the user prefers. It also customizes the detailed information of tourist attractions to suit the user's preferences. For example, it highlights and displays activities and attractions that interest the user. The generation AI also customizes the detailed information of accommodations and tourist attractions and provides it to the user. For example, it displays reviews and ratings that suit the user's preferences. In this way, more appropriate information can be provided by customizing the detailed information of accommodations and tourist attractions to suit the user's preferences.

[0043] The detailed information providing unit can provide a detailed guide about specific tourist spots and activities that interest the user. For example, the generation AI provides a detailed guide about specific tourist spots that interest the user. For example, it provides a detailed explanation of the history and highlights of the tourist spot. It also provides a detailed guide about activities that interest the user. For example, it provides a detailed explanation of the content of the activity and how to participate. The generation AI also provides a detailed guide about specific tourist spots and activities to provide useful information to the user. For example, it introduces how to access the tourist spot and recommended spots in the area. This enriches the user's travel experience by providing a detailed guide about specific tourist spots and activities that interest the user.

[0044] The detailed information providing unit can add information about recommended local restaurants and cafes to the proposed travel plan. For example, the detailed information providing unit adds information about recommended local restaurants to the travel plan proposed by the generation AI. For example, it suggests popular local restaurants and cafes. It also collects information about local cafes and restaurants, which the generation AI incorporates into the travel plan. For example, it suggests local gourmet spots. It also adds information about recommended local restaurants and cafes and provides it to the user. For example, it suggests restaurants where you can enjoy local specialties. In this way, adding information about recommended local restaurants and cafes can increase the appeal of the travel plan.

[0045] The detailed information providing unit can provide coupons and discount information that the user can use during the trip. In the detailed information providing unit, for example, the generation AI provides the user with coupons and discount information that can be used during the trip. For example, discount coupons for accommodations and restaurants are suggested. In addition, discount information that the user can use during the trip is collected and provided by the generation AI. For example, discount information on admission fees to tourist attractions and discount information on activities is suggested. In addition, the generation AI provides coupons and discount information that can be used during the trip, providing the user with valuable information. For example, discount coupons for transportation are suggested. In this way, by providing coupons and discount information that the user can use during the trip, the cost of the trip can be reduced.

[0046] The reservation support unit can analyze the user's past reservation history and suggest the optimal reservation option. In the reservation support unit, for example, the generation AI analyzes the user's past reservation history and suggests the optimal reservation option. For example, it suggests the optimal reservation based on accommodations and transportation methods used in the past. The generation AI also suggests the optimal reservation option based on the user's reservation history. For example, it suggests accommodations and transportation methods that have received high ratings in the past. The generation AI also analyzes the user's past reservation history and suggests the optimal reservation option. For example, it checks the availability of accommodations and transportation methods used in the past and suggests a reservation. In this way, the optimal reservation option can be suggested by analyzing the user's past reservation history.

[0047] The reservation support unit can automatically select the optimal plan and complete the reservation when the user goes through the reservation procedure. In the reservation support unit, for example, the generation AI automatically selects the optimal plan based on the user's preferences and completes the reservation procedure. For example, it automatically reserves the user's desired accommodation and means of transportation. Also, when the user goes through the reservation procedure, the generation AI automatically selects the optimal plan and completes the reservation. For example, it automatically selects a plan that matches the user's budget and preferences. Also, the generation AI selects the optimal plan based on the user's preferences and automatically completes the reservation procedure. For example, it checks the availability of the user's desired accommodation and means of transportation and completes the reservation. In this way, when the user goes through the reservation procedure, the optimal plan is automatically selected and the reservation is completed, reducing the user's effort.

[0048] The reservation support unit can also include reservations for local activities and tours in the proposed travel plan. For example, the reservation support unit includes reservations for local activities and tours in the travel plan proposed by the generation AI. For example, it suggests booking sightseeing tours and activities. In addition, it collects information on local activities and tours, which the generation AI incorporates into the travel plan. For example, it suggests booking local guided tours and activities. In addition, the generation AI proposes travel plans that include bookings for local activities and tours. For example, it checks the availability of local activities and tours and suggests booking them. This makes it possible to increase the consistency of travel plans by including reservations for local activities and tours.

[0049] The reservation support unit can make reservations for transportation that a user will use during a trip in one go. In the reservation support unit, for example, the generation AI makes reservations for transportation in one go based on the user's travel plan. For example, it makes reservations for airplane and train tickets in one go. In addition, the generation AI collects information on transportation that a user will use during a trip and makes reservations in one go. For example, it makes reservations for rental cars and taxis in one go. In addition, the generation AI makes reservations for transportation in one go based on the user's travel plan. For example, it makes reservations for bus and ferry tickets in one go. This allows the user to make reservations for transportation that a user will use during a trip in one go, thereby improving the efficiency of the reservation process.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The travel image acquisition unit acquires the user's travel image. For example, it analyzes the user's spoken language or free-word prompts to understand the user's travel image and wishes. The generation AI analyzes vague wishes, such as "I want to relax in a place full of nature" or "I want to go somewhere where I can enjoy delicious food within a budget of 100,000 yen." The plan generation unit generates an optimal travel plan based on the travel image acquired by the travel image acquisition unit. For example, the generation AI suggests tourist spots rich in nature and relaxing accommodations. It also suggests travel destinations and restaurants where you can enjoy gourmet food within your budget. The customization unit customizes the travel plan generated by the plan generation unit based on user feedback. For example, if the user inputs requests such as "I want to keep the budget a little lower" or "I want to visit more tourist spots," the generation AI regenerates the plan according to those requests. The detailed information provision unit provides detailed information about the travel plan customized by the customization unit. For example, this information may include photos and reviews of accommodations, detailed information about tourist spots, and transportation information. The reservation support unit assists the reservation process based on the travel plan provided by the detailed information provision unit. For example, the system checks availability of accommodations and handles the reservation procedure on behalf of the user. It also assists with booking transportation. This allows the travel plan proposal system according to the embodiment to understand the user's vague travel wishes and image, propose and customize the optimal travel plan, provide detailed information, and assist with the reservation procedure.

[0052] The travel image acquisition unit can analyze a user's past travel history and social media posts to understand travel preferences and trends. For example, the generation AI analyzes a user's past travel history to understand travel preferences based on data on visited places and accommodations. For example, it analyzes ratings of previously visited tourist spots and accommodations to identify the user's preferences. It also analyzes social media posts to understand travel trends from travel photos and comments shared by the user. For example, it analyzes the locations of posted photos and tagged activities. It also analyzes the user's past travel reviews and ratings to identify travel preferences and trends. For example, it analyzes rating comments on accommodations and tourist spots to understand the user's preferences. In this way, by analyzing a user's past travel history and social media posts, it is possible to understand travel preferences and trends and propose more appropriate travel plans.

[0053] The travel image acquisition unit can automatically search for related images and videos for free words entered by the user to complement the visual image. For example, based on the free words entered by the user, the generation AI automatically searches for images of related tourist spots and activities to provide a visual image. For example, if "beach resort" is entered, images of beaches will be displayed. It also automatically searches for videos related to the free words to provide the user with a visual image. For example, if "mountain climbing" is entered, videos of mountain climbing will be displayed. The generation AI also searches for images of related tourist spots and accommodations based on the free words to provide the user with a visual image. For example, if "hot spring" is entered, images of hot springs will be displayed. This allows the system to automatically search for related images and videos for the free words entered by the user to complement the visual image, thereby suggesting more specific travel plans.

[0054] The travel image acquisition unit can suggest related music and podcasts for free words entered by the user, broadening the user's travel image. For example, the generation AI can suggest related music based on the free words entered by the user, broadening the user's travel image. For example, if the user enters "beach resort," music that suits the beach will be suggested. The system can also automatically search for podcasts related to the free words and suggest them to the user. For example, if the user enters "mountain climbing," podcasts related to mountain climbing will be suggested. The generation AI can also suggest related music and podcasts based on the free words, broadening the user's travel image. For example, if the user enters "hot spring," music and podcasts that suit hot springs will be suggested. This allows the system to broaden the user's travel image by suggesting related music and podcasts for the free words entered by the user.

[0055] The travel image acquisition unit can automatically translate free words entered in different languages ​​and generate multilingual travel itineraries. For example, free words entered by a user in different languages ​​are automatically translated, and the generation AI generates a multilingual travel itinerary. For example, if "beach resort" is entered, beach resort information in each language is provided. Free words are also automatically translated, and the generation AI proposes multilingual travel itineraries. For example, if "mountain climbing" is entered, mountain climbing information in each language is provided. The generation AI also automatically translates free words entered in different languages ​​and generates multilingual travel itineraries. For example, if "hot spring" is entered, hot spring information in each language is provided. In this way, by automatically translating free words entered in different languages ​​and generating multilingual travel itineraries, it is possible to accommodate international users.

[0056] The plan generation unit can propose optimal travel plans by taking into account real-time weather information and event information. For example, the generation AI analyzes real-time weather information and proposes optimal travel plans according to the weather. For example, if it rains, indoor activities are proposed. The generation AI also proposes optimal travel plans by taking into account real-time event information. For example, it proposes travel plans that match events being held locally. The generation AI also integrates weather information and event information to propose optimal travel plans. For example, it suggests outdoor activities on sunny days and provides plans that match the event dates. This makes it possible to propose more appropriate travel plans by taking into account real-time weather information and event information.

[0057] The plan generation unit can propose health-conscious travel plans by taking into account the user's health condition and allergy information. For example, the generation AI analyzes the user's health condition and proposes health-conscious travel plans. For example, for a user with a chronic illness, it proposes accommodations near medical facilities. The generation AI also considers the user's allergy information and proposes allergy-friendly travel plans. For example, it proposes restaurants and accommodations that cater to allergies. The generation AI also integrates the health condition and allergy information and proposes health-conscious travel plans. For example, it proposes accommodations and activities that provide healthy meals. In this way, it is possible to propose health-conscious travel plans by taking into account the user's health condition and allergy information.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The travel image acquisition unit acquires the user's travel image. For example, it analyzes the spoken language or free-word prompts entered by the user to understand the user's travel image and wishes. The generation AI analyzes vague wishes such as, "I want to relax in a place full of nature" or "I want to go somewhere where I can enjoy delicious food within a budget of 100,000 yen." Step 2: The plan generation unit generates an optimal travel plan based on the travel image acquired by the travel image acquisition unit. For example, the generation AI suggests tourist spots rich in nature and relaxing accommodations. It also suggests travel destinations and restaurants where you can enjoy gourmet food within your budget. Step 3: The customization unit customizes the travel plan generated by the plan generation unit based on user feedback. For example, if the user inputs requests such as "I want to keep the budget a little lower" or "I want to include more tourist spots," the generation AI will regenerate the plan according to those requests. Step 4: The detailed information providing unit provides detailed information about the travel plan customized by the customization unit, such as photos and reviews of accommodations, detailed information about tourist spots, and information about transportation. Step 5: The reservation support unit assists with the reservation process based on the travel plan provided by the detailed information providing unit. For example, it checks the availability of accommodations and handles the reservation process on behalf of the user. It also assists with the reservation of transportation.

[0060] (Example 2) The travel plan proposal system according to the embodiment of the present invention is a system that reads a user's vague travel wishes and images and proposes the most suitable travel plan. As a result, the travel plan proposal system can provide a travel plan based on the user's wishes, making travel planning easy.

[0061] A travel plan proposal system according to an embodiment includes a travel image acquisition unit, a plan generation unit, a customization unit, a detailed information provision unit, and a reservation support unit. The travel image acquisition unit acquires a user's travel image. For example, it analyzes spoken language or free-word prompts entered by the user to understand the user's travel image and wishes. The generation AI analyzes vague wishes, such as "I want to relax in a place full of nature" or "I want to go somewhere where I can enjoy delicious food within a budget of 100,000 yen." The plan generation unit generates an optimal travel plan based on the travel image acquired by the travel image acquisition unit. For example, the generation AI suggests tourist spots rich in nature and relaxing accommodations. It also suggests travel destinations and restaurants where you can enjoy gourmet food within your budget. The customization unit customizes the travel plan generated by the plan generation unit based on user feedback. For example, if the user inputs requests such as "I want to keep the budget a little lower" or "I want to visit more tourist spots," the generation AI regenerates the plan according to the requests. The detailed information provision unit provides detailed information about the travel plan customized by the customization unit. For example, the detailed information may include photos and reviews of accommodations, detailed information about tourist spots, and transportation information. The reservation support unit supports the reservation procedure based on the travel plan provided by the detailed information providing unit. For example, it checks the availability of accommodations and handles the reservation procedure on behalf of the user. It also supports the reservation of transportation means. As a result, the travel plan proposal system according to the embodiment can read the user's vague travel wishes and image, propose and customize the optimal travel plan, provide detailed information, and support the reservation procedure.

[0062] The travel image acquisition unit can analyze a user's past travel history and social media posts to understand travel preferences and trends. For example, the generation AI in the travel image acquisition unit analyzes the user's past travel history and understands travel preferences based on data on visited places and accommodations. For example, it analyzes ratings of tourist spots and accommodations visited in the past to identify the user's preferences. It also analyzes social media posts to understand travel trends from travel photos and comments shared by the user. For example, it analyzes the locations of posted photos and tagged activities. It also analyzes the user's past travel reviews and ratings to identify travel preferences and trends. For example, it analyzes rating comments on accommodations and tourist spots to understand the user's preferences. In this way, by analyzing the user's past travel history and social media posts, it is possible to understand travel preferences and trends and propose more appropriate travel plans.

[0063] The travel image acquisition unit can automatically search for related images and videos for free words entered by the user to complement the visual image. For example, the travel image acquisition unit uses a generation AI to automatically search for images of related tourist spots and activities based on free words entered by the user, and provide a visual image. For example, if "beach resort" is entered, images of beaches are displayed. Also, videos related to the free words are automatically searched for to provide the user with a visual image. For example, if "mountain climbing" is entered, videos of mountain climbing are displayed. Also, the generation AI searches for images of related tourist spots and accommodations based on the free words, and provides the user with a visual image. For example, if "hot spring" is entered, images of hot springs are displayed. In this way, by automatically searching for related images and videos for the free words entered by the user and complementing the visual image, more specific travel plans can be proposed.

[0064] The travel image acquisition unit can use the emotion estimation function to read emotions from free words entered by the user and suggest travel plans based on those emotions. For example, the travel image acquisition unit uses a generation AI to perform emotion analysis on free words entered by the user and suggest travel plans with positive emotions. For example, if the user enters "I want to relax," a relaxing travel plan will be suggested. The emotion estimation function can also read emotions from free words entered by the user and suggest travel plans based on those emotions. For example, if the user enters "I want to be adventurous," an adventurous travel plan will be suggested. The user's emotions can also be analyzed and travel plans based on those emotions will be suggested. For example, if the user enters "I want to be soothed," a travel plan that provides healing will be suggested. In this way, by suggesting travel plans based on the user's emotions, a more satisfying travel experience can be provided.

[0065] The travel image acquisition unit can suggest related music and podcasts for free words entered by the user, thereby broadening the user's travel image. For example, the travel image acquisition unit has a generation AI suggest related music based on free words entered by the user, broadening the user's travel image. For example, if the user enters "beach resort," music that suits the beach will be suggested. The unit also automatically searches for podcasts related to the free words and suggests them to the user. For example, if the user enters "mountain climbing," podcasts related to mountain climbing will be suggested. The generation AI also suggests related music and podcasts based on the free words, broadening the user's travel image. For example, if the user enters "hot spring," music and podcasts that suit hot springs will be suggested. In this way, the unit can broaden the user's travel image by suggesting related music and podcasts for the free words entered by the user.

[0066] The travel image acquisition unit can automatically translate free words entered in different languages ​​and generate a multilingual travel itinerary. The travel image acquisition unit, for example, automatically translates free words entered by a user in different languages, and the generation AI generates a multilingual travel itinerary. For example, if "beach resort" is entered, beach resort information in each language is provided. The free words are also automatically translated, and the generation AI proposes multilingual travel itineraries. For example, if "mountain climbing" is entered, mountain climbing information in each language is provided. The generation AI also automatically translates free words entered in different languages ​​and generates a multilingual travel itinerary. For example, if "hot spring" is entered, hot spring information in each language is provided. In this way, by automatically translating free words entered in different languages ​​and generating a multilingual travel itinerary, it is possible to accommodate international users.

[0067] The travel image acquisition unit can use the emotion estimation function to collect other users' emotional reactions to free words entered by the user and propose travel plans that are highly relatable. For example, the travel image acquisition unit uses the emotion estimation function to collect other users' emotional reactions to free words entered by the user and propose travel plans that are highly relatable. For example, if the user enters "I want to relax," travel plans that other users can relate to are proposed. The travel image acquisition unit also collects other users' emotional reactions and proposes travel plans that are highly relatable. For example, if the user enters "I want to be adventurous," an adventurous travel plan that other users can relate to is proposed. The emotion estimation function also collects other users' emotional reactions and proposes travel plans that are highly relatable. For example, if the user enters "I want to be relaxed," a relaxing travel plan that other users can relate to is proposed. In this way, by collecting other users' emotional reactions and proposing travel plans that are highly relatable, user satisfaction is improved.

[0068] The plan generation unit can propose optimal travel plans by taking into account real-time weather information and event information. In the plan generation unit, for example, the generation AI analyzes real-time weather information and proposes optimal travel plans according to the weather. For example, if it rains, indoor activities are proposed. The generation AI also considers real-time event information and proposes optimal travel plans. For example, a travel plan tailored to an event being held locally is proposed. The generation AI also integrates weather information and event information and proposes optimal travel plans. For example, outdoor activities are proposed on sunny days, and a plan tailored to the event date is provided. In this way, more appropriate travel plans can be proposed by taking into account real-time weather information and event information.

[0069] The plan generation unit can propose health-conscious travel plans by taking into account the user's health condition and allergy information. In the plan generation unit, for example, the generation AI analyzes the user's health condition and proposes health-conscious travel plans. For example, for a user with a chronic illness, it proposes accommodations that are close to medical facilities. In addition, the generation AI considers the user's allergy information and proposes allergy-friendly travel plans. For example, it proposes restaurants and accommodations that are allergy-friendly. In addition, the generation AI integrates the health condition and allergy information and proposes health-conscious travel plans. For example, it proposes accommodations and activities that provide healthy meals. In this way, it is possible to propose health-conscious travel plans by taking into account the user's health condition and allergy information.

[0070] The plan generation unit can use the emotion estimation function to propose travel plans that provide relaxation or excitement based on the user's emotions. The plan generation unit, for example, uses the emotion estimation function to propose relaxing travel plans based on the user's emotions. For example, a relaxing hot spring trip is proposed for a user who is feeling stressed. The plan generation unit also analyzes the user's emotions and proposes travel plans that provide excitement. For example, an adventure tour is proposed for a user with a strong sense of adventure. The emotion estimation function also proposes travel plans that provide relaxation or excitement based on the user's emotions. For example, a spa resort is proposed for a user who wants to relax, and a theme park is proposed for a user who wants excitement. In this way, by proposing travel plans that provide relaxation or excitement based on the user's emotions, a more satisfying travel experience can be provided.

[0071] The plan generation unit can incorporate experiences that allow users to experience local culture and history into the proposed travel plans. For example, the plan generation unit may incorporate experiences that allow users to experience local culture and history into the travel plans proposed by the generation AI. For example, it may suggest experiences with local traditional crafts or tours of historical buildings. The generation AI may also collect information about local culture and history and incorporate it into the travel plans. For example, it may suggest plans to participate in local festivals and events. The generation AI may also suggest experiences that allow users to experience local culture and history. For example, it may provide a travel plan that incorporates local guided tours and visits to museums. In this way, the value of the trip can be increased by incorporating experiences that allow users to experience local culture and history.

[0072] The plan generation unit can suggest pet-friendly accommodations and tourist spots when a user is traveling with a pet. For example, the generation AI of the plan generation unit suggests pet-friendly accommodations. For example, it suggests hotels and pensions that allow pets. In addition, it suggests pet-friendly tourist spots for users traveling with pets. For example, it suggests parks and beaches that allow pets. In addition, the generation AI suggests pet-friendly travel plans. For example, it provides plans that include restaurants and cafes that allow pets. In this way, by suggesting pet-friendly accommodations and tourist spots, it is possible to accommodate travelers with pets.

[0073] The plan generation unit can use the emotion estimation function to propose a travel plan that includes a surprise element based on the user's emotions. The plan generation unit, for example, uses the emotion estimation function to propose a travel plan that includes a surprise element based on the user's emotions. For example, a surprise event may be incorporated for a birthday or anniversary. The plan generation unit may also analyze the user's emotions and propose a surprise element based on the emotions. For example, a spot where a breathtaking view can be enjoyed may be incorporated. The emotion estimation function may also be used to propose a travel plan that includes a surprise element based on the user's emotions. For example, a special dinner or a surprise gift may be incorporated. In this way, by proposing a travel plan that includes a surprise element based on the user's emotions, the enjoyment of the trip may be increased.

[0074] The customization unit can analyze user feedback in real time and instantly regenerate a plan. In the customization unit, for example, the generation AI analyzes user feedback in real time and instantly regenerates a travel plan. For example, in response to feedback such as "I want to keep the budget down," the generation AI re-proposes the optimal plan within that budget. The generation AI also regenerates a travel plan based on user feedback. For example, in response to a request such as "I want to add more tourist attractions," the generation AI proposes a plan that adds additional tourist attractions. The generation AI also analyzes user feedback in real time and regenerates the optimal travel plan. For example, in response to a request such as "I want to change accommodation," the generation AI proposes new accommodation. In this way, by analyzing user feedback in real time and instantly regenerating a plan, it is possible to quickly respond to user wishes.

[0075] The customization unit can instantly propose a new plan if the user requests a change during the trip. For example, if the user requests a change during the trip, the customization unit allows the generation AI to instantly propose a new travel plan. For example, in response to a request such as "I want to change my plans and go to another tourist spot," the generation AI will propose a new tourist spot. The generation AI also generates a new plan in real time in response to requests for changes during the trip. For example, in response to a request such as "I want to change my accommodation," the generation AI will propose a new accommodation. The generation AI also analyzes the user's desired changes and instantly proposes a new travel plan. For example, in response to a request such as "I want to change my plans and add another activity," the generation AI will propose a new activity. This allows for flexible responses by instantly proposing a new plan if the user requests a change during the trip.

[0076] The customization unit can use the emotion estimation function to dynamically adjust the travel plan according to changes in the user's emotions. The customization unit, for example, uses the emotion estimation function to dynamically adjust the travel plan according to changes in the user's emotions. For example, a relaxing activity is added to a user who is feeling stressed. The customization unit also analyzes the user's emotions in real time and adjusts the travel plan according to changes in emotions. For example, an adventurous activity is added to a user who is excited. The emotion estimation function also dynamically adjusts the travel plan according to changes in the user's emotions. For example, a spa or massage is added to a user who wants to relax. In this way, by dynamically adjusting the travel plan according to changes in the user's emotions, a more satisfying travel experience can be provided.

[0077] The customization unit can incorporate the opinions of the user's friends and family to propose the best plan for a group trip. For example, the customization unit allows the generation AI to incorporate the opinions of the user's friends and family to propose the best plan for a group trip. For example, it can propose tourist spots and accommodations that take everyone's preferences into consideration. The generation AI can also collect the opinions of all members of a group trip to propose the best travel plan. For example, it can propose a plan that includes activities that everyone can enjoy. The generation AI can also incorporate the opinions of the user's friends and family to propose the best plan for a group trip. For example, it can provide a travel plan that takes everyone's budget and preferences into consideration. In this way, the generation AI can propose the best plan for a group trip by incorporating the opinions of the user's friends and family.

[0078] The customization unit can propose customized travel plans based on the user's specific hobbies and interests. In the customization unit, for example, the generation AI proposes customized travel plans based on the user's specific hobbies and interests. For example, it proposes photogenic spots to a user whose hobby is photography. The generation AI also analyzes the user's hobbies and interests and proposes customized travel plans. For example, it proposes a gourmet tour to a user who enjoys eating out. The generation AI also proposes customized travel plans based on the user's specific hobbies and interests. For example, it proposes historical tourist spots to a user who loves history. In this way, a more personalized travel experience can be provided by proposing customized travel plans based on the user's specific hobbies and interests.

[0079] The customization unit can use the emotion estimation function to add special events and activities based on the user's emotions. For example, the customization unit uses the emotion estimation function to add special events and activities based on the user's emotions. For example, it suggests spots where users can enjoy impressive scenery. It also analyzes the user's emotions and adds special events and activities based on the emotions. For example, it suggests a relaxing spa or massage. It also uses the emotion estimation function to add special events and activities based on the user's emotions. For example, it suggests an adventure tour that provides excitement. In this way, adding special events and activities based on the user's emotions increases the enjoyment of the trip.

[0080] The detailed information providing unit can customize the detailed information of suggested accommodations and tourist attractions to suit the user's preferences. For example, the detailed information providing unit customizes the detailed information of accommodations suggested by the generation AI to suit the user's preferences. For example, it highlights and displays facilities and services that the user prefers. It also customizes the detailed information of tourist attractions to suit the user's preferences. For example, it highlights and displays activities and attractions that interest the user. The generation AI also customizes the detailed information of accommodations and tourist attractions and provides it to the user. For example, it displays reviews and ratings that suit the user's preferences. In this way, more appropriate information can be provided by customizing the detailed information of accommodations and tourist attractions to suit the user's preferences.

[0081] The detailed information providing unit can provide a detailed guide about specific tourist spots and activities that interest the user. For example, the generation AI provides a detailed guide about specific tourist spots that interest the user. For example, it provides a detailed explanation of the history and highlights of the tourist spot. It also provides a detailed guide about activities that interest the user. For example, it provides a detailed explanation of the content of the activity and how to participate. The generation AI also provides a detailed guide about specific tourist spots and activities to provide useful information to the user. For example, it introduces how to access the tourist spot and recommended spots in the area. This enriches the user's travel experience by providing a detailed guide about specific tourist spots and activities that interest the user.

[0082] The detailed information providing unit can use the emotion estimation function to preferentially display information that the user is most interested in. The detailed information providing unit, for example, uses the emotion estimation function to preferentially display information that the user is most interested in. For example, information about tourist spots and activities that the user has shown interest in is highlighted and displayed. Furthermore, the detailed information providing unit analyzes the user's emotions and preferentially displays information that the user is most interested in. For example, information about accommodations for which the user has shown positive emotions is highlighted and displayed. Furthermore, the emotion estimation function is used to preferentially display information that the user is most interested in. For example, information about restaurants and cafes that the user has shown interest in is highlighted and displayed. In this way, by preferentially displaying information that the user is most interested in, it is possible to provide information that matches the user's interests.

[0083] The detailed information providing unit can add information about recommended local restaurants and cafes to the proposed travel plan. For example, the detailed information providing unit adds information about recommended local restaurants to the travel plan proposed by the generation AI. For example, it suggests popular local restaurants and cafes. It also collects information about local cafes and restaurants, which the generation AI incorporates into the travel plan. For example, it suggests local gourmet spots. It also adds information about recommended local restaurants and cafes and provides it to the user. For example, it suggests restaurants where you can enjoy local specialties. In this way, adding information about recommended local restaurants and cafes can increase the appeal of the travel plan.

[0084] The detailed information providing unit can provide coupons and discount information that the user can use during the trip. In the detailed information providing unit, for example, the generation AI provides the user with coupons and discount information that can be used during the trip. For example, discount coupons for accommodations and restaurants are suggested. In addition, discount information that the user can use during the trip is collected and provided by the generation AI. For example, discount information on admission fees to tourist attractions and discount information on activities is suggested. In addition, the generation AI provides coupons and discount information that can be used during the trip, providing the user with valuable information. For example, discount coupons for transportation are suggested. In this way, by providing coupons and discount information that the user can use during the trip, the cost of the trip can be reduced.

[0085] The detailed information providing unit can use the emotion estimation function to suggest special offers and promotions based on the user's emotions. The detailed information providing unit, for example, uses the emotion estimation function to suggest special offers and promotions based on the user's emotions. For example, it suggests special offers for accommodations for which the user has expressed positive emotions. It also analyzes the user's emotions and suggests special offers and promotions based on the emotions. For example, it suggests promotions for tourist destinations in which the user has shown interest. It also uses the emotion estimation function to suggest special offers and promotions based on the user's emotions. For example, it suggests special menus for restaurants in which the user has shown interest. In this way, by suggesting special offers and promotions based on the user's emotions, user satisfaction is improved.

[0086] The reservation support unit can analyze the user's past reservation history and suggest the optimal reservation option. In the reservation support unit, for example, the generation AI analyzes the user's past reservation history and suggests the optimal reservation option. For example, it suggests the optimal reservation based on accommodations and transportation methods used in the past. The generation AI also suggests the optimal reservation option based on the user's reservation history. For example, it suggests accommodations and transportation methods that have received high ratings in the past. The generation AI also analyzes the user's past reservation history and suggests the optimal reservation option. For example, it checks the availability of accommodations and transportation methods used in the past and suggests a reservation. In this way, the optimal reservation option can be suggested by analyzing the user's past reservation history.

[0087] The reservation support unit can automatically select the optimal plan and complete the reservation when the user goes through the reservation procedure. In the reservation support unit, for example, the generation AI automatically selects the optimal plan based on the user's preferences and completes the reservation procedure. For example, it automatically reserves the user's desired accommodation and means of transportation. Also, when the user goes through the reservation procedure, the generation AI automatically selects the optimal plan and completes the reservation. For example, it automatically selects a plan that matches the user's budget and preferences. Also, the generation AI selects the optimal plan based on the user's preferences and automatically completes the reservation procedure. For example, it checks the availability of the user's desired accommodation and means of transportation and completes the reservation. In this way, when the user goes through the reservation procedure, the optimal plan is automatically selected and the reservation is completed, reducing the user's effort.

[0088] The reservation support unit can use the emotion estimation function to provide support to reduce stress felt by the user during the reservation procedure. The reservation support unit, for example, uses the emotion estimation function to provide support to reduce stress felt by the user during the reservation procedure. For example, it provides a guide to ensure the reservation procedure proceeds smoothly. It also analyzes the user's emotions and provides support to reduce stress felt by the user during the reservation procedure. For example, it displays the progress of the reservation procedure in real time. It also uses the emotion estimation function to provide support to reduce stress felt by the user during the reservation procedure. For example, it provides a help function to quickly resolve problems that occur during the reservation procedure. In this way, support to reduce stress felt by the user during the reservation procedure is provided, allowing the reservation procedure to proceed smoothly.

[0089] The reservation support unit can also include reservations for local activities and tours in the proposed travel plan. For example, the reservation support unit includes reservations for local activities and tours in the travel plan proposed by the generation AI. For example, it suggests booking sightseeing tours and activities. In addition, it collects information on local activities and tours, which the generation AI incorporates into the travel plan. For example, it suggests booking local guided tours and activities. In addition, the generation AI proposes travel plans that include bookings for local activities and tours. For example, it checks the availability of local activities and tours and suggests booking them. This makes it possible to increase the consistency of travel plans by including reservations for local activities and tours.

[0090] The reservation support unit can make reservations for transportation that a user will use during a trip in one go. In the reservation support unit, for example, the generation AI makes reservations for transportation in one go based on the user's travel plan. For example, it makes reservations for airplane and train tickets in one go. In addition, the generation AI collects information on transportation that a user will use during a trip and makes reservations in one go. For example, it makes reservations for rental cars and taxis in one go. In addition, the generation AI makes reservations for transportation in one go based on the user's travel plan. For example, it makes reservations for bus and ferry tickets in one go. This allows the user to make reservations for transportation that a user will use during a trip in one go, thereby improving the efficiency of the reservation process.

[0091] The reservation support unit can use the emotion estimation function to provide guidance to reduce anxiety felt by the user during the reservation procedure. The reservation support unit, for example, uses the emotion estimation function to provide guidance to reduce anxiety felt by the user during the reservation procedure. For example, each step of the reservation procedure is explained in an easy-to-understand manner. The reservation support unit also analyzes the user's emotions and provides guidance to reduce anxiety felt by the user during the reservation procedure. For example, the progress of the reservation procedure is displayed in real time. The emotion estimation function also provides guidance to reduce anxiety felt by the user during the reservation procedure. For example, a help function is provided to quickly resolve problems that occur during the reservation procedure. In this way, guidance to reduce anxiety felt by the user during the reservation procedure is provided, allowing the reservation procedure to proceed smoothly.

[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 image acquisition unit acquires the user's travel image. For example, it analyzes the user's spoken language or free-word prompts to understand the user's travel image and wishes. The generation AI analyzes vague wishes, such as "I want to relax in a place full of nature" or "I want to go somewhere where I can enjoy delicious food within a budget of 100,000 yen." The plan generation unit generates an optimal travel plan based on the travel image acquired by the travel image acquisition unit. For example, the generation AI suggests tourist spots rich in nature and relaxing accommodations. It also suggests travel destinations and restaurants where you can enjoy gourmet food within your budget. The customization unit customizes the travel plan generated by the plan generation unit based on user feedback. For example, if the user inputs requests such as "I want to keep the budget a little lower" or "I want to visit more tourist spots," the generation AI regenerates the plan according to those requests. The detailed information provision unit provides detailed information about the travel plan customized by the customization unit. For example, this information may include photos and reviews of accommodations, detailed information about tourist spots, and transportation information. The reservation support unit assists the reservation process based on the travel plan provided by the detailed information provision unit. For example, the system checks availability of accommodations and handles the reservation procedure on behalf of the user. It also assists with booking transportation. This allows the travel plan proposal system according to the embodiment to understand the user's vague travel wishes and image, propose and customize the optimal travel plan, provide detailed information, and assist with the reservation procedure.

[0094] The travel image acquisition unit can analyze a user's past travel history and social media posts to understand travel preferences and trends. For example, the generation AI analyzes a user's past travel history to understand travel preferences based on data on visited places and accommodations. For example, it analyzes ratings of previously visited tourist spots and accommodations to identify the user's preferences. It also analyzes social media posts to understand travel trends from travel photos and comments shared by the user. For example, it analyzes the locations of posted photos and tagged activities. It also analyzes the user's past travel reviews and ratings to identify travel preferences and trends. For example, it analyzes rating comments on accommodations and tourist spots to understand the user's preferences. In this way, by analyzing a user's past travel history and social media posts, it is possible to understand travel preferences and trends and propose more appropriate travel plans.

[0095] The travel image acquisition unit can automatically search for related images and videos for free words entered by the user to complement the visual image. For example, based on the free words entered by the user, the generation AI automatically searches for images of related tourist spots and activities to provide a visual image. For example, if "beach resort" is entered, images of beaches will be displayed. It also automatically searches for videos related to the free words to provide the user with a visual image. For example, if "mountain climbing" is entered, videos of mountain climbing will be displayed. The generation AI also searches for images of related tourist spots and accommodations based on the free words to provide the user with a visual image. For example, if "hot spring" is entered, images of hot springs will be displayed. This allows the system to automatically search for related images and videos for the free words entered by the user to complement the visual image, thereby suggesting more specific travel plans.

[0096] The travel image acquisition unit can use the emotion estimation function to read emotions from free words entered by the user and suggest travel plans based on those emotions. For example, the generation AI performs emotion analysis on free words entered by the user and suggests travel plans with positive emotions. For example, if the user enters "I want to relax," it will suggest a relaxing travel plan. The emotion estimation function can also read emotions from free words entered by the user and suggest travel plans based on those emotions. For example, if the user enters "I want to be adventurous," it will suggest an adventurous travel plan. The unit can also analyze the user's emotions and suggest travel plans based on those emotions. For example, if the user enters "I want to be soothed," it will suggest a travel plan that provides healing. In this way, by suggesting travel plans based on the user's emotions, it is possible to provide a more satisfying travel experience.

[0097] The travel image acquisition unit can suggest related music and podcasts for free words entered by the user, broadening the user's travel image. For example, the generation AI can suggest related music based on the free words entered by the user, broadening the user's travel image. For example, if the user enters "beach resort," music that suits the beach will be suggested. The system can also automatically search for podcasts related to the free words and suggest them to the user. For example, if the user enters "mountain climbing," podcasts related to mountain climbing will be suggested. The generation AI can also suggest related music and podcasts based on the free words, broadening the user's travel image. For example, if the user enters "hot spring," music and podcasts that suit hot springs will be suggested. This allows the system to broaden the user's travel image by suggesting related music and podcasts for the free words entered by the user.

[0098] The travel image acquisition unit can automatically translate free words entered in different languages ​​and generate multilingual travel itineraries. For example, free words entered by a user in different languages ​​are automatically translated, and the generation AI generates a multilingual travel itinerary. For example, if "beach resort" is entered, beach resort information in each language is provided. Free words are also automatically translated, and the generation AI proposes multilingual travel itineraries. For example, if "mountain climbing" is entered, mountain climbing information in each language is provided. The generation AI also automatically translates free words entered in different languages ​​and generates multilingual travel itineraries. For example, if "hot spring" is entered, hot spring information in each language is provided. In this way, by automatically translating free words entered in different languages ​​and generating multilingual travel itineraries, it is possible to accommodate international users.

[0099] The travel image acquisition unit can use the emotion estimation function to collect other users' emotional reactions to free words entered by the user and suggest travel plans that are highly relatable. For example, the emotion estimation function can be used to collect other users' emotional reactions to free words entered by the user and suggest travel plans that are highly relatable. For example, if the user enters "I want to relax," travel plans that other users can relate to are suggested. The emotion estimation function can also be used to collect other users' emotional reactions and suggest travel plans that are highly relatable. For example, if the user enters "I want to be adventurous," an adventurous travel plan that other users can relate to is suggested. The emotion estimation function can also be used to collect other users' emotional reactions and suggest travel plans that are highly relatable. For example, if the user enters "I want to be relaxed," a relaxing travel plan that other users can relate to is suggested. In this way, by collecting other users' emotional reactions and suggesting travel plans that are highly relatable, user satisfaction is improved.

[0100] The plan generation unit can propose optimal travel plans by taking into account real-time weather information and event information. For example, the generation AI analyzes real-time weather information and proposes optimal travel plans according to the weather. For example, if it rains, indoor activities are proposed. The generation AI also proposes optimal travel plans by taking into account real-time event information. For example, it proposes travel plans that match events being held locally. The generation AI also integrates weather information and event information to propose optimal travel plans. For example, it suggests outdoor activities on sunny days and provides plans that match the event dates. This makes it possible to propose more appropriate travel plans by taking into account real-time weather information and event information.

[0101] The plan generation unit can propose health-conscious travel plans by taking into account the user's health condition and allergy information. For example, the generation AI analyzes the user's health condition and proposes health-conscious travel plans. For example, for a user with a chronic illness, it proposes accommodations near medical facilities. The generation AI also considers the user's allergy information and proposes allergy-friendly travel plans. For example, it proposes restaurants and accommodations that cater to allergies. The generation AI also integrates the health condition and allergy information and proposes health-conscious travel plans. For example, it proposes accommodations and activities that provide healthy meals. In this way, it is possible to propose health-conscious travel plans by taking into account the user's health condition and allergy information.

[0102] The plan generation unit can use the emotion estimation function to propose travel plans that provide relaxation or excitement based on the user's emotions. For example, the emotion estimation function can be used to propose relaxing travel plans based on the user's emotions. For example, a relaxing hot spring trip can be proposed to a user who is feeling stressed. The plan generation unit can also analyze the user's emotions and propose travel plans that provide excitement. For example, an adventure tour can be proposed to a user with a strong sense of adventure. The emotion estimation function can also be used to propose travel plans that provide relaxation or excitement based on the user's emotions. For example, a spa resort can be proposed to a user who wants to relax, and a theme park can be proposed to a user who wants to be excited. In this way, by proposing travel plans that provide relaxation or excitement based on the user's emotions, a more satisfying travel experience can be provided.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The travel image acquisition unit acquires the user's travel image. For example, it analyzes the spoken language or free-word prompts entered by the user to understand the user's travel image and wishes. The generation AI analyzes vague wishes such as, "I want to relax in a place full of nature" or "I want to go somewhere where I can enjoy delicious food within a budget of 100,000 yen." Step 2: The plan generation unit generates an optimal travel plan based on the travel image acquired by the travel image acquisition unit. For example, the generation AI suggests tourist spots rich in nature and relaxing accommodations. It also suggests travel destinations and restaurants where you can enjoy gourmet food within your budget. Step 3: The customization unit customizes the travel plan generated by the plan generation unit based on user feedback. For example, if the user inputs requests such as "I want to keep the budget a little lower" or "I want to include more tourist spots," the generation AI will regenerate the plan according to those requests. Step 4: The detailed information providing unit provides detailed information about the travel plan customized by the customization unit, such as photos and reviews of accommodations, detailed information about tourist spots, and information about transportation. Step 5: The reservation support unit assists with the reservation process based on the travel plan provided by the detailed information providing unit. For example, it checks the availability of accommodations and handles the reservation process on behalf of the user. It also assists with the reservation of transportation.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[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 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.

[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. 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.

[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 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.

[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 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.

[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 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.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[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 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.

[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 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).

[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] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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]

[0172] 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 travel image acquisition unit that acquires a user's travel image; a plan generation unit that generates an optimal travel plan based on the travel image acquired by the travel image acquisition unit; a customization unit that customizes the travel plan generated by the plan generation unit based on user feedback; a detailed information providing unit that provides detailed information about the travel plan customized by the customization unit; a reservation support unit that supports a reservation procedure based on the travel plan provided by the detailed information providing unit; A system characterized by:

2. The travel image acquisition unit Analyzing the user's past travel history and social media posts to understand the user's travel preferences and trends 2. The system of claim 1.

3. The travel image acquisition unit The system automatically searches for related images and videos for the free words entered by the user to complete the visual image.

2. The system of claim 1.

4. The travel image acquisition unit The emotion is read from the free words input by the user, and the travel plan is proposed based on the emotion.

2. The system of claim 1.

5. The travel image acquisition unit The app suggests related music and podcasts for the free words entered by the user to broaden travel ideas.

2. The system of claim 1.

6. The travel image acquisition unit Automatically translates free words entered in different languages ​​and generates multilingual travel plans.

2. The system of claim 1.

7. The travel image acquisition unit Collecting other users' emotional responses to the free words entered by the user and proposing the travel plan that is most relatable to the user 2. The system of claim 1.

8. The plan generation unit Taking real-time weather and event information into consideration, we propose the most suitable travel plan.

2. The system of claim 1.

Images