system

Abstract

An object of a system according to an embodiment is to generate and arrange a travel plan based on a hobby and a preference of a user.SOLUTION: A system according to an embodiment includes a data analysis unit, a plan generation unit, a transportation arrangement unit, and a ticket arrangement unit. The data analysis unit analyzes data such as SNS and photographs of the user. The plan generation unit generates a travel plan based on the data analyzed by the data analysis unit. The transportation arrangement unit arranges transportation based on the travel plan generated by the plan generation unit. The ticket arranger arranges a ticket based on the travel plan generated by the plan generator.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not adequately automated the generation and arrangement of travel plans based on a user's hobbies and preferences, and there is room for improvement.

[0005] The system according to the embodiment aims to generate and arrange a travel plan based on the user's hobbies and preferences. [Means for solving the problem]

[0006] The system according to the embodiment includes a data analysis unit, a plan generation unit, a transportation arrangement unit, and a ticket arrangement unit. The data analysis unit analyzes data such as a user's social media accounts and photos. The plan generation unit generates a travel plan based on the data analyzed by the data analysis unit. The transportation arrangement unit arranges transportation based on the travel plan generated by the plan generation unit. The ticket arrangement unit arranges tickets based on the travel plan generated by the plan generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate and arrange a travel plan based on the user's hobbies and preferences. [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 creation system according to an embodiment of the present invention analyzes data such as a smartphone user's social media accounts and photos, understands the user's hobbies and preferences, and then automatically creates an optimal travel plan and automatically arranges transportation and tickets. This allows the travel plan creation system to analyze user data, generate an optimal travel plan, and automatically arrange transportation and tickets.

[0029] A travel plan creation system according to an embodiment includes a data analysis unit, a plan generation unit, a transportation arrangement unit, and a ticket arrangement unit. The data analysis unit analyzes data such as a user's social media accounts and photos. For example, the data analysis unit collects the user's social media posts and photos and performs text analysis and image analysis. The data analysis unit can also perform sentiment analysis to estimate the user's emotions. For example, the data analysis unit analyzes text data from social media posts and extracts keywords indicating positive emotions. The plan generation unit generates a travel plan based on the data analyzed by the data analysis unit. For example, the plan generation unit proposes a plan including optimal tourist spots and activities based on the user's hobbies and preferences. The plan generation unit can also generate a travel plan based on the user's emotions using a generation AI. The transportation arrangement unit arranges transportation based on the travel plan generated by the plan generation unit. For example, the transportation arrangement unit reserves airplane and train tickets and provides them to the user. The transportation arrangement unit can also propose the optimal transportation method taking into account the user's budget and time constraints. The ticket arrangement unit arranges tickets based on the travel plan generated by the plan generation unit. For example, the ticket arrangement unit reserves tickets to tourist attractions and events and provides them to the user. The ticket arrangement unit can also arrange tickets based on the user's emotions. As a result, the travel plan creation system according to the embodiment can analyze user data, generate an optimal travel plan, and automatically arrange transportation and tickets. For example, if the user likes nature, a plan including tourist spots rich in nature will be proposed, and airplane and train tickets, admission tickets to tourist attractions, etc. will be automatically arranged. This allows the user to significantly reduce the time and effort required to plan a trip.

[0030] The data analysis unit can also analyze travel history or reviews to more accurately understand hobbies and preferences. For example, using generation AI, the data analysis unit can also analyze the user's past travel history and reviews to more accurately understand hobbies and preferences. For example, the generation AI can analyze the user's past travel history and understand hobbies and preferences based on information about places visited and accommodations. For example, it can analyze the characteristics of tourist spots visited in the past and suggest similar tourist spots. It can analyze travel reviews posted by the user and identify places and activities that have received positive reviews. For example, it can create a travel plan based on tourist spots and activities with many highly rated reviews. It can integrate the user's past travel history and reviews to understand the user's overall hobbies and preferences. For example, it can identify the user's preferred travel style based on the frequency of places visited and the content of reviews. This makes it possible to more accurately understand the user's hobbies and preferences by taking into account the user's past travel history and reviews.

[0031] The data analysis unit can also consider seasonal or weather information to suggest the optimal travel time. The data analysis unit, for example, uses a generation AI to consider seasonal and weather information to suggest the optimal travel time. For example, the generation AI incorporates seasonal and weather information into the user's data analysis to suggest the optimal travel time. For example, it analyzes the weather and the time when a photo posted by the user was taken and suggests a trip with similar conditions. It integrates the user's past travel history with seasonal and weather data to identify the optimal travel time. For example, it suggests a trip with similar conditions based on the season and weather of tourist spots visited in the past. It collects seasonal and weather data in real time to suggest the optimal travel time that suits the user's hobbies and preferences. For example, for a user who loves nature, it suggests the season when flowers bloom or the season when autumn leaves change color. In this way, it is possible to suggest the optimal travel time to the user by taking season and weather into consideration.

[0032] The data analysis unit can grasp hobbies and preferences from a more multifaceted perspective, including audio data or video data. The data analysis unit, for example, uses generation AI to grasp hobbies and preferences from a more multifaceted perspective, including audio data and video data. For example, the generation AI analyzes the user's audio data to grasp hobbies and preferences. For example, it analyzes the audio of audio notes and videos recorded by the user while traveling to identify places and activities of interest. It analyzes video data posted by the user to grasp hobbies and preferences based on the scenery and activities contained in the video. For example, it analyzes tourist spots and activities shown in the video to suggest similar places. It integrates audio data and video data to grasp comprehensive hobbies and preferences. For example, it combines keywords extracted from audio data with footage from the video data to identify the user's preferred travel style. In this way, by including audio data and video data, it becomes possible to grasp hobbies and preferences from a more multifaceted perspective.

[0033] The data analysis unit can integrate data from different SNS platforms and perform comprehensive analysis. The data analysis unit, for example, uses generation AI to integrate data from different SNS platforms and perform comprehensive analysis. For example, the generation AI collects data from multiple SNS platforms used by a user and analyzes it in an integrated manner. For example, it centralizes and analyzes post data from Facebook, Instagram, Twitter, etc. Based on the data collected from different SNS platforms, it comprehensively understands the user's hobbies and preferences. For example, it analyzes the content and reactions of posts on each platform to identify common interests. It collects data from multiple SNS platforms in real time and performs comprehensive analysis. For example, it identifies the user's current interests and concerns based on the most recent post data. This makes it possible to perform comprehensive analysis by integrating data from different SNS platforms.

[0034] The plan generation unit can propose a reasonable plan by taking into account the user's health condition or physical strength. The plan generation unit, for example, uses a generation AI to propose a reasonable plan by taking into account the user's health condition and physical strength. For example, the generation AI analyzes the user's health data and proposes a reasonable travel plan. For example, it creates a plan with an appropriate amount of exercise based on the user's step count data and heart rate data. It generates a reasonable travel plan based on the user's physical strength data. For example, it proposes a plan that includes appropriate activities by taking into account the user's age and physical strength level. It collects health condition and physical strength data in real time and proposes the optimal travel plan for the user. For example, if the user's health condition is not good, it proposes a relaxing plan. In this way, it is possible to propose a reasonable travel plan by taking into account the user's health condition and physical strength.

[0035] The plan generation unit can arouse the user's interest by including information about local culture or history. The plan generation unit, for example, uses a generation AI to include information about local culture and history to arouse the user's interest. For example, the generation AI may include information about local culture and history in a travel plan to arouse the user's interest. For example, the generation AI may propose a plan that introduces the history and cultural background of tourist destinations. Based on the user's interests, the plan may generate a travel plan that includes activities related to local culture and history. For example, a plan to visit traditional festivals or historical buildings may be proposed. Information about local culture and history may be collected in real time to propose the optimal travel plan to the user. For example, a plan that interests the user may be created based on information about local events. In this way, the inclusion of information about local culture and history can arouse the user's interest.

[0036] The plan generation unit can propose a group travel plan by taking into account data of friends or family. The plan generation unit, for example, uses generation AI to propose a group travel plan by taking into account data of the user's friends and family. For example, the generation AI analyzes data of the user's friends and family to propose a group travel plan. For example, it takes into account the hobbies and preferences of friends and family to create a plan that everyone can enjoy. It collects social media data of the user's friends and family to generate a group travel plan. For example, it proposes a plan that includes tourist spots and activities that everyone is interested in. It collects data of friends and family in real time to propose a group travel plan. For example, it takes into account everyone's schedules and interests to propose the best time and place to travel. In this way, it is possible to propose a group travel plan by taking into account the data of the user's friends and family.

[0037] The plan generation unit can compare the plan with the user's past travel plans and generate a plan that provides a new experience. The plan generation unit, for example, uses a generation AI to compare the plan with the user's past travel plans and generate a plan that provides a new experience. For example, the generation AI analyzes the user's past travel plans and generates a plan that provides a new experience. For example, it may suggest tourist spots and activities that have not been visited before. It may compare the plan with the user's past travel history and generate a travel plan that provides a different experience. For example, it may suggest regions and cultures that are different from tourist spots visited in the past. It may integrate past travel plans with current interests and generate a plan that provides a new experience. For example, it may suggest experiences in new places based on activities that were popular on past trips. In this way, it is possible to generate a plan that provides a new experience by comparing the plan with the user's past travel plans.

[0038] The transportation arrangement unit can also consider budget or time constraints and propose the optimal means. The transportation arrangement unit, for example, uses generation AI to consider the user's budget and time constraints and propose the optimal means. For example, the generation AI analyzes the user's budget data and proposes the optimal means of transportation within the budget. For example, it presents options such as plane, train, and bus depending on the user's budget. It proposes the optimal means of transportation taking into account the user's time constraints. For example, it proposes the means of transportation that takes the shortest time according to the user's schedule. It integrates budget and time data to propose the optimal means of transportation to the user. For example, it selects and proposes the most time-efficient means of transportation within the budget. This makes it possible to propose the optimal means of transportation by considering the user's budget and time constraints.

[0039] The transportation arrangement unit can propose eco-friendly options by taking into account the impact on the environment. The transportation arrangement unit, for example, uses generation AI to propose eco-friendly options by taking into account the impact on the environment. For example, the generation AI analyzes the environmental impact of transportation methods and proposes eco-friendly options. For example, it prioritizes proposals of low-carbon transportation methods such as trains and buses. It proposes eco-friendly transportation methods based on the user's environmental awareness data. For example, if the user is interested in environmental protection, it presents options such as electric vehicles and bicycles. It collects environmental data in real time and proposes the most suitable eco-friendly transportation method for the user. For example, it proposes environmentally friendly options based on the CO2 emissions of the transportation method. This makes it possible to propose eco-friendly transportation methods by taking into account the impact on the environment.

[0040] The transportation arrangement unit can consider the user's past transportation preferences and propose the optimal means. The transportation arrangement unit, for example, uses a generation AI to consider the user's past transportation preferences and propose the optimal means. For example, the generation AI analyzes the user's past transportation data and proposes transportation that suits their preferences. For example, it proposes similar means of transportation based on evaluations of transportation used in the past. It proposes the optimal transportation means based on the user's past travel history. For example, it prioritizes proposals of transportation means that were comfortable in the past. It collects data on past transportation means in real time and proposes the optimal transportation means to the user. For example, it selects the current transportation means based on past transportation preferences. In this way, it is possible to propose the optimal transportation means by considering the user's past transportation preferences.

[0041] The transportation arrangement unit can integrate data from different transportation modes and propose the optimal travel route. The transportation arrangement unit, for example, uses generation AI to integrate data from different transportation modes and propose the optimal travel route. For example, the generation AI integrates data from different transportation modes and proposes the optimal travel route. For example, it centralizes data from airplanes, trains, buses, etc. and proposes the route that allows travel in the shortest time. It analyzes data from different transportation modes and proposes the optimal travel route for the user. For example, it proposes a route that combines multiple transportation modes. It collects transportation data in real time and proposes the optimal travel route for the user. For example, it selects the optimal route based on traffic conditions and operation schedules. In this way, it is possible to propose the optimal travel route by integrating data from different transportation modes.

[0042] The ticket arrangement unit can also consider the user's past event participation history to suggest the most suitable ticket. The ticket arrangement unit, for example, uses a generation AI to also consider the user's past event participation history to suggest the most suitable ticket. For example, the generation AI analyzes the user's past event participation history to suggest the most suitable ticket. For example, based on the evaluation of an event previously attended, tickets for a similar event are suggested. Based on the user's past event participation history, the most suitable ticket is suggested. For example, taking into account the genre and content of an event previously attended, tickets for a similar event are suggested. Past event participation history is collected in real time to suggest the most suitable ticket to the user. For example, based on the evaluation of an event previously attended, tickets for the current event are suggested. In this way, the most suitable ticket can be suggested by considering the user's past event participation history.

[0043] The ticket arrangement unit can draw the user's interest by including information about local events. The ticket arrangement unit, for example, uses a generation AI to draw the user's interest by including information about local events. For example, the generation AI includes local event information in a travel plan to draw the user's interest. For example, it suggests tickets for concerts and festivals held locally. It generates a travel plan that includes local event information based on the user's interests. For example, it suggests a plan that includes events in a genre that the user is interested in. It collects local event information in real time and suggests the most suitable tickets for the user. For example, it suggests tickets that the user is interested in based on the latest information about events held locally. In this way, it is possible to draw the user's interest by including local event information.

[0044] The ticket arrangement unit can also consider data on friends and family to suggest group participation. The ticket arrangement unit, for example, uses generation AI to also consider data on the user's friends and family to suggest group participation. For example, the generation AI analyzes data on the user's friends and family to suggest group participation. For example, it considers the hobbies and preferences of friends and family to suggest tickets for an event that everyone can enjoy. It collects SNS data on the user's friends and family to suggest group participation. For example, it suggests tickets for an event that everyone is interested in. It collects data on friends and family in real time to suggest group participation. For example, it considers everyone's schedules and interests to suggest tickets for the most suitable event. In this way, it is possible to suggest group participation by considering the data of the user's friends and family.

[0045] The ticket arrangement unit can integrate tickets for different events and propose the optimal schedule. The ticket arrangement unit, for example, uses generation AI to integrate tickets for different events and propose the optimal schedule. For example, the generation AI can integrate tickets for different events and propose the optimal schedule. For example, it can propose a schedule that combines multiple events. It analyzes tickets for different events and proposes the optimal schedule for the user. For example, it can propose an efficient schedule taking into account the time and location of the events. It collects event ticket data in real time and proposes the optimal schedule for the user. For example, it can propose a schedule for events that the user is interested in based on the latest event information. This makes it possible to propose the optimal schedule by integrating tickets for different events.

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

[0047] The data analysis unit analyzes data such as the user's social media accounts and photos. For example, the data analysis unit collects the user's social media posts and photos and performs text analysis and image analysis. The data analysis unit can also perform sentiment analysis to estimate the user's emotions. For example, the data analysis unit analyzes text data from social media posts and extracts keywords that indicate positive emotions. The plan generation unit generates a travel plan based on the data analyzed by the data analysis unit. For example, the plan generation unit proposes a plan including optimal tourist spots and activities based on the user's hobbies and preferences. The plan generation unit can also generate a travel plan based on the user's emotions using generation AI. The transportation arrangement unit arranges transportation based on the travel plan generated by the plan generation unit. For example, the transportation arrangement unit reserves plane and train tickets and provides them to the user. The transportation arrangement unit can also propose the optimal transportation method taking into account the user's budget and time constraints. The ticket arrangement unit arranges tickets based on the travel plan generated by the plan generation unit. For example, the ticket arrangement unit reserves tickets to tourist spots and events and provides them to the user. The ticket arrangement unit can also arrange tickets based on the user's emotions. As a result, the travel plan creation system according to the embodiment can analyze user data, generate an optimal travel plan, and automatically arrange transportation and tickets. For example, if the user likes nature, a plan that includes tourist spots rich in nature will be proposed, and airplane and train tickets, admission tickets to tourist spots, etc. will be automatically arranged. This allows the user to significantly reduce the time and effort required to plan a trip.

[0048] The data analysis unit can also analyze travel history or reviews to more accurately understand hobbies and preferences. For example, using generation AI, the data analysis unit can also analyze the user's past travel history and reviews to more accurately understand hobbies and preferences. For example, the generation AI can analyze the user's past travel history and understand hobbies and preferences based on information about places visited and accommodations. For example, it can analyze the characteristics of tourist spots visited in the past and suggest similar tourist spots. It can analyze travel reviews posted by the user and identify places and activities that have received positive reviews. For example, it can create a travel plan based on tourist spots and activities with many highly rated reviews. It can integrate the user's past travel history and reviews to understand the user's overall hobbies and preferences. For example, it can identify the user's preferred travel style based on the frequency of places visited and the content of reviews. This makes it possible to more accurately understand the user's hobbies and preferences by taking into account the user's past travel history and reviews.

[0049] The data analysis unit can also consider seasonal or weather information to suggest the optimal travel time. The data analysis unit, for example, uses a generation AI to consider seasonal and weather information to suggest the optimal travel time. For example, the generation AI incorporates seasonal and weather information into the user's data analysis to suggest the optimal travel time. For example, it analyzes the weather and the time when a photo posted by the user was taken and suggests a trip with similar conditions. It integrates the user's past travel history with seasonal and weather data to identify the optimal travel time. For example, it suggests a trip with similar conditions based on the season and weather of tourist spots visited in the past. It collects seasonal and weather data in real time to suggest the optimal travel time that suits the user's hobbies and preferences. For example, for a user who loves nature, it suggests the season when flowers bloom or the season when autumn leaves change color. In this way, it is possible to suggest the optimal travel time to the user by taking season and weather into consideration.

[0050] The data analysis unit can grasp hobbies and preferences from a more multifaceted perspective, including audio data or video data. The data analysis unit, for example, uses generation AI to grasp hobbies and preferences from a more multifaceted perspective, including audio data and video data. For example, the generation AI analyzes the user's audio data to grasp hobbies and preferences. For example, it analyzes the audio of audio notes and videos recorded by the user while traveling to identify places and activities of interest. It analyzes video data posted by the user to grasp hobbies and preferences based on the scenery and activities contained in the video. For example, it analyzes tourist spots and activities shown in the video to suggest similar places. It integrates audio data and video data to grasp comprehensive hobbies and preferences. For example, it combines keywords extracted from audio data with footage from the video data to identify the user's preferred travel style. In this way, by including audio data and video data, it becomes possible to grasp hobbies and preferences from a more multifaceted perspective.

[0051] The data analysis unit can integrate data from different SNS platforms and perform comprehensive analysis. The data analysis unit, for example, uses generation AI to integrate data from different SNS platforms and perform comprehensive analysis. For example, the generation AI collects data from multiple SNS platforms used by a user and analyzes it in an integrated manner. For example, it centralizes and analyzes post data from Facebook, Instagram, Twitter, etc. Based on the data collected from different SNS platforms, it comprehensively understands the user's hobbies and preferences. For example, it analyzes the content and reactions of posts on each platform to identify common interests. It collects data from multiple SNS platforms in real time and performs comprehensive analysis. For example, it identifies the user's current interests and concerns based on the most recent post data. This makes it possible to perform comprehensive analysis by integrating data from different SNS platforms.

[0052] The plan generation unit can propose a reasonable plan by taking into account the user's health condition or physical strength. The plan generation unit, for example, uses a generation AI to propose a reasonable plan by taking into account the user's health condition and physical strength. For example, the generation AI analyzes the user's health data and proposes a reasonable travel plan. For example, it creates a plan with an appropriate amount of exercise based on the user's step count data and heart rate data. It generates a reasonable travel plan based on the user's physical strength data. For example, it proposes a plan that includes appropriate activities by taking into account the user's age and physical strength level. It collects health condition and physical strength data in real time and proposes the optimal travel plan for the user. For example, if the user's health condition is not good, it proposes a relaxing plan. In this way, it is possible to propose a reasonable travel plan by taking into account the user's health condition and physical strength.

[0053] The plan generation unit can arouse the user's interest by including information about local culture or history. The plan generation unit, for example, uses a generation AI to include information about local culture and history to arouse the user's interest. For example, the generation AI may include information about local culture and history in a travel plan to arouse the user's interest. For example, the generation AI may propose a plan that introduces the history and cultural background of tourist destinations. Based on the user's interests, the plan may generate a travel plan that includes activities related to local culture and history. For example, a plan to visit traditional festivals or historical buildings may be proposed. Information about local culture and history may be collected in real time to propose the optimal travel plan to the user. For example, a plan that interests the user may be created based on information about local events. In this way, the inclusion of information about local culture and history can arouse the user's interest.

[0054] The plan generation unit can propose a group travel plan by taking into account data of friends or family. The plan generation unit, for example, uses generation AI to propose a group travel plan by taking into account data of the user's friends and family. For example, the generation AI analyzes data of the user's friends and family to propose a group travel plan. For example, it takes into account the hobbies and preferences of friends and family to create a plan that everyone can enjoy. It collects social media data of the user's friends and family to generate a group travel plan. For example, it proposes a plan that includes tourist spots and activities that everyone is interested in. It collects data of friends and family in real time to propose a group travel plan. For example, it takes into account everyone's schedules and interests to propose the best time and place to travel. In this way, it is possible to propose a group travel plan by taking into account the data of the user's friends and family.

[0055] The plan generation unit can compare the plan with the user's past travel plans and generate a plan that provides a new experience. The plan generation unit, for example, uses a generation AI to compare the plan with the user's past travel plans and generate a plan that provides a new experience. For example, the generation AI analyzes the user's past travel plans and generates a plan that provides a new experience. For example, it may suggest tourist spots and activities that have not been visited before. It may compare the plan with the user's past travel history and generate a travel plan that provides a different experience. For example, it may suggest regions and cultures that are different from tourist spots visited in the past. It may integrate past travel plans with current interests and generate a plan that provides a new experience. For example, it may suggest experiences in new places based on activities that were popular on past trips. In this way, it is possible to generate a plan that provides a new experience by comparing the plan with the user's past travel plans.

[0056] The transportation arrangement unit can also consider budget or time constraints and propose the optimal means. The transportation arrangement unit, for example, uses generation AI to consider the user's budget and time constraints and propose the optimal means. For example, the generation AI analyzes the user's budget data and proposes the optimal means of transportation within the budget. For example, it presents options such as plane, train, and bus depending on the user's budget. It proposes the optimal means of transportation taking into account the user's time constraints. For example, it proposes the means of transportation that takes the shortest time according to the user's schedule. It integrates budget and time data to propose the optimal means of transportation to the user. For example, it selects and proposes the most time-efficient means of transportation within the budget. This makes it possible to propose the optimal means of transportation by considering the user's budget and time constraints.

[0057] The transportation arrangement unit can propose eco-friendly options by taking into account the impact on the environment. The transportation arrangement unit, for example, uses generation AI to propose eco-friendly options by taking into account the impact on the environment. For example, the generation AI analyzes the environmental impact of transportation methods and proposes eco-friendly options. For example, it prioritizes proposals of low-carbon transportation methods such as trains and buses. It proposes eco-friendly transportation methods based on the user's environmental awareness data. For example, if the user is interested in environmental protection, it presents options such as electric vehicles and bicycles. It collects environmental data in real time and proposes the most suitable eco-friendly transportation method for the user. For example, it proposes environmentally friendly options based on the CO2 emissions of the transportation method. This makes it possible to propose eco-friendly transportation methods by taking into account the impact on the environment.

[0058] The transportation arrangement unit can consider the user's past transportation preferences and propose the optimal means. The transportation arrangement unit, for example, uses a generation AI to consider the user's past transportation preferences and propose the optimal means. For example, the generation AI analyzes the user's past transportation data and proposes transportation that suits their preferences. For example, it proposes similar means of transportation based on evaluations of transportation used in the past. It proposes the optimal transportation means based on the user's past travel history. For example, it prioritizes proposals of transportation means that were comfortable in the past. It collects data on past transportation means in real time and proposes the optimal transportation means to the user. For example, it selects the current transportation means based on past transportation preferences. In this way, it is possible to propose the optimal transportation means by considering the user's past transportation preferences.

[0059] The transportation arrangement unit can integrate data from different transportation modes and propose the optimal travel route. The transportation arrangement unit, for example, uses generation AI to integrate data from different transportation modes and propose the optimal travel route. For example, the generation AI integrates data from different transportation modes and proposes the optimal travel route. For example, it centralizes data from airplanes, trains, buses, etc. and proposes the route that allows travel in the shortest time. It analyzes data from different transportation modes and proposes the optimal travel route for the user. For example, it proposes a route that combines multiple transportation modes. It collects transportation data in real time and proposes the optimal travel route for the user. For example, it selects the optimal route based on traffic conditions and operation schedules. In this way, it is possible to propose the optimal travel route by integrating data from different transportation modes.

[0060] The ticket arrangement unit can also consider the user's past event participation history to suggest the most suitable ticket. The ticket arrangement unit, for example, uses a generation AI to also consider the user's past event participation history to suggest the most suitable ticket. For example, the generation AI analyzes the user's past event participation history to suggest the most suitable ticket. For example, based on the evaluation of an event previously attended, tickets for a similar event are suggested. Based on the user's past event participation history, the most suitable ticket is suggested. For example, taking into account the genre and content of an event previously attended, tickets for a similar event are suggested. Past event participation history is collected in real time to suggest the most suitable ticket to the user. For example, based on the evaluation of an event previously attended, tickets for the current event are suggested. In this way, the most suitable ticket can be suggested by considering the user's past event participation history.

[0061] The ticket arrangement unit can draw the user's interest by including information about local events. The ticket arrangement unit, for example, uses a generation AI to draw the user's interest by including information about local events. For example, the generation AI includes local event information in a travel plan to draw the user's interest. For example, it suggests tickets for concerts and festivals held locally. It generates a travel plan that includes local event information based on the user's interests. For example, it suggests a plan that includes events in a genre that the user is interested in. It collects local event information in real time and suggests the most suitable tickets for the user. For example, it suggests tickets that the user is interested in based on the latest information about events held locally. In this way, it is possible to draw the user's interest by including local event information.

[0062] The ticket arrangement unit can also consider data on friends and family to suggest group participation. The ticket arrangement unit, for example, uses generation AI to also consider data on the user's friends and family to suggest group participation. For example, the generation AI analyzes data on the user's friends and family to suggest group participation. For example, it considers the hobbies and preferences of friends and family to suggest tickets for an event that everyone can enjoy. It collects SNS data on the user's friends and family to suggest group participation. For example, it suggests tickets for an event that everyone is interested in. It collects data on friends and family in real time to suggest group participation. For example, it considers everyone's schedules and interests to suggest tickets for the most suitable event. In this way, it is possible to suggest group participation by considering the data of the user's friends and family.

[0063] The ticket arrangement unit can integrate tickets for different events and propose the optimal schedule. The ticket arrangement unit, for example, uses generation AI to integrate tickets for different events and propose the optimal schedule. For example, the generation AI can integrate tickets for different events and propose the optimal schedule. For example, it can propose a schedule that combines multiple events. It analyzes tickets for different events and proposes the optimal schedule for the user. For example, it can propose an efficient schedule taking into account the time and location of the events. It collects event ticket data in real time and proposes the optimal schedule for the user. For example, it can propose a schedule for events that the user is interested in based on the latest event information. This makes it possible to propose the optimal schedule by integrating tickets for different events.

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

[0065] Step 1: The data analysis unit analyzes data such as the user's social media posts and photos. For example, the data analysis unit collects the user's social media posts and photos and performs text analysis and image analysis. The data analysis unit can also perform sentiment analysis to estimate the user's emotions. For example, it analyzes the text data of social media posts and extracts keywords that indicate positive emotions. Step 2: The plan generation unit generates a travel plan based on the data analyzed by the data analysis unit. For example, the plan generation unit proposes a plan including optimal sightseeing spots and activities based on the user's hobbies and preferences. The plan generation unit can also use generation AI to generate a travel plan based on the user's emotions. Step 3: The transportation arrangement unit arranges transportation based on the travel plan generated by the plan generation unit. For example, the transportation arrangement unit reserves plane or train tickets and provides them to the user. The transportation arrangement unit can also propose the most suitable transportation method, taking into account the user's budget and time constraints. Step 4: The ticket arrangement unit arranges tickets based on the travel plan generated by the plan generation unit. For example, the ticket arrangement unit reserves tickets for tourist attractions or events and provides them to the user. The ticket arrangement unit can also arrange tickets based on the user's emotions.

[0066] (Example 2) The travel plan creation system according to an embodiment of the present invention analyzes data such as a smartphone user's social media accounts and photos, understands the user's hobbies and preferences, and then automatically creates an optimal travel plan and automatically arranges transportation and tickets. This allows the travel plan creation system to analyze user data, generate an optimal travel plan, and automatically arrange transportation and tickets.

[0067] A travel plan creation system according to an embodiment includes a data analysis unit, a plan generation unit, a transportation arrangement unit, and a ticket arrangement unit. The data analysis unit analyzes data such as a user's social media accounts and photos. For example, the data analysis unit collects the user's social media posts and photos and performs text analysis and image analysis. The data analysis unit can also perform sentiment analysis to estimate the user's emotions. For example, the data analysis unit analyzes text data from social media posts and extracts keywords indicating positive emotions. The plan generation unit generates a travel plan based on the data analyzed by the data analysis unit. For example, the plan generation unit proposes a plan including optimal tourist spots and activities based on the user's hobbies and preferences. The plan generation unit can also generate a travel plan based on the user's emotions using a generation AI. The transportation arrangement unit arranges transportation based on the travel plan generated by the plan generation unit. For example, the transportation arrangement unit reserves airplane and train tickets and provides them to the user. The transportation arrangement unit can also propose the optimal transportation method taking into account the user's budget and time constraints. The ticket arrangement unit arranges tickets based on the travel plan generated by the plan generation unit. For example, the ticket arrangement unit reserves tickets to tourist attractions and events and provides them to the user. The ticket arrangement unit can also arrange tickets based on the user's emotions. As a result, the travel plan creation system according to the embodiment can analyze user data, generate an optimal travel plan, and automatically arrange transportation and tickets. For example, if the user likes nature, a plan including tourist spots rich in nature will be proposed, and airplane and train tickets, admission tickets to tourist attractions, etc. will be automatically arranged. This allows the user to significantly reduce the time and effort required to plan a trip.

[0068] The data analysis unit can infer emotions from social media posts or photos and propose travel plans that elicit positive emotions. For example, the data analysis unit uses a generation AI to infer emotions from a user's social media posts and photos and propose travel plans that elicit positive emotions. For example, the generation AI analyzes the user's social media posts and photos and proposes travel plans that elicit positive emotions. For example, if a user posts many photos of smiling faces, the travel plans include the locations where those photos were taken and similar locations. The text data of the user's social media posts is analyzed to extract keywords that indicate positive emotions. For example, a travel plan is created based on posts that frequently contain keywords such as "fun" and "beautiful." The scenery and activities included in the user's photos are analyzed to propose travel plans that elicit positive emotions. For example, tourist spots rich in nature are proposed based on photos that frequently include natural scenery and activities. This allows the system to propose travel plans that take the user's emotions into consideration, thereby improving user satisfaction.

[0069] The data analysis unit can also analyze travel history or reviews to more accurately understand hobbies and preferences. For example, using generation AI, the data analysis unit can also analyze the user's past travel history and reviews to more accurately understand hobbies and preferences. For example, the generation AI can analyze the user's past travel history and understand hobbies and preferences based on information about places visited and accommodations. For example, it can analyze the characteristics of tourist spots visited in the past and suggest similar tourist spots. It can analyze travel reviews posted by the user and identify places and activities that have received positive reviews. For example, it can create a travel plan based on tourist spots and activities with many highly rated reviews. It can integrate the user's past travel history and reviews to understand the user's overall hobbies and preferences. For example, it can identify the user's preferred travel style based on the frequency of places visited and the content of reviews. This makes it possible to more accurately understand the user's hobbies and preferences by taking into account the user's past travel history and reviews.

[0070] The data analysis unit can also consider seasonal or weather information to suggest the optimal travel time. The data analysis unit, for example, uses a generation AI to consider seasonal and weather information to suggest the optimal travel time. For example, the generation AI incorporates seasonal and weather information into the user's data analysis to suggest the optimal travel time. For example, it analyzes the weather and the time when a photo posted by the user was taken and suggests a trip with similar conditions. It integrates the user's past travel history with seasonal and weather data to identify the optimal travel time. For example, it suggests a trip with similar conditions based on the season and weather of tourist spots visited in the past. It collects seasonal and weather data in real time to suggest the optimal travel time that suits the user's hobbies and preferences. For example, for a user who loves nature, it suggests the season when flowers bloom or the season when autumn leaves change color. In this way, it is possible to suggest the optimal travel time to the user by taking season and weather into consideration.

[0071] The data analysis unit can grasp hobbies and preferences from a more multifaceted perspective, including audio data or video data. The data analysis unit, for example, uses generation AI to grasp hobbies and preferences from a more multifaceted perspective, including audio data and video data. For example, the generation AI analyzes the user's audio data to grasp hobbies and preferences. For example, it analyzes the audio of audio notes and videos recorded by the user while traveling to identify places and activities of interest. It analyzes video data posted by the user to grasp hobbies and preferences based on the scenery and activities contained in the video. For example, it analyzes tourist spots and activities shown in the video to suggest similar places. It integrates audio data and video data to grasp comprehensive hobbies and preferences. For example, it combines keywords extracted from audio data with footage from the video data to identify the user's preferred travel style. In this way, by including audio data and video data, it becomes possible to grasp hobbies and preferences from a more multifaceted perspective.

[0072] The data analysis unit can integrate data from different SNS platforms and perform comprehensive analysis. The data analysis unit, for example, uses generation AI to integrate data from different SNS platforms and perform comprehensive analysis. For example, the generation AI collects data from multiple SNS platforms used by a user and analyzes it in an integrated manner. For example, it centralizes and analyzes post data from Facebook, Instagram, Twitter, etc. Based on the data collected from different SNS platforms, it comprehensively understands the user's hobbies and preferences. For example, it analyzes the content and reactions of posts on each platform to identify common interests. It collects data from multiple SNS platforms in real time and performs comprehensive analysis. For example, it identifies the user's current interests and concerns based on the most recent post data. This makes it possible to perform comprehensive analysis by integrating data from different SNS platforms.

[0073] The data analysis unit can use the emotion estimation function to analyze the emotions of a user when browsing travel plans in real time and optimize plan suggestions. The data analysis unit, for example, uses the generation AI and the emotion estimation function to analyze the emotions of a user when browsing travel plans in real time and optimize plan suggestions. For example, the generation AI analyzes the user's facial expressions and voice when browsing travel plans and estimates emotions in real time. For example, if the user is smiling while browsing a plan, that plan is preferentially suggested. The user's emotional responses are analyzed in real time and plans that elicit positive emotions are suggested. For example, similar plans are suggested based on plans in which the user has shown interest. Based on the emotion estimation data, plans that evoke the most positive emotions in the user are identified and suggestions are optimized. For example, plans with a high user emotion score are preferentially displayed. This makes it possible to suggest optimal travel plans by analyzing the user's emotions in real time.

[0074] The plan generation unit can estimate emotions and generate a travel plan based on the emotions. The plan generation unit, for example, uses a generation AI to estimate a user's emotions and generate a travel plan based on the emotions. For example, the generation AI estimates emotions from the user's social media posts and photos and generates a travel plan based on the emotions. For example, if the user feels like relaxing, a plan including a resort is suggested. The user's emotional data is analyzed to generate a travel plan that elicits positive emotions. For example, if the user feels like having fun, tourist spots with plenty of activities are suggested. Based on the emotion estimation data, a travel plan optimal for the user's current emotional state is generated. For example, if the user is feeling stressed, a place where they can relax is suggested. In this way, by generating a travel plan based on the user's emotions, user satisfaction can be improved.

[0075] The plan generation unit can propose a reasonable plan by taking into account the user's health condition or physical strength. The plan generation unit, for example, uses a generation AI to propose a reasonable plan by taking into account the user's health condition and physical strength. For example, the generation AI analyzes the user's health data and proposes a reasonable travel plan. For example, it creates a plan with an appropriate amount of exercise based on the user's step count data and heart rate data. It generates a reasonable travel plan based on the user's physical strength data. For example, it proposes a plan that includes appropriate activities by taking into account the user's age and physical strength level. It collects health condition and physical strength data in real time and proposes the optimal travel plan for the user. For example, if the user's health condition is not good, it proposes a relaxing plan. In this way, it is possible to propose a reasonable travel plan by taking into account the user's health condition and physical strength.

[0076] The plan generation unit can arouse the user's interest by including information about local culture or history. The plan generation unit, for example, uses a generation AI to include information about local culture and history to arouse the user's interest. For example, the generation AI may include information about local culture and history in a travel plan to arouse the user's interest. For example, the generation AI may propose a plan that introduces the history and cultural background of tourist destinations. Based on the user's interests, the plan may generate a travel plan that includes activities related to local culture and history. For example, a plan to visit traditional festivals or historical buildings may be proposed. Information about local culture and history may be collected in real time to propose the optimal travel plan to the user. For example, a plan that interests the user may be created based on information about local events. In this way, the inclusion of information about local culture and history can arouse the user's interest.

[0077] The plan generation unit can propose a group travel plan by taking into account data of friends or family. The plan generation unit, for example, uses generation AI to propose a group travel plan by taking into account data of the user's friends and family. For example, the generation AI analyzes data of the user's friends and family to propose a group travel plan. For example, it takes into account the hobbies and preferences of friends and family to create a plan that everyone can enjoy. It collects social media data of the user's friends and family to generate a group travel plan. For example, it proposes a plan that includes tourist spots and activities that everyone is interested in. It collects data of friends and family in real time to propose a group travel plan. For example, it takes into account everyone's schedules and interests to propose the best time and place to travel. In this way, it is possible to propose a group travel plan by taking into account the data of the user's friends and family.

[0078] The plan generation unit can compare the plan with the user's past travel plans and generate a plan that provides a new experience. The plan generation unit, for example, uses a generation AI to compare the plan with the user's past travel plans and generate a plan that provides a new experience. For example, the generation AI analyzes the user's past travel plans and generates a plan that provides a new experience. For example, it may suggest tourist spots and activities that have not been visited before. It may compare the plan with the user's past travel history and generate a travel plan that provides a different experience. For example, it may suggest regions and cultures that are different from tourist spots visited in the past. It may integrate past travel plans with current interests and generate a plan that provides a new experience. For example, it may suggest experiences in new places based on activities that were popular on past trips. In this way, it is possible to generate a plan that provides a new experience by comparing the plan with the user's past travel plans.

[0079] The plan generation unit can use the emotion estimation function to analyze the emotions of the user when selecting a travel plan and propose the optimal plan. The plan generation unit, for example, uses the generation AI to use the emotion estimation function to analyze the emotions of the user when selecting a travel plan and propose the optimal plan. For example, the generation AI analyzes the user's facial expressions and voice when selecting a travel plan and predicts the emotion in real time. For example, if the user selects a plan with a smile, that plan is preferentially proposed. The user's emotional response is analyzed in real time and plans that elicit positive emotions are proposed. For example, similar plans are proposed based on plans in which the user has shown interest. Based on the emotion estimation data, plans that evoke the most positive emotions in the user are identified and the proposals are optimized. For example, plans with a high user emotion score are preferentially displayed. In this way, the optimal travel plan can be proposed by analyzing the user's emotions.

[0080] The transportation arrangement unit can estimate emotions and suggest comfortable transportation methods. The transportation arrangement unit, for example, uses generation AI to estimate the user's emotions and suggest comfortable transportation methods. For example, the generation AI estimates emotions from the user's social media posts and photos and suggests comfortable transportation methods. For example, if the user feels like relaxing, it suggests transportation methods that offer comfortable seats and services. It analyzes the user's emotional data and suggests transportation methods that elicit positive emotions. For example, if the user feels like having fun, it suggests transportation methods that offer plenty of entertainment. Based on the emotion estimation data, it suggests transportation methods that are optimal for the user's current emotional state. For example, if the user is feeling stressed, it suggests transportation methods that allow them to relax. In this way, it is possible to suggest comfortable transportation methods by taking the user's emotions into consideration.

[0081] The transportation arrangement unit can also consider budget or time constraints and propose the optimal means. The transportation arrangement unit, for example, uses generation AI to consider the user's budget and time constraints and propose the optimal means. For example, the generation AI analyzes the user's budget data and proposes the optimal means of transportation within the budget. For example, it presents options such as plane, train, and bus depending on the user's budget. It proposes the optimal means of transportation taking into account the user's time constraints. For example, it proposes the means of transportation that takes the shortest time according to the user's schedule. It integrates budget and time data to propose the optimal means of transportation to the user. For example, it selects and proposes the most time-efficient means of transportation within the budget. This makes it possible to propose the optimal means of transportation by considering the user's budget and time constraints.

[0082] The transportation arrangement unit can propose eco-friendly options by taking into account the impact on the environment. The transportation arrangement unit, for example, uses generation AI to propose eco-friendly options by taking into account the impact on the environment. For example, the generation AI analyzes the environmental impact of transportation methods and proposes eco-friendly options. For example, it prioritizes proposals of low-carbon transportation methods such as trains and buses. It proposes eco-friendly transportation methods based on the user's environmental awareness data. For example, if the user is interested in environmental protection, it presents options such as electric vehicles and bicycles. It collects environmental data in real time and proposes the most suitable eco-friendly transportation method for the user. For example, it proposes environmentally friendly options based on the CO2 emissions of the transportation method. This makes it possible to propose eco-friendly transportation methods by taking into account the impact on the environment.

[0083] The transportation arrangement unit can consider the user's past transportation preferences and propose the optimal means. The transportation arrangement unit, for example, uses a generation AI to consider the user's past transportation preferences and propose the optimal means. For example, the generation AI analyzes the user's past transportation data and proposes transportation that suits their preferences. For example, it proposes similar means of transportation based on evaluations of transportation used in the past. It proposes the optimal transportation means based on the user's past travel history. For example, it prioritizes proposals of transportation means that were comfortable in the past. It collects data on past transportation means in real time and proposes the optimal transportation means to the user. For example, it selects the current transportation means based on past transportation preferences. In this way, it is possible to propose the optimal transportation means by considering the user's past transportation preferences.

[0084] The transportation arrangement unit can integrate data from different transportation modes and propose the optimal travel route. The transportation arrangement unit, for example, uses generation AI to integrate data from different transportation modes and propose the optimal travel route. For example, the generation AI integrates data from different transportation modes and proposes the optimal travel route. For example, it centralizes data from airplanes, trains, buses, etc. and proposes the route that allows travel in the shortest time. It analyzes data from different transportation modes and proposes the optimal travel route for the user. For example, it proposes a route that combines multiple transportation modes. It collects transportation data in real time and proposes the optimal travel route for the user. For example, it selects the optimal route based on traffic conditions and operation schedules. In this way, it is possible to propose the optimal travel route by integrating data from different transportation modes.

[0085] The transportation arrangement unit can use the emotion estimation function to analyze the emotions of the user when selecting a transportation means and suggest the optimal means. The transportation arrangement unit can use, for example, a generation AI to use the emotion estimation function to analyze the emotions of the user when selecting a transportation means and suggest the optimal means. For example, the generation AI analyzes the user's facial expressions and voice when selecting a transportation means and estimates the emotions in real time. For example, if the user wants to relax, a comfortable transportation means is suggested. The user's emotional response is analyzed in real time and a transportation means that elicits positive emotions is suggested. For example, if the user wants to have fun, a transportation means that offers plenty of entertainment is suggested. Based on the emotion estimation data, a transportation means that is optimal for the user's current emotional state is suggested. For example, if the user is feeling stressed, a transportation means that will help them relax is suggested. In this way, the optimal transportation means can be suggested by analyzing the user's emotions.

[0086] The ticket arrangement unit can estimate emotions and arrange tickets based on the emotions. The ticket arrangement unit, for example, uses a generation AI to estimate a user's emotions and arrange tickets based on the emotions. For example, the generation AI estimates emotions from the user's social media posts and photos and arranges tickets based on the emotions. For example, if the user feels like having fun, entertainment tickets are suggested. The user's emotional data is analyzed and tickets that elicit positive emotions are arranged. For example, if the user feels like relaxing, tickets to a relaxation facility are suggested. Based on the emotion estimation data, tickets that are optimal for the user's current emotional state are arranged. For example, if the user is feeling stressed, tickets to a relaxation facility are suggested. In this way, by arranging tickets based on the user's emotions, user satisfaction can be improved.

[0087] The ticket arrangement unit can also consider the user's past event participation history to suggest the most suitable ticket. The ticket arrangement unit, for example, uses a generation AI to also consider the user's past event participation history to suggest the most suitable ticket. For example, the generation AI analyzes the user's past event participation history to suggest the most suitable ticket. For example, based on the evaluation of an event previously attended, tickets for a similar event are suggested. Based on the user's past event participation history, the most suitable ticket is suggested. For example, taking into account the genre and content of an event previously attended, tickets for a similar event are suggested. Past event participation history is collected in real time to suggest the most suitable ticket to the user. For example, based on the evaluation of an event previously attended, tickets for the current event are suggested. In this way, the most suitable ticket can be suggested by considering the user's past event participation history.

[0088] The ticket arrangement unit can draw the user's interest by including information about local events. The ticket arrangement unit, for example, uses a generation AI to draw the user's interest by including information about local events. For example, the generation AI includes local event information in a travel plan to draw the user's interest. For example, it suggests tickets for concerts and festivals held locally. It generates a travel plan that includes local event information based on the user's interests. For example, it suggests a plan that includes events in a genre that the user is interested in. It collects local event information in real time and suggests the most suitable tickets for the user. For example, it suggests tickets that the user is interested in based on the latest information about events held locally. In this way, it is possible to draw the user's interest by including local event information.

[0089] The ticket arrangement unit can also consider data on friends and family to suggest group participation. The ticket arrangement unit, for example, uses generation AI to also consider data on the user's friends and family to suggest group participation. For example, the generation AI analyzes data on the user's friends and family to suggest group participation. For example, it considers the hobbies and preferences of friends and family to suggest tickets for an event that everyone can enjoy. It collects SNS data on the user's friends and family to suggest group participation. For example, it suggests tickets for an event that everyone is interested in. It collects data on friends and family in real time to suggest group participation. For example, it considers everyone's schedules and interests to suggest tickets for the most suitable event. In this way, it is possible to suggest group participation by considering the data of the user's friends and family.

[0090] The ticket arrangement unit can integrate tickets for different events and propose the optimal schedule. The ticket arrangement unit, for example, uses generation AI to integrate tickets for different events and propose the optimal schedule. For example, the generation AI can integrate tickets for different events and propose the optimal schedule. For example, it can propose a schedule that combines multiple events. It analyzes tickets for different events and proposes the optimal schedule for the user. For example, it can propose an efficient schedule taking into account the time and location of the events. It collects event ticket data in real time and proposes the optimal schedule for the user. For example, it can propose a schedule for events that the user is interested in based on the latest event information. This makes it possible to propose the optimal schedule by integrating tickets for different events.

[0091] The ticket arrangement unit can use the emotion estimation function to analyze the emotions of the user when selecting a ticket and suggest the most suitable ticket. The ticket arrangement unit can use, for example, a generation AI to use the emotion estimation function to analyze the emotions of the user when selecting a ticket and suggest the most suitable ticket. For example, the generation AI analyzes the facial expressions and voice of the user when selecting a ticket and estimates the emotion in real time. For example, if the user feels like having fun, entertainment tickets are suggested. The user's emotional response is analyzed in real time and tickets that elicit positive emotions are suggested. For example, if the user feels like relaxing, tickets to a relaxation facility are suggested. Based on the emotion estimation data, tickets that are most suitable for the user's current emotional state are suggested. For example, if the user is feeling stressed, tickets to a relaxation facility are suggested. In this way, the most suitable ticket can be suggested by analyzing the user's emotions.

[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 data analysis unit analyzes data such as the user's social media accounts and photos. For example, the data analysis unit collects the user's social media posts and photos and performs text analysis and image analysis. The data analysis unit can also perform sentiment analysis to estimate the user's emotions. For example, the data analysis unit analyzes text data from social media posts and extracts keywords that indicate positive emotions. The plan generation unit generates a travel plan based on the data analyzed by the data analysis unit. For example, the plan generation unit proposes a plan including optimal tourist spots and activities based on the user's hobbies and preferences. The plan generation unit can also generate a travel plan based on the user's emotions using generation AI. The transportation arrangement unit arranges transportation based on the travel plan generated by the plan generation unit. For example, the transportation arrangement unit reserves plane and train tickets and provides them to the user. The transportation arrangement unit can also propose the optimal transportation method taking into account the user's budget and time constraints. The ticket arrangement unit arranges tickets based on the travel plan generated by the plan generation unit. For example, the ticket arrangement unit reserves tickets to tourist spots and events and provides them to the user. The ticket arrangement unit can also arrange tickets based on the user's emotions. As a result, the travel plan creation system according to the embodiment can analyze user data, generate an optimal travel plan, and automatically arrange transportation and tickets. For example, if the user likes nature, a plan that includes tourist spots rich in nature will be proposed, and airplane and train tickets, admission tickets to tourist spots, etc. will be automatically arranged. This allows the user to significantly reduce the time and effort required to plan a trip.

[0094] The data analysis unit can infer emotions from social media posts or photos and propose travel plans that elicit positive emotions. For example, the data analysis unit uses a generation AI to infer emotions from a user's social media posts and photos and propose travel plans that elicit positive emotions. For example, the generation AI analyzes the user's social media posts and photos and proposes travel plans that elicit positive emotions. For example, if a user posts many photos of smiling faces, the travel plans include the locations where those photos were taken and similar locations. The text data of the user's social media posts is analyzed to extract keywords that indicate positive emotions. For example, a travel plan is created based on posts that frequently contain keywords such as "fun" and "beautiful." The scenery and activities included in the user's photos are analyzed to propose travel plans that elicit positive emotions. For example, tourist spots rich in nature are proposed based on photos that frequently include natural scenery and activities. This allows the system to propose travel plans that take the user's emotions into consideration, thereby improving user satisfaction.

[0095] The data analysis unit can also analyze travel history or reviews to more accurately understand hobbies and preferences. For example, using generation AI, the data analysis unit can also analyze the user's past travel history and reviews to more accurately understand hobbies and preferences. For example, the generation AI can analyze the user's past travel history and understand hobbies and preferences based on information about places visited and accommodations. For example, it can analyze the characteristics of tourist spots visited in the past and suggest similar tourist spots. It can analyze travel reviews posted by the user and identify places and activities that have received positive reviews. For example, it can create a travel plan based on tourist spots and activities with many highly rated reviews. It can integrate the user's past travel history and reviews to understand the user's overall hobbies and preferences. For example, it can identify the user's preferred travel style based on the frequency of places visited and the content of reviews. This makes it possible to more accurately understand the user's hobbies and preferences by taking into account the user's past travel history and reviews.

[0096] The data analysis unit can also consider seasonal or weather information to suggest the optimal travel time. The data analysis unit, for example, uses a generation AI to consider seasonal and weather information to suggest the optimal travel time. For example, the generation AI incorporates seasonal and weather information into the user's data analysis to suggest the optimal travel time. For example, it analyzes the weather and the time when a photo posted by the user was taken and suggests a trip with similar conditions. It integrates the user's past travel history with seasonal and weather data to identify the optimal travel time. For example, it suggests a trip with similar conditions based on the season and weather of tourist spots visited in the past. It collects seasonal and weather data in real time to suggest the optimal travel time that suits the user's hobbies and preferences. For example, for a user who loves nature, it suggests the season when flowers bloom or the season when autumn leaves change color. In this way, it is possible to suggest the optimal travel time to the user by taking season and weather into consideration.

[0097] The data analysis unit can grasp hobbies and preferences from a more multifaceted perspective, including audio data or video data. The data analysis unit, for example, uses generation AI to grasp hobbies and preferences from a more multifaceted perspective, including audio data and video data. For example, the generation AI analyzes the user's audio data to grasp hobbies and preferences. For example, it analyzes the audio of audio notes and videos recorded by the user while traveling to identify places and activities of interest. It analyzes video data posted by the user to grasp hobbies and preferences based on the scenery and activities contained in the video. For example, it analyzes tourist spots and activities shown in the video to suggest similar places. It integrates audio data and video data to grasp comprehensive hobbies and preferences. For example, it combines keywords extracted from audio data with footage from the video data to identify the user's preferred travel style. In this way, by including audio data and video data, it becomes possible to grasp hobbies and preferences from a more multifaceted perspective.

[0098] The data analysis unit can integrate data from different SNS platforms and perform comprehensive analysis. The data analysis unit, for example, uses generation AI to integrate data from different SNS platforms and perform comprehensive analysis. For example, the generation AI collects data from multiple SNS platforms used by a user and analyzes it in an integrated manner. For example, it centralizes and analyzes post data from Facebook, Instagram, Twitter, etc. Based on the data collected from different SNS platforms, it comprehensively understands the user's hobbies and preferences. For example, it analyzes the content and reactions of posts on each platform to identify common interests. It collects data from multiple SNS platforms in real time and performs comprehensive analysis. For example, it identifies the user's current interests and concerns based on the most recent post data. This makes it possible to perform comprehensive analysis by integrating data from different SNS platforms.

[0099] The data analysis unit can use the emotion estimation function to analyze the emotions of a user when browsing travel plans in real time and optimize plan suggestions. The data analysis unit, for example, uses the generation AI and the emotion estimation function to analyze the emotions of a user when browsing travel plans in real time and optimize plan suggestions. For example, the generation AI analyzes the user's facial expressions and voice when browsing travel plans and estimates emotions in real time. For example, if the user is smiling while browsing a plan, that plan is preferentially suggested. The user's emotional responses are analyzed in real time and plans that elicit positive emotions are suggested. For example, similar plans are suggested based on plans in which the user has shown interest. Based on the emotion estimation data, plans that evoke the most positive emotions in the user are identified and suggestions are optimized. For example, plans with a high user emotion score are preferentially displayed. This makes it possible to suggest optimal travel plans by analyzing the user's emotions in real time.

[0100] The plan generation unit can estimate emotions and generate a travel plan based on the emotions. The plan generation unit, for example, uses a generation AI to estimate a user's emotions and generate a travel plan based on the emotions. For example, the generation AI estimates emotions from the user's social media posts and photos and generates a travel plan based on the emotions. For example, if the user feels like relaxing, a plan including a resort is suggested. The user's emotional data is analyzed to generate a travel plan that elicits positive emotions. For example, if the user feels like having fun, tourist spots with plenty of activities are suggested. Based on the emotion estimation data, a travel plan optimal for the user's current emotional state is generated. For example, if the user is feeling stressed, a place where they can relax is suggested. In this way, by generating a travel plan based on the user's emotions, user satisfaction can be improved.

[0101] The plan generation unit can propose a reasonable plan by taking into account the user's health condition or physical strength. The plan generation unit, for example, uses a generation AI to propose a reasonable plan by taking into account the user's health condition and physical strength. For example, the generation AI analyzes the user's health data and proposes a reasonable travel plan. For example, it creates a plan with an appropriate amount of exercise based on the user's step count data and heart rate data. It generates a reasonable travel plan based on the user's physical strength data. For example, it proposes a plan that includes appropriate activities by taking into account the user's age and physical strength level. It collects health condition and physical strength data in real time and proposes the optimal travel plan for the user. For example, if the user's health condition is not good, it proposes a relaxing plan. In this way, it is possible to propose a reasonable travel plan by taking into account the user's health condition and physical strength.

[0102] The plan generation unit can arouse the user's interest by including information about local culture or history. The plan generation unit, for example, uses a generation AI to include information about local culture and history to arouse the user's interest. For example, the generation AI may include information about local culture and history in a travel plan to arouse the user's interest. For example, the generation AI may propose a plan that introduces the history and cultural background of tourist destinations. Based on the user's interests, the plan may generate a travel plan that includes activities related to local culture and history. For example, a plan to visit traditional festivals or historical buildings may be proposed. Information about local culture and history may be collected in real time to propose the optimal travel plan to the user. For example, a plan that interests the user may be created based on information about local events. In this way, the inclusion of information about local culture and history can arouse the user's interest.

[0103] The plan generation unit can propose a group travel plan by taking into account data of friends or family. The plan generation unit, for example, uses generation AI to propose a group travel plan by taking into account data of the user's friends and family. For example, the generation AI analyzes data of the user's friends and family to propose a group travel plan. For example, it takes into account the hobbies and preferences of friends and family to create a plan that everyone can enjoy. It collects social media data of the user's friends and family to generate a group travel plan. For example, it proposes a plan that includes tourist spots and activities that everyone is interested in. It collects data of friends and family in real time to propose a group travel plan. For example, it takes into account everyone's schedules and interests to propose the best time and place to travel. In this way, it is possible to propose a group travel plan by taking into account the data of the user's friends and family.

[0104] The plan generation unit can compare the plan with the user's past travel plans and generate a plan that provides a new experience. The plan generation unit, for example, uses a generation AI to compare the plan with the user's past travel plans and generate a plan that provides a new experience. For example, the generation AI analyzes the user's past travel plans and generates a plan that provides a new experience. For example, it may suggest tourist spots and activities that have not been visited before. It may compare the plan with the user's past travel history and generate a travel plan that provides a different experience. For example, it may suggest regions and cultures that are different from tourist spots visited in the past. It may integrate past travel plans with current interests and generate a plan that provides a new experience. For example, it may suggest experiences in new places based on activities that were popular on past trips. In this way, it is possible to generate a plan that provides a new experience by comparing the plan with the user's past travel plans.

[0105] The plan generation unit can use the emotion estimation function to analyze the emotions of the user when selecting a travel plan and propose the optimal plan. The plan generation unit, for example, uses the generation AI to use the emotion estimation function to analyze the emotions of the user when selecting a travel plan and propose the optimal plan. For example, the generation AI analyzes the user's facial expressions and voice when selecting a travel plan and predicts the emotion in real time. For example, if the user selects a plan with a smile, that plan is preferentially proposed. The user's emotional response is analyzed in real time and plans that elicit positive emotions are proposed. For example, similar plans are proposed based on plans in which the user has shown interest. Based on the emotion estimation data, plans that evoke the most positive emotions in the user are identified and the proposals are optimized. For example, plans with a high user emotion score are preferentially displayed. In this way, the optimal travel plan can be proposed by analyzing the user's emotions.

[0106] The transportation arrangement unit can estimate emotions and suggest comfortable transportation methods. The transportation arrangement unit, for example, uses generation AI to estimate the user's emotions and suggest comfortable transportation methods. For example, the generation AI estimates emotions from the user's social media posts and photos and suggests comfortable transportation methods. For example, if the user feels like relaxing, it suggests transportation methods that offer comfortable seats and services. It analyzes the user's emotional data and suggests transportation methods that elicit positive emotions. For example, if the user feels like having fun, it suggests transportation methods that offer plenty of entertainment. Based on the emotion estimation data, it suggests transportation methods that are optimal for the user's current emotional state. For example, if the user is feeling stressed, it suggests transportation methods that allow them to relax. In this way, it is possible to suggest comfortable transportation methods by taking the user's emotions into consideration.

[0107] The transportation arrangement unit can also consider budget or time constraints and propose the optimal means. The transportation arrangement unit, for example, uses generation AI to consider the user's budget and time constraints and propose the optimal means. For example, the generation AI analyzes the user's budget data and proposes the optimal means of transportation within the budget. For example, it presents options such as plane, train, and bus depending on the user's budget. It proposes the optimal means of transportation taking into account the user's time constraints. For example, it proposes the means of transportation that takes the shortest time according to the user's schedule. It integrates budget and time data to propose the optimal means of transportation to the user. For example, it selects and proposes the most time-efficient means of transportation within the budget. This makes it possible to propose the optimal means of transportation by considering the user's budget and time constraints.

[0108] The transportation arrangement unit can propose eco-friendly options by taking into account the impact on the environment. The transportation arrangement unit, for example, uses generation AI to propose eco-friendly options by taking into account the impact on the environment. For example, the generation AI analyzes the environmental impact of transportation methods and proposes eco-friendly options. For example, it prioritizes proposals of low-carbon transportation methods such as trains and buses. It proposes eco-friendly transportation methods based on the user's environmental awareness data. For example, if the user is interested in environmental protection, it presents options such as electric vehicles and bicycles. It collects environmental data in real time and proposes the most suitable eco-friendly transportation method for the user. For example, it proposes environmentally friendly options based on the CO2 emissions of the transportation method. This makes it possible to propose eco-friendly transportation methods by taking into account the impact on the environment.

[0109] The transportation arrangement unit can consider the user's past transportation preferences and propose the optimal means. The transportation arrangement unit, for example, uses a generation AI to consider the user's past transportation preferences and propose the optimal means. For example, the generation AI analyzes the user's past transportation data and proposes transportation that suits their preferences. For example, it proposes similar means of transportation based on evaluations of transportation used in the past. It proposes the optimal transportation means based on the user's past travel history. For example, it prioritizes proposals of transportation means that were comfortable in the past. It collects data on past transportation means in real time and proposes the optimal transportation means to the user. For example, it selects the current transportation means based on past transportation preferences. In this way, it is possible to propose the optimal transportation means by considering the user's past transportation preferences.

[0110] The transportation arrangement unit can integrate data from different transportation modes and propose the optimal travel route. The transportation arrangement unit, for example, uses generation AI to integrate data from different transportation modes and propose the optimal travel route. For example, the generation AI integrates data from different transportation modes and proposes the optimal travel route. For example, it centralizes data from airplanes, trains, buses, etc. and proposes the route that allows travel in the shortest time. It analyzes data from different transportation modes and proposes the optimal travel route for the user. For example, it proposes a route that combines multiple transportation modes. It collects transportation data in real time and proposes the optimal travel route for the user. For example, it selects the optimal route based on traffic conditions and operation schedules. In this way, it is possible to propose the optimal travel route by integrating data from different transportation modes.

[0111] The transportation arrangement unit can use the emotion estimation function to analyze the emotions of the user when selecting a transportation means and suggest the optimal means. The transportation arrangement unit can use, for example, a generation AI to use the emotion estimation function to analyze the emotions of the user when selecting a transportation means and suggest the optimal means. For example, the generation AI analyzes the user's facial expressions and voice when selecting a transportation means and estimates the emotions in real time. For example, if the user wants to relax, a comfortable transportation means is suggested. The user's emotional response is analyzed in real time and a transportation means that elicits positive emotions is suggested. For example, if the user wants to have fun, a transportation means that offers plenty of entertainment is suggested. Based on the emotion estimation data, a transportation means that is optimal for the user's current emotional state is suggested. For example, if the user is feeling stressed, a transportation means that will help them relax is suggested. In this way, the optimal transportation means can be suggested by analyzing the user's emotions.

[0112] The ticket arrangement unit can estimate emotions and arrange tickets based on the emotions. The ticket arrangement unit, for example, uses a generation AI to estimate a user's emotions and arrange tickets based on the emotions. For example, the generation AI estimates emotions from the user's social media posts and photos and arranges tickets based on the emotions. For example, if the user feels like having fun, entertainment tickets are suggested. The user's emotional data is analyzed and tickets that elicit positive emotions are arranged. For example, if the user feels like relaxing, tickets to a relaxation facility are suggested. Based on the emotion estimation data, tickets that are optimal for the user's current emotional state are arranged. For example, if the user is feeling stressed, tickets to a relaxation facility are suggested. In this way, by arranging tickets based on the user's emotions, user satisfaction can be improved.

[0113] The ticket arrangement unit can also consider the user's past event participation history to suggest the most suitable ticket. The ticket arrangement unit, for example, uses a generation AI to also consider the user's past event participation history to suggest the most suitable ticket. For example, the generation AI analyzes the user's past event participation history to suggest the most suitable ticket. For example, based on the evaluation of an event previously attended, tickets for a similar event are suggested. Based on the user's past event participation history, the most suitable ticket is suggested. For example, taking into account the genre and content of an event previously attended, tickets for a similar event are suggested. Past event participation history is collected in real time to suggest the most suitable ticket to the user. For example, based on the evaluation of an event previously attended, tickets for the current event are suggested. In this way, the most suitable ticket can be suggested by considering the user's past event participation history.

[0114] The ticket arrangement unit can draw the user's interest by including information about local events. The ticket arrangement unit, for example, uses a generation AI to draw the user's interest by including information about local events. For example, the generation AI includes local event information in a travel plan to draw the user's interest. For example, it suggests tickets for concerts and festivals held locally. It generates a travel plan that includes local event information based on the user's interests. For example, it suggests a plan that includes events in a genre that the user is interested in. It collects local event information in real time and suggests the most suitable tickets for the user. For example, it suggests tickets that the user is interested in based on the latest information about events held locally. In this way, it is possible to draw the user's interest by including local event information.

[0115] The ticket arrangement unit can also consider data on friends and family to suggest group participation. The ticket arrangement unit, for example, uses generation AI to also consider data on the user's friends and family to suggest group participation. For example, the generation AI analyzes data on the user's friends and family to suggest group participation. For example, it considers the hobbies and preferences of friends and family to suggest tickets for an event that everyone can enjoy. It collects SNS data on the user's friends and family to suggest group participation. For example, it suggests tickets for an event that everyone is interested in. It collects data on friends and family in real time to suggest group participation. For example, it considers everyone's schedules and interests to suggest tickets for the most suitable event. In this way, it is possible to suggest group participation by considering the data of the user's friends and family.

[0116] The ticket arrangement unit can integrate tickets for different events and propose the optimal schedule. The ticket arrangement unit, for example, uses generation AI to integrate tickets for different events and propose the optimal schedule. For example, the generation AI can integrate tickets for different events and propose the optimal schedule. For example, it can propose a schedule that combines multiple events. It analyzes tickets for different events and proposes the optimal schedule for the user. For example, it can propose an efficient schedule taking into account the time and location of the events. It collects event ticket data in real time and proposes the optimal schedule for the user. For example, it can propose a schedule for events that the user is interested in based on the latest event information. This makes it possible to propose the optimal schedule by integrating tickets for different events.

[0117] The ticket arrangement unit can use the emotion estimation function to analyze the emotions of the user when selecting a ticket and suggest the most suitable ticket. The ticket arrangement unit can use, for example, a generation AI to use the emotion estimation function to analyze the emotions of the user when selecting a ticket and suggest the most suitable ticket. For example, the generation AI analyzes the facial expressions and voice of the user when selecting a ticket and estimates the emotion in real time. For example, if the user feels like having fun, entertainment tickets are suggested. The user's emotional response is analyzed in real time and tickets that elicit positive emotions are suggested. For example, if the user feels like relaxing, tickets to a relaxation facility are suggested. Based on the emotion estimation data, tickets that are most suitable for the user's current emotional state are suggested. For example, if the user is feeling stressed, tickets to a relaxation facility are suggested. In this way, the most suitable ticket can be suggested by analyzing the user's emotions.

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

[0119] Step 1: The data analysis unit analyzes data such as the user's social media posts and photos. For example, the data analysis unit collects the user's social media posts and photos and performs text analysis and image analysis. The data analysis unit can also perform sentiment analysis to estimate the user's emotions. For example, it analyzes the text data of social media posts and extracts keywords that indicate positive emotions. Step 2: The plan generation unit generates a travel plan based on the data analyzed by the data analysis unit. For example, the plan generation unit proposes a plan including optimal sightseeing spots and activities based on the user's hobbies and preferences. The plan generation unit can also use generation AI to generate a travel plan based on the user's emotions. Step 3: The transportation arrangement unit arranges transportation based on the travel plan generated by the plan generation unit. For example, the transportation arrangement unit reserves plane or train tickets and provides them to the user. The transportation arrangement unit can also propose the most suitable transportation method, taking into account the user's budget and time constraints. Step 4: The ticket arrangement unit arranges tickets based on the travel plan generated by the plan generation unit. For example, the ticket arrangement unit reserves tickets for tourist attractions or events and provides them to the user. The ticket arrangement unit can also arrange tickets based on the user's emotions.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

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

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

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

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

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

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

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

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

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

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

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

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

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

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

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

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

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

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

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

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

[0152] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0154] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

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

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

[0160] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

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

[0162] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

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

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

[0174] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

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

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

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

[0178] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0179] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

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

[0181] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

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

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

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

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

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

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

Claims

1. A data analysis section that analyzes data such as users' SNS and photos, and a plan generation unit that generates a travel plan based on the data analyzed by the data analysis unit; a transportation arrangement unit that arranges transportation based on the travel plan generated by the plan generation unit; a ticket arrangement unit that arranges tickets based on the travel plan generated by the plan generation unit. A system characterized by:

2. The data analysis unit Estimating emotions from the SNS post or the photo and proposing the travel plan to elicit positive emotions 2. The system of claim 1.

3. The data analysis unit Analyze travel history or reviews to gain a more accurate understanding of your preferences.

2. The system of claim 1.

4. The data analysis unit Considering seasonal or weather information, we suggest the best time to travel 2. The system of claim 1.

5. The data analysis unit Gain a more comprehensive understanding of interests and preferences, including audio and video data 2. The system of claim 1.

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