system

Abstract

We provide the system. [Solution] An input method for receiving travel preferences from the user, A processing means for generating the optimal travel plan based on the received desired conditions, A means of presenting the generated travel plan and obtaining user approval, A booking method that executes all related reservations based on the approved travel plan, A means of obtaining real-time information during travel and making changes to the plan, A learning method that incorporates the feedback received into the next plan, A system that includes this.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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 Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 Travel plans need to collect, organize a lot of information and configure an optimal plan, which requires time and effort. In addition, the ability to quickly respond to unexpected situations during travel is also required. However, it is not easy to appropriately respond according to the diverse needs and situations of individual users. Therefore, the development of a system that can efficiently and flexibly make and execute travel plans is required. 【Means for Solving the Problems】 【0005】 This invention provides a system that acquires travel preferences from users via an input means and generates an optimal travel plan using AI based on the received data. The generated plan is presented to the user via a confirmation means, and if approved, the necessary reservations are executed all at once via a reservation means. The system also includes a modification means that dynamically modifies the travel plan based on external information acquired in real time. Furthermore, a learning means collects feedback from users after their trip and uses this feedback to further individualize and optimize future travel plans, thereby flexibly responding to the diverse needs of users. 【0006】 "Input means" refers to functions or devices that allow users to input their travel preferences into the system. 【0007】 "Processing means" refers to a function or program that generates an optimal travel plan based on data received via input means. 【0008】 "Confirmation methods" refer to functions and processes for presenting the generated travel plan to the user and obtaining their approval. 【0009】 A "booking method" refers to a function or software that allows users to make all necessary reservations related to an approved travel plan in one go. 【0010】 "Methods of modification" refer to functions or algorithms that dynamically modify travel plans based on real-time information acquired during the trip. 【0011】 "Learning tools" refer to data analysis and machine learning functions that acquire user feedback and personalize and optimize future travel plans. [Brief explanation of the drawing] 【0012】 [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2]This is a conceptual diagram showing an example of the essential functions of the data processing device and smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention] 【0013】 Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings. 【0014】 First, the terms used in the following description will be explained. 【0015】 In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of the arithmetic unit 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), and the like. 【0016】 In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0017】 In the following embodiments, a storage with a reference numeral is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of the non-volatile storage device include a flash memory (SSD (Solid State Drive)), a magnetic disk (e.g., a hard disk), or a magnetic tape, and the like. 【0018】 In the following embodiments, a communication I / F (Interface) with a reference numeral is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of the communication standard applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like. 【0019】 In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or." 【0020】 [First Embodiment] 【0021】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0022】 As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server. 【0023】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0024】 The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52. 【0025】 The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input. 【0026】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor. 【0027】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54. 【0028】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0029】 As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30. 【0030】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0031】 In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0032】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0033】 The AI ​​trip planner system of the present invention is a system that integrates several important functions to efficiently and flexibly support users' travel planning. The system's operation and specific examples are shown below. 【0034】 This system first implements a user interface using a terminal, providing a mechanism for users to easily input their travel preferences. The terminal receives input from the user and transfers that data to the server. 【0035】 The server analyzes the received user data and generates the optimal travel plan using an AI algorithm. The AI ​​consults a large database, taking into account the latest information on each destination, flight schedules, accommodation availability, and local activities. 【0036】 The generated travel plan is sent to the device via the server. The device presents the plan to the user through a visual plan display function. The user can review the plan details and request changes as needed. 【0037】 After obtaining user approval, the server makes bulk reservations for flights, accommodations, restaurants, activities, and more. The server integrates with external reservation systems to automate these reservation processes. 【0038】 During your trip, the server monitors flight delays, weather changes, and other local information in real time, and modifies your travel plan as needed. This ensures that you are always acting based on the most up-to-date plan. 【0039】 After the trip, the device requests feedback from the user through a simple interface and sends that feedback to the server. The server uses this information to improve future planning using a learning algorithm. 【0040】 Specific example: 【0041】 For example, suppose a user enters their desired conditions as, "A family of four, under 100,000 yen, to go to a beach resort, and enjoy sightseeing and shopping." 【0042】 Based on this, the server selects Okinawa as the destination and generates a plan that includes suitable flights, hotels, local attractions, and shopping malls. Once the booking process is complete, if a typhoon approaches during the trip, the server updates the schedule based on weather information and presents an itinerary that avoids risks. 【0043】 In this way, the present invention accurately responds to the user's travel needs and realizes cost-effective planning. 【0044】 The following describes the processing flow. 【0045】 Step 1: 【0046】 The user uses the terminal's interface to enter their travel preferences (budget, dates, activities of interest, etc.). The terminal verifies the entered data and then sends it to the server. 【0047】 Step 2: 【0048】 The server analyzes the user's preferences received and collects relevant information from its internal database. This includes potential destinations, flight information, accommodation options, and activity lists. The server then passes this information to an AI algorithm. 【0049】 Step 3: 【0050】 The server uses an AI algorithm to generate a travel plan optimized for the user's requirements. The AI ​​efficiently constructs the plan while considering budget, schedule, and activity requirements. The generated plan is then sent from the server to the user's device. 【0051】 Step 4: 【0052】 The device presents the travel plan to the user in a visually easy-to-understand format. The user reviews the plan and requests changes if necessary. If no changes are needed, the plan is approved. 【0053】 Step 5: 【0054】 The server, upon user approval, makes all reservations for flights, accommodations, restaurants, and activities based on the travel plan. It integrates with external reservation systems via APIs. Once all reservations are complete, it sends confirmation information to the user. 【0055】 Step 6: 【0056】 During your trip, the server continuously monitors real-time data. If it detects flight delays, weather changes, or other issues, it dynamically modifies your travel plan based on this information. The revised plan is immediately sent to your device. 【0057】 Step 7: 【0058】 After the trip ends, the device prompts the user for feedback. The user enters feedback about their travel experience and sends it from the device to the server. 【0059】 Step 8: 【0060】 The server passes the received feedback to a learning algorithm, which uses it as data to create the next travel plan. This allows the system to optimize itself so that it can provide the user with a more suitable plan next time. 【0061】 (Example 1) 【0062】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0063】 In modern times, travel planning has become increasingly complex, making it difficult for users to find the optimal travel plan that best suits their needs. Furthermore, responding quickly to changing circumstances during a trip places a significant burden on users. In addition, there is a lack of mechanisms to effectively utilize feedback from past travel experiences and incorporate it into future travel planning. To address these challenges, there is a need to develop a system that can generate and modify plans quickly and flexibly. 【0064】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0065】 In this invention, the server includes an input means for acquiring travel preferences from the user, a processing means for generating an optimal travel plan using a generation AI model based on the received conditions, and a modification means for updating the plan based on dynamic information acquired during the trip. This allows the user to efficiently and flexibly create a travel plan that meets their preferences and to respond quickly to unforeseen circumstances during the trip. Furthermore, by utilizing feedback and reflecting it in future plans, a better travel experience can be provided. 【0066】 "Input means" refers to an interface or device for obtaining travel preferences from the user. 【0067】 A "generative AI model" is an artificial intelligence algorithm or computational model used to generate the optimal travel plan based on received conditions. 【0068】 "Processing means" refers to a device or program that uses a generative AI model to create a travel plan based on the user's desired conditions. 【0069】 "Confirmation means" refers to a device or function used to present the generated travel plan to the user and obtain their approval. 【0070】 "Reservation method" refers to a device or program for executing all relevant reservations in accordance with an approved travel plan, in conjunction with an external reservation system. 【0071】 "Means of modification" refers to devices or procedures used to update travel plans based on dynamic information acquired during the trip. 【0072】 A "learning tool" is a device or program that collects user feedback and incorporates it into future travel plans. 【0073】 This AI trip planner system efficiently and flexibly supports users' travel planning. Users input their travel preferences using a terminal. These preferences include the number of travelers, budget, desired destination, and activities. Based on this input, the terminal sends data to the server. 【0074】 The server generates the optimal travel plan using a generative AI model based on the received data. This process utilizes an advanced database management system and AI algorithms. The server accesses a large database to obtain the latest flight schedules, accommodation availability, and destination information. This data is analyzed to formulate the most suitable travel plan for the user. 【0075】 The generated travel plan is sent from the server to the terminal, which visually presents the plan to the user. The user can review the plan and request changes if necessary. This request is then sent back to the server to adjust the plan. 【0076】 After the user approves the plan, the server integrates with an external booking system to process flight, accommodation, and activity reservations all at once. This eliminates the need for the user to go through individual procedures. Furthermore, the server monitors information in real time during the trip and updates the plan as needed. For example, the plan is automatically adjusted to account for weather changes or flight delays. 【0077】 After the trip, the device requests feedback from the user through a simple interface. This feedback is sent to the server and used for planning the next trip. By utilizing this feedback, the system continuously learns and improves the accuracy of travel planning. 【0078】 As a concrete example, a user might input their desired conditions, such as "a family of four, under 100,000 yen, to a beach resort, where we want to enjoy sightseeing and shopping." In response to this request, the server would suggest Okinawa as the destination and propose appropriate flights and hotels. In this way, the present invention appropriately responds to the user's travel needs and realizes a cost-effective travel plan. 【0079】 Examples of prompt statements are as follows: 【0080】 "We'll help you plan your next trip. Please enter the following information: the number of people traveling, your budget, the type of destination you'd like (beach resort, city, mountains, etc.), and the activities you'd like to enjoy (sightseeing, shopping, relaxation, etc.)." 【0081】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0082】 Step 1: 【0083】 The user uses a terminal to enter their travel preferences. The data entered includes the number of travelers, budget, desired destination, and activities. The terminal collects this input data, formats it, and prepares it for transmission to the server. The output generated in this step is structured and in a format suitable for transmission to the server. 【0084】 Step 2: 【0085】 The terminal sends the input data received from the user to the server. The server receives this data and stores it in a database. The server then preprocesses the received data and converts it into the format necessary for input into the generating AI model. The output is the travel preferences in a format suitable for analysis. 【0086】 Step 3: 【0087】 The server uses a generative AI model to create an optimal travel plan based on the received data. This plan generation involves referencing data from multiple databases, particularly flight schedules, accommodations, retail stores, and activity information. The output generated through data calculations is a proposed travel plan. 【0088】 Step 4: 【0089】 The server sends the generated travel plan to the terminal. The terminal receives this data and uses a GUI (Graphical User Interface) to display it visually in an easy-to-understand format for the user. The terminal also provides interactive options that allow the user to review the plan and request approval or changes. The output is the travel plan details presented through the user interface. 【0090】 Step 5: 【0091】 Users can review the plan details through their device and request changes as needed. If the user approves the plan, the device sends that information back to the server. The output generated based on the user's actions is the approved plan information, which is then used to proceed to the next booking process. 【0092】 Step 6: 【0093】 After receiving user approval, the server integrates with an external booking system to handle all necessary booking procedures. Specifically, it processes flight, accommodation, and activity bookings in batches via an automated API. The server checks the status of each booking, and successful bookings are output as booking details for the completed trip. 【0094】 Step 7: 【0095】 During your trip, the server monitors flight information, weather changes, event cancellations, and other dynamic data in real time. It updates your travel plan based on this information as needed. The output generated by the server's information gathering and plan adjustments is an updated itinerary designed for a safe and smooth journey. 【0096】 Step 8: 【0097】 After the trip, the device collects feedback from the user and sends it to the server. The server stores this feedback in a database and uses it to update the AI ​​model's learning algorithm. The output obtained in this step is feedback data that can be used to improve the quality of future travel plans. 【0098】 (Application Example 1) 【0099】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0100】 Existing travel planning methods have limitations in efficiently and effectively generating travel plans that meet user preferences and acquiring local information, as well as in responding quickly to unexpected situations during travel. Furthermore, they fail to fully utilize travelers' past preferences and feedback, making it difficult to provide travel experiences tailored to individual tastes. There is a need for a system that solves these problems and enriches users' travel experiences. 【0101】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0102】 In this invention, the server includes an information input means for receiving travel preferences from the user, an information processing means for generating an optimal travel plan based on the received preferences, and a content distribution means for providing the user with information about destinations during the trip. This allows the user to enjoy their trip based on a travel plan that is updated in real time. Furthermore, personalized information enables the provision of services that are more closely tailored to the needs of travelers. 【0103】 An "information input means" is an interface that has the function of receiving travel preferences from the user. 【0104】 An "information processing system" is a system with a calculation function that generates the optimal travel plan based on the received desired conditions. 【0105】 An "information verification tool" is an interface that has the function of presenting the generated travel plan to the user and obtaining their approval. 【0106】 An "information booking system" is an automated mechanism for executing all relevant bookings based on an approved travel plan. 【0107】 An "information modification system" is a system that has the function of modifying travel plans based on information acquired in real time during a trip. 【0108】 An "information learning tool" is a system that has a data processing function to incorporate user feedback into the next plan. 【0109】 A "content distribution method" is a system that has a distribution function to provide users with information about their destinations while they are traveling. 【0110】 An "information recommendation system" is a system that has a recommendation function to provide personalized information about places to visit based on the user's past preference data. 【0111】 This system is built by integrating various hardware and software technologies. Users input their preferences and travel requirements from their smartphones or similar devices, and this information is sent to a server in the cloud. This server operates an API using Node.js and the Express framework, receiving requests from users. 【0112】 The received information is processed using a machine learning model that utilizes Python and TENSORFLOW®. At this stage, a personalized travel plan is generated based on the user's past travel data and preferences. The database (MongoDB) stores a large amount of travel destination information and past user feedback, enabling the provision of the latest and most appropriate travel plans. 【0113】 Once a plan is generated, it is presented to the device in a visual format for the user to review. During the trip, the server monitors real-time weather and traffic information and has the capability to dynamically modify the travel plan as needed. This information is then pushed to the user's smartphone. 【0114】 Information about travel destinations, including tourist spots and local cultural events, is provided through the content distribution function. The AI ​​model dynamically optimizes the information presented based on the individual user's preferences. 【0115】 As a concrete example, suppose a user enters "I want to have a unique food experience in Tokyo." The system could refer to past food preference data and, through an AI model, suggest up-and-coming restaurants and local food festivals. An example of a prompt to the generating AI model would be, "Please suggest a local plan based on the activities the user wants to enjoy and their food preferences at their destination." 【0116】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0117】 Step 1: 【0118】 Users enter their travel preferences using their smartphones. This includes attributes such as destination, type of activity, budget, and dates. The entered data is sent directly from the device to the server. 【0119】 Step 2: 【0120】 The server retrieves relevant travel destination information from the MongoDB database based on the received preferences. This search collects data such as tourist attractions, accommodations, and experience plans related to the destination. The data is converted to an intermediate format and passed on to the next processing step. 【0121】 Step 3: 【0122】 The server launches a machine learning model using Python and TensorFlow. Intermediate travel information and historical user preference data are used as input. Based on this data, the machine learning model calculates the optimal travel plan and generates a plan tailored to the user's individual needs. This output is returned to the server in a format that is easy to visualize. 【0123】 Step 4: 【0124】 The server sends the generated travel plan to the terminal. The terminal receives this data and displays it to the user using an interface that visually presents the travel plan. The user can review the plan and enter modification requests if necessary. 【0125】 Step 5: 【0126】 Upon receiving an approval or modification request from a user, the server executes an automated booking process. It integrates with an external booking system to make bulk bookings for selected flights and activities. The results of this process are logged as a success / failure status. 【0127】 Step 6: 【0128】 During the trip, the server retrieves real-time weather and traffic data from external sources. By analyzing this data, the travel plan is modified as needed. The revised plan is immediately notified to the user's device, allowing the user to always act based on the latest information. 【0129】 Step 7: 【0130】 After a user completes their trip, the server displays an interface on the user's terminal requesting feedback. The feedback is stored in a database and used to generate future travel plans. The feedback data is also used to improve the AI ​​model and optimize prompt messages. Through this feedback process, the overall accuracy of the system is improved. 【0131】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0132】 This invention provides a more personalized travel experience by incorporating emotion recognition into the user's travel plan. The emotion engine analyzes the user's voice and text to recognize the user's emotional state in real time, and based on this, it enables adjustments to the travel plan. 【0133】 First, the user enters their travel preferences using a device. The device sends this information to the server, and at the same time, the emotion engine analyzes the user's emotions during input (for example, the tone of their text or voice). In addition to the normal planning process, the server utilizes the information from the emotion engine to generate a travel plan optimized for the user's current emotions. 【0134】 After a plan is presented, if the user shows an emotional response to it, the emotion engine analyzes it and re-evaluates the plan's applicability. For example, if the user expresses anxiety or interest in the suggested activity, the server takes this information into account and fine-tunes the plan. Once the user approves, the server proceeds with the booking process. 【0135】 During travel, the device and emotion engine continuously monitor the user's emotions. Emotional states are also obtained from inputs the user provides to the device during specific activities and between travel. Based on this information, the server makes new suggestions at the appropriate time as needed and flexibly modifies the plan. 【0136】 After the trip ends, the device prompts the user to provide feedback. This post-trip feedback is also analyzed by the emotion engine, and the results are reflected in the next trip plan. The server analyzes this emotion information along with past feedback and uses a learning algorithm to optimize future plans. 【0137】 Specific example: 【0138】 If a user enters "I want to travel to Hawaii and relax," the emotion engine analyzes whether the user's language tone indicates stress. The server generates and presents a plan that emphasizes activities effective in reducing stress (such as spa treatments and relaxing time on quiet beaches). Furthermore, if the user shows new signs of stress during the trip, the emotion engine reacts immediately and instructs appropriate changes to ensure the user has the best possible travel experience. 【0139】 The following describes the processing flow. 【0140】 Step 1: 【0141】 The user enters their desired travel plan criteria (e.g., destination, budget, dates, desired activities, etc.) into the device. This input also includes voice input. The device sends the given text or voice to an emotion engine to analyze the user's emotional state. 【0142】 Step 2: 【0143】 The device sends the user's desired conditions, along with analyzed emotional information, to the server. Based on the received information, the server uses an AI algorithm to generate the optimal travel plan. In this process, the user's emotional state (e.g., excitement, relaxation, stress) is also taken into consideration. 【0144】 Step 3: 【0145】 The server displays the generated travel plan on the terminal. The terminal visually displays the plan details to the user, and the emotion engine analyzes the user's reaction in real time. If the user's emotions indicate anxiety or dissatisfaction, the terminal requests corrections from the server. 【0146】 Step 4: 【0147】 The server readjusts the travel plan based on the user's emotional feedback. It suggests new activities or modifies existing plans as needed to create a more suitable plan for the user and sends it to their device. 【0148】 Step 5: 【0149】 Once the user is satisfied with and approves the proposed plan, the server will execute bookings for flights, accommodations, activities, and other services all at once. The booking information will be notified to the user via their device. 【0150】 Step 6: 【0151】 Even while traveling, the device sends voice and text data from the user to the emotion engine, continuously monitoring their emotional state. If the server detects a change in emotion, it adjusts the schedule in real time and sends relevant suggestions to the device. 【0152】 Step 7: 【0153】 After the trip ends, the device prompts the user for feedback. The feedback provided by the user, along with emotional information in audio and text, is sent to the server and used in a learning process to improve future travel plans. 【0154】 Step 8: 【0155】 Based on past feedback and sentiment data, the server optimizes future travel plans and prepares to provide more personalized suggestions. 【0156】 (Example 2) 【0157】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0158】 Traditional travel planning systems have struggled to personalize plans by considering the user's emotional state and have been unable to respond promptly to their changing emotions. Furthermore, they have been unable to effectively collect and utilize real-time feedback during travel and incorporate it into future plans. Therefore, new systems are expected to provide users with a more satisfying travel experience. 【0159】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0160】 In this invention, the server includes an analysis means for analyzing the user's emotional state from their voice or documents, a construction means for generating an optimized travel plan based on the received request and the analyzed emotional data, and a modification means for taking in information from the user during the trip and dynamically modifying the plan. This enables the provision of personalized travel plans that reflect the user's emotional state and real-time plan adjustments. 【0161】 "Receiving means" refers to a function for receiving basic travel-related requests from the user. 【0162】 "Analysis means" refers to a function for analyzing the emotional state of a user from their voice or documents. 【0163】 The "construction means" refers to a function for generating optimized travel plans based on received requests and analyzed sentiment data. 【0164】 The "confirmation mechanism" is a function that presents the generated travel plan and adjusts the plan based on the user's emotional response. 【0165】 "Procedural means" refers to the function of carrying out all relevant procedures based on the approved travel plan. 【0166】 The "modification mechanism" refers to a function that incorporates information from users during their trip and dynamically modifies the plan. 【0167】 "Improvement measures" refer to a function for accumulating post-trip evaluations and incorporating them into future plans. 【0168】 "Optimization means" refers to a function that uses a learning algorithm to evolve travel plans. 【0169】 This invention is a system that analyzes user emotions and incorporates them into travel planning to provide a more personalized experience. The system consists of three main components: a terminal, a server, and an emotion engine. 【0170】 The user first enters their basic travel requests using a terminal. This terminal can be a general-purpose information processing device such as a smartphone or personal computer. Input can be done via voice or text, and the user can choose whichever method suits them best. 【0171】 The terminal sends the acquired input data to the server. The server works in conjunction with an emotion engine, which uses natural language processing (NLP) and speech analysis techniques to analyze the user's emotional characteristics. This emotion analysis extracts emotional information, such as whether the user is stressed or excited. 【0172】 The server uses a generative AI model to construct travel plans based on the user's input, including requests and emotional state data. This AI model is optimized using an extensive travel database and past user data, enabling it to provide suggestions tailored to the user's individual needs. For example, if a user inputs "I want to travel to Hawaii and relax," the server will generate and present a plan centered around relaxing activities. 【0173】 During travel, the device receives real-time feedback from the user, and the server dynamically adjusts the plan based on that information. This allows users to enjoy a plan optimized for changing circumstances, even while traveling. 【0174】 After the trip ends, the device prompts the user for feedback again, and this data is analyzed by an emotion engine before being collected by a server. This feedback information is used to create future travel plans, and an AI model generates even more refined suggestions. 【0175】 Example of a prompt: 【0176】 "Please generate a Hawaii travel plan for users who are feeling stressed. Include relaxing activities." 【0177】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0178】 Step 1: 【0179】 The user enters their basic travel requests. The terminal receives the user's requests via voice or text. The entered data includes the desired travel destination, dates, and purpose (e.g., relaxation, adventure). The terminal prepares the entered data to be sent directly to the next processing step. 【0180】 Step 2: 【0181】 The terminal sends the acquired user input data to the server. The server forwards the received data to the emotion engine, which uses natural language processing (NLP) and speech analysis techniques to analyze the user's emotional state. The emotion engine measures stress and excitement from the content and tone of the input data and returns the analysis results to the server. 【0182】 Step 3: 【0183】 The server combines user requests and emotional state data, and uses a generative AI model to generate optimal travel suggestions. The server then creates a personalized travel plan, taking the emotional analysis results into account, and sends the output data to the user's device. This plan includes specific details such as suggested activities and accommodations. 【0184】 Step 4: 【0185】 The device presents the generated travel plan to the user and observes their emotional response in real time. The user's response is sent from the device to an emotion engine, which analyzes the new emotional input. The server receives the data again, reflecting the analysis results, and fine-tunes the plan. 【0186】 Step 5: 【0187】 Users approve or request changes to the presented plan. The server collects user feedback and performs predetermined procedures based on the approved plan. This includes booking activities and confirming transportation options. 【0188】 Step 6: 【0189】 During the trip, the device continuously receives input from the user and sends it to the server. The server uses this information to dynamically adjust the travel plan and make new suggestions as needed. The results are sent to the device and presented to the user in real time. 【0190】 Step 7: 【0191】 After the trip ends, the device prompts the user to provide feedback. This feedback is analyzed by an emotion engine, and the results are collected on a server. The server then integrates this data into a generative AI model to improve future travel plans, evolving the suggested trips. 【0192】 (Application Example 2) 【0193】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal". 【0194】 In recent years, there has been a growing demand for personalized travel experiences that take into account the user's emotional state, from the planning stage to during the trip itself. However, conventional systems have struggled to adjust plans based on user emotions, making it difficult to guarantee the optimal travel experience for each user. This problem needs to be solved. 【0195】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0196】 In this invention, the server includes an input means for receiving travel preferences and emotional states from the user; a processing means for generating an optimal travel plan based on the received preferences and emotional states and adjusting the plan considering the emotional data; and a modification means for acquiring real-time emotional information during the trip and dynamically modifying the plan according to the user's emotional state. This makes it possible to provide a flexible travel experience that reflects the user's emotional state in real time. 【0197】 The "input method" refers to a function for receiving travel preferences and emotional states from the user. 【0198】 The "processing means" is a function that generates the optimal travel plan based on the received desired conditions and emotional state, and adjusts the plan while taking emotional data into consideration. 【0199】 The "confirmation method" is a function that presents the generated travel plan to the user and obtains approval while analyzing their emotional response. 【0200】 "Booking method" refers to the function for executing all related bookings based on the approved travel plan. 【0201】 The "modification method" refers to a function that acquires real-time emotional information from the user during their trip and dynamically modifies the travel plan according to that emotional state. 【0202】 A "learning tool" is a function that uses acquired feedback and emotional information to inform the next plan and optimize it using a learning algorithm. 【0203】 The system for realizing this invention operates by integrating a user terminal, a server, and an emotion engine. The user inputs their travel preferences through a specific terminal and provides their emotional state in voice or text. The terminal then transmits this data to the server. 【0204】 The server converts speech data into text using tools such as Google® Cloud Speech-to-Text and analyzes the user's emotional state using emotion analysis tools such as Azure® Emotion API. Based on this, it generates a travel plan and adjusts the plan by incorporating the emotional data. 【0205】 The generated travel plan is presented to the user's device. The user's reaction is analyzed again by the emotion engine, and the server re-evaluates and readjusts the plan based on that information. In this process, feedback is obtained according to the emotional state, which is used to create the next travel plan. 【0206】 Throughout the trip, the device continuously captures the user's real-time emotions. Based on this, the server makes necessary plan changes to provide the optimal travel experience. For example, if the user feels stressed by a particular activity, a new activity that helps them relax can be immediately suggested. 【0207】 For example, if a user asks, "I want to plan a family trip with my children for our next vacation," the server will generate plans such as visiting a zoo or going to a family-friendly resort, and the robot will propose these plans. If the user responds positively with "This is good!", the plan is approved, and the process moves to the next step. 【0208】 An example of a prompt for a generative AI model might be: "The user wants to take a family trip on their next vacation. Based on sentiment analysis, please suggest a travel plan that will lead to a positive experience by incorporating friendly activities." 【0209】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0210】 Step 1: 【0211】 The user inputs their travel preferences and feelings into the device via voice or text. The device captures the user's input as audio data and sends it to the server. In this step, the input is the user's voice or text, and the output is audio data in digital format. 【0212】 Step 2: 【0213】 The server receives the audio data and uses Google Cloud Speech-to-Text to convert the speech to text. The input is digital audio data, and the output is the converted text. This process transforms the audio data into a parseable format. 【0214】 Step 3: 【0215】 The server uses the Azure Emotion API and other tools to analyze the user's emotional state from the converted text. The input is text data, and the output is emotional state data based on that text. Sentiment analysis is performed here to identify the user's current emotional state. 【0216】 Step 4: 【0217】 The server generates a travel plan based on the received desired conditions and analyzed emotional state. A generative AI model is used to create a plan suitable for the conditions, and the plan is adjusted, particularly taking emotional data into consideration. In this step, desired conditions and emotional state are the inputs, and the travel plan is the output. 【0218】 Step 5: 【0219】 The server generates a travel plan and sends it to the terminal for the user to see. The user reviews the plan and provides an emotional response. The input is the travel plan, and the output is emotional data based on the user's response. User feedback is collected at this stage. 【0220】 Step 6: 【0221】 The server re-analyzes the user's emotional response and readjusts the plan as needed. Here, user feedback is the input, and the adjusted travel plan is the output. The plan is optimized based on the emotional analysis. 【0222】 Step 7: 【0223】 During the trip, the device continuously monitors the user's emotional state in real time and sends data to the server as needed. The input is data on the user's emotional changes, and the output is new activities suggested by the server in real time. In this process, the plan is flexibly modified to optimize the user's travel experience. 【0224】 The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data. 【0225】 Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0226】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14. 【0227】 [Second Embodiment] 【0228】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0229】 As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server. 【0230】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0231】 The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52. 【0232】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0233】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0234】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0235】 Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0236】 The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30. 【0237】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0238】 In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0239】 Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0240】 The AI ​​trip planner system of the present invention is a system that integrates several important functions to efficiently and flexibly support users' travel planning. The system's operation and specific examples are shown below. 【0241】 This system first implements a user interface using a terminal, providing a mechanism for users to easily input their travel preferences. The terminal receives input from the user and transfers that data to the server. 【0242】 The server analyzes the received user data and generates the optimal travel plan using an AI algorithm. The AI ​​consults a large database, taking into account the latest information on each destination, flight schedules, accommodation availability, and local activities. 【0243】 The generated travel plan is sent to the device via the server. The device presents the plan to the user through a visual plan display function. The user can review the plan details and request changes as needed. 【0244】 After obtaining user approval, the server makes bulk reservations for flights, accommodations, restaurants, activities, and more. The server integrates with external reservation systems to automate these reservation processes. 【0245】 During your trip, the server monitors flight delays, weather changes, and other local information in real time, and modifies your travel plan as needed. This ensures that you are always acting based on the most up-to-date plan. 【0246】 After the trip, the device requests feedback from the user through a simple interface and sends that feedback to the server. The server uses this information to improve future planning using a learning algorithm. 【0247】 Specific example: 【0248】 For example, suppose a user enters their desired conditions as, "A family of four, under 100,000 yen, to go to a beach resort, and enjoy sightseeing and shopping." 【0249】 Based on this, the server selects Okinawa as the destination and generates a plan that includes suitable flights, hotels, local attractions, and shopping malls. Once the booking process is complete, if a typhoon approaches during the trip, the server updates the schedule based on weather information and presents an itinerary that avoids risks. 【0250】 In this way, the present invention accurately responds to the user's travel needs and realizes cost-effective planning. 【0251】 The following describes the processing flow. 【0252】 Step 1: 【0253】 The user uses the terminal's interface to enter their travel preferences (budget, dates, activities of interest, etc.). The terminal verifies the entered data and then sends it to the server. 【0254】 Step 2: 【0255】 The server analyzes the user's preferences received and collects relevant information from its internal database. This includes potential destinations, flight information, accommodation options, and activity lists. The server then passes this information to an AI algorithm. 【0256】 Step 3: 【0257】 The server uses an AI algorithm to generate a travel plan optimized for the user's requirements. The AI ​​efficiently constructs the plan while considering budget, schedule, and activity requirements. The generated plan is then sent from the server to the user's device. 【0258】 Step 4: 【0259】 The device presents the travel plan to the user in a visually easy-to-understand format. The user reviews the plan and requests changes if necessary. If no changes are needed, the plan is approved. 【0260】 Step 5: 【0261】 The server, upon user approval, makes all reservations for flights, accommodations, restaurants, and activities based on the travel plan. It integrates with external reservation systems via APIs. Once all reservations are complete, it sends confirmation information to the user. 【0262】 Step 6: 【0263】 During your trip, the server continuously monitors real-time data. If it detects flight delays, weather changes, or other issues, it dynamically modifies your travel plan based on this information. The revised plan is immediately sent to your device. 【0264】 Step 7: 【0265】 After the trip ends, the device prompts the user for feedback. The user enters feedback about their travel experience and sends it from the device to the server. 【0266】 Step 8: 【0267】 The server passes the received feedback to a learning algorithm, which uses it as data to create the next travel plan. This allows the system to optimize itself so that it can provide the user with a more suitable plan next time. 【0268】 (Example 1) 【0269】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0270】 In modern times, travel planning has become increasingly complex, making it difficult for users to find the optimal travel plan that best suits their needs. Furthermore, responding quickly to changing circumstances during a trip places a significant burden on users. In addition, there is a lack of mechanisms to effectively utilize feedback from past travel experiences and incorporate it into future travel planning. To address these challenges, there is a need to develop a system that can generate and modify plans quickly and flexibly. 【0271】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0272】 In this invention, the server includes an input means for acquiring travel preferences from the user, a processing means for generating an optimal travel plan using a generation AI model based on the received conditions, and a modification means for updating the plan based on dynamic information acquired during the trip. This allows the user to efficiently and flexibly create a travel plan that meets their preferences and to respond quickly to unforeseen circumstances during the trip. Furthermore, by utilizing feedback and reflecting it in future plans, a better travel experience can be provided. 【0273】 "Input means" refers to an interface or device for obtaining travel preferences from the user. 【0274】 A "generative AI model" is an artificial intelligence algorithm or computational model used to generate the optimal travel plan based on received conditions. 【0275】 "Processing means" refers to a device or program that uses a generative AI model to create a travel plan based on the user's desired conditions. 【0276】 "Confirmation means" refers to a device or function used to present the generated travel plan to the user and obtain their approval. 【0277】 "Reservation method" refers to a device or program for executing all relevant reservations in accordance with an approved travel plan, in conjunction with an external reservation system. 【0278】 "Means of modification" refers to devices or procedures used to update travel plans based on dynamic information acquired during the trip. 【0279】 A "learning tool" is a device or program that collects user feedback and incorporates it into future travel plans. 【0280】 This AI trip planner system efficiently and flexibly supports users' travel planning. Users input their travel preferences using a terminal. These preferences include the number of travelers, budget, desired destination, and activities. Based on this input, the terminal sends data to the server. 【0281】 The server generates the optimal travel plan using a generative AI model based on the received data. This process utilizes an advanced database management system and AI algorithms. The server accesses a large database to obtain the latest flight schedules, accommodation availability, and destination information. This data is analyzed to formulate the most suitable travel plan for the user. 【0282】 The generated travel plan is sent from the server to the terminal, which visually presents the plan to the user. The user can review the plan and request changes if necessary. This request is then sent back to the server to adjust the plan. 【0283】 After the user approves the plan, the server integrates with an external booking system to process flight, accommodation, and activity reservations all at once. This eliminates the need for the user to go through individual procedures. Furthermore, the server monitors information in real time during the trip and updates the plan as needed. For example, the plan is automatically adjusted to account for weather changes or flight delays. 【0284】 After the trip, the terminal requests feedback from the user through a simple interface. This feedback is sent to the server and utilized for the next planning. By leveraging such feedback, the system continuously learns and improves the accuracy of travel plans. 【0285】 As a specific an specific example, it is conceivable that the user inputs desired conditions such as "want to go to a beach resort with four family members within 100,000 yen and enjoy sightseeing and shopping". In response to this request, the server proposes Okinawa as the destination and appropriate flights and hotels. In this way, the present invention appropriately responds to the user's travel needs and realizes a cost-effective travel plan. 【0286】 Examples of prompt sentences are as follows: 【0287】 "I will help you with your next travel plan. Please enter the following information: the number of family members traveling, the budget, the type of destination you hope for (beach resort, city, mountain, etc.), and the activities you want to enjoy (sightseeing, shopping, relaxation, etc.)." 【0288】 The flow of the specific process in Example 1 will be described using FIG. 11. 【0289】 Step 1: 【0290】 The user uses the terminal to input the desired conditions for the trip. The data to be input are the number of travelers, the budget, the desired destination, and the activity content. The terminal secures these input data and prepares to format and send them to the server. The output generated in this step is in a structured data format that can be sent to the server. 【0291】 Step 2: 【0292】 The terminal sends the input data received from the user to the server. The server receives this data and stores it in a database. The server then preprocesses the received data and converts it into the format necessary for input into the generating AI model. The output is the travel preferences in a format suitable for analysis. 【0293】 Step 3: 【0294】 The server uses a generative AI model to create an optimal travel plan based on the received data. This plan generation involves referencing data from multiple databases, particularly flight schedules, accommodations, retail stores, and activity information. The output generated through data calculations is a proposed travel plan. 【0295】 Step 4: 【0296】 The server sends the generated travel plan to the terminal. The terminal receives this data and uses a GUI (Graphical User Interface) to display it visually in an easy-to-understand format for the user. The terminal also provides interactive options that allow the user to review the plan and request approval or changes. The output is the travel plan details presented through the user interface. 【0297】 Step 5: 【0298】 Users can review the plan details through their device and request changes as needed. If the user approves the plan, the device sends that information back to the server. The output generated based on the user's actions is the approved plan information, which is then used to proceed to the next booking process. 【0299】 Step 6: 【0300】 After receiving the user's approval, the server coordinates with an external reservation system to perform all necessary reservation procedures. In particular, it batch processes flight, accommodation, and activity reservations through automated APIs. The server checks the status of each reservation, and the output for which success has been confirmed is the reservation details of the completed trip. 【0301】 Step 7: 【0302】 During the trip, the server monitors flight information, weather changes, event cancellations, etc. in real time. As needed, it updates the travel plan based on this dynamic information. The output generated by the server's information collection and plan adjustment is the updated itinerary for a safe and smooth trip. 【0303】 Step 8: 【0304】 After the trip, the terminal collects feedback from the user and sends it to the server. The server stores the feedback in the database and uses it to update the learning algorithm of the AI model. The output obtained in this step is the feedback data for improving the quality of future travel plans. 【0305】 (Application Example 1) 【0306】 Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal". 【0307】 Existing travel planning methods have problems in that they cannot efficiently and effectively generate the travel plans desired by users and obtain local information, and cannot quickly respond to unexpected situations during the trip. Also, they cannot fully utilize the past preferences and feedback of travelers, and it is difficult to provide a travel experience tailored to individual preferences. There is a need to provide a system that solves these problems and enriches the user's travel experience. 【0308】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0309】 In this invention, the server includes an information input means for receiving travel preferences from the user, an information processing means for generating an optimal travel plan based on the received preferences, and a content distribution means for providing the user with information about destinations during the trip. This allows the user to enjoy their trip based on a travel plan that is updated in real time. Furthermore, personalized information enables the provision of services that are more closely tailored to the needs of travelers. 【0310】 An "information input means" is an interface that has the function of receiving travel preferences from the user. 【0311】 An "information processing system" is a system with a calculation function that generates the optimal travel plan based on the received desired conditions. 【0312】 An "information verification tool" is an interface that has the function of presenting the generated travel plan to the user and obtaining their approval. 【0313】 An "information booking system" is an automated mechanism for executing all relevant bookings based on an approved travel plan. 【0314】 An "information modification system" is a system that has the function of modifying travel plans based on information acquired in real time during a trip. 【0315】 An "information learning tool" is a system that has a data processing function to incorporate user feedback into the next plan. 【0316】 A "content distribution method" is a system that has a distribution function to provide users with information about their destinations while they are traveling. 【0317】 An "information recommendation system" is a system that has a recommendation function to provide personalized information about places to visit based on the user's past preference data. 【0318】 This system is built by integrating various hardware and software technologies. Users input their preferences and travel requirements from their smartphones or similar devices, and this information is sent to a server in the cloud. This server operates an API using Node.js and the Express framework, receiving requests from users. 【0319】 The received information is processed using a machine learning model powered by Python and TensorFlow. At this stage, a personalized travel plan is generated based on the user's past travel data and preferences. The database (MongoDB) contains a large amount of travel destination information and past user feedback, enabling the provision of the latest and most appropriate travel plans. 【0320】 Once a plan is generated, it is presented to the device in a visual format for the user to review. During the trip, the server monitors real-time weather and traffic information and has the capability to dynamically modify the travel plan as needed. This information is then pushed to the user's smartphone. 【0321】 Information about travel destinations, including tourist spots and local cultural events, is provided through the content distribution function. The AI ​​model dynamically optimizes the information presented based on the individual user's preferences. 【0322】 As a concrete example, suppose a user enters "I want to have a unique food experience in Tokyo." The system could refer to past food preference data and, through an AI model, suggest up-and-coming restaurants and local food festivals. An example of a prompt to the generating AI model would be, "Please suggest a local plan based on the activities the user wants to enjoy and their food preferences at their destination." 【0323】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0324】 Step 1: 【0325】 Users enter their travel preferences using their smartphones. This includes attributes such as destination, type of activity, budget, and dates. The entered data is sent directly from the device to the server. 【0326】 Step 2: 【0327】 The server retrieves relevant travel destination information from the MongoDB database based on the received preferences. This search collects data such as tourist attractions, accommodations, and experience plans related to the destination. The data is converted to an intermediate format and passed on to the next processing step. 【0328】 Step 3: 【0329】 The server launches a machine learning model using Python and TensorFlow. Intermediate travel information and historical user preference data are used as input. Based on this data, the machine learning model calculates the optimal travel plan and generates a plan tailored to the user's individual needs. This output is returned to the server in a format that is easy to visualize. 【0330】 Step 4: 【0331】 The server sends the generated travel plan to the terminal. The terminal receives this data and displays it to the user using an interface that visually presents the travel plan. The user can review the plan and enter modification requests if necessary. 【0332】 Step 5: 【0333】 Upon receiving an approval or modification request from a user, the server executes an automated booking process. It integrates with an external booking system to make bulk bookings for selected flights and activities. The results of this process are logged as a success / failure status. 【0334】 Step 6: 【0335】 During the trip, the server retrieves real-time weather and traffic data from external sources. By analyzing this data, the travel plan is modified as needed. The revised plan is immediately notified to the user's device, allowing the user to always act based on the latest information. 【0336】 Step 7: 【0337】 After a user completes their trip, the server displays an interface on the user's terminal requesting feedback. The feedback is stored in a database and used to generate future travel plans. The feedback data is also used to improve the AI ​​model and optimize prompt messages. Through this feedback process, the overall accuracy of the system is improved. 【0338】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0339】 This invention provides a more personalized travel experience by incorporating emotion recognition into the user's travel plan. The emotion engine analyzes the user's voice and text to recognize the user's emotional state in real time, and based on this, it enables adjustments to the travel plan. 【0340】 First, the user enters their travel preferences using a device. The device sends this information to the server, and at the same time, the emotion engine analyzes the user's emotions during input (for example, the tone of their text or voice). In addition to the normal planning process, the server utilizes the information from the emotion engine to generate a travel plan optimized for the user's current emotions. 【0341】 After a plan is presented, if the user shows an emotional response to it, the emotion engine analyzes it and re-evaluates the plan's applicability. For example, if the user expresses anxiety or interest in the suggested activity, the server takes this information into account and fine-tunes the plan. Once the user approves, the server proceeds with the booking process. 【0342】 During travel, the device and emotion engine continuously monitor the user's emotions. Emotional states are also obtained from inputs the user provides to the device during specific activities and between travel. Based on this information, the server makes new suggestions at the appropriate time as needed and flexibly modifies the plan. 【0343】 After the trip ends, the device prompts the user to provide feedback. This post-trip feedback is also analyzed by the emotion engine, and the results are reflected in the next trip plan. The server analyzes this emotion information along with past feedback and uses a learning algorithm to optimize future plans. 【0344】 Specific example: 【0345】 If a user enters "I want to travel to Hawaii and relax," the emotion engine analyzes whether the user's language tone indicates stress. The server generates and presents a plan that emphasizes activities effective in reducing stress (such as spa treatments and relaxing time on quiet beaches). Furthermore, if the user shows new signs of stress during the trip, the emotion engine reacts immediately and instructs appropriate changes to ensure the user has the best possible travel experience. 【0346】 The following describes the processing flow. 【0347】 Step 1: 【0348】 The user enters their desired travel plan criteria (e.g., destination, budget, dates, desired activities, etc.) into the device. This input also includes voice input. The device sends the given text or voice to an emotion engine to analyze the user's emotional state. 【0349】 Step 2: 【0350】 The device sends the user's desired conditions, along with analyzed emotional information, to the server. Based on the received information, the server uses an AI algorithm to generate the optimal travel plan. In this process, the user's emotional state (e.g., excitement, relaxation, stress) is also taken into consideration. 【0351】 Step 3: 【0352】 The server displays the generated travel plan on the terminal. The terminal visually displays the plan details to the user, and the emotion engine analyzes the user's reaction in real time. If the user's emotions indicate anxiety or dissatisfaction, the terminal requests corrections from the server. 【0353】 Step 4: 【0354】 The server readjusts the travel plan based on the user's emotional feedback. It suggests new activities or modifies existing plans as needed to create a more suitable plan for the user and sends it to their device. 【0355】 Step 5: 【0356】 Once the user is satisfied with and approves the proposed plan, the server will execute bookings for flights, accommodations, activities, and other services all at once. The booking information will be notified to the user via their device. 【0357】 Step 6: 【0358】 Even while traveling, the device sends voice and text data from the user to the emotion engine, continuously monitoring their emotional state. If the server detects a change in emotion, it adjusts the schedule in real time and sends relevant suggestions to the device. 【0359】 Step 7: 【0360】 After the trip ends, the device prompts the user for feedback. The feedback provided by the user, along with emotional information in audio and text, is sent to the server and used in a learning process to improve future travel plans. 【0361】 Step 8: 【0362】 Based on past feedback and sentiment data, the server optimizes future travel plans and prepares to provide more personalized suggestions. 【0363】 (Example 2) 【0364】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0365】 Traditional travel planning systems have struggled to personalize plans by considering the user's emotional state and have been unable to respond promptly to their changing emotions. Furthermore, they have been unable to effectively collect and utilize real-time feedback during travel and incorporate it into future plans. Therefore, new systems are expected to provide users with a more satisfying travel experience. 【0366】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0367】 In this invention, the server includes an analysis means for analyzing the user's emotional state from their voice or documents, a construction means for generating an optimized travel plan based on the received request and the analyzed emotional data, and a modification means for taking in information from the user during the trip and dynamically modifying the plan. This enables the provision of personalized travel plans that reflect the user's emotional state and real-time plan adjustments. 【0368】 "Receiving means" refers to a function for receiving basic travel-related requests from the user. 【0369】 "Analysis means" refers to a function for analyzing the emotional state of a user from their voice or documents. 【0370】 The "construction means" refers to a function for generating optimized travel plans based on received requests and analyzed sentiment data. 【0371】 The "confirmation mechanism" is a function that presents the generated travel plan and adjusts the plan based on the user's emotional response. 【0372】 "Procedural means" refers to the function of carrying out all relevant procedures based on the approved travel plan. 【0373】 The "modification mechanism" refers to a function that incorporates information from users during their trip and dynamically modifies the plan. 【0374】 "Improvement measures" refer to a function for accumulating post-trip evaluations and incorporating them into future plans. 【0375】 "Optimization means" refers to a function that uses a learning algorithm to evolve travel plans. 【0376】 This invention is a system that analyzes user emotions and incorporates them into travel planning to provide a more personalized experience. The system consists of three main components: a terminal, a server, and an emotion engine. 【0377】 The user first enters their basic travel requests using a terminal. This terminal can be a general-purpose information processing device such as a smartphone or personal computer. Input can be done via voice or text, and the user can choose whichever method suits them best. 【0378】 The terminal sends the acquired input data to the server. The server works in conjunction with an emotion engine, which uses natural language processing (NLP) and speech analysis techniques to analyze the user's emotional characteristics. This emotion analysis extracts emotional information, such as whether the user is stressed or excited. 【0379】 The server uses a generative AI model to construct travel plans based on the user's input, including requests and emotional state data. This AI model is optimized using an extensive travel database and past user data, enabling it to provide suggestions tailored to the user's individual needs. For example, if a user inputs "I want to travel to Hawaii and relax," the server will generate and present a plan centered around relaxing activities. 【0380】 During travel, the device receives real-time feedback from the user, and the server dynamically adjusts the plan based on that information. This allows users to enjoy a plan optimized for changing circumstances, even while traveling. 【0381】 After the trip ends, the device prompts the user for feedback again, and this data is analyzed by an emotion engine before being collected by a server. This feedback information is used to create future travel plans, and an AI model generates even more refined suggestions. 【0382】 Example of a prompt: 【0383】 "Please generate a Hawaii travel plan for users who are feeling stressed. Include relaxing activities." 【0384】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0385】 Step 1: 【0386】 The user enters their basic travel requests. The terminal receives the user's requests via voice or text. The entered data includes the desired travel destination, dates, and purpose (e.g., relaxation, adventure). The terminal prepares the entered data to be sent directly to the next processing step. 【0387】 Step 2: 【0388】 The terminal sends the acquired user input data to the server. The server forwards the received data to the emotion engine, which uses natural language processing (NLP) and speech analysis techniques to analyze the user's emotional state. The emotion engine measures stress and excitement from the content and tone of the input data and returns the analysis results to the server. 【0389】 Step 3: 【0390】 The server combines user requests and emotional state data, and uses a generative AI model to generate optimal travel suggestions. The server then creates a personalized travel plan, taking the emotional analysis results into account, and sends the output data to the user's device. This plan includes specific details such as suggested activities and accommodations. 【0391】 Step 4: 【0392】 The device presents the generated travel plan to the user and observes their emotional response in real time. The user's response is sent from the device to an emotion engine, which analyzes the new emotional input. The server receives the data again, reflecting the analysis results, and fine-tunes the plan. 【0393】 Step 5: 【0394】 Users approve or request changes to the presented plan. The server collects user feedback and performs predetermined procedures based on the approved plan. This includes booking activities and confirming transportation options. 【0395】 Step 6: 【0396】 During the trip, the device continuously receives input from the user and sends it to the server. The server uses this information to dynamically adjust the travel plan and make new suggestions as needed. The results are sent to the device and presented to the user in real time. 【0397】 Step 7: 【0398】 After the trip ends, the device prompts the user to provide feedback. This feedback is analyzed by an emotion engine, and the results are collected on a server. The server then integrates this data into a generative AI model to improve future travel plans, evolving the suggested trips. 【0399】 (Application Example 2) 【0400】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal". 【0401】 In recent years, there has been a growing demand for personalized travel experiences that take into account the user's emotional state, from the planning stage to during the trip itself. However, conventional systems have struggled to adjust plans based on user emotions, making it difficult to guarantee the optimal travel experience for each user. This problem needs to be solved. 【0402】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0403】 In this invention, the server includes an input means for receiving travel preferences and emotional states from the user; a processing means for generating an optimal travel plan based on the received preferences and emotional states and adjusting the plan considering the emotional data; and a modification means for acquiring real-time emotional information during the trip and dynamically modifying the plan according to the user's emotional state. This makes it possible to provide a flexible travel experience that reflects the user's emotional state in real time. 【0404】 The "input method" refers to a function for receiving travel preferences and emotional states from the user. 【0405】 The "processing means" is a function that generates the optimal travel plan based on the received desired conditions and emotional state, and adjusts the plan while taking emotional data into consideration. 【0406】 The "confirmation method" is a function that presents the generated travel plan to the user and obtains approval while analyzing their emotional response. 【0407】 "Booking method" refers to the function for executing all related bookings based on the approved travel plan. 【0408】 The "modification method" refers to a function that acquires real-time emotional information from the user during their trip and dynamically modifies the travel plan according to that emotional state. 【0409】 A "learning tool" is a function that uses acquired feedback and emotional information to inform the next plan and optimize it using a learning algorithm. 【0410】 The system for realizing this invention operates by integrating a user terminal, a server, and an emotion engine. The user inputs their travel preferences through a specific terminal and provides their emotional state in voice or text. The terminal then transmits this data to the server. 【0411】 The server converts speech data into text using tools such as Google Cloud Speech-to-Text and analyzes the user's emotional state using sentiment analysis tools such as the Azure Emotion API. Based on this, it generates a travel plan and adjusts it by incorporating the emotional data. 【0412】 The generated travel plan is presented to the user's device. The user's reaction is analyzed again by the emotion engine, and the server re-evaluates and readjusts the plan based on that information. In this process, feedback is obtained according to the emotional state, which is used to create the next travel plan. 【0413】 Throughout the trip, the device continuously captures the user's real-time emotions. Based on this, the server makes necessary plan changes to provide the optimal travel experience. For example, if the user feels stressed by a particular activity, a new activity that helps them relax can be immediately suggested. 【0414】 For example, if a user asks, "I want to plan a family trip with my children for our next vacation," the server will generate plans such as visiting a zoo or going to a family-friendly resort, and the robot will propose these plans. If the user responds positively with "This is good!", the plan is approved, and the process moves to the next step. 【0415】 An example of a prompt for a generative AI model might be: "The user wants to take a family trip on their next vacation. Based on sentiment analysis, please suggest a travel plan that will lead to a positive experience by incorporating friendly activities." 【0416】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0417】 Step 1: 【0418】 The user inputs their travel preferences and feelings into the device via voice or text. The device captures the user's input as audio data and sends it to the server. In this step, the input is the user's voice or text, and the output is audio data in digital format. 【0419】 Step 2: 【0420】 The server receives the audio data and uses Google Cloud Speech-to-Text to convert the speech to text. The input is digital audio data, and the output is the converted text. This process transforms the audio data into a parseable format. 【0421】 Step 3: 【0422】 The server uses the Azure Emotion API and other tools to analyze the user's emotional state from the converted text. The input is text data, and the output is emotional state data based on that text. Sentiment analysis is performed here to identify the user's current emotional state. 【0423】 Step 4: 【0424】 The server generates a travel plan based on the received desired conditions and analyzed emotional state. A generative AI model is used to create a plan suitable for the conditions, and the plan is adjusted, particularly taking emotional data into consideration. In this step, desired conditions and emotional state are the inputs, and the travel plan is the output. 【0425】 Step 5: 【0426】 The server generates a travel plan and sends it to the terminal for the user to see. The user reviews the plan and provides an emotional response. The input is the travel plan, and the output is emotional data based on the user's response. User feedback is collected at this stage. 【0427】 Step 6: 【0428】 The server re-analyzes the user's emotional response and readjusts the plan as needed. Here, user feedback is the input, and the adjusted travel plan is the output. The plan is optimized based on the emotional analysis. 【0429】 Step 7: 【0430】 During the trip, the device continuously monitors the user's emotional state in real time and sends data to the server as needed. The input is data on the user's emotional changes, and the output is new activities suggested by the server in real time. In this process, the plan is flexibly modified to optimize the user's travel experience. 【0431】 The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0432】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0433】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214. 【0434】 [Third Embodiment] 【0435】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0436】 As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server. 【0437】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0438】 The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52. 【0439】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0440】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0441】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0442】 Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0443】 The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30. 【0444】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0445】 In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0446】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal". 【0447】 The AI ​​trip planner system of the present invention is a system that integrates several important functions to efficiently and flexibly support users' travel planning. The system's operation and specific examples are shown below. 【0448】 This system first implements a user interface using a terminal, providing a mechanism for users to easily input their travel preferences. The terminal receives input from the user and transfers that data to the server. 【0449】 The server analyzes the received user data and generates the optimal travel plan using an AI algorithm. The AI ​​consults a large database, taking into account the latest information on each destination, flight schedules, accommodation availability, and local activities. 【0450】 The generated travel plan is sent to the device via the server. The device presents the plan to the user through a visual plan display function. The user can review the plan details and request changes as needed. 【0451】 After obtaining user approval, the server makes bulk reservations for flights, accommodations, restaurants, activities, and more. The server integrates with external reservation systems to automate these reservation processes. 【0452】 During your trip, the server monitors flight delays, weather changes, and other local information in real time, and modifies your travel plan as needed. This ensures that you are always acting based on the most up-to-date plan. 【0453】 After the trip, the device requests feedback from the user through a simple interface and sends that feedback to the server. The server uses this information to improve future planning using a learning algorithm. 【0454】 Specific example: 【0455】 For example, suppose a user enters their desired conditions as, "A family of four, under 100,000 yen, to go to a beach resort, and enjoy sightseeing and shopping." 【0456】 Based on this, the server selects Okinawa as the destination and generates a plan that includes suitable flights, hotels, local attractions, and shopping malls. Once the booking process is complete, if a typhoon approaches during the trip, the server updates the schedule based on weather information and presents an itinerary that avoids risks. 【0457】 In this way, the present invention accurately responds to the user's travel needs and realizes cost-effective planning. 【0458】 The following describes the processing flow. 【0459】 Step 1: 【0460】 The user uses the terminal's interface to enter their travel preferences (budget, dates, activities of interest, etc.). The terminal verifies the entered data and then sends it to the server. 【0461】 Step 2: 【0462】 The server analyzes the user's preferences received and collects relevant information from its internal database. This includes potential destinations, flight information, accommodation options, and activity lists. The server then passes this information to an AI algorithm. 【0463】 Step 3: 【0464】 The server uses an AI algorithm to generate a travel plan optimized for the user's requirements. The AI ​​efficiently constructs the plan while considering budget, schedule, and activity requirements. The generated plan is then sent from the server to the user's device. 【0465】 Step 4: 【0466】 The device presents the travel plan to the user in a visually easy-to-understand format. The user reviews the plan and requests changes if necessary. If no changes are needed, the plan is approved. 【0467】 Step 5: 【0468】 The server, upon user approval, makes all reservations for flights, accommodations, restaurants, and activities based on the travel plan. It integrates with external reservation systems via APIs. Once all reservations are complete, it sends confirmation information to the user. 【0469】 Step 6: 【0470】 During your trip, the server continuously monitors real-time data. If it detects flight delays, weather changes, or other issues, it dynamically modifies your travel plan based on this information. The revised plan is immediately sent to your device. 【0471】 Step 7: 【0472】 After the trip ends, the device prompts the user for feedback. The user enters feedback about their travel experience and sends it from the device to the server. 【0473】 Step 8: 【0474】 The server passes the received feedback to a learning algorithm, which uses it as data to create the next travel plan. This allows the system to optimize itself so that it can provide the user with a more suitable plan next time. 【0475】 (Example 1) 【0476】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0477】 In modern times, travel planning has become increasingly complex, making it difficult for users to find the optimal travel plan that best suits their needs. Furthermore, responding quickly to changing circumstances during a trip places a significant burden on users. In addition, there is a lack of mechanisms to effectively utilize feedback from past travel experiences and incorporate it into future travel planning. To address these challenges, there is a need to develop a system that can generate and modify plans quickly and flexibly. 【0478】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0479】 In this invention, the server includes an input means for acquiring travel preferences from the user, a processing means for generating an optimal travel plan using a generation AI model based on the received conditions, and a modification means for updating the plan based on dynamic information acquired during the trip. This allows the user to efficiently and flexibly create a travel plan that meets their preferences and to respond quickly to unforeseen circumstances during the trip. Furthermore, by utilizing feedback and reflecting it in future plans, a better travel experience can be provided. 【0480】 "Input means" refers to an interface or device for obtaining travel preferences from the user. 【0481】 A "generative AI model" is an artificial intelligence algorithm or computational model used to generate the optimal travel plan based on received conditions. 【0482】 "Processing means" refers to a device or program that uses a generative AI model to create a travel plan based on the user's desired conditions. 【0483】 "Confirmation means" refers to a device or function used to present the generated travel plan to the user and obtain their approval. 【0484】 "Reservation method" refers to a device or program for executing all relevant reservations in accordance with an approved travel plan, in conjunction with an external reservation system. 【0485】 "Means of modification" refers to devices or procedures used to update travel plans based on dynamic information acquired during the trip. 【0486】 A "learning tool" is a device or program that collects user feedback and incorporates it into future travel plans. 【0487】 This AI trip planner system efficiently and flexibly supports users' travel planning. Users input their travel preferences using a terminal. These preferences include the number of travelers, budget, desired destination, and activities. Based on this input, the terminal sends data to the server. 【0488】 The server generates the optimal travel plan using a generative AI model based on the received data. This process utilizes an advanced database management system and AI algorithms. The server accesses a large database to obtain the latest flight schedules, accommodation availability, and destination information. This data is analyzed to formulate the most suitable travel plan for the user. 【0489】 The generated travel plan is sent from the server to the terminal, which visually presents the plan to the user. The user can review the plan and request changes if necessary. This request is then sent back to the server to adjust the plan. 【0490】 After the user approves the plan, the server integrates with an external booking system to process flight, accommodation, and activity reservations all at once. This eliminates the need for the user to go through individual procedures. Furthermore, the server monitors information in real time during the trip and updates the plan as needed. For example, the plan is automatically adjusted to account for weather changes or flight delays. 【0491】 After the trip, the device requests feedback from the user through a simple interface. This feedback is sent to the server and used for planning the next trip. By utilizing this feedback, the system continuously learns and improves the accuracy of travel planning. 【0492】 As a concrete example, a user might input their desired conditions, such as "a family of four, under 100,000 yen, to a beach resort, where we want to enjoy sightseeing and shopping." In response to this request, the server would suggest Okinawa as the destination and propose appropriate flights and hotels. In this way, the present invention appropriately responds to the user's travel needs and realizes a cost-effective travel plan. 【0493】 Examples of prompt statements are as follows: 【0494】 "We'll help you plan your next trip. Please enter the following information: the number of people traveling, your budget, the type of destination you'd like (beach resort, city, mountains, etc.), and the activities you'd like to enjoy (sightseeing, shopping, relaxation, etc.)." 【0495】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0496】 Step 1: 【0497】 The user uses a terminal to enter their travel preferences. The data entered includes the number of travelers, budget, desired destination, and activities. The terminal collects this input data, formats it, and prepares it for transmission to the server. The output generated in this step is structured and in a format suitable for transmission to the server. 【0498】 Step 2: 【0499】 The terminal sends the input data received from the user to the server. The server receives this data and stores it in a database. The server then preprocesses the received data and converts it into the format necessary for input into the generating AI model. The output is the travel preferences in a format suitable for analysis. 【0500】 Step 3: 【0501】 The server uses a generative AI model to create an optimal travel plan based on the received data. This plan generation involves referencing data from multiple databases, particularly flight schedules, accommodations, retail stores, and activity information. The output generated through data calculations is a proposed travel plan. 【0502】 Step 4: 【0503】 The server sends the generated travel plan to the terminal. The terminal receives this data and uses a GUI (Graphical User Interface) to display it visually in an easy-to-understand format for the user. The terminal also provides interactive options that allow the user to review the plan and request approval or changes. The output is the travel plan details presented through the user interface. 【0504】 Step 5: 【0505】 Users can review the plan details through their device and request changes as needed. If the user approves the plan, the device sends that information back to the server. The output generated based on the user's actions is the approved plan information, which is then used to proceed to the next booking process. 【0506】 Step 6: 【0507】 After receiving user approval, the server integrates with an external booking system to handle all necessary booking procedures. Specifically, it processes flight, accommodation, and activity bookings in batches via an automated API. The server checks the status of each booking, and successful bookings are output as booking details for the completed trip. 【0508】 Step 7: 【0509】 During your trip, the server monitors flight information, weather changes, event cancellations, and other dynamic data in real time. It updates your travel plan based on this information as needed. The output generated by the server's information gathering and plan adjustments is an updated itinerary designed for a safe and smooth journey. 【0510】 Step 8: 【0511】 After the trip, the device collects feedback from the user and sends it to the server. The server stores this feedback in a database and uses it to update the AI ​​model's learning algorithm. The output obtained in this step is feedback data that can be used to improve the quality of future travel plans. 【0512】 (Application Example 1) 【0513】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0514】 Existing travel planning methods have limitations in efficiently and effectively generating travel plans that meet user preferences and acquiring local information, as well as in responding quickly to unexpected situations during travel. Furthermore, they fail to fully utilize travelers' past preferences and feedback, making it difficult to provide travel experiences tailored to individual tastes. There is a need for a system that solves these problems and enriches users' travel experiences. 【0515】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0516】 In this invention, the server includes an information input means for receiving travel preferences from the user, an information processing means for generating an optimal travel plan based on the received preferences, and a content distribution means for providing the user with information about destinations during the trip. This allows the user to enjoy their trip based on a travel plan that is updated in real time. Furthermore, personalized information enables the provision of services that are more closely tailored to the needs of travelers. 【0517】 An "information input means" is an interface that has the function of receiving travel preferences from the user. 【0518】 An "information processing system" is a system with a calculation function that generates the optimal travel plan based on the received desired conditions. 【0519】 An "information verification tool" is an interface that has the function of presenting the generated travel plan to the user and obtaining their approval. 【0520】 An "information booking system" is an automated mechanism for executing all relevant bookings based on an approved travel plan. 【0521】 An "information modification system" is a system that has the function of modifying travel plans based on information acquired in real time during a trip. 【0522】 An "information learning tool" is a system that has a data processing function to incorporate user feedback into the next plan. 【0523】 A "content distribution method" is a system that has a distribution function to provide users with information about their destinations while they are traveling. 【0524】 An "information recommendation system" is a system that has a recommendation function to provide personalized information about places to visit based on the user's past preference data. 【0525】 This system is built by integrating various hardware and software technologies. Users input their preferences and travel requirements from their smartphones or similar devices, and this information is sent to a server in the cloud. This server operates an API using Node.js and the Express framework, receiving requests from users. 【0526】 The received information is processed using a machine learning model powered by Python and TensorFlow. At this stage, a personalized travel plan is generated based on the user's past travel data and preferences. The database (MongoDB) contains a large amount of travel destination information and past user feedback, enabling the provision of the latest and most appropriate travel plans. 【0527】 Once a plan is generated, it is presented to the device in a visual format for the user to review. During the trip, the server monitors real-time weather and traffic information and has the capability to dynamically modify the travel plan as needed. This information is then pushed to the user's smartphone. 【0528】 Information about travel destinations, including tourist spots and local cultural events, is provided through the content distribution function. The AI ​​model dynamically optimizes the information presented based on the individual user's preferences. 【0529】 As a concrete example, suppose a user enters "I want to have a unique food experience in Tokyo." The system could refer to past food preference data and, through an AI model, suggest up-and-coming restaurants and local food festivals. An example of a prompt to the generating AI model would be, "Please suggest a local plan based on the activities the user wants to enjoy and their food preferences at their destination." 【0530】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0531】 Step 1: 【0532】 Users enter their travel preferences using their smartphones. This includes attributes such as destination, type of activity, budget, and dates. The entered data is sent directly from the device to the server. 【0533】 Step 2: 【0534】 The server retrieves relevant travel destination information from the MongoDB database based on the received preferences. This search collects data such as tourist attractions, accommodations, and experience plans related to the destination. The data is converted to an intermediate format and passed on to the next processing step. 【0535】 Step 3: 【0536】 The server launches a machine learning model using Python and TensorFlow. Intermediate travel information and historical user preference data are used as input. Based on this data, the machine learning model calculates the optimal travel plan and generates a plan tailored to the user's individual needs. This output is returned to the server in a format that is easy to visualize. 【0537】 Step 4: 【0538】 The server sends the generated travel plan to the terminal. The terminal receives this data and displays it to the user using an interface that visually presents the travel plan. The user can review the plan and enter modification requests if necessary. 【0539】 Step 5: 【0540】 Upon receiving an approval or modification request from a user, the server executes an automated booking process. It integrates with an external booking system to make bulk bookings for selected flights and activities. The results of this process are logged as a success / failure status. 【0541】 Step 6: 【0542】 During the trip, the server retrieves real-time weather and traffic data from external sources. By analyzing this data, the travel plan is modified as needed. The revised plan is immediately notified to the user's device, allowing the user to always act based on the latest information. 【0543】 Step 7: 【0544】 After a user completes their trip, the server displays an interface on the user's terminal requesting feedback. The feedback is stored in a database and used to generate future travel plans. The feedback data is also used to improve the AI ​​model and optimize prompt messages. Through this feedback process, the overall accuracy of the system is improved. 【0545】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0546】 This invention provides a more personalized travel experience by incorporating emotion recognition into the user's travel plan. The emotion engine analyzes the user's voice and text to recognize the user's emotional state in real time, and based on this, it enables adjustments to the travel plan. 【0547】 First, the user enters their travel preferences using a device. The device sends this information to the server, and at the same time, the emotion engine analyzes the user's emotions during input (for example, the tone of their text or voice). In addition to the normal planning process, the server utilizes the information from the emotion engine to generate a travel plan optimized for the user's current emotions. 【0548】 After a plan is presented, if the user shows an emotional response to it, the emotion engine analyzes it and re-evaluates the plan's applicability. For example, if the user expresses anxiety or interest in the suggested activity, the server takes this information into account and fine-tunes the plan. Once the user approves, the server proceeds with the booking process. 【0549】 During travel, the device and emotion engine continuously monitor the user's emotions. Emotional states are also obtained from inputs the user provides to the device during specific activities and between travel. Based on this information, the server makes new suggestions at the appropriate time as needed and flexibly modifies the plan. 【0550】 After the trip ends, the device prompts the user to provide feedback. This post-trip feedback is also analyzed by the emotion engine, and the results are reflected in the next trip plan. The server analyzes this emotion information along with past feedback and uses a learning algorithm to optimize future plans. 【0551】 Specific example: 【0552】 If a user enters "I want to travel to Hawaii and relax," the emotion engine analyzes whether the user's language tone indicates stress. The server generates and presents a plan that emphasizes activities effective in reducing stress (such as spa treatments and relaxing time on quiet beaches). Furthermore, if the user shows new signs of stress during the trip, the emotion engine reacts immediately and instructs appropriate changes to ensure the user has the best possible travel experience. 【0553】 The following describes the processing flow. 【0554】 Step 1: 【0555】 The user enters their desired travel plan criteria (e.g., destination, budget, dates, desired activities, etc.) into the device. This input also includes voice input. The device sends the given text or voice to an emotion engine to analyze the user's emotional state. 【0556】 Step 2: 【0557】 The device sends the user's desired conditions, along with analyzed emotional information, to the server. Based on the received information, the server uses an AI algorithm to generate the optimal travel plan. In this process, the user's emotional state (e.g., excitement, relaxation, stress) is also taken into consideration. 【0558】 Step 3: 【0559】 The server displays the generated travel plan on the terminal. The terminal visually displays the plan details to the user, and the emotion engine analyzes the user's reaction in real time. If the user's emotions indicate anxiety or dissatisfaction, the terminal requests corrections from the server. 【0560】 Step 4: 【0561】 The server readjusts the travel plan based on the user's emotional feedback. It suggests new activities or modifies existing plans as needed to create a more suitable plan for the user and sends it to their device. 【0562】 Step 5: 【0563】 Once the user is satisfied with and approves the proposed plan, the server will execute bookings for flights, accommodations, activities, and other services all at once. The booking information will be notified to the user via their device. 【0564】 Step 6: 【0565】 Even while traveling, the device sends voice and text data from the user to the emotion engine, continuously monitoring their emotional state. If the server detects a change in emotion, it adjusts the schedule in real time and sends relevant suggestions to the device. 【0566】 Step 7: 【0567】 After the trip ends, the device prompts the user for feedback. The feedback provided by the user, along with emotional information in audio and text, is sent to the server and used in a learning process to improve future travel plans. 【0568】 Step 8: 【0569】 Based on past feedback and sentiment data, the server optimizes future travel plans and prepares to provide more personalized suggestions. 【0570】 (Example 2) 【0571】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0572】 Traditional travel planning systems have struggled to personalize plans by considering the user's emotional state and have been unable to respond promptly to their changing emotions. Furthermore, they have been unable to effectively collect and utilize real-time feedback during travel and incorporate it into future plans. Therefore, new systems are expected to provide users with a more satisfying travel experience. 【0573】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0574】 In this invention, the server includes an analysis means for analyzing the user's emotional state from their voice or documents, a construction means for generating an optimized travel plan based on the received request and the analyzed emotional data, and a modification means for taking in information from the user during the trip and dynamically modifying the plan. This enables the provision of personalized travel plans that reflect the user's emotional state and real-time plan adjustments. 【0575】 "Receiving means" refers to a function for receiving basic travel-related requests from the user. 【0576】 "Analysis means" refers to a function for analyzing the emotional state of a user from their voice or documents. 【0577】 The "construction means" refers to a function for generating optimized travel plans based on received requests and analyzed sentiment data. 【0578】 The "confirmation mechanism" is a function that presents the generated travel plan and adjusts the plan based on the user's emotional response. 【0579】 "Procedural means" refers to the function of carrying out all relevant procedures based on the approved travel plan. 【0580】 The "modification mechanism" refers to a function that incorporates information from users during their trip and dynamically modifies the plan. 【0581】 "Improvement measures" refer to a function for accumulating post-trip evaluations and incorporating them into future plans. 【0582】 "Optimization means" refers to a function that uses a learning algorithm to evolve travel plans. 【0583】 This invention is a system that analyzes user emotions and incorporates them into travel planning to provide a more personalized experience. The system consists of three main components: a terminal, a server, and an emotion engine. 【0584】 The user first enters their basic travel requests using a terminal. This terminal can be a general-purpose information processing device such as a smartphone or personal computer. Input can be done via voice or text, and the user can choose whichever method suits them best. 【0585】 The terminal sends the acquired input data to the server. The server works in conjunction with an emotion engine, which uses natural language processing (NLP) and speech analysis techniques to analyze the user's emotional characteristics. This emotion analysis extracts emotional information, such as whether the user is stressed or excited. 【0586】 The server uses a generative AI model to construct travel plans based on the user's input, including requests and emotional state data. This AI model is optimized using an extensive travel database and past user data, enabling it to provide suggestions tailored to the user's individual needs. For example, if a user inputs "I want to travel to Hawaii and relax," the server will generate and present a plan centered around relaxing activities. 【0587】 During travel, the device receives real-time feedback from the user, and the server dynamically adjusts the plan based on that information. This allows users to enjoy a plan optimized for changing circumstances, even while traveling. 【0588】 After the trip ends, the device prompts the user for feedback again, and this data is analyzed by an emotion engine before being collected by a server. This feedback information is used to create future travel plans, and an AI model generates even more refined suggestions. 【0589】 Example of a prompt: 【0590】 "Please generate a Hawaii travel plan for users who are feeling stressed. Include relaxing activities." 【0591】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0592】 Step 1: 【0593】 The user enters their basic travel requests. The terminal receives the user's requests via voice or text. The entered data includes the desired travel destination, dates, and purpose (e.g., relaxation, adventure). The terminal prepares the entered data to be sent directly to the next processing step. 【0594】 Step 2: 【0595】 The terminal sends the acquired user input data to the server. The server forwards the received data to the emotion engine, which uses natural language processing (NLP) and speech analysis techniques to analyze the user's emotional state. The emotion engine measures stress and excitement from the content and tone of the input data and returns the analysis results to the server. 【0596】 Step 3: 【0597】 The server combines user requests and emotional state data, and uses a generative AI model to generate optimal travel suggestions. The server then creates a personalized travel plan, taking the emotional analysis results into account, and sends the output data to the user's device. This plan includes specific details such as suggested activities and accommodations. 【0598】 Step 4: 【0599】 The device presents the generated travel plan to the user and observes their emotional response in real time. The user's response is sent from the device to an emotion engine, which analyzes the new emotional input. The server receives the data again, reflecting the analysis results, and fine-tunes the plan. 【0600】 Step 5: 【0601】 Users approve or request changes to the presented plan. The server collects user feedback and performs predetermined procedures based on the approved plan. This includes booking activities and confirming transportation options. 【0602】 Step 6: 【0603】 During the trip, the device continuously receives input from the user and sends it to the server. The server uses this information to dynamically adjust the travel plan and make new suggestions as needed. The results are sent to the device and presented to the user in real time. 【0604】 Step 7: 【0605】 After the trip ends, the device prompts the user to provide feedback. This feedback is analyzed by an emotion engine, and the results are collected on a server. The server then integrates this data into a generative AI model to improve future travel plans, evolving the suggested trips. 【0606】 (Application Example 2) 【0607】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0608】 In recent years, there has been a growing demand for personalized travel experiences that take into account the user's emotional state, from the planning stage to during the trip itself. However, conventional systems have struggled to adjust plans based on user emotions, making it difficult to guarantee the optimal travel experience for each user. This problem needs to be solved. 【0609】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0610】 In this invention, the server includes an input means for receiving travel preferences and emotional states from the user; a processing means for generating an optimal travel plan based on the received preferences and emotional states and adjusting the plan considering the emotional data; and a modification means for acquiring real-time emotional information during the trip and dynamically modifying the plan according to the user's emotional state. This makes it possible to provide a flexible travel experience that reflects the user's emotional state in real time. 【0611】 The "input method" refers to a function for receiving travel preferences and emotional states from the user. 【0612】 The "processing means" is a function that generates the optimal travel plan based on the received desired conditions and emotional state, and adjusts the plan while taking emotional data into consideration. 【0613】 The "confirmation method" is a function that presents the generated travel plan to the user and obtains approval while analyzing their emotional response. 【0614】 "Booking method" refers to the function for executing all related bookings based on the approved travel plan. 【0615】 The "modification method" refers to a function that acquires real-time emotional information from the user during their trip and dynamically modifies the travel plan according to that emotional state. 【0616】 A "learning tool" is a function that uses acquired feedback and emotional information to inform the next plan and optimize it using a learning algorithm. 【0617】 The system for realizing this invention operates by integrating a user terminal, a server, and an emotion engine. The user inputs their travel preferences through a specific terminal and provides their emotional state in voice or text. The terminal then transmits this data to the server. 【0618】 The server converts speech data into text using tools such as Google Cloud Speech-to-Text and analyzes the user's emotional state using sentiment analysis tools such as the Azure Emotion API. Based on this, it generates a travel plan and adjusts it by incorporating the emotional data. 【0619】 The generated travel plan is presented to the user's device. The user's reaction is analyzed again by the emotion engine, and the server re-evaluates and readjusts the plan based on that information. In this process, feedback is obtained according to the emotional state, which is used to create the next travel plan. 【0620】 Throughout the trip, the device continuously captures the user's real-time emotions. Based on this, the server makes necessary plan changes to provide the optimal travel experience. For example, if the user feels stressed by a particular activity, a new activity that helps them relax can be immediately suggested. 【0621】 For example, if a user asks, "I want to plan a family trip with my children for our next vacation," the server will generate plans such as visiting a zoo or going to a family-friendly resort, and the robot will propose these plans. If the user responds positively with "This is good!", the plan is approved, and the process moves to the next step. 【0622】 An example of a prompt for a generative AI model might be: "The user wants to take a family trip on their next vacation. Based on sentiment analysis, please suggest a travel plan that will lead to a positive experience by incorporating friendly activities." 【0623】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0624】 Step 1: 【0625】 The user inputs their travel preferences and feelings into the device via voice or text. The device captures the user's input as audio data and sends it to the server. In this step, the input is the user's voice or text, and the output is audio data in digital format. 【0626】 Step 2: 【0627】 The server receives the audio data and uses Google Cloud Speech-to-Text to convert the speech to text. The input is digital audio data, and the output is the converted text. This process transforms the audio data into a parseable format. 【0628】 Step 3: 【0629】 The server uses the Azure Emotion API and other tools to analyze the user's emotional state from the converted text. The input is text data, and the output is emotional state data based on that text. Sentiment analysis is performed here to identify the user's current emotional state. 【0630】 Step 4: 【0631】 The server generates a travel plan based on the received desired conditions and analyzed emotional state. A generative AI model is used to create a plan suitable for the conditions, and the plan is adjusted, particularly taking emotional data into consideration. In this step, desired conditions and emotional state are the inputs, and the travel plan is the output. 【0632】 Step 5: 【0633】 The server generates a travel plan and sends it to the terminal for the user to see. The user reviews the plan and provides an emotional response. The input is the travel plan, and the output is emotional data based on the user's response. User feedback is collected at this stage. 【0634】 Step 6: 【0635】 The server re-analyzes the user's emotional response and readjusts the plan as needed. Here, user feedback is the input, and the adjusted travel plan is the output. The plan is optimized based on the emotional analysis. 【0636】 Step 7: 【0637】 During the trip, the device continuously monitors the user's emotional state in real time and sends data to the server as needed. The input is data on the user's emotional changes, and the output is new activities suggested by the server in real time. In this process, the plan is flexibly modified to optimize the user's travel experience. 【0638】 The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0639】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0640】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314. 【0641】 [Fourth Embodiment] 【0642】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0643】 As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server. 【0644】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0645】 The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52. 【0646】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0647】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0648】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0649】 The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes. 【0650】 Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0651】 The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30. 【0652】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0653】 In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0654】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0655】 The AI ​​trip planner system of the present invention is a system that integrates several important functions to efficiently and flexibly support users' travel planning. The system's operation and specific examples are shown below. 【0656】 This system first implements a user interface using a terminal, providing a mechanism for users to easily input their travel preferences. The terminal receives input from the user and transfers that data to the server. 【0657】 The server analyzes the received user data and generates the optimal travel plan using an AI algorithm. The AI ​​consults a large database, taking into account the latest information on each destination, flight schedules, accommodation availability, and local activities. 【0658】 The generated travel plan is sent to the device via the server. The device presents the plan to the user through a visual plan display function. The user can review the plan details and request changes as needed. 【0659】 After obtaining user approval, the server makes bulk reservations for flights, accommodations, restaurants, activities, and more. The server integrates with external reservation systems to automate these reservation processes. 【0660】 During your trip, the server monitors flight delays, weather changes, and other local information in real time, and modifies your travel plan as needed. This ensures that you are always acting based on the most up-to-date plan. 【0661】 After the trip, the device requests feedback from the user through a simple interface and sends that feedback to the server. The server uses this information to improve future planning using a learning algorithm. 【0662】 Specific example: 【0663】 For example, suppose a user enters their desired conditions as, "A family of four, under 100,000 yen, to go to a beach resort, and enjoy sightseeing and shopping." 【0664】 Based on this, the server selects Okinawa as the destination and generates a plan that includes suitable flights, hotels, local attractions, and shopping malls. Once the booking process is complete, if a typhoon approaches during the trip, the server updates the schedule based on weather information and presents an itinerary that avoids risks. 【0665】 In this way, the present invention accurately responds to the user's travel needs and realizes cost-effective planning. 【0666】 The following describes the processing flow. 【0667】 Step 1: 【0668】 The user uses the terminal's interface to enter their travel preferences (budget, dates, activities of interest, etc.). The terminal verifies the entered data and then sends it to the server. 【0669】 Step 2: 【0670】 The server analyzes the user's preferences received and collects relevant information from its internal database. This includes potential destinations, flight information, accommodation options, and activity lists. The server then passes this information to an AI algorithm. 【0671】 Step 3: 【0672】 The server uses an AI algorithm to generate a travel plan optimized for the user's requirements. The AI ​​efficiently constructs the plan while considering budget, schedule, and activity requirements. The generated plan is then sent from the server to the user's device. 【0673】 Step 4: 【0674】 The device presents the travel plan to the user in a visually easy-to-understand format. The user reviews the plan and requests changes if necessary. If no changes are needed, the plan is approved. 【0675】 Step 5: 【0676】 The server, upon user approval, makes all reservations for flights, accommodations, restaurants, and activities based on the travel plan. It integrates with external reservation systems via APIs. Once all reservations are complete, it sends confirmation information to the user. 【0677】 Step 6: 【0678】 During your trip, the server continuously monitors real-time data. If it detects flight delays, weather changes, or other issues, it dynamically modifies your travel plan based on this information. The revised plan is immediately sent to your device. 【0679】 Step 7: 【0680】 After the trip ends, the device prompts the user for feedback. The user enters feedback about their travel experience and sends it from the device to the server. 【0681】 Step 8: 【0682】 The server passes the received feedback to a learning algorithm, which uses it as data to create the next travel plan. This allows the system to optimize itself so that it can provide the user with a more suitable plan next time. 【0683】 (Example 1) 【0684】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0685】 In modern times, travel planning has become increasingly complex, making it difficult for users to find the optimal travel plan that best suits their needs. Furthermore, responding quickly to changing circumstances during a trip places a significant burden on users. In addition, there is a lack of mechanisms to effectively utilize feedback from past travel experiences and incorporate it into future travel planning. To address these challenges, there is a need to develop a system that can generate and modify plans quickly and flexibly. 【0686】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0687】 In this invention, the server includes an input means for acquiring travel preferences from the user, a processing means for generating an optimal travel plan using a generation AI model based on the received conditions, and a modification means for updating the plan based on dynamic information acquired during the trip. This allows the user to efficiently and flexibly create a travel plan that meets their preferences and to respond quickly to unforeseen circumstances during the trip. Furthermore, by utilizing feedback and reflecting it in future plans, a better travel experience can be provided. 【0688】 "Input means" refers to an interface or device for obtaining travel preferences from the user. 【0689】 A "generative AI model" is an artificial intelligence algorithm or computational model used to generate the optimal travel plan based on received conditions. 【0690】 "Processing means" refers to a device or program that uses a generative AI model to create a travel plan based on the user's desired conditions. 【0691】 "Confirmation means" refers to a device or function used to present the generated travel plan to the user and obtain their approval. 【0692】 "Reservation method" refers to a device or program for executing all relevant reservations in accordance with an approved travel plan, in conjunction with an external reservation system. 【0693】 "Means of modification" refers to devices or procedures used to update travel plans based on dynamic information acquired during the trip. 【0694】 A "learning tool" is a device or program that collects user feedback and incorporates it into future travel plans. 【0695】 This AI trip planner system efficiently and flexibly supports users' travel planning. Users input their travel preferences using a terminal. These preferences include the number of travelers, budget, desired destination, and activities. Based on this input, the terminal sends data to the server. 【0696】 The server generates the optimal travel plan using a generative AI model based on the received data. This process utilizes an advanced database management system and AI algorithms. The server accesses a large database to obtain the latest flight schedules, accommodation availability, and destination information. This data is analyzed to formulate the most suitable travel plan for the user. 【0697】 The generated travel plan is sent from the server to the terminal, which visually presents the plan to the user. The user can review the plan and request changes if necessary. This request is then sent back to the server to adjust the plan. 【0698】 After the user approves the plan, the server integrates with an external booking system to process flight, accommodation, and activity reservations all at once. This eliminates the need for the user to go through individual procedures. Furthermore, the server monitors information in real time during the trip and updates the plan as needed. For example, the plan is automatically adjusted to account for weather changes or flight delays. 【0699】 After the trip, the device requests feedback from the user through a simple interface. This feedback is sent to the server and used for planning the next trip. By utilizing this feedback, the system continuously learns and improves the accuracy of travel planning. 【0700】 As a concrete example, a user might input their desired conditions, such as "a family of four, under 100,000 yen, to a beach resort, where we want to enjoy sightseeing and shopping." In response to this request, the server would suggest Okinawa as the destination and propose appropriate flights and hotels. In this way, the present invention appropriately responds to the user's travel needs and realizes a cost-effective travel plan. 【0701】 Examples of prompt statements are as follows: 【0702】 "We'll help you plan your next trip. Please enter the following information: the number of people traveling, your budget, the type of destination you'd like (beach resort, city, mountains, etc.), and the activities you'd like to enjoy (sightseeing, shopping, relaxation, etc.)." 【0703】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0704】 Step 1: 【0705】 The user uses a terminal to enter their travel preferences. The data entered includes the number of travelers, budget, desired destination, and activities. The terminal collects this input data, formats it, and prepares it for transmission to the server. The output generated in this step is structured and in a format suitable for transmission to the server. 【0706】 Step 2: 【0707】 The terminal sends the input data received from the user to the server. The server receives this data and stores it in a database. The server then preprocesses the received data and converts it into the format necessary for input into the generating AI model. The output is the travel preferences in a format suitable for analysis. 【0708】 Step 3: 【0709】 The server uses a generative AI model to create an optimal travel plan based on the received data. This plan generation involves referencing data from multiple databases, particularly flight schedules, accommodations, retail stores, and activity information. The output generated through data calculations is a proposed travel plan. 【0710】 Step 4: 【0711】 The server sends the generated travel plan to the terminal. The terminal receives this data and uses a GUI (Graphical User Interface) to display it visually in an easy-to-understand format for the user. The terminal also provides interactive options that allow the user to review the plan and request approval or changes. The output is the travel plan details presented through the user interface. 【0712】 Step 5: 【0713】 Users can review the plan details through their device and request changes as needed. If the user approves the plan, the device sends that information back to the server. The output generated based on the user's actions is the approved plan information, which is then used to proceed to the next booking process. 【0714】 Step 6: 【0715】 After receiving user approval, the server integrates with an external booking system to handle all necessary booking procedures. Specifically, it processes flight, accommodation, and activity bookings in batches via an automated API. The server checks the status of each booking, and successful bookings are output as booking details for the completed trip. 【0716】 Step 7: 【0717】 During your trip, the server monitors flight information, weather changes, event cancellations, and other dynamic data in real time. It updates your travel plan based on this information as needed. The output generated by the server's information gathering and plan adjustments is an updated itinerary designed for a safe and smooth journey. 【0718】 Step 8: 【0719】 After the trip, the device collects feedback from the user and sends it to the server. The server stores this feedback in a database and uses it to update the AI ​​model's learning algorithm. The output obtained in this step is feedback data that can be used to improve the quality of future travel plans. 【0720】 (Application Example 1) 【0721】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0722】 Existing travel planning methods have limitations in efficiently and effectively generating travel plans that meet user preferences and acquiring local information, as well as in responding quickly to unexpected situations during travel. Furthermore, they fail to fully utilize travelers' past preferences and feedback, making it difficult to provide travel experiences tailored to individual tastes. There is a need for a system that solves these problems and enriches users' travel experiences. 【0723】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0724】 In this invention, the server includes an information input means for receiving travel preferences from the user, an information processing means for generating an optimal travel plan based on the received preferences, and a content distribution means for providing the user with information about destinations during the trip. This allows the user to enjoy their trip based on a travel plan that is updated in real time. Furthermore, personalized information enables the provision of services that are more closely tailored to the needs of travelers. 【0725】 An "information input means" is an interface that has the function of receiving travel preferences from the user. 【0726】 An "information processing system" is a system with a calculation function that generates the optimal travel plan based on the received desired conditions. 【0727】 An "information verification tool" is an interface that has the function of presenting the generated travel plan to the user and obtaining their approval. 【0728】 An "information booking system" is an automated mechanism for executing all relevant bookings based on an approved travel plan. 【0729】 An "information modification system" is a system that has the function of modifying travel plans based on information acquired in real time during a trip. 【0730】 An "information learning tool" is a system that has a data processing function to incorporate user feedback into the next plan. 【0731】 A "content distribution method" is a system that has a distribution function to provide users with information about their destinations while they are traveling. 【0732】 An "information recommendation system" is a system that has a recommendation function to provide personalized information about places to visit based on the user's past preference data. 【0733】 This system is built by integrating various hardware and software technologies. Users input their preferences and travel requirements from their smartphones or similar devices, and this information is sent to a server in the cloud. This server operates an API using Node.js and the Express framework, receiving requests from users. 【0734】 The received information is processed using a machine learning model powered by Python and TensorFlow. At this stage, a personalized travel plan is generated based on the user's past travel data and preferences. The database (MongoDB) contains a large amount of travel destination information and past user feedback, enabling the provision of the latest and most appropriate travel plans. 【0735】 Once a plan is generated, it is presented to the device in a visual format for the user to review. During the trip, the server monitors real-time weather and traffic information and has the capability to dynamically modify the travel plan as needed. This information is then pushed to the user's smartphone. 【0736】 Information about travel destinations, including tourist spots and local cultural events, is provided through the content distribution function. The AI ​​model dynamically optimizes the information presented based on the individual user's preferences. 【0737】 As a concrete example, suppose a user enters "I want to have a unique food experience in Tokyo." The system could refer to past food preference data and, through an AI model, suggest up-and-coming restaurants and local food festivals. An example of a prompt to the generating AI model would be, "Please suggest a local plan based on the activities the user wants to enjoy and their food preferences at their destination." 【0738】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0739】 Step 1: 【0740】 Users enter their travel preferences using their smartphones. This includes attributes such as destination, type of activity, budget, and dates. The entered data is sent directly from the device to the server. 【0741】 Step 2: 【0742】 The server retrieves relevant travel destination information from the MongoDB database based on the received preferences. This search collects data such as tourist attractions, accommodations, and experience plans related to the destination. The data is converted to an intermediate format and passed on to the next processing step. 【0743】 Step 3: 【0744】 The server launches a machine learning model using Python and TensorFlow. Intermediate travel information and historical user preference data are used as input. Based on this data, the machine learning model calculates the optimal travel plan and generates a plan tailored to the user's individual needs. This output is returned to the server in a format that is easy to visualize. 【0745】 Step 4: 【0746】 The server sends the generated travel plan to the terminal. The terminal receives this data and displays it to the user using an interface that visually presents the travel plan. The user can review the plan and enter modification requests if necessary. 【0747】 Step 5: 【0748】 Upon receiving an approval or modification request from a user, the server executes an automated booking process. It integrates with an external booking system to make bulk bookings for selected flights and activities. The results of this process are logged as a success / failure status. 【0749】 Step 6: 【0750】 During the trip, the server retrieves real-time weather and traffic data from external sources. By analyzing this data, the travel plan is modified as needed. The revised plan is immediately notified to the user's device, allowing the user to always act based on the latest information. 【0751】 Step 7: 【0752】 After a user completes their trip, the server displays an interface on the user's terminal requesting feedback. The feedback is stored in a database and used to generate future travel plans. The feedback data is also used to improve the AI ​​model and optimize prompt messages. Through this feedback process, the overall accuracy of the system is improved. 【0753】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0754】 This invention provides a more personalized travel experience by incorporating emotion recognition into the user's travel plan. The emotion engine analyzes the user's voice and text to recognize the user's emotional state in real time, and based on this, it enables adjustments to the travel plan. 【0755】 First, the user enters their travel preferences using a device. The device sends this information to the server, and at the same time, the emotion engine analyzes the user's emotions during input (for example, the tone of their text or voice). In addition to the normal planning process, the server utilizes the information from the emotion engine to generate a travel plan optimized for the user's current emotions. 【0756】 After a plan is presented, if the user shows an emotional response to it, the emotion engine analyzes it and re-evaluates the plan's applicability. For example, if the user expresses anxiety or interest in the suggested activity, the server takes this information into account and fine-tunes the plan. Once the user approves, the server proceeds with the booking process. 【0757】 During travel, the device and emotion engine continuously monitor the user's emotions. Emotional states are also obtained from inputs the user provides to the device during specific activities and between travel. Based on this information, the server makes new suggestions at the appropriate time as needed and flexibly modifies the plan. 【0758】 After the trip ends, the device prompts the user to provide feedback. This post-trip feedback is also analyzed by the emotion engine, and the results are reflected in the next trip plan. The server analyzes this emotion information along with past feedback and uses a learning algorithm to optimize future plans. 【0759】 Specific example: 【0760】 If a user enters "I want to travel to Hawaii and relax," the emotion engine analyzes whether the user's language tone indicates stress. The server generates and presents a plan that emphasizes activities effective in reducing stress (such as spa treatments and relaxing time on quiet beaches). Furthermore, if the user shows new signs of stress during the trip, the emotion engine reacts immediately and instructs appropriate changes to ensure the user has the best possible travel experience. 【0761】 The following describes the processing flow. 【0762】 Step 1: 【0763】 The user enters their desired travel plan criteria (e.g., destination, budget, dates, desired activities, etc.) into the device. This input also includes voice input. The device sends the given text or voice to an emotion engine to analyze the user's emotional state. 【0764】 Step 2: 【0765】 The device sends the user's desired conditions, along with analyzed emotional information, to the server. Based on the received information, the server uses an AI algorithm to generate the optimal travel plan. In this process, the user's emotional state (e.g., excitement, relaxation, stress) is also taken into consideration. 【0766】 Step 3: 【0767】 The server displays the generated travel plan on the terminal. The terminal visually displays the plan details to the user, and the emotion engine analyzes the user's reaction in real time. If the user's emotions indicate anxiety or dissatisfaction, the terminal requests corrections from the server. 【0768】 Step 4: 【0769】 The server readjusts the travel plan based on the user's emotional feedback. It suggests new activities or modifies existing plans as needed to create a more suitable plan for the user and sends it to their device. 【0770】 Step 5: 【0771】 Once the user is satisfied with and approves the proposed plan, the server will execute bookings for flights, accommodations, activities, and other services all at once. The booking information will be notified to the user via their device. 【0772】 Step 6: 【0773】 Even while traveling, the device sends voice and text data from the user to the emotion engine, continuously monitoring their emotional state. If the server detects a change in emotion, it adjusts the schedule in real time and sends relevant suggestions to the device. 【0774】 Step 7: 【0775】 After the trip ends, the device prompts the user for feedback. The feedback provided by the user, along with emotional information in audio and text, is sent to the server and used in a learning process to improve future travel plans. 【0776】 Step 8: 【0777】 Based on past feedback and sentiment data, the server optimizes future travel plans and prepares to provide more personalized suggestions. 【0778】 (Example 2) 【0779】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0780】 Traditional travel planning systems have struggled to personalize plans by considering the user's emotional state and have been unable to respond promptly to their changing emotions. Furthermore, they have been unable to effectively collect and utilize real-time feedback during travel and incorporate it into future plans. Therefore, new systems are expected to provide users with a more satisfying travel experience. 【0781】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0782】 In this invention, the server includes an analysis means for analyzing the user's emotional state from their voice or documents, a construction means for generating an optimized travel plan based on the received request and the analyzed emotional data, and a modification means for taking in information from the user during the trip and dynamically modifying the plan. This enables the provision of personalized travel plans that reflect the user's emotional state and real-time plan adjustments. 【0783】 "Receiving means" refers to a function for receiving basic travel-related requests from the user. 【0784】 "Analysis means" refers to a function for analyzing the emotional state of a user from their voice or documents. 【0785】 The "construction means" refers to a function for generating optimized travel plans based on received requests and analyzed sentiment data. 【0786】 The "confirmation mechanism" is a function that presents the generated travel plan and adjusts the plan based on the user's emotional response. 【0787】 "Procedural means" refers to the function of carrying out all relevant procedures based on the approved travel plan. 【0788】 The "modification mechanism" refers to a function that incorporates information from users during their trip and dynamically modifies the plan. 【0789】 "Improvement measures" refer to a function for accumulating post-trip evaluations and incorporating them into future plans. 【0790】 "Optimization means" refers to a function that uses a learning algorithm to evolve travel plans. 【0791】 This invention is a system that analyzes user emotions and incorporates them into travel planning to provide a more personalized experience. The system consists of three main components: a terminal, a server, and an emotion engine. 【0792】 The user first enters their basic travel requests using a terminal. This terminal can be a general-purpose information processing device such as a smartphone or personal computer. Input can be done via voice or text, and the user can choose whichever method suits them best. 【0793】 The terminal sends the acquired input data to the server. The server works in conjunction with an emotion engine, which uses natural language processing (NLP) and speech analysis techniques to analyze the user's emotional characteristics. This emotion analysis extracts emotional information, such as whether the user is stressed or excited. 【0794】 The server uses a generative AI model to construct travel plans based on the user's input, including requests and emotional state data. This AI model is optimized using an extensive travel database and past user data, enabling it to provide suggestions tailored to the user's individual needs. For example, if a user inputs "I want to travel to Hawaii and relax," the server will generate and present a plan centered around relaxing activities. 【0795】 During travel, the device receives real-time feedback from the user, and the server dynamically adjusts the plan based on that information. This allows users to enjoy a plan optimized for changing circumstances, even while traveling. 【0796】 After the trip ends, the device prompts the user for feedback again, and this data is analyzed by an emotion engine before being collected by a server. This feedback information is used to create future travel plans, and an AI model generates even more refined suggestions. 【0797】 Example of a prompt: 【0798】 "Please generate a Hawaii travel plan for users who are feeling stressed. Include relaxing activities." 【0799】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0800】 Step 1: 【0801】 The user enters their basic travel requests. The terminal receives the user's requests via voice or text. The entered data includes the desired travel destination, dates, and purpose (e.g., relaxation, adventure). The terminal prepares the entered data to be sent directly to the next processing step. 【0802】 Step 2: 【0803】 The terminal sends the acquired user input data to the server. The server forwards the received data to the emotion engine, which uses natural language processing (NLP) and speech analysis techniques to analyze the user's emotional state. The emotion engine measures stress and excitement from the content and tone of the input data and returns the analysis results to the server. 【0804】 Step 3: 【0805】 The server combines user requests and emotional state data, and uses a generative AI model to generate optimal travel suggestions. The server then creates a personalized travel plan, taking the emotional analysis results into account, and sends the output data to the user's device. This plan includes specific details such as suggested activities and accommodations. 【0806】 Step 4: 【0807】 The device presents the generated travel plan to the user and observes their emotional response in real time. The user's response is sent from the device to an emotion engine, which analyzes the new emotional input. The server receives the data again, reflecting the analysis results, and fine-tunes the plan. 【0808】 Step 5: 【0809】 Users approve or request changes to the presented plan. The server collects user feedback and performs predetermined procedures based on the approved plan. This includes booking activities and confirming transportation options. 【0810】 Step 6: 【0811】 During the trip, the device continuously receives input from the user and sends it to the server. The server uses this information to dynamically adjust the travel plan and make new suggestions as needed. The results are sent to the device and presented to the user in real time. 【0812】 Step 7: 【0813】 After the trip ends, the device prompts the user to provide feedback. This feedback is analyzed by an emotion engine, and the results are collected on a server. The server then integrates this data into a generative AI model to improve future travel plans, evolving the suggested trips. 【0814】 (Application Example 2) 【0815】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0816】 In recent years, there has been a growing demand for personalized travel experiences that take into account the user's emotional state, from the planning stage to during the trip itself. However, conventional systems have struggled to adjust plans based on user emotions, making it difficult to guarantee the optimal travel experience for each user. This problem needs to be solved. 【0817】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0818】 In this invention, the server includes an input means for receiving travel preferences and emotional states from the user; a processing means for generating an optimal travel plan based on the received preferences and emotional states and adjusting the plan considering the emotional data; and a modification means for acquiring real-time emotional information during the trip and dynamically modifying the plan according to the user's emotional state. This makes it possible to provide a flexible travel experience that reflects the user's emotional state in real time. 【0819】 The "input method" refers to a function for receiving travel preferences and emotional states from the user. 【0820】 The "processing means" is a function that generates the optimal travel plan based on the received desired conditions and emotional state, and adjusts the plan while taking emotional data into consideration. 【0821】 The "confirmation method" is a function that presents the generated travel plan to the user and obtains approval while analyzing their emotional response. 【0822】 "Booking method" refers to the function for executing all related bookings based on the approved travel plan. 【0823】 The "modification method" refers to a function that acquires real-time emotional information from the user during their trip and dynamically modifies the travel plan according to that emotional state. 【0824】 A "learning tool" is a function that uses acquired feedback and emotional information to inform the next plan and optimize it using a learning algorithm. 【0825】 The system for realizing this invention operates by integrating a user terminal, a server, and an emotion engine. The user inputs their travel preferences through a specific terminal and provides their emotional state in voice or text. The terminal then transmits this data to the server. 【0826】 The server converts speech data into text using tools such as Google Cloud Speech-to-Text and analyzes the user's emotional state using sentiment analysis tools such as the Azure Emotion API. Based on this, it generates a travel plan and adjusts it by incorporating the emotional data. 【0827】 The generated travel plan is presented to the user's device. The user's reaction is analyzed again by the emotion engine, and the server re-evaluates and readjusts the plan based on that information. In this process, feedback is obtained according to the emotional state, which is used to create the next travel plan. 【0828】 Throughout the trip, the device continuously captures the user's real-time emotions. Based on this, the server makes necessary plan changes to provide the optimal travel experience. For example, if the user feels stressed by a particular activity, a new activity that helps them relax can be immediately suggested. 【0829】 For example, if a user asks, "I want to plan a family trip with my children for our next vacation," the server will generate plans such as visiting a zoo or going to a family-friendly resort, and the robot will propose these plans. If the user responds positively with "This is good!", the plan is approved, and the process moves to the next step. 【0830】 An example of a prompt for a generative AI model might be: "The user wants to take a family trip on their next vacation. Based on sentiment analysis, please suggest a travel plan that will lead to a positive experience by incorporating friendly activities." 【0831】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0832】 Step 1: 【0833】 The user inputs their travel preferences and feelings into the device via voice or text. The device captures the user's input as audio data and sends it to the server. In this step, the input is the user's voice or text, and the output is audio data in digital format. 【0834】 Step 2: 【0835】 The server receives the audio data and uses Google Cloud Speech-to-Text to convert the speech to text. The input is digital audio data, and the output is the converted text. This process transforms the audio data into a parseable format. 【0836】 Step 3: 【0837】 The server uses the Azure Emotion API and other tools to analyze the user's emotional state from the converted text. The input is text data, and the output is emotional state data based on that text. Sentiment analysis is performed here to identify the user's current emotional state. 【0838】 Step 4: 【0839】 The server generates a travel plan based on the received desired conditions and analyzed emotional state. A generative AI model is used to create a plan suitable for the conditions, and the plan is adjusted, particularly taking emotional data into consideration. In this step, desired conditions and emotional state are the inputs, and the travel plan is the output. 【0840】 Step 5: 【0841】 The server generates a travel plan and sends it to the terminal for the user to see. The user reviews the plan and provides an emotional response. The input is the travel plan, and the output is emotional data based on the user's response. User feedback is collected at this stage. 【0842】 Step 6: 【0843】 The server re-analyzes the user's emotional response and readjusts the plan as needed. Here, user feedback is the input, and the adjusted travel plan is the output. The plan is optimized based on the emotional analysis. 【0844】 Step 7: 【0845】 During the trip, the device continuously monitors the user's emotional state in real time and sends data to the server as needed. The input is data on the user's emotional changes, and the output is new activities suggested by the server in real time. In this process, the plan is flexibly modified to optimize the user's travel experience. 【0846】 The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0847】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0848】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414. 【0849】 Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion. 【0850】 Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together. 【0851】 These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression. 【0852】 The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become. 【0853】 Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant. 【0854】 The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more." 【0855】 The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values. 【0856】 The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format. 【0857】 In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data. 【0858】 In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56. 【0859】 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. 【0860】 Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56. 【0861】 The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using this memory. 【0862】 The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor. 【0863】 Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources. 【0864】 Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose. 【0865】 The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above. 【0866】 All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference. 【0867】 The following is further disclosed regarding the embodiments described above. 【0868】 (Claim 1) 【0869】 An input method for receiving travel preferences from the user, 【0870】 A processing means for generating the optimal travel plan based on the received desired conditions, 【0871】 A means of presenting the generated travel plan and obtaining user approval, 【0872】 A booking method that executes all related reservations based on the approved travel plan, 【0873】 A means of obtaining real-time information during travel and making changes to the plan, 【0874】 A learning method that incorporates the feedback received into the next plan, 【0875】 A system that includes this. 【0876】 (Claim 2) 【0877】 The system according to claim 1, which dynamically modifies a travel plan based on external information obtained in real time. 【0878】 (Claim 3) 【0879】 The system according to claim 1, which accumulates user feedback and optimizes future travel plans. 【0880】 "Example 1" 【0881】 (Claim 1) 【0882】 An input method for obtaining travel preferences from the user, 【0883】 A processing means for generating an optimal travel plan using a generated AI model based on received conditions, 【0884】 A means of displaying the generated travel plan and obtaining confirmation from the user, 【0885】 A booking method that executes all related reservations in conjunction with an external booking system according to the approved travel plan, 【0886】 A means of updating the plan based on dynamic information acquired during the trip, 【0887】 A learning method that collects user feedback and uses it to inform future planning, 【0888】 A system that includes this. 【0889】 (Claim 2) 【0890】 The system according to claim 1, which modifies a travel plan based on dynamically acquired external information. 【0891】 (Claim 3) 【0892】 The system according to claim 1, which accumulates user feedback to optimize future travel plans. 【0893】 "Application Example 1" 【0894】 (Claim 1) 【0895】 A means of receiving information input from the user regarding their travel preferences, 【0896】 Information processing means for generating the optimal travel plan based on received desired conditions, 【0897】 A means of verifying information by presenting the generated travel plan and obtaining user approval, 【0898】 Information booking means to execute all related reservations based on the approved travel plan, 【0899】 A means of obtaining real-time information during travel and modifying plans, 【0900】 An information learning tool that incorporates the feedback received into the next plan, 【0901】 A content distribution method that provides users with information about their destinations while they are traveling, 【0902】 An information recommendation method that personalizes information about places to visit based on the user's past preference data, 【0903】 A system that includes this. 【0904】 (Claim 2) 【0905】 The system according to claim 1, which dynamically modifies travel plans based on external data obtained in real time and provides users with the latest destination information. 【0906】 (Claim 3) 【0907】 The system according to claim 1, which accumulates user feedback and preference data and optimizes future travel plans and destination information. 【0908】 "Example 2 of combining an emotion engine" 【0909】 (Claim 1) 【0910】 A receiving means for receiving basic travel-related requests from the user, 【0911】 An analysis means for analyzing the emotional state from the user's voice or document, 【0912】 A construction means for generating optimized travel plans based on received requests and analyzed sentiment data, 【0913】 A confirmation mechanism that presents generated travel plans and adjusts them based on the user's emotional response, 【0914】 The procedural means for carrying out all relevant procedures based on the approved travel plan, 【0915】 A means of dynamically modifying the plan by incorporating information from users during their travels, 【0916】 A means of improvement that involves accumulating post-trip evaluations and reflecting them in the next plan, 【0917】 An optimization method that evolves travel plans using a learning algorithm, 【0918】 A system that includes this. 【0919】 (Claim 2) 【0920】 The system according to claim 1, which modifies travel plans based on user sentiment analysis data obtained in real time. 【0921】 (Claim 3) 【0922】 The system according to claim 1, which manages user evaluations after a trip and improves future travel plans. 【0923】 "Application example 2 when combining with an emotional engine" 【0924】 (Claim 1) 【0925】 An input means for receiving travel preferences and emotional states from the user, 【0926】 A processing means that generates an optimal travel plan based on received desired conditions and emotional state, and adjusts the plan considering emotional data, 【0927】 A confirmation method that presents a generated travel plan, analyzes the user's emotional response, and obtains approval, 【0928】 A booking method that executes all related reservations based on the approved travel plan, 【0929】 A means of dynamically modifying the plan based on the user's emotional state by acquiring real-time emotional information during travel, 【0930】 A learning method that incorporates the acquired feedback and emotional information into the next plan and optimizes it using a learning algorithm, 【0931】 A system that includes this. 【0932】 (Claim 2) 【0933】 The system according to claim 1, which dynamically modifies a travel plan based on the user's emotional state. 【0934】 (Claim 3) 【0935】 The system according to claim 1, which accumulates user emotional information and optimizes future travel plans based on those emotions. [Explanation of Symbols] 【0936】 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

[Claim 1] An input method for receiving travel preferences from the user, A processing means for generating the optimal travel plan based on the received desired conditions, A means of presenting the generated travel plan and obtaining user approval, A booking method that executes all related reservations based on the approved travel plan, A means of obtaining real-time information during travel and making changes to the plan, A learning method that incorporates the feedback received into the next plan, A system that includes this. [Claim 2] The system according to claim 1, which dynamically modifies a travel plan based on external information obtained in real time. [Claim 3] The system according to claim 1, which accumulates user feedback and optimizes future travel plans.

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