system

Abstract

We provide the system. [Solution] Information processing means that receives travel preferences from a user and generates a travel plan based on them, A booking mechanism that automatically executes reservations with related services based on the generated travel plan, A learning method that analyzes the user's past transaction history and evaluation data to propose an optimized travel 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 persona chatbot control method performed by at least one processor, the method including: 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] For many modern individuals and groups, travel planning is enjoyable, but it can be time-consuming and laborious to prepare, and can cause stress. In particular, complex tasks involved in planning, such as schedule adjustment, selection of accommodation facilities, and arrangement of transportation means, can be factors hindering the execution of travel. There is a need for a mechanism to solve this problem, reduce the travel planning work, and enable more people to enjoy traveling easily.

Means for Solving the Problems

[0005] This invention provides an information processing means that receives travel preferences from a user and automatically generates a travel plan, and a booking means that automatically executes reservations with related services based on the generated travel plan. Furthermore, by providing a learning means that proposes an optimized travel plan based on the user's past transaction history and evaluation data, it realizes travel planning tailored to the individual preferences of each user. As a result, it is possible to reduce the effort and time involved in travel planning and provide users with a highly satisfying travel experience.

[0006] An "information processing means" is a mechanism for performing a process of analyzing travel-related conditions and data provided by the user and generating a plan based on that analysis.

[0007] A "booking method" refers to a system or method for automatically making reservations for travel-related services such as transportation, accommodation, and activities, based on a generated travel plan.

[0008] A "learning tool" is an algorithm or function that uses past user usage history and evaluation data to analyze user preferences and patterns, and then proposes an optimized travel plan accordingly.

[0009] A "modification mechanism" is a system that has the ability to update and modify an existing travel plan as needed in response to changes in the user's requests or circumstances after the plan has been created.

[0010] "Group support methods" refer to a mechanism or process for creating a travel plan suitable for the entire group by considering requests from multiple users, integrating their individual wishes and constraints. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [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 when an emotion engine is combined. [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]

[0012] 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.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the numbered processor (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 arithmetic units 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.

[0015] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface that includes a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0018] 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."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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".

[0032] This invention provides a system that enables users to easily plan and book their trips. The system includes information processing means, booking means, learning means, modification means, and group support means.

[0033] First, the user enters their travel itinerary, destination, budget, desired activities, and travel companions through the device. The device then verifies all entered information to ensure nothing is missing before sending it to the server.

[0034] Next, the server uses information processing tools to generate a basic travel plan based on the received information. At this time, it extracts the most suitable options from databases of transportation, accommodation, and tourist spots to form a provisional plan.

[0035] The server extracts the user's hobbies and preferences from their past travel data and optimizes the plan using machine learning-based learning methods. This step creates a more personalized plan that reflects the user's evaluations and preferences from past trips.

[0036] Once a plan is generated, the server sends it back to the terminal, displaying the detailed plan to the user. The user can review the proposed travel plan and confirm or fine-tune the booking.

[0037] The terminal then sends the user's final selection to the server. This is where the booking process begins, and the server, in conjunction with relevant external services, automatically makes reservations for flights, accommodations, tourist attractions, and other services.

[0038] For example, if a user enters a request such as "I want to take a resort trip within Japan with friends this summer," the server will suggest several popular resort destinations and present a plan combining corresponding flights, hotels, and local activities. The user can then choose the plan that best suits their preferences and complete the entire booking process with just a few clicks.

[0039] Furthermore, if unexpected weather changes or other circumstances necessitate changes in the plan, modification mechanisms are available, allowing for quick changes to the plan in response to new user requests.

[0040] Therefore, the burden on users regarding travel planning is significantly reduced, and a comfortable and highly satisfying travel experience can be provided.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The user enters their travel preferences into the terminal. These preferences include travel dates, destination, budget, desired activities, and travel companions. The terminal verifies the entered information and ensures data integrity by prompting the user for corrections as needed.

[0044] Step 2:

[0045] The terminal sends the final confirmed travel conditions data to the server. During this process, it converts the data into a usable format and prepares it for transmission.

[0046] Step 3:

[0047] The server uses information processing tools to analyze the received data. Based on the conditions specified by the user, it gathers data on available transportation, accommodation, and tourist attractions from the database and assembles a basic travel plan.

[0048] Step 4:

[0049] The server uses learning mechanisms to optimize generated travel plans by referencing the user's past travel history and evaluation data. It considers options based on individual user preferences to create personalized suggestions.

[0050] Step 5:

[0051] The server sends an optimized travel plan back to the device. The device displays the received plan to the user, allowing them to review the plan details. The cost, duration, and reviews of the plan are also displayed.

[0052] Step 6:

[0053] The user reviews the proposed plan and requests revisions if necessary, making minor adjustments as needed. Finally, they confirm their chosen travel plan.

[0054] Step 7:

[0055] The terminal sends the confirmed plan information to the server. The server then uses the booking method to automatically execute reservations for flights, accommodations, sightseeing activities, and other services by collaborating with external services.

[0056] Step 8:

[0057] The server sends a reservation completion notification to the terminal, which then provides the user with reservation confirmation information. This includes details such as a hotel reservation confirmation, an electronic ticket, and a schedule summary.

[0058] Step 9:

[0059] If a user needs to change their travel plans immediately before or during their trip, they send a change request from their device to the server. The server then regenerates or adjusts the plan based on the requested changes and presents it to the user again.

[0060] (Example 1)

[0061] 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."

[0062] Traditional travel planning systems required users to manually search for and select from multiple pieces of information, which was time-consuming and cumbersome. Furthermore, they struggled to provide personalized plans that reflected individual user preferences and were inadequate in handling unexpected changes. Therefore, there was a need for a system that would allow users to easily create more efficient and personalized travel plans, while also being flexible enough to accommodate changes.

[0063] 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.

[0064] In this invention, the server includes terminal means, information processing means, learning means, adjustment means, and reservation means. This allows users to automatically generate travel plans with simple information input, provide personalized plans utilizing past data, and respond quickly and appropriately to changes.

[0065] "Terminal means" refers to devices or interfaces that allow users to input travel information and send it to a server.

[0066] "Information processing means" refers to devices and software that analyze information provided by users and manage the process of creating travel plans.

[0067] "Learning method" refers to machine learning technology that uses users' past data to analyze their preferences and optimize travel plans based on that analysis.

[0068] "Adjustment means" refers to devices or processes that reflect any adjustments made by the user to the plan and then optimize the plan again.

[0069] "Reservation methods" refer to devices and processes that automatically make necessary reservations in conjunction with external services based on a confirmed travel plan.

[0070] "Means of modification" refers to functions and processes that allow for a quick and appropriate response when a user requests a change to their travel plan, and to generate a new plan.

[0071] "Group response methods" refer to processes or devices that integrate requests from multiple users and generate travel plans tailored to the individual preferences of each participant.

[0072] This invention provides a system that allows users to easily plan trips and consistently make related reservations. The system includes a user-accessible terminal, a server with powerful data processing capabilities, and a reservation function that integrates with external services.

[0073] First, the user uses a personal computer or mobile device to input their desired travel plan through an interactive interface. This input includes travel dates, destination, budget, desired activities, and travel companions. The device uses validation functions such as JavaScript (registered trademark) to verify the input data in real time. After verification is complete, this information is sent to the server via a secure communication protocol.

[0074] The server activates information processing tools to analyze the received information and extracts data from an SQL database that matches the user's criteria. In this process, information such as transportation, accommodation, and tourist attractions is combined to generate a basic hypothetical travel plan. Next, the server utilizes machine learning models (e.g., TENSORFLOW® or Scikit-learn) to analyze the user's past data, extract preferences, and optimize the hypothetical plan.

[0075] Once the plan is finalized, the server uses a RESTful API to send the optimized plan back to the device. On the device, the plan is visually displayed through the user interface, allowing the user to review the contents and make adjustments as needed. This makes it easy for users to customize their plans.

[0076] Once the user confirms the plan details, that information is sent back to the server from the device. The server then uses the booking method to automatically execute the flight and hotel reservations via relevant external service APIs.

[0077] For example, if a user enters conditions such as "I would like to take a resort trip within Japan with a friend this summer," the server will suggest potential resort locations and present an overall travel plan based on those suggestions. An example of a prompt from this system would be, "I'm planning a resort trip to Okinawa from August 15th to 22nd. My budget is under 150,000 yen, and I would like to do scuba diving and relax on the beach."

[0078] This system allows users to easily obtain sophisticated travel plans, significantly reducing the time and effort required for planning.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] Users enter basic travel information through the terminal's interface. This includes information such as travel itinerary, destination, budget, desired activities, and travel companions, which are filled into input forms. The terminal validates the input in real time using JavaScript or similar technologies to check for errors. The input data is organized and packaged in JSON format. This packaged data is ready to be sent to the server.

[0082] Step 2:

[0083] The terminal encrypts the entered information using SSL / TLS and sends it to the server using the HTTPS protocol. This communication ensures secure data transmission. The input at this time is travel information prepared on the terminal, which the server receives.

[0084] Step 3:

[0085] The server parses the received JSON data and generates a travel plan using information processing tools. At this stage, it searches databases of transportation, accommodations, tourist spots, etc., using SQL queries and extracts data that matches the criteria. These become the components of a provisional travel plan, and the provisional travel plan is formed as output.

[0086] Step 4:

[0087] The server uses a hypothetical travel plan as a basis, retrieves historical travel data and user preference data, and optimizes the plan using a machine learning model. Models such as Scikit-learn and TensorFlow are used, and personalization reflecting past preferences is performed. The input here is the hypothetical plan and historical data, and the output is an individualized, optimized plan.

[0088] Step 5:

[0089] The server sends an optimized plan back to the device via a RESTful API, and the device visually displays the received data on the user's screen. Specifically, HTML / CSS and JavaScript are used to create a user-friendly and well-organized plan screen. The output is the state in which the user can review the plan details.

[0090] Step 6:

[0091] The user reviews the displayed travel plan and makes minor adjustments to dates and activities as needed. During this process, the device repackages the adjusted data based on the user's actions and prepares to send it to the server. The input here is the user's adjustments, and the output is the adjusted data.

[0092] Step 7:

[0093] The server automatically initiates the booking process based on the coordinated travel plan. It uses the API of an external booking service to confirm flight and accommodation reservations. The input here is the final travel plan, and the output is booking confirmation information.

[0094] This series of steps allows users to efficiently plan their trips and complete bookings automatically.

[0095] (Application Example 1)

[0096] 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."

[0097] Planning and executing a trip involves many choices and adjustments, which can be time-consuming for users. In particular, the inability to experience the destination beforehand makes it difficult to visualize the details of the plan. Furthermore, if users intend to change their plans, it is difficult to quickly and flexibly adapt the schedule.

[0098] 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.

[0099] In this invention, the server includes information processing means that receive travel preferences from the user and generate a travel plan based on them; reservation means that automatically execute reservations with related services based on the generated travel plan; learning means that analyze the user's past transaction history and evaluation data and propose an optimized travel plan; and virtual experience means that use a virtual reality environment for the user to experience a travel destination and adjust the travel plan. This allows the user to experience a specific travel plan in a virtual environment and to flexibly change the plan according to their wishes and circumstances.

[0100] "Information processing means" refers to a device or software that performs processing to generate a travel plan based on travel preferences received from a user.

[0101] A "booking system" is a mechanism that automatically books related services such as airline tickets and accommodations based on a generated travel plan.

[0102] The "learning method" is a technology that analyzes the user's past transaction history and evaluation data, and proposes an optimized travel plan that reflects the results.

[0103] A "virtual experience system" is a system that allows users to experience a travel destination in advance using a virtual reality environment, enabling them to adjust their travel plans.

[0104] The system that realizes this invention operates with a configuration comprising a server, a user terminal, and a virtual reality environment surrounding it. The server acts as an information processing means for receiving travel preferences from the user. When the user enters information such as destination, dates, budget, and travel companions on the terminal, this information is sent to the server. Based on this, the server generates a basic travel plan. In generating the plan, it refers to databases of transportation, accommodation, and tourist attractions to construct a plan that is optimal for the user's conditions.

[0105] The server also has learning capabilities to analyze users' past transaction history and rating data, and optimizes travel plans based on the insights gained. This process uses machine learning algorithms to suggest personalized plans tailored to the user's preferences.

[0106] Furthermore, a distinctive feature of this invention is the virtual experience mechanism. Users can use a VR headset to experience a proposed travel destination in advance within a virtual reality environment. This process utilizes a game engine such as Unity, providing a realistic visual and auditory environment of the travel destination within the virtual space. This allows users to visualize their travel plan more concretely and make adjustments as needed.

[0107] As a concrete example of a prompt message, if you enter text such as "I want to visit a domestic resort with my friends this summer," the server will select popular resort destinations and provide a virtual tour. In this virtual tour, you can experience the characteristics and activities of each resort, make the best choice, and proceed to make an actual reservation.

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The user enters their travel preferences through the terminal. This input data includes destination, dates, budget, and travel companions. The terminal verifies that all this data has been entered correctly, formats it, and then sends it to the server.

[0111] Step 2:

[0112] The server retrieves relevant information from the database based on the received travel preferences. Specifically, it extracts the most suitable options from data on transportation, accommodation, and tourist attractions. This generates an initial travel plan. The output is a basic travel plan.

[0113] Step 3:

[0114] The server analyzes the user's past transaction history and rating data, and optimizes travel plans using a generative AI model. Based on the user's preferences, it proposes a more personalized plan. In this process, machine learning algorithms are used to compare the new plan with similar past data and output the results of the refinement.

[0115] Step 4:

[0116] The server sends an optimized travel plan back to the terminal. The user receives this proposed plan through the terminal and reviews its contents. Based on the outputted information, the user can adjust the details of the travel plan.

[0117] Step 5:

[0118] Once the user selects a virtual experience method, the server prepares a virtual reality environment and provides a virtual tour of the selected travel destination. A game engine such as Unity is used here, and the virtual trip is experienced through a VR headset. The input is the user's selected travel plan information, and a visual and auditory virtual environment is output.

[0119] Step 6:

[0120] When the user finalizes their travel plan, the terminal sends that information to the server. The server then uses a booking system to automatically make reservations for flights and accommodations based on the final travel plan. The input is the user's final decision information, and the output is reservation confirmation information.

[0121] 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.

[0122] This invention is a system that uses emotion recognition to adjust a user's travel plan and provide a highly personalized experience. This system includes an emotion engine in addition to information processing means, booking means, learning means, modification means, and group handling means.

[0123] The user enters their travel preferences into the terminal. These preferences include dates, destination, budget, activities, and travel companions. The terminal verifies the input before sending it to the server.

[0124] The server uses information processing tools to analyze the input data and create a basic travel plan. It also utilizes the user's past data to understand their preferences and uses learning tools to optimize the plan. Here, the emotion engine analyzes the user's emotions from the input data and interactions with the device.

[0125] When a user reviews their travel plan, the emotion engine evaluates their reaction in real time and influences the plan's options. For example, if the system determines the user is stressed, more relaxing spots will be added to the plan. Conversely, if the user is excited, more active activities will be recommended.

[0126] In the case of group travel, the Emotion Engine analyzes the emotions of each participant and creates a plan that brings optimal harmony. It takes into account the different emotional states of each participant and proposes content that will satisfy everyone.

[0127] As a concrete example, when a user reviews a plan displayed on a monitor, changes in their facial expressions and tone of voice are detected by the device. The server analyzes this through an emotion engine and fine-tunes the plan as needed. For instance, if the user shows signs of fatigue, new suggestions will be made emphasizing less strenuous modes of transportation or relaxation facilities.

[0128] Finally, once the user selects and confirms their adjusted travel plan, the server uses the booking mechanism to make all related reservations in a single batch. In particular, if the user needs to change their plan at the last minute, a change mechanism will immediately suggest new options.

[0129] This system allows travel plans to adapt to the user's mood and provide a more satisfying experience.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The user enters their travel preferences using an interface connected to the device. This includes destination, travel dates, budget, and desired activities. The device temporarily saves the entered data and verifies that the information is in the correct format.

[0133] Step 2:

[0134] The terminal sends the confirmed travel preference data to the server. During data transmission, the data is formatted appropriately to ensure smooth progress in each business process.

[0135] Step 3:

[0136] The server uses information processing tools to analyze the received data and construct the initial travel plan. It retrieves relevant tourist spots, accommodations, and transportation data from the database to generate a basic plan.

[0137] Step 4:

[0138] The server optimizes travel plans through learning mechanisms using the user's past travel history and evaluation data. It creates more detailed plans tailored to the user's preferences.

[0139] Step 5:

[0140] The server uses an emotion engine to analyze the user's emotions based on their input and past behavior. Based on this information, it fine-tunes the travel plan to provide more personalized suggestions.

[0141] Step 6:

[0142] The server sends the optimized plan back to the device. The device displays the plan details to the user, allowing the user to evaluate the content. At this point, the sentiment engine may analyze the user's immediate response and further adjust the plan.

[0143] Step 7:

[0144] The user reviews the displayed plan and requests adjustments if necessary. The user then confirms their final travel plan and sends that information to the server via their device.

[0145] Step 8:

[0146] The server uses booking methods to automatically make reservations for flights, accommodations, and various activities based on the selected travel plan. It works in conjunction with external related services to execute an efficient booking process.

[0147] Step 9:

[0148] If a user needs to change their travel plans, they send a request for the change to the server via their device. The server then uses a change mechanism and an emotional engine to immediately generate a plan that suits the new conditions and presents it to the user again.

[0149] This processing step enables flexible travel planning based on the user's emotions, resulting in a more satisfying travel experience.

[0150] (Example 2)

[0151] 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".

[0152] Modern travel planning faces the challenge of providing highly satisfying experiences for users with diverse needs and emotions. Traditional systems struggle to accurately reflect individual user feelings and preferences, and lack mechanisms to accommodate the individual wishes of participants in group tours. As a result, user satisfaction often declines, and travel experiences frequently fall short of expectations.

[0153] 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.

[0154] In this invention, the server includes data processing means that receive travel preferences from the user and generate a travel plan based on them; emotion analysis means that analyze the user's emotions through user input data and interactions with network terminals and adjust the travel plan based on those emotions; and data reservation means that automatically make reservations for related services based on the generated travel plan. This makes it possible to create detailed plans that are tailored to the emotions and preferences of individual users, and in the case of group travel, it is possible to make adjustments that increase the satisfaction of all participants.

[0155] "Data processing means" refers to a device or system for analyzing travel preferences provided by users and constructing a basic travel plan.

[0156] "Emotional analysis means" refers to technologies and methods that identify a user's emotional state based on user input information and interactions with the device, and then optimize travel plans in real time based on that information.

[0157] "Data booking methods" refer to technologies and processes that automatically execute bookings for related services, including accommodations and transportation, in accordance with a generated travel plan.

[0158] "Data learning tools" refer to functions or systems that analyze a user's past travel history and evaluation information, and use that information to improve the accuracy and personalization of travel plans.

[0159] "Group response methods" refer to a process or system for integrating requests and information from multiple users, taking into account the emotional state of each participant, and creating a harmonious travel plan as a whole.

[0160] This invention comprises a system for highly personalizing users' travel plans and providing an experience tailored to each individual. The entire system is operated primarily by three entities: a server, terminals, and users.

[0161] First, the user enters their travel preferences using a device. The device then verifies this information and sends it to the server. Specific devices and platforms that can be used here include smartphones, tablets, and personal computers.

[0162] Next, the server analyzes the received data using data processing tools and formulates a basic travel plan. The server utilizes a generative AI model to optimize the travel plan, taking into account the user's past history and evaluation data. In addition, sentiment analysis tools analyze the user's emotions in real time through user input data and interactions with network terminals, and adjust the travel content accordingly.

[0163] In particular, emotion analysis can utilize facial recognition and voice tone analysis technologies to gain a detailed understanding of the user's emotional state. Based on this, the server can add relaxing and exciting activities to the travel plan.

[0164] As a concrete example, while a user is reviewing their travel plan, the device captures the user's facial expression. This data is sent to a server, where an emotion analysis system begins its analysis. If the system determines that the user is calm or excited, it adjusts the plan accordingly.

[0165] This system allows for adjustments to the generated AI model using prompts. For example, a possible prompt might be, "The user is showing an excited expression, so please suggest an active schedule." In this way, the system can automatically suggest and provide the user with an appropriate travel plan based on their emotions.

[0166] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0167] Step 1:

[0168] Users enter their travel preferences using a terminal. This includes dates, destination, budget, activities, and travel companions. The entered data is written through the terminal's input form and validated for correct formatting. After the input is confirmed, the data is sent to the server.

[0169] Step 2:

[0170] The server receives user preference data from the terminal using data processing tools. Data analysis is then performed to generate a basic travel plan. Specifically, it collects information about the selected destination and uses an AI model to suggest optimal travel routes and sightseeing spots. The generated plan is stored for future sentiment analysis.

[0171] Step 3:

[0172] The server uses emotion analysis tools to analyze additional data from the user, specifically real-time data on facial expressions and voice tone. Each piece of data is analyzed by an emotion recognition algorithm to determine the user's emotional state. For example, facial expression data can be used to determine whether the user is relaxed or excited. This analysis provides useful indicators for adjusting the travel plan, and the plan data is updated accordingly.

[0173] Step 4:

[0174] The server uses a generative AI model to optimize the plan based on information obtained from emotion analysis. Specifically, this includes changing activity suggestions according to emotions and fine-tuning the schedule. For example, if fatigue is detected, suggestions for relaxation facilities will be enhanced. Once this process is complete, the final travel plan is generated and sent to the device.

[0175] Step 5:

[0176] Users review their final travel plans on their devices, making adjustments or finalizing them as needed. Once finalized, the server automatically uses data booking mechanisms to execute reservations for accommodation and transportation. At this stage, additional data and options, such as cancellation policies and price recalculations, are handled as required.

[0177] This entire system's program processing allows travel plans to be flexibly and precisely adjusted based on the user's emotions and specific preferences.

[0178] (Application Example 2)

[0179] 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".

[0180] In modern travel planning, it's common for travel plans to be suggested based on the user's interests and schedule. However, suggesting plans that take into account the traveler's emotional state is extremely difficult, and it's rare to provide content and plans that adapt to the traveler's real-time emotions. As a result, there is a challenge in creating travel plans that enhance satisfaction because they cannot flexibly respond to changes in the traveler's emotions.

[0181] 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.

[0182] In this invention, the server includes information processing means that receive travel preferences from the user and generate a travel plan based on them; reservation means that automatically make reservations for related services based on the generated travel plan; learning means that analyze the user's past usage history and evaluation information and propose an optimized travel plan; and emotion recognition means that analyze the user's facial expressions and vocalizations and recommend content that matches their emotions. This makes it possible to provide travel plans and content optimized for the user's emotions.

[0183] "Information processing means" refers to a device that has the function of generating a travel plan based on the travel preferences entered by the user.

[0184] A "booking device" is a device that automatically makes reservations for related services based on a generated travel plan.

[0185] A "learning tool" is a device that analyzes a user's past usage history and evaluation information and has the function of proposing an optimized travel plan.

[0186] An "emotion recognition device" is a device that analyzes a user's facial expressions and vocalizations and recommends content that corresponds to their emotions.

[0187] "User preferences" refer to the conditions that users desire when traveling, such as the date and time of the trip, destination, budget, activities, and travel companions.

[0188] The system for carrying out this invention consists of a server and a terminal. The server is equipped with information processing means, reservation means, learning means, and emotion recognition means. The terminal is a device for the user to input their travel preferences and is equipped with a camera and a microphone.

[0189] First, the user uses a terminal to enter their travel preferences. These preferences include the date and time of the trip, destination, budget, activities, and travel companions. The terminal then sends the entered preferences to the server.

[0190] On the server, information processing tools analyze these conditions and generate a basic travel plan. During this process, learning tools analyze past user usage history and evaluation information to propose an optimized travel plan.

[0191] Furthermore, emotion recognition measures capture the user's facial expressions and voice through the camera and microphone and analyze their emotions. By analyzing emotions using software such as Google® Cloud Vision API and Amazon Rekognition, content tailored to the user's emotions is recommended. For example, if the user is tired, relaxing plans are suggested, while if they are excited, active activities are suggested.

[0192] For example, if a user is reviewing travel plans on their device after returning home from work, and their facial expression is analyzed as indicating fatigue, various relaxation-oriented content can be provided. This allows the user to enjoy a more appropriate travel plan.

[0193] An example of a prompt message for a generative AI model is, "The user looks tired. Please recommend relaxing content." Based on this prompt message, the server flexibly adjusts and optimizes the plan based on the user's emotional state, thereby creating a highly satisfying travel experience.

[0194] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0195] Step 1:

[0196] The user uses a terminal to enter their travel preferences (dates and times, destination, budget, activities, and travel companions). This input is verified on the terminal and then sent to the server. The entered data is used directly in the next processing step.

[0197] Step 2:

[0198] The server's information processing system analyzes the travel preferences submitted by the user and generates a basic travel plan for the user. The data used here is the input data of the user's preferences, and the output is a travel plan based on those preferences.

[0199] Step 3:

[0200] The server's learning mechanism utilizes past user usage history and evaluation information to process data and optimize the generated basic travel plan. The input here is past usage data and the current basic plan, and the output is an optimized travel plan.

[0201] Step 4:

[0202] The server's emotion recognition system captures and analyzes the user's facial expressions and vocalizations in real time through the device's camera and microphone. The input data consists of real-time image and audio data acquired from the camera and microphone, while the output is the analyzed emotional state of the user. Google Cloud Vision API and Amazon Rekognition are used for this analysis.

[0203] Step 5:

[0204] The server readjusts content and travel plans to suit the user's emotions based on emotion data obtained from emotion recognition devices. In this step, the user's emotional state is taken as input, and a plan including more suitable content and activities is output. Using a generative AI model, the plan is adjusted based on the prompt message, "The user looks tired. Please recommend relaxing content."

[0205] 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.

[0206] 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.

[0207] 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.

[0208] [Second Embodiment]

[0209] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0210] 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.

[0211] 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).

[0212] 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.

[0213] 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.

[0214] 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).

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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".

[0221] This invention provides a system that enables users to easily plan and book their trips. The system includes information processing means, booking means, learning means, modification means, and group support means.

[0222] First, the user enters their travel itinerary, destination, budget, desired activities, and travel companions through the device. The device then verifies all entered information to ensure nothing is missing before sending it to the server.

[0223] Next, the server uses information processing tools to generate a basic travel plan based on the received information. At this time, it extracts the most suitable options from databases of transportation, accommodation, and tourist spots to form a provisional plan.

[0224] The server extracts the user's hobbies and preferences from their past travel data and optimizes the plan using machine learning-based learning methods. This step creates a more personalized plan that reflects the user's evaluations and preferences from past trips.

[0225] Once a plan is generated, the server sends it back to the terminal, displaying the detailed plan to the user. The user can review the proposed travel plan and confirm or fine-tune the booking.

[0226] The terminal then sends the user's final selection to the server. This is where the booking process begins, and the server, in conjunction with relevant external services, automatically makes reservations for flights, accommodations, tourist attractions, and other services.

[0227] For example, if a user enters a request such as "I want to take a resort trip within Japan with friends this summer," the server will suggest several popular resort destinations and present a plan combining corresponding flights, hotels, and local activities. The user can then choose the plan that best suits their preferences and complete the entire booking process with just a few clicks.

[0228] Furthermore, if unexpected weather changes or other circumstances necessitate changes in the plan, modification mechanisms are available, allowing for quick changes to the plan in response to new user requests.

[0229] Therefore, the burden on users regarding travel planning is significantly reduced, and a comfortable and highly satisfying travel experience can be provided.

[0230] The following describes the processing flow.

[0231] Step 1:

[0232] The user enters their travel preferences into the terminal. These preferences include travel dates, destination, budget, desired activities, and travel companions. The terminal verifies the entered information and ensures data integrity by prompting the user for corrections as needed.

[0233] Step 2:

[0234] The terminal sends the final confirmed travel conditions data to the server. During this process, it converts the data into a usable format and prepares it for transmission.

[0235] Step 3:

[0236] The server uses information processing tools to analyze the received data. Based on the conditions specified by the user, it gathers data on available transportation, accommodation, and tourist attractions from the database and assembles a basic travel plan.

[0237] Step 4:

[0238] The server uses learning mechanisms to optimize generated travel plans by referencing the user's past travel history and evaluation data. It considers options based on individual user preferences to create personalized suggestions.

[0239] Step 5:

[0240] The server sends an optimized travel plan back to the device. The device displays the received plan to the user, allowing them to review the plan details. The cost, duration, and reviews of the plan are also displayed.

[0241] Step 6:

[0242] The user reviews the proposed plan and requests revisions if necessary, making minor adjustments as needed. Finally, they confirm their chosen travel plan.

[0243] Step 7:

[0244] The terminal sends the confirmed plan information to the server. The server then uses the booking method to automatically execute reservations for flights, accommodations, sightseeing activities, and other services by collaborating with external services.

[0245] Step 8:

[0246] The server sends a reservation completion notification to the terminal, which then provides the user with reservation confirmation information. This includes details such as a hotel reservation confirmation, an electronic ticket, and a schedule summary.

[0247] Step 9:

[0248] If a user needs to change their travel plans immediately before or during their trip, they send a change request from their device to the server. The server then regenerates or adjusts the plan based on the requested changes and presents it to the user again.

[0249] (Example 1)

[0250] 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."

[0251] Traditional travel planning systems required users to manually search for and select from multiple pieces of information, which was time-consuming and cumbersome. Furthermore, they struggled to provide personalized plans that reflected individual user preferences and were inadequate in handling unexpected changes. Therefore, there was a need for a system that would allow users to easily create more efficient and personalized travel plans, while also being flexible enough to accommodate changes.

[0252] 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.

[0253] In this invention, the server includes terminal means, information processing means, learning means, adjustment means, and reservation means. This allows users to automatically generate travel plans with simple information input, provide personalized plans utilizing past data, and respond quickly and appropriately to changes.

[0254] "Terminal means" refers to devices or interfaces that allow users to input travel information and send it to a server.

[0255] "Information processing means" refers to devices and software that analyze information provided by users and manage the process of creating travel plans.

[0256] "Learning method" refers to machine learning technology that uses users' past data to analyze their preferences and optimize travel plans based on that analysis.

[0257] "Adjustment means" refers to devices or processes that reflect any adjustments made by the user to the plan and then optimize the plan again.

[0258] "Reservation methods" refer to devices and processes that automatically make necessary reservations in conjunction with external services based on a confirmed travel plan.

[0259] "Means of modification" refers to functions and processes that allow for a quick and appropriate response when a user requests a change to their travel plan, and to generate a new plan.

[0260] "Group response methods" refer to processes or devices that integrate requests from multiple users and generate travel plans tailored to the individual preferences of each participant.

[0261] This invention provides a system that allows users to easily plan trips and consistently make related reservations. The system includes a user-accessible terminal, a server with powerful data processing capabilities, and a reservation function that integrates with external services.

[0262] First, the user uses a personal computer or mobile device to input their desired travel plan through an interactive interface. This input includes travel dates, destination, budget, desired activities, and travel companions. The device uses validation functions such as JavaScript to verify the input data in real time. After verification is complete, this information is sent to the server via a secure communication protocol.

[0263] The server uses information processing tools to analyze the received information and extracts data from an SQL database that matches the user's criteria. In this process, information such as transportation, accommodation, and tourist attractions is combined to generate a basic hypothetical travel plan. Next, the server utilizes machine learning models (e.g., TensorFlow or Scikit-learn) to analyze the user's past data, extract preferences, and optimize the hypothetical plan.

[0264] Once the plan is finalized, the server uses a RESTful API to send the optimized plan back to the device. On the device, the plan is visually displayed through the user interface, allowing the user to review the contents and make adjustments as needed. This makes it easy for users to customize their plans.

[0265] Once the user confirms the plan details, that information is sent back to the server from the device. The server then uses the booking method to automatically execute the flight and hotel reservations via relevant external service APIs.

[0266] For example, if a user enters conditions such as "I would like to take a resort trip within Japan with a friend this summer," the server will suggest potential resort locations and present an overall travel plan based on those suggestions. An example of a prompt from this system would be, "I'm planning a resort trip to Okinawa from August 15th to 22nd. My budget is under 150,000 yen, and I would like to do scuba diving and relax on the beach."

[0267] This system allows users to easily obtain sophisticated travel plans, significantly reducing the time and effort required for planning.

[0268] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0269] Step 1:

[0270] Users enter basic travel information through the terminal's interface. This includes information such as travel itinerary, destination, budget, desired activities, and travel companions, which are filled into input forms. The terminal validates the input in real time using JavaScript or similar technologies to check for errors. The input data is organized and packaged in JSON format. This packaged data is ready to be sent to the server.

[0271] Step 2:

[0272] The terminal encrypts the entered information using SSL / TLS and sends it to the server using the HTTPS protocol. This communication ensures secure data transmission. The input at this time is travel information prepared on the terminal, which the server receives.

[0273] Step 3:

[0274] The server parses the received JSON data and generates a travel plan using information processing tools. At this stage, it searches databases of transportation, accommodations, tourist spots, etc., using SQL queries and extracts data that matches the criteria. These become the components of a provisional travel plan, and the provisional travel plan is formed as output.

[0275] Step 4:

[0276] Based on the tentative travel plan, the server acquires past travel data and user preference data, and optimizes the plan using a machine learning model. Models such as Scikit-learn and TensorFlow are used, and personalization reflecting past preferences is performed. The inputs here are the tentative plan and past data, and the output is the individualized optimized plan.

[0277] Step 5:

[0278] The server returns the optimized plan to the terminal through the RESTful API, and the terminal visually displays the received data on the user's screen. As a specific operation, HTML / CSS and JavaScript are used to construct an easy-to-view and organized plan screen. The state where the user can check the plan content is the output.

[0279] Step 6:

[0280] The user checks the displayed travel plan and makes fine adjustments to the date and activities as needed. At this time, depending on the user's operation, the terminal repackages the adjusted data and prepares to send it to the server. The input here is the user's adjustment content, and the output is the adjusted data.

[0281] Step 7:

[0282] Based on the adjusted travel plan, the server starts the process for automatically making reservations. The API of an external reservation service is used to confirm reservations for air tickets and accommodation facilities. The input here is the final travel plan, and the output is the reservation confirmation information.

[0283] Through this series of steps, the user can efficiently formulate a travel plan and automatically complete the reservation.

[0284] (Application Example 1)

[0285] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0286] In the formulation and execution of travel plans, many options and adjustments are required, which is a problem in that it is time-consuming for users. In particular, there is a problem that it is impossible to have a prior experience of the travel destination and it is difficult to specifically imagine the details of the plan. Furthermore, when a user intends to change the plan, it is difficult to quickly and flexibly adapt the schedule.

[0287] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0288] In this invention, the server includes information processing means for receiving travel wish conditions from a user and generating a travel plan based on them, reservation means for automatically executing a reservation with a related service based on the generated travel plan, learning means for analyzing the user's past transaction history and evaluation data and proposing an optimized travel plan, and virtual experience means for allowing the user to experience the travel destination using a virtual reality environment and adjusting the travel plan. Thereby, the user can experience a specific travel plan in a virtual environment and can make flexible plan changes according to wishes and situations.

[0289] The "information processing means" is a device or software that performs processing for generating a travel plan based on the travel wish conditions received from the user.

[0290] The "reservation means" is a mechanism for automatically reserving related services such as air tickets and accommodation facilities based on the generated travel plan.

[0291] The "learning means" is a technology for analyzing the user's past transaction history and evaluation data and proposing an optimized travel plan that reflects the results.

[0292] A "virtual experience system" is a system that allows users to experience a travel destination in advance using a virtual reality environment, enabling them to adjust their travel plans.

[0293] The system that realizes this invention operates with a configuration comprising a server, a user terminal, and a virtual reality environment surrounding it. The server acts as an information processing means for receiving travel preferences from the user. When the user enters information such as destination, dates, budget, and travel companions on the terminal, this information is sent to the server. Based on this, the server generates a basic travel plan. In generating the plan, it refers to databases of transportation, accommodation, and tourist attractions to construct a plan that is optimal for the user's conditions.

[0294] The server also has learning capabilities to analyze users' past transaction history and rating data, and optimizes travel plans based on the insights gained. This process uses machine learning algorithms to suggest personalized plans tailored to the user's preferences.

[0295] Furthermore, a distinctive feature of this invention is the virtual experience mechanism. Users can use a VR headset to experience a proposed travel destination in advance within a virtual reality environment. This process utilizes a game engine such as Unity, providing a realistic visual and auditory environment of the travel destination within the virtual space. This allows users to visualize their travel plan more concretely and make adjustments as needed.

[0296] As a concrete example of a prompt message, if you enter text such as "I want to visit a domestic resort with my friends this summer," the server will select popular resort destinations and provide a virtual tour. In this virtual tour, you can experience the characteristics and activities of each resort, make the best choice, and proceed to make an actual reservation.

[0297] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0298] Step 1:

[0299] The user inputs the desired travel conditions through the terminal. The input data includes the travel destination, schedule, budget, companions, etc. The terminal checks whether all these data are correctly input, formats them, and then sends them to the server.

[0300] Step 2:

[0301] Based on the received desired travel conditions, the server retrieves relevant information from the database. Specifically, it extracts the most suitable options for the user's conditions from the data on transportation means, accommodation facilities, and tourist attractions. Thereby, an initial travel plan proposal is generated. The output is a basic travel plan.

[0302] Step 3:

[0303] The server analyzes the user's past transaction history and evaluation data, and uses the generated AI model to optimize the travel plan. Based on the user's preference information, a more personalized plan is proposed. At this time, machine learning algorithms are used, and the result of comparing and scrutinizing the similar past data with the new plan is output.

[0304] Step 4:

[0305] The server returns the optimized travel plan to the terminal. The user receives this proposed plan through the terminal and checks the content. Based on the output information, the user can adjust the details of the travel plan.

[0306] Step 5:

[0307] When the user selects virtual experience means, the server prepares a virtual reality environment and provides a virtual tour of the selected travel destination. Here, game engines such as Unity are used, and the virtual travel is experienced through a VR headset. The input is the planned information selected by the user, and a visual and auditory virtual environment is output.

[0308] Step 6:

[0309] When the user finalizes their travel plan, the terminal sends that information to the server. The server then uses a booking system to automatically make reservations for flights and accommodations based on the final travel plan. The input is the user's final decision information, and the output is reservation confirmation information.

[0310] 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.

[0311] This invention is a system that uses emotion recognition to adjust a user's travel plan and provide a highly personalized experience. This system includes an emotion engine in addition to information processing means, booking means, learning means, modification means, and group handling means.

[0312] The user enters their travel preferences into the terminal. These preferences include dates, destination, budget, activities, and travel companions. The terminal verifies the input before sending it to the server.

[0313] The server uses information processing tools to analyze the input data and create a basic travel plan. It also utilizes the user's past data to understand their preferences and uses learning tools to optimize the plan. Here, the emotion engine analyzes the user's emotions from the input data and interactions with the device.

[0314] When a user reviews their travel plan, the emotion engine evaluates their reaction in real time and influences the plan's options. For example, if the system determines the user is stressed, more relaxing spots will be added to the plan. Conversely, if the user is excited, more active activities will be recommended.

[0315] In the case of group travel, the Emotion Engine analyzes the emotions of each participant and creates a plan that brings optimal harmony. It takes into account the different emotional states of each participant and proposes content that will satisfy everyone.

[0316] As a concrete example, when a user reviews a plan displayed on a monitor, changes in their facial expressions and tone of voice are detected by the device. The server analyzes this through an emotion engine and fine-tunes the plan as needed. For instance, if the user shows signs of fatigue, new suggestions will be made emphasizing less strenuous modes of transportation or relaxation facilities.

[0317] Finally, once the user selects and confirms their adjusted travel plan, the server uses the booking mechanism to make all related reservations in a single batch. In particular, if the user needs to change their plan at the last minute, a change mechanism will immediately suggest new options.

[0318] This system allows travel plans to adapt to the user's mood and provide a more satisfying experience.

[0319] The following describes the processing flow.

[0320] Step 1:

[0321] The user enters their travel preferences using an interface connected to the device. This includes destination, travel dates, budget, and desired activities. The device temporarily saves the entered data and verifies that the information is in the correct format.

[0322] Step 2:

[0323] The terminal sends the confirmed travel preference data to the server. During data transmission, the data is formatted appropriately to ensure smooth progress in each business process.

[0324] Step 3:

[0325] The server uses information processing tools to analyze the received data and construct the initial travel plan. It retrieves relevant tourist spots, accommodations, and transportation data from the database to generate a basic plan.

[0326] Step 4:

[0327] The server optimizes travel plans through learning mechanisms using the user's past travel history and evaluation data. It creates more detailed plans tailored to the user's preferences.

[0328] Step 5:

[0329] The server uses an emotion engine to analyze the user's emotions based on their input and past behavior. Based on this information, it fine-tunes the travel plan to provide more personalized suggestions.

[0330] Step 6:

[0331] The server sends the optimized plan back to the device. The device displays the plan details to the user, allowing the user to evaluate the content. At this point, the sentiment engine may analyze the user's immediate response and further adjust the plan.

[0332] Step 7:

[0333] The user reviews the displayed plan and requests adjustments if necessary. The user then confirms their final travel plan and sends that information to the server via their device.

[0334] Step 8:

[0335] The server uses booking methods to automatically make reservations for flights, accommodations, and various activities based on the selected travel plan. It works in conjunction with external related services to execute an efficient booking process.

[0336] Step 9:

[0337] If a user needs to change their travel plans, they send a request for the change to the server via their device. The server then uses a change mechanism and an emotional engine to immediately generate a plan that suits the new conditions and presents it to the user again.

[0338] This processing step enables flexible travel planning based on the user's emotions, resulting in a more satisfying travel experience.

[0339] (Example 2)

[0340] 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".

[0341] Modern travel planning faces the challenge of providing highly satisfying experiences for users with diverse needs and emotions. Traditional systems struggle to accurately reflect individual user feelings and preferences, and lack mechanisms to accommodate the individual wishes of participants in group tours. As a result, user satisfaction often declines, and travel experiences frequently fall short of expectations.

[0342] 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.

[0343] In this invention, the server includes data processing means that receive travel preferences from the user and generate a travel plan based on them; emotion analysis means that analyze the user's emotions through user input data and interactions with network terminals and adjust the travel plan based on those emotions; and data reservation means that automatically make reservations for related services based on the generated travel plan. This makes it possible to create detailed plans that are tailored to the emotions and preferences of individual users, and in the case of group travel, it is possible to make adjustments that increase the satisfaction of all participants.

[0344] "Data processing means" refers to a device or system for analyzing travel preferences provided by users and constructing a basic travel plan.

[0345] "Emotional analysis means" refers to technologies and methods that identify a user's emotional state based on user input information and interactions with the device, and then optimize travel plans in real time based on that information.

[0346] "Data booking methods" refer to technologies and processes that automatically execute bookings for related services, including accommodations and transportation, in accordance with a generated travel plan.

[0347] "Data learning tools" refer to functions or systems that analyze a user's past travel history and evaluation information, and use that information to improve the accuracy and personalization of travel plans.

[0348] "Group response methods" refer to a process or system for integrating requests and information from multiple users, taking into account the emotional state of each participant, and creating a harmonious travel plan as a whole.

[0349] This invention comprises a system for highly personalizing users' travel plans and providing an experience tailored to each individual. The entire system is operated primarily by three entities: a server, terminals, and users.

[0350] First, the user enters their travel preferences using a device. The device then verifies this information and sends it to the server. Specific devices and platforms that can be used here include smartphones, tablets, and personal computers.

[0351] Next, the server analyzes the received data using data processing tools and formulates a basic travel plan. The server utilizes a generative AI model to optimize the travel plan, taking into account the user's past history and evaluation data. In addition, sentiment analysis tools analyze the user's emotions in real time through user input data and interactions with network terminals, and adjust the travel content accordingly.

[0352] In particular, emotion analysis can utilize facial recognition and voice tone analysis technologies to gain a detailed understanding of the user's emotional state. Based on this, the server can add relaxing and exciting activities to the travel plan.

[0353] As a concrete example, while a user is reviewing their travel plan, the device captures the user's facial expression. This data is sent to a server, where an emotion analysis system begins its analysis. If the system determines that the user is calm or excited, it adjusts the plan accordingly.

[0354] This system allows for adjustments to the generated AI model using prompts. For example, a possible prompt might be, "The user is showing an excited expression, so please suggest an active schedule." In this way, the system can automatically suggest and provide the user with an appropriate travel plan based on their emotions.

[0355] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0356] Step 1:

[0357] Users enter their travel preferences using a terminal. This includes dates, destination, budget, activities, and travel companions. The entered data is written through the terminal's input form and validated for correct formatting. After the input is confirmed, the data is sent to the server.

[0358] Step 2:

[0359] The server receives user preference data from the terminal using data processing tools. Data analysis is then performed to generate a basic travel plan. Specifically, it collects information about the selected destination and uses an AI model to suggest optimal travel routes and sightseeing spots. The generated plan is stored for future sentiment analysis.

[0360] Step 3:

[0361] The server uses emotion analysis tools to analyze additional data from the user, specifically real-time data on facial expressions and voice tone. Each piece of data is analyzed by an emotion recognition algorithm to determine the user's emotional state. For example, facial expression data can be used to determine whether the user is relaxed or excited. This analysis provides useful indicators for adjusting the travel plan, and the plan data is updated accordingly.

[0362] Step 4:

[0363] The server uses a generative AI model to optimize the plan based on information obtained from emotion analysis. Specifically, this includes changing activity suggestions according to emotions and fine-tuning the schedule. For example, if fatigue is detected, suggestions for relaxation facilities will be enhanced. Once this process is complete, the final travel plan is generated and sent to the device.

[0364] Step 5:

[0365] Users review their final travel plans on their devices, making adjustments or finalizing them as needed. Once finalized, the server automatically uses data booking mechanisms to execute reservations for accommodation and transportation. At this stage, additional data and options, such as cancellation policies and price recalculations, are handled as required.

[0366] This entire system's program processing allows travel plans to be flexibly and precisely adjusted based on the user's emotions and specific preferences.

[0367] (Application Example 2)

[0368] 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 will be referred to as the "terminal."

[0369] In modern travel planning, it's common for travel plans to be suggested based on the user's interests and schedule. However, suggesting plans that take into account the traveler's emotional state is extremely difficult, and it's rare to provide content and plans that adapt to the traveler's real-time emotions. As a result, there is a challenge in creating travel plans that enhance satisfaction because they cannot flexibly respond to changes in the traveler's emotions.

[0370] 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.

[0371] In this invention, the server includes information processing means that receive travel preferences from the user and generate a travel plan based on them; reservation means that automatically make reservations for related services based on the generated travel plan; learning means that analyze the user's past usage history and evaluation information and propose an optimized travel plan; and emotion recognition means that analyze the user's facial expressions and vocalizations and recommend content that matches their emotions. This makes it possible to provide travel plans and content optimized for the user's emotions.

[0372] "Information processing means" refers to a device that has the function of generating a travel plan based on the travel preferences entered by the user.

[0373] A "booking device" is a device that automatically makes reservations for related services based on a generated travel plan.

[0374] A "learning tool" is a device that analyzes a user's past usage history and evaluation information and has the function of proposing an optimized travel plan.

[0375] An "emotion recognition device" is a device that analyzes a user's facial expressions and vocalizations and recommends content that corresponds to their emotions.

[0376] "User preferences" refer to the conditions that users desire when traveling, such as the date and time of the trip, destination, budget, activities, and travel companions.

[0377] The system for carrying out this invention consists of a server and a terminal. The server is equipped with information processing means, reservation means, learning means, and emotion recognition means. The terminal is a device for the user to input their travel preferences and is equipped with a camera and a microphone.

[0378] First, the user uses a terminal to enter their travel preferences. These preferences include the date and time of the trip, destination, budget, activities, and travel companions. The terminal then sends the entered preferences to the server.

[0379] On the server, information processing tools analyze these conditions and generate a basic travel plan. During this process, learning tools analyze past user usage history and evaluation information to propose an optimized travel plan.

[0380] Furthermore, emotion recognition technology captures the user's facial expressions and voice through the camera and microphone, and analyzes their emotions. By analyzing emotions using software such as Google Cloud Vision API and Amazon Rekognition, content tailored to the user's emotions is recommended. For example, if the user is tired, a relaxing plan will be suggested, while if they are excited, an active activity will be suggested.

[0381] For example, if a user is reviewing travel plans on their device after returning home from work, and their facial expression is analyzed as indicating fatigue, various relaxation-oriented content can be provided. This allows the user to enjoy a more appropriate travel plan.

[0382] An example of a prompt message for a generative AI model is, "The user looks tired. Please recommend relaxing content." Based on this prompt message, the server flexibly adjusts and optimizes the plan based on the user's emotional state, thereby creating a highly satisfying travel experience.

[0383] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0384] Step 1:

[0385] The user uses a terminal to enter their travel preferences (dates and times, destination, budget, activities, and travel companions). This input is verified on the terminal and then sent to the server. The entered data is used directly in the next processing step.

[0386] Step 2:

[0387] The server's information processing system analyzes the travel preferences submitted by the user and generates a basic travel plan for the user. The data used here is the input data of the user's preferences, and the output is a travel plan based on those preferences.

[0388] Step 3:

[0389] The server's learning mechanism utilizes past user usage history and evaluation information to process data and optimize the generated basic travel plan. The input here is past usage data and the current basic plan, and the output is an optimized travel plan.

[0390] Step 4:

[0391] The server's emotion recognition system captures and analyzes the user's facial expressions and vocalizations in real time through the device's camera and microphone. The input data consists of real-time image and audio data acquired from the camera and microphone, while the output is the analyzed emotional state of the user. Google Cloud Vision API and Amazon Rekognition are used for this analysis.

[0392] Step 5:

[0393] The server readjusts content and travel plans to suit the user's emotions based on emotion data obtained from emotion recognition devices. In this step, the user's emotional state is taken as input, and a plan including more suitable content and activities is output. Using a generative AI model, the plan is adjusted based on the prompt message, "The user looks tired. Please recommend relaxing content."

[0394] 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.

[0395] 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.

[0396] 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.

[0397] [Third Embodiment]

[0398] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0399] 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.

[0400] 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).

[0401] 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.

[0402] 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.

[0403] 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).

[0404] 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.

[0405] 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.

[0406] 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.

[0407] 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.

[0408] 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.

[0409] 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".

[0410] This invention provides a system that enables users to easily plan and book their trips. The system includes information processing means, booking means, learning means, modification means, and group support means.

[0411] First, the user enters their travel itinerary, destination, budget, desired activities, and travel companions through the device. The device then verifies all entered information to ensure nothing is missing before sending it to the server.

[0412] Next, the server uses information processing tools to generate a basic travel plan based on the received information. At this time, it extracts the most suitable options from databases of transportation, accommodation, and tourist spots to form a provisional plan.

[0413] The server extracts the user's hobbies and preferences from their past travel data and optimizes the plan using machine learning-based learning methods. This step creates a more personalized plan that reflects the user's evaluations and preferences from past trips.

[0414] Once a plan is generated, the server sends it back to the terminal, displaying the detailed plan to the user. The user can review the proposed travel plan and confirm or fine-tune the booking.

[0415] The terminal then sends the user's final selection to the server. This is where the booking process begins, and the server, in conjunction with relevant external services, automatically makes reservations for flights, accommodations, tourist attractions, and other services.

[0416] For example, if a user enters a request such as "I want to take a resort trip within Japan with friends this summer," the server will suggest several popular resort destinations and present a plan combining corresponding flights, hotels, and local activities. The user can then choose the plan that best suits their preferences and complete the entire booking process with just a few clicks.

[0417] Furthermore, if unexpected weather changes or other circumstances necessitate changes in the plan, modification mechanisms are available, allowing for quick changes to the plan in response to new user requests.

[0418] Therefore, the burden on users regarding travel planning is significantly reduced, and a comfortable and highly satisfying travel experience can be provided.

[0419] The following describes the processing flow.

[0420] Step 1:

[0421] The user enters their travel preferences into the terminal. These preferences include travel dates, destination, budget, desired activities, and travel companions. The terminal verifies the entered information and ensures data integrity by prompting the user for corrections as needed.

[0422] Step 2:

[0423] The terminal sends the final confirmed travel conditions data to the server. During this process, it converts the data into a usable format and prepares it for transmission.

[0424] Step 3:

[0425] The server uses information processing tools to analyze the received data. Based on the conditions specified by the user, it gathers data on available transportation, accommodation, and tourist attractions from the database and assembles a basic travel plan.

[0426] Step 4:

[0427] The server uses learning mechanisms to optimize generated travel plans by referencing the user's past travel history and evaluation data. It considers options based on individual user preferences to create personalized suggestions.

[0428] Step 5:

[0429] The server sends an optimized travel plan back to the device. The device displays the received plan to the user, allowing them to review the plan details. The cost, duration, and reviews of the plan are also displayed.

[0430] Step 6:

[0431] The user reviews the proposed plan and requests revisions if necessary, making minor adjustments as needed. Finally, they confirm their chosen travel plan.

[0432] Step 7:

[0433] The terminal sends the confirmed plan information to the server. The server then uses the booking method to automatically execute reservations for flights, accommodations, sightseeing activities, and other services by collaborating with external services.

[0434] Step 8:

[0435] The server sends a reservation completion notification to the terminal, which then provides the user with reservation confirmation information. This includes details such as a hotel reservation confirmation, an electronic ticket, and a schedule summary.

[0436] Step 9:

[0437] If a user needs to change their travel plans immediately before or during their trip, they send a change request from their device to the server. The server then regenerates or adjusts the plan based on the requested changes and presents it to the user again.

[0438] (Example 1)

[0439] 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."

[0440] Traditional travel planning systems required users to manually search for and select from multiple pieces of information, which was time-consuming and cumbersome. Furthermore, they struggled to provide personalized plans that reflected individual user preferences and were inadequate in handling unexpected changes. Therefore, there was a need for a system that would allow users to easily create more efficient and personalized travel plans, while also being flexible enough to accommodate changes.

[0441] 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.

[0442] In this invention, the server includes terminal means, information processing means, learning means, adjustment means, and reservation means. This allows users to automatically generate travel plans with simple information input, provide personalized plans utilizing past data, and respond quickly and appropriately to changes.

[0443] "Terminal means" refers to devices or interfaces that allow users to input travel information and send it to a server.

[0444] "Information processing means" refers to devices and software that analyze information provided by users and manage the process of creating travel plans.

[0445] "Learning method" refers to machine learning technology that uses users' past data to analyze their preferences and optimize travel plans based on that analysis.

[0446] "Adjustment means" refers to devices or processes that reflect any adjustments made by the user to the plan and then optimize the plan again.

[0447] "Reservation methods" refer to devices and processes that automatically make necessary reservations in conjunction with external services based on a confirmed travel plan.

[0448] "Means of modification" refers to functions and processes that allow for a quick and appropriate response when a user requests a change to their travel plan, and to generate a new plan.

[0449] "Group response methods" refer to processes or devices that integrate requests from multiple users and generate travel plans tailored to the individual preferences of each participant.

[0450] This invention provides a system that allows users to easily plan trips and consistently make related reservations. The system includes a user-accessible terminal, a server with powerful data processing capabilities, and a reservation function that integrates with external services.

[0451] First, the user uses a personal computer or mobile device to input their desired travel plan through an interactive interface. This input includes travel dates, destination, budget, desired activities, and travel companions. The device uses validation functions such as JavaScript to verify the input data in real time. After verification is complete, this information is sent to the server via a secure communication protocol.

[0452] The server uses information processing tools to analyze the received information and extracts data from an SQL database that matches the user's criteria. In this process, information such as transportation, accommodation, and tourist attractions is combined to generate a basic hypothetical travel plan. Next, the server utilizes machine learning models (e.g., TensorFlow or Scikit-learn) to analyze the user's past data, extract preferences, and optimize the hypothetical plan.

[0453] Once the plan is finalized, the server uses a RESTful API to send the optimized plan back to the device. On the device, the plan is visually displayed through the user interface, allowing the user to review the contents and make adjustments as needed. This makes it easy for users to customize their plans.

[0454] Once the user confirms the plan details, that information is sent back to the server from the device. The server then uses the booking method to automatically execute the flight and hotel reservations via relevant external service APIs.

[0455] For example, if a user enters conditions such as "I would like to take a resort trip within Japan with a friend this summer," the server will suggest potential resort locations and present an overall travel plan based on those suggestions. An example of a prompt from this system would be, "I'm planning a resort trip to Okinawa from August 15th to 22nd. My budget is under 150,000 yen, and I would like to do scuba diving and relax on the beach."

[0456] This system allows users to easily obtain sophisticated travel plans, significantly reducing the time and effort required for planning.

[0457] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0458] Step 1:

[0459] Users enter basic travel information through the terminal's interface. This includes information such as travel itinerary, destination, budget, desired activities, and travel companions, which are filled into input forms. The terminal validates the input in real time using JavaScript or similar technologies to check for errors. The input data is organized and packaged in JSON format. This packaged data is ready to be sent to the server.

[0460] Step 2:

[0461] The terminal encrypts the entered information using SSL / TLS and sends it to the server using the HTTPS protocol. This communication ensures secure data transmission. The input at this time is travel information prepared on the terminal, which the server receives.

[0462] Step 3:

[0463] The server parses the received JSON data and generates a travel plan using information processing tools. At this stage, it searches databases of transportation, accommodations, tourist spots, etc., using SQL queries and extracts data that matches the criteria. These become the components of a provisional travel plan, and the provisional travel plan is formed as output.

[0464] Step 4:

[0465] The server uses a hypothetical travel plan as a basis, retrieves historical travel data and user preference data, and optimizes the plan using a machine learning model. Models such as Scikit-learn and TensorFlow are used, and personalization reflecting past preferences is performed. The input here is the hypothetical plan and historical data, and the output is an individualized, optimized plan.

[0466] Step 5:

[0467] The server sends an optimized plan back to the device via a RESTful API, and the device visually displays the received data on the user's screen. Specifically, HTML / CSS and JavaScript are used to create a user-friendly and well-organized plan screen. The output is the state in which the user can review the plan details.

[0468] Step 6:

[0469] The user reviews the displayed travel plan and makes minor adjustments to dates and activities as needed. During this process, the device repackages the adjusted data based on the user's actions and prepares to send it to the server. The input here is the user's adjustments, and the output is the adjusted data.

[0470] Step 7:

[0471] The server automatically initiates the booking process based on the coordinated travel plan. It uses the API of an external booking service to confirm flight and accommodation reservations. The input here is the final travel plan, and the output is booking confirmation information.

[0472] This series of steps allows users to efficiently plan their trips and complete bookings automatically.

[0473] (Application Example 1)

[0474] 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."

[0475] Planning and executing a trip involves many choices and adjustments, which can be time-consuming for users. In particular, the inability to experience the destination beforehand makes it difficult to visualize the details of the plan. Furthermore, if users intend to change their plans, it is difficult to quickly and flexibly adapt the schedule.

[0476] 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.

[0477] In this invention, the server includes information processing means that receive travel preferences from the user and generate a travel plan based on them; reservation means that automatically execute reservations with related services based on the generated travel plan; learning means that analyze the user's past transaction history and evaluation data and propose an optimized travel plan; and virtual experience means that use a virtual reality environment for the user to experience a travel destination and adjust the travel plan. This allows the user to experience a specific travel plan in a virtual environment and to flexibly change the plan according to their wishes and circumstances.

[0478] "Information processing means" refers to a device or software that performs processing to generate a travel plan based on travel preferences received from a user.

[0479] A "booking system" is a mechanism that automatically books related services such as airline tickets and accommodations based on a generated travel plan.

[0480] The "learning method" is a technology that analyzes the user's past transaction history and evaluation data, and proposes an optimized travel plan that reflects the results.

[0481] A "virtual experience system" is a system that allows users to experience a travel destination in advance using a virtual reality environment, enabling them to adjust their travel plans.

[0482] The system that realizes this invention operates with a configuration comprising a server, a user terminal, and a virtual reality environment surrounding it. The server acts as an information processing means for receiving travel preferences from the user. When the user enters information such as destination, dates, budget, and travel companions on the terminal, this information is sent to the server. Based on this, the server generates a basic travel plan. In generating the plan, it refers to databases of transportation, accommodation, and tourist attractions to construct a plan that is optimal for the user's conditions.

[0483] The server also has learning capabilities to analyze users' past transaction history and rating data, and optimizes travel plans based on the insights gained. This process uses machine learning algorithms to suggest personalized plans tailored to the user's preferences.

[0484] Furthermore, a distinctive feature of this invention is the virtual experience mechanism. Users can use a VR headset to experience a proposed travel destination in advance within a virtual reality environment. This process utilizes a game engine such as Unity, providing a realistic visual and auditory environment of the travel destination within the virtual space. This allows users to visualize their travel plan more concretely and make adjustments as needed.

[0485] As a concrete example of a prompt message, if you enter text such as "I want to visit a domestic resort with my friends this summer," the server will select popular resort destinations and provide a virtual tour. In this virtual tour, you can experience the characteristics and activities of each resort, make the best choice, and proceed to make an actual reservation.

[0486] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0487] Step 1:

[0488] The user enters their travel preferences through the terminal. This input data includes destination, dates, budget, and travel companions. The terminal verifies that all this data has been entered correctly, formats it, and then sends it to the server.

[0489] Step 2:

[0490] The server retrieves relevant information from the database based on the received travel preferences. Specifically, it extracts the most suitable options from data on transportation, accommodation, and tourist attractions. This generates an initial travel plan. The output is a basic travel plan.

[0491] Step 3:

[0492] The server analyzes the user's past transaction history and rating data, and optimizes travel plans using a generative AI model. Based on the user's preferences, it proposes a more personalized plan. In this process, machine learning algorithms are used to compare the new plan with similar past data and output the results of the refinement.

[0493] Step 4:

[0494] The server sends an optimized travel plan back to the terminal. The user receives this proposed plan through the terminal and reviews its contents. Based on the outputted information, the user can adjust the details of the travel plan.

[0495] Step 5:

[0496] Once the user selects a virtual experience method, the server prepares a virtual reality environment and provides a virtual tour of the selected travel destination. A game engine such as Unity is used here, and the virtual trip is experienced through a VR headset. The input is the user's selected travel plan information, and a visual and auditory virtual environment is output.

[0497] Step 6:

[0498] When the user finalizes their travel plan, the terminal sends that information to the server. The server then uses a booking system to automatically make reservations for flights and accommodations based on the final travel plan. The input is the user's final decision information, and the output is reservation confirmation information.

[0499] 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.

[0500] This invention is a system that uses emotion recognition to adjust a user's travel plan and provide a highly personalized experience. This system includes an emotion engine in addition to information processing means, booking means, learning means, modification means, and group handling means.

[0501] The user enters their travel preferences into the terminal. These preferences include dates, destination, budget, activities, and travel companions. The terminal verifies the input before sending it to the server.

[0502] The server uses information processing tools to analyze the input data and create a basic travel plan. It also utilizes the user's past data to understand their preferences and uses learning tools to optimize the plan. Here, the emotion engine analyzes the user's emotions from the input data and interactions with the device.

[0503] When a user reviews their travel plan, the emotion engine evaluates their reaction in real time and influences the plan's options. For example, if the system determines the user is stressed, more relaxing spots will be added to the plan. Conversely, if the user is excited, more active activities will be recommended.

[0504] In the case of group travel, the Emotion Engine analyzes the emotions of each participant and creates a plan that brings optimal harmony. It takes into account the different emotional states of each participant and proposes content that will satisfy everyone.

[0505] As a concrete example, when a user reviews a plan displayed on a monitor, changes in their facial expressions and tone of voice are detected by the device. The server analyzes this through an emotion engine and fine-tunes the plan as needed. For instance, if the user shows signs of fatigue, new suggestions will be made emphasizing less strenuous modes of transportation or relaxation facilities.

[0506] Finally, once the user selects and confirms their adjusted travel plan, the server uses the booking mechanism to make all related reservations in a single batch. In particular, if the user needs to change their plan at the last minute, a change mechanism will immediately suggest new options.

[0507] This system allows travel plans to adapt to the user's mood and provide a more satisfying experience.

[0508] The following describes the processing flow.

[0509] Step 1:

[0510] The user enters their travel preferences using an interface connected to the device. This includes destination, travel dates, budget, and desired activities. The device temporarily saves the entered data and verifies that the information is in the correct format.

[0511] Step 2:

[0512] The terminal sends the confirmed travel preference data to the server. During data transmission, the data is formatted appropriately to ensure smooth progress in each business process.

[0513] Step 3:

[0514] The server uses information processing tools to analyze the received data and construct the initial travel plan. It retrieves relevant tourist spots, accommodations, and transportation data from the database to generate a basic plan.

[0515] Step 4:

[0516] The server optimizes travel plans through learning mechanisms using the user's past travel history and evaluation data. It creates more detailed plans tailored to the user's preferences.

[0517] Step 5:

[0518] The server uses an emotion engine to analyze the user's emotions based on their input and past behavior. Based on this information, it fine-tunes the travel plan to provide more personalized suggestions.

[0519] Step 6:

[0520] The server sends the optimized plan back to the device. The device displays the plan details to the user, allowing the user to evaluate the content. At this point, the sentiment engine may analyze the user's immediate response and further adjust the plan.

[0521] Step 7:

[0522] The user reviews the displayed plan and requests adjustments if necessary. The user then confirms their final travel plan and sends that information to the server via their device.

[0523] Step 8:

[0524] The server uses booking methods to automatically make reservations for flights, accommodations, and various activities based on the selected travel plan. It works in conjunction with external related services to execute an efficient booking process.

[0525] Step 9:

[0526] If a user needs to change their travel plans, they send a request for the change to the server via their device. The server then uses a change mechanism and an emotional engine to immediately generate a plan that suits the new conditions and presents it to the user again.

[0527] This processing step enables flexible travel planning based on the user's emotions, resulting in a more satisfying travel experience.

[0528] (Example 2)

[0529] 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."

[0530] Modern travel planning faces the challenge of providing highly satisfying experiences for users with diverse needs and emotions. Traditional systems struggle to accurately reflect individual user feelings and preferences, and lack mechanisms to accommodate the individual wishes of participants in group tours. As a result, user satisfaction often declines, and travel experiences frequently fall short of expectations.

[0531] 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.

[0532] In this invention, the server includes data processing means that receive travel preferences from the user and generate a travel plan based on them; emotion analysis means that analyze the user's emotions through user input data and interactions with network terminals and adjust the travel plan based on those emotions; and data reservation means that automatically make reservations for related services based on the generated travel plan. This makes it possible to create detailed plans that are tailored to the emotions and preferences of individual users, and in the case of group travel, it is possible to make adjustments that increase the satisfaction of all participants.

[0533] "Data processing means" refers to a device or system for analyzing travel preferences provided by users and constructing a basic travel plan.

[0534] "Emotional analysis means" refers to technologies and methods that identify a user's emotional state based on user input information and interactions with the device, and then optimize travel plans in real time based on that information.

[0535] "Data booking methods" refer to technologies and processes that automatically execute bookings for related services, including accommodations and transportation, in accordance with a generated travel plan.

[0536] "Data learning tools" refer to functions or systems that analyze a user's past travel history and evaluation information, and use that information to improve the accuracy and personalization of travel plans.

[0537] "Group response methods" refer to a process or system for integrating requests and information from multiple users, taking into account the emotional state of each participant, and creating a harmonious travel plan as a whole.

[0538] This invention comprises a system for highly personalizing users' travel plans and providing an experience tailored to each individual. The entire system is operated primarily by three entities: a server, terminals, and users.

[0539] First, the user enters their travel preferences using a device. The device then verifies this information and sends it to the server. Specific devices and platforms that can be used here include smartphones, tablets, and personal computers.

[0540] Next, the server analyzes the received data using data processing tools and formulates a basic travel plan. The server utilizes a generative AI model to optimize the travel plan, taking into account the user's past history and evaluation data. In addition, sentiment analysis tools analyze the user's emotions in real time through user input data and interactions with network terminals, and adjust the travel content accordingly.

[0541] In particular, emotion analysis can utilize facial recognition and voice tone analysis technologies to gain a detailed understanding of the user's emotional state. Based on this, the server can add relaxing and exciting activities to the travel plan.

[0542] As a concrete example, while a user is reviewing their travel plan, the device captures the user's facial expression. This data is sent to a server, where an emotion analysis system begins its analysis. If the system determines that the user is calm or excited, it adjusts the plan accordingly.

[0543] This system allows for adjustments to the generated AI model using prompts. For example, a possible prompt might be, "The user is showing an excited expression, so please suggest an active schedule." In this way, the system can automatically suggest and provide the user with an appropriate travel plan based on their emotions.

[0544] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0545] Step 1:

[0546] Users enter their travel preferences using a terminal. This includes dates, destination, budget, activities, and travel companions. The entered data is written through the terminal's input form and validated for correct formatting. After the input is confirmed, the data is sent to the server.

[0547] Step 2:

[0548] The server receives user preference data from the terminal using data processing tools. Data analysis is then performed to generate a basic travel plan. Specifically, it collects information about the selected destination and uses an AI model to suggest optimal travel routes and sightseeing spots. The generated plan is stored for future sentiment analysis.

[0549] Step 3:

[0550] The server uses emotion analysis tools to analyze additional data from the user, specifically real-time data on facial expressions and voice tone. Each piece of data is analyzed by an emotion recognition algorithm to determine the user's emotional state. For example, facial expression data can be used to determine whether the user is relaxed or excited. This analysis provides useful indicators for adjusting the travel plan, and the plan data is updated accordingly.

[0551] Step 4:

[0552] The server uses a generative AI model to optimize the plan based on information obtained from emotion analysis. Specifically, this includes changing activity suggestions according to emotions and fine-tuning the schedule. For example, if fatigue is detected, suggestions for relaxation facilities will be enhanced. Once this process is complete, the final travel plan is generated and sent to the device.

[0553] Step 5:

[0554] Users review their final travel plans on their devices, making adjustments or finalizing them as needed. Once finalized, the server automatically uses data booking mechanisms to execute reservations for accommodation and transportation. At this stage, additional data and options, such as cancellation policies and price recalculations, are handled as required.

[0555] This entire system's program processing allows travel plans to be flexibly and precisely adjusted based on the user's emotions and specific preferences.

[0556] (Application Example 2)

[0557] 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."

[0558] In modern travel planning, it's common for travel plans to be suggested based on the user's interests and schedule. However, suggesting plans that take into account the traveler's emotional state is extremely difficult, and it's rare to provide content and plans that adapt to the traveler's real-time emotions. As a result, there is a challenge in creating travel plans that enhance satisfaction because they cannot flexibly respond to changes in the traveler's emotions.

[0559] 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.

[0560] In this invention, the server includes information processing means that receive travel preferences from the user and generate a travel plan based on them; reservation means that automatically make reservations for related services based on the generated travel plan; learning means that analyze the user's past usage history and evaluation information and propose an optimized travel plan; and emotion recognition means that analyze the user's facial expressions and vocalizations and recommend content that matches their emotions. This makes it possible to provide travel plans and content optimized for the user's emotions.

[0561] "Information processing means" refers to a device that has the function of generating a travel plan based on the travel preferences entered by the user.

[0562] A "booking device" is a device that automatically makes reservations for related services based on a generated travel plan.

[0563] A "learning tool" is a device that analyzes a user's past usage history and evaluation information and has the function of proposing an optimized travel plan.

[0564] An "emotion recognition device" is a device that analyzes a user's facial expressions and vocalizations and recommends content that corresponds to their emotions.

[0565] "User preferences" refer to the conditions that users desire when traveling, such as the date and time of the trip, destination, budget, activities, and travel companions.

[0566] The system for carrying out this invention consists of a server and a terminal. The server is equipped with information processing means, reservation means, learning means, and emotion recognition means. The terminal is a device for the user to input their travel preferences and is equipped with a camera and a microphone.

[0567] First, the user uses a terminal to enter their travel preferences. These preferences include the date and time of the trip, destination, budget, activities, and travel companions. The terminal then sends the entered preferences to the server.

[0568] On the server, information processing tools analyze these conditions and generate a basic travel plan. During this process, learning tools analyze past user usage history and evaluation information to propose an optimized travel plan.

[0569] Furthermore, emotion recognition technology captures the user's facial expressions and voice through the camera and microphone, and analyzes their emotions. By analyzing emotions using software such as Google Cloud Vision API and Amazon Rekognition, content tailored to the user's emotions is recommended. For example, if the user is tired, a relaxing plan will be suggested, while if they are excited, an active activity will be suggested.

[0570] For example, if a user is reviewing travel plans on their device after returning home from work, and their facial expression is analyzed as indicating fatigue, various relaxation-oriented content can be provided. This allows the user to enjoy a more appropriate travel plan.

[0571] An example of a prompt message for a generative AI model is, "The user looks tired. Please recommend relaxing content." Based on this prompt message, the server flexibly adjusts and optimizes the plan based on the user's emotional state, thereby creating a highly satisfying travel experience.

[0572] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0573] Step 1:

[0574] The user uses a terminal to enter their travel preferences (dates and times, destination, budget, activities, and travel companions). This input is verified on the terminal and then sent to the server. The entered data is used directly in the next processing step.

[0575] Step 2:

[0576] The server's information processing system analyzes the travel preferences submitted by the user and generates a basic travel plan for the user. The data used here is the input data of the user's preferences, and the output is a travel plan based on those preferences.

[0577] Step 3:

[0578] The server's learning mechanism utilizes past user usage history and evaluation information to process data and optimize the generated basic travel plan. The input here is past usage data and the current basic plan, and the output is an optimized travel plan.

[0579] Step 4:

[0580] The server's emotion recognition system captures and analyzes the user's facial expressions and vocalizations in real time through the device's camera and microphone. The input data consists of real-time image and audio data acquired from the camera and microphone, while the output is the analyzed emotional state of the user. Google Cloud Vision API and Amazon Rekognition are used for this analysis.

[0581] Step 5:

[0582] The server readjusts content and travel plans to suit the user's emotions based on emotion data obtained from emotion recognition devices. In this step, the user's emotional state is taken as input, and a plan including more suitable content and activities is output. Using a generative AI model, the plan is adjusted based on the prompt message, "The user looks tired. Please recommend relaxing content."

[0583] 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.

[0584] 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.

[0585] 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.

[0586] [Fourth Embodiment]

[0587] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0588] 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.

[0589] 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).

[0590] 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.

[0591] 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.

[0592] 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).

[0593] 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.

[0594] 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 in 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.

[0595] 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.

[0596] 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.

[0597] 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.

[0598] 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.

[0599] 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".

[0600] This invention provides a system that enables users to easily plan and book their trips. The system includes information processing means, booking means, learning means, modification means, and group support means.

[0601] First, the user enters their travel itinerary, destination, budget, desired activities, and travel companions through the device. The device then verifies all entered information to ensure nothing is missing before sending it to the server.

[0602] Next, the server uses information processing tools to generate a basic travel plan based on the received information. At this time, it extracts the most suitable options from databases of transportation, accommodation, and tourist spots to form a provisional plan.

[0603] The server extracts the user's hobbies and preferences from their past travel data and optimizes the plan using machine learning-based learning methods. This step creates a more personalized plan that reflects the user's evaluations and preferences from past trips.

[0604] Once a plan is generated, the server sends it back to the terminal, displaying the detailed plan to the user. The user can review the proposed travel plan and confirm or fine-tune the booking.

[0605] The terminal then sends the user's final selection to the server. This is where the booking process begins, and the server, in conjunction with relevant external services, automatically makes reservations for flights, accommodations, tourist attractions, and other services.

[0606] For example, if a user enters a request such as "I want to take a resort trip within Japan with friends this summer," the server will suggest several popular resort destinations and present a plan combining corresponding flights, hotels, and local activities. The user can then choose the plan that best suits their preferences and complete the entire booking process with just a few clicks.

[0607] Furthermore, if unexpected weather changes or other circumstances necessitate changes in the plan, modification mechanisms are available, allowing for quick changes to the plan in response to new user requests.

[0608] Therefore, the burden on users regarding travel planning is significantly reduced, and a comfortable and highly satisfying travel experience can be provided.

[0609] The following describes the processing flow.

[0610] Step 1:

[0611] The user enters their travel preferences into the terminal. These preferences include travel dates, destination, budget, desired activities, and travel companions. The terminal verifies the entered information and ensures data integrity by prompting the user for corrections as needed.

[0612] Step 2:

[0613] The terminal sends the final confirmed travel conditions data to the server. During this process, it converts the data into a usable format and prepares it for transmission.

[0614] Step 3:

[0615] The server uses information processing tools to analyze the received data. Based on the conditions specified by the user, it gathers data on available transportation, accommodation, and tourist attractions from the database and assembles a basic travel plan.

[0616] Step 4:

[0617] The server uses learning mechanisms to optimize generated travel plans by referencing the user's past travel history and evaluation data. It considers options based on individual user preferences to create personalized suggestions.

[0618] Step 5:

[0619] The server sends an optimized travel plan back to the device. The device displays the received plan to the user, allowing them to review the plan details. The cost, duration, and reviews of the plan are also displayed.

[0620] Step 6:

[0621] The user reviews the proposed plan and requests revisions if necessary, making minor adjustments as needed. Finally, they confirm their chosen travel plan.

[0622] Step 7:

[0623] The terminal sends the confirmed plan information to the server. The server then uses the booking method to automatically execute reservations for flights, accommodations, sightseeing activities, and other services by collaborating with external services.

[0624] Step 8:

[0625] The server sends a reservation completion notification to the terminal, which then provides the user with reservation confirmation information. This includes details such as a hotel reservation confirmation, an electronic ticket, and a schedule summary.

[0626] Step 9:

[0627] If a user needs to change their travel plans immediately before or during their trip, they send a change request from their device to the server. The server then regenerates or adjusts the plan based on the requested changes and presents it to the user again.

[0628] (Example 1)

[0629] 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".

[0630] Traditional travel planning systems required users to manually search for and select from multiple pieces of information, which was time-consuming and cumbersome. Furthermore, they struggled to provide personalized plans that reflected individual user preferences and were inadequate in handling unexpected changes. Therefore, there was a need for a system that would allow users to easily create more efficient and personalized travel plans, while also being flexible enough to accommodate changes.

[0631] 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.

[0632] In this invention, the server includes terminal means, information processing means, learning means, adjustment means, and reservation means. This allows users to automatically generate travel plans with simple information input, provide personalized plans utilizing past data, and respond quickly and appropriately to changes.

[0633] "Terminal means" refers to devices or interfaces that allow users to input travel information and send it to a server.

[0634] "Information processing means" refers to devices and software that analyze information provided by users and manage the process of creating travel plans.

[0635] "Learning method" refers to machine learning technology that uses users' past data to analyze their preferences and optimize travel plans based on that analysis.

[0636] "Adjustment means" refers to devices or processes that reflect any adjustments made by the user to the plan and then optimize the plan again.

[0637] "Reservation methods" refer to devices and processes that automatically make necessary reservations in conjunction with external services based on a confirmed travel plan.

[0638] "Means of modification" refers to functions and processes that allow for a quick and appropriate response when a user requests a change to their travel plan, and to generate a new plan.

[0639] "Group response methods" refer to processes or devices that integrate requests from multiple users and generate travel plans tailored to the individual preferences of each participant.

[0640] This invention provides a system that allows users to easily plan trips and consistently make related reservations. The system includes a user-accessible terminal, a server with powerful data processing capabilities, and a reservation function that integrates with external services.

[0641] First, the user uses a personal computer or mobile device to input their desired travel plan through an interactive interface. This input includes travel dates, destination, budget, desired activities, and travel companions. The device uses validation functions such as JavaScript to verify the input data in real time. After verification is complete, this information is sent to the server via a secure communication protocol.

[0642] The server uses information processing tools to analyze the received information and extracts data from an SQL database that matches the user's criteria. In this process, information such as transportation, accommodation, and tourist attractions is combined to generate a basic hypothetical travel plan. Next, the server utilizes machine learning models (e.g., TensorFlow or Scikit-learn) to analyze the user's past data, extract preferences, and optimize the hypothetical plan.

[0643] Once the plan is finalized, the server uses a RESTful API to send the optimized plan back to the device. On the device, the plan is visually displayed through the user interface, allowing the user to review the contents and make adjustments as needed. This makes it easy for users to customize their plans.

[0644] Once the user confirms the plan details, that information is sent back to the server from the device. The server then uses the booking method to automatically execute the flight and hotel reservations via relevant external service APIs.

[0645] For example, if a user enters conditions such as "I would like to take a resort trip within Japan with a friend this summer," the server will suggest potential resort locations and present an overall travel plan based on those suggestions. An example of a prompt from this system would be, "I'm planning a resort trip to Okinawa from August 15th to 22nd. My budget is under 150,000 yen, and I would like to do scuba diving and relax on the beach."

[0646] This system allows users to easily obtain sophisticated travel plans, significantly reducing the time and effort required for planning.

[0647] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0648] Step 1:

[0649] Users enter basic travel information through the terminal's interface. This includes information such as travel itinerary, destination, budget, desired activities, and travel companions, which are filled into input forms. The terminal validates the input in real time using JavaScript or similar technologies to check for errors. The input data is organized and packaged in JSON format. This packaged data is ready to be sent to the server.

[0650] Step 2:

[0651] The terminal encrypts the entered information using SSL / TLS and sends it to the server using the HTTPS protocol. This communication ensures secure data transmission. The input at this time is travel information prepared on the terminal, which the server receives.

[0652] Step 3:

[0653] The server parses the received JSON data and generates a travel plan using information processing tools. At this stage, it searches databases of transportation, accommodations, tourist spots, etc., using SQL queries and extracts data that matches the criteria. These become the components of a provisional travel plan, and the provisional travel plan is formed as output.

[0654] Step 4:

[0655] The server uses a hypothetical travel plan as a basis, retrieves historical travel data and user preference data, and optimizes the plan using a machine learning model. Models such as Scikit-learn and TensorFlow are used, and personalization reflecting past preferences is performed. The input here is the hypothetical plan and historical data, and the output is an individualized, optimized plan.

[0656] Step 5:

[0657] The server sends an optimized plan back to the device via a RESTful API, and the device visually displays the received data on the user's screen. Specifically, HTML / CSS and JavaScript are used to create a user-friendly and well-organized plan screen. The output is the state in which the user can review the plan details.

[0658] Step 6:

[0659] The user reviews the displayed travel plan and makes minor adjustments to dates and activities as needed. During this process, the device repackages the adjusted data based on the user's actions and prepares to send it to the server. The input here is the user's adjustments, and the output is the adjusted data.

[0660] Step 7:

[0661] The server automatically initiates the booking process based on the coordinated travel plan. It uses the API of an external booking service to confirm flight and accommodation reservations. The input here is the final travel plan, and the output is booking confirmation information.

[0662] This series of steps allows users to efficiently plan their trips and complete bookings automatically.

[0663] (Application Example 1)

[0664] 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".

[0665] Planning and executing a trip involves many choices and adjustments, which can be time-consuming for users. In particular, the inability to experience the destination beforehand makes it difficult to visualize the details of the plan. Furthermore, if users intend to change their plans, it is difficult to quickly and flexibly adapt the schedule.

[0666] 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.

[0667] In this invention, the server includes information processing means that receive travel preferences from the user and generate a travel plan based on them; reservation means that automatically execute reservations with related services based on the generated travel plan; learning means that analyze the user's past transaction history and evaluation data and propose an optimized travel plan; and virtual experience means that use a virtual reality environment for the user to experience a travel destination and adjust the travel plan. This allows the user to experience a specific travel plan in a virtual environment and to flexibly change the plan according to their wishes and circumstances.

[0668] "Information processing means" refers to a device or software that performs processing to generate a travel plan based on travel preferences received from a user.

[0669] A "booking system" is a mechanism that automatically books related services such as airline tickets and accommodations based on a generated travel plan.

[0670] The "learning method" is a technology that analyzes the user's past transaction history and evaluation data, and proposes an optimized travel plan that reflects the results.

[0671] A "virtual experience system" is a system that allows users to experience a travel destination in advance using a virtual reality environment, enabling them to adjust their travel plans.

[0672] The system that realizes this invention operates with a configuration comprising a server, a user terminal, and a virtual reality environment surrounding it. The server acts as an information processing means for receiving travel preferences from the user. When the user enters information such as destination, dates, budget, and travel companions on the terminal, this information is sent to the server. Based on this, the server generates a basic travel plan. In generating the plan, it refers to databases of transportation, accommodation, and tourist attractions to construct a plan that is optimal for the user's conditions.

[0673] The server also has learning capabilities to analyze users' past transaction history and rating data, and optimizes travel plans based on the insights gained. This process uses machine learning algorithms to suggest personalized plans tailored to the user's preferences.

[0674] Furthermore, a distinctive feature of this invention is the virtual experience mechanism. Users can use a VR headset to experience a proposed travel destination in advance within a virtual reality environment. This process utilizes a game engine such as Unity, providing a realistic visual and auditory environment of the travel destination within the virtual space. This allows users to visualize their travel plan more concretely and make adjustments as needed.

[0675] As a concrete example of a prompt message, if you enter text such as "I want to visit a domestic resort with my friends this summer," the server will select popular resort destinations and provide a virtual tour. In this virtual tour, you can experience the characteristics and activities of each resort, make the best choice, and proceed to make an actual reservation.

[0676] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0677] Step 1:

[0678] The user enters their travel preferences through the terminal. This input data includes destination, dates, budget, and travel companions. The terminal verifies that all this data has been entered correctly, formats it, and then sends it to the server.

[0679] Step 2:

[0680] The server retrieves relevant information from the database based on the received travel preferences. Specifically, it extracts the most suitable options from data on transportation, accommodation, and tourist attractions. This generates an initial travel plan. The output is a basic travel plan.

[0681] Step 3:

[0682] The server analyzes the user's past transaction history and rating data, and optimizes travel plans using a generative AI model. Based on the user's preferences, it proposes a more personalized plan. In this process, machine learning algorithms are used to compare the new plan with similar past data and output the results of the refinement.

[0683] Step 4:

[0684] The server sends an optimized travel plan back to the terminal. The user receives this proposed plan through the terminal and reviews its contents. Based on the outputted information, the user can adjust the details of the travel plan.

[0685] Step 5:

[0686] Once the user selects a virtual experience method, the server prepares a virtual reality environment and provides a virtual tour of the selected travel destination. A game engine such as Unity is used here, and the virtual trip is experienced through a VR headset. The input is the user's selected travel plan information, and a visual and auditory virtual environment is output.

[0687] Step 6:

[0688] When the user finalizes their travel plan, the terminal sends that information to the server. The server then uses a booking system to automatically make reservations for flights and accommodations based on the final travel plan. The input is the user's final decision information, and the output is reservation confirmation information.

[0689] 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.

[0690] This invention is a system that uses emotion recognition to adjust a user's travel plan and provide a highly personalized experience. This system includes an emotion engine in addition to information processing means, booking means, learning means, modification means, and group handling means.

[0691] The user enters their travel preferences into the terminal. These preferences include dates, destination, budget, activities, and travel companions. The terminal verifies the input before sending it to the server.

[0692] The server uses information processing tools to analyze the input data and create a basic travel plan. It also utilizes the user's past data to understand their preferences and uses learning tools to optimize the plan. Here, the emotion engine analyzes the user's emotions from the input data and interactions with the device.

[0693] When a user reviews their travel plan, the emotion engine evaluates their reaction in real time and influences the plan's options. For example, if the system determines the user is stressed, more relaxing spots will be added to the plan. Conversely, if the user is excited, more active activities will be recommended.

[0694] In the case of group travel, the Emotion Engine analyzes the emotions of each participant and creates a plan that brings optimal harmony. It takes into account the different emotional states of each participant and proposes content that will satisfy everyone.

[0695] As a concrete example, when a user reviews a plan displayed on a monitor, changes in their facial expressions and tone of voice are detected by the device. The server analyzes this through an emotion engine and fine-tunes the plan as needed. For instance, if the user shows signs of fatigue, new suggestions will be made emphasizing less strenuous modes of transportation or relaxation facilities.

[0696] Finally, once the user selects and confirms their adjusted travel plan, the server uses the booking mechanism to make all related reservations in a single batch. In particular, if the user needs to change their plan at the last minute, a change mechanism will immediately suggest new options.

[0697] This system allows travel plans to adapt to the user's mood and provide a more satisfying experience.

[0698] The following describes the processing flow.

[0699] Step 1:

[0700] The user enters their travel preferences using an interface connected to the device. This includes destination, travel dates, budget, and desired activities. The device temporarily saves the entered data and verifies that the information is in the correct format.

[0701] Step 2:

[0702] The terminal sends the confirmed travel preference data to the server. During data transmission, the data is formatted appropriately to ensure smooth progress in each business process.

[0703] Step 3:

[0704] The server uses information processing tools to analyze the received data and construct the initial travel plan. It retrieves relevant tourist spots, accommodations, and transportation data from the database to generate a basic plan.

[0705] Step 4:

[0706] The server optimizes travel plans through learning mechanisms using the user's past travel history and evaluation data. It creates more detailed plans tailored to the user's preferences.

[0707] Step 5:

[0708] The server uses an emotion engine to analyze the user's emotions based on their input and past behavior. Based on this information, it fine-tunes the travel plan to provide more personalized suggestions.

[0709] Step 6:

[0710] The server sends the optimized plan back to the device. The device displays the plan details to the user, allowing the user to evaluate the content. At this point, the sentiment engine may analyze the user's immediate response and further adjust the plan.

[0711] Step 7:

[0712] The user reviews the displayed plan and requests adjustments if necessary. The user then confirms their final travel plan and sends that information to the server via their device.

[0713] Step 8:

[0714] The server uses booking methods to automatically make reservations for flights, accommodations, and various activities based on the selected travel plan. It works in conjunction with external related services to execute an efficient booking process.

[0715] Step 9:

[0716] If a user needs to change their travel plans, they send a request for the change to the server via their device. The server then uses a change mechanism and an emotional engine to immediately generate a plan that suits the new conditions and presents it to the user again.

[0717] This processing step enables flexible travel planning based on the user's emotions, resulting in a more satisfying travel experience.

[0718] (Example 2)

[0719] 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".

[0720] Modern travel planning faces the challenge of providing highly satisfying experiences for users with diverse needs and emotions. Traditional systems struggle to accurately reflect individual user feelings and preferences, and lack mechanisms to accommodate the individual wishes of participants in group tours. As a result, user satisfaction often declines, and travel experiences frequently fall short of expectations.

[0721] 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.

[0722] In this invention, the server includes data processing means that receive travel preferences from the user and generate a travel plan based on them; emotion analysis means that analyze the user's emotions through user input data and interactions with network terminals and adjust the travel plan based on those emotions; and data reservation means that automatically make reservations for related services based on the generated travel plan. This makes it possible to create detailed plans that are tailored to the emotions and preferences of individual users, and in the case of group travel, it is possible to make adjustments that increase the satisfaction of all participants.

[0723] "Data processing means" refers to a device or system for analyzing travel preferences provided by users and constructing a basic travel plan.

[0724] "Emotional analysis means" refers to technologies and methods that identify a user's emotional state based on user input information and interactions with the device, and then optimize travel plans in real time based on that information.

[0725] "Data booking methods" refer to technologies and processes that automatically execute bookings for related services, including accommodations and transportation, in accordance with a generated travel plan.

[0726] "Data learning tools" refer to functions or systems that analyze a user's past travel history and evaluation information, and use that information to improve the accuracy and personalization of travel plans.

[0727] "Group response methods" refer to a process or system for integrating requests and information from multiple users, taking into account the emotional state of each participant, and creating a harmonious travel plan as a whole.

[0728] This invention comprises a system for highly personalizing users' travel plans and providing an experience tailored to each individual. The entire system is operated primarily by three entities: a server, terminals, and users.

[0729] First, the user enters their travel preferences using a device. The device then verifies this information and sends it to the server. Specific devices and platforms that can be used here include smartphones, tablets, and personal computers.

[0730] Next, the server analyzes the received data using data processing tools and formulates a basic travel plan. The server utilizes a generative AI model to optimize the travel plan, taking into account the user's past history and evaluation data. In addition, sentiment analysis tools analyze the user's emotions in real time through user input data and interactions with network terminals, and adjust the travel content accordingly.

[0731] In particular, emotion analysis can utilize facial recognition and voice tone analysis technologies to gain a detailed understanding of the user's emotional state. Based on this, the server can add relaxing and exciting activities to the travel plan.

[0732] As a concrete example, while a user is reviewing their travel plan, the device captures the user's facial expression. This data is sent to a server, where an emotion analysis system begins its analysis. If the system determines that the user is calm or excited, it adjusts the plan accordingly.

[0733] This system allows for adjustments to the generated AI model using prompts. For example, a possible prompt might be, "The user is showing an excited expression, so please suggest an active schedule." In this way, the system can automatically suggest and provide the user with an appropriate travel plan based on their emotions.

[0734] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0735] Step 1:

[0736] Users enter their travel preferences using a terminal. This includes dates, destination, budget, activities, and travel companions. The entered data is written through the terminal's input form and validated for correct formatting. After the input is confirmed, the data is sent to the server.

[0737] Step 2:

[0738] The server receives user preference data from the terminal using data processing tools. Data analysis is then performed to generate a basic travel plan. Specifically, it collects information about the selected destination and uses an AI model to suggest optimal travel routes and sightseeing spots. The generated plan is stored for future sentiment analysis.

[0739] Step 3:

[0740] The server uses emotion analysis tools to analyze additional data from the user, specifically real-time data on facial expressions and voice tone. Each piece of data is analyzed by an emotion recognition algorithm to determine the user's emotional state. For example, facial expression data can be used to determine whether the user is relaxed or excited. This analysis provides useful indicators for adjusting the travel plan, and the plan data is updated accordingly.

[0741] Step 4:

[0742] The server uses a generative AI model to optimize the plan based on information obtained from emotion analysis. Specifically, this includes changing activity suggestions according to emotions and fine-tuning the schedule. For example, if fatigue is detected, suggestions for relaxation facilities will be enhanced. Once this process is complete, the final travel plan is generated and sent to the device.

[0743] Step 5:

[0744] Users review their final travel plans on their devices, making adjustments or finalizing them as needed. Once finalized, the server automatically uses data booking mechanisms to execute reservations for accommodation and transportation. At this stage, additional data and options, such as cancellation policies and price recalculations, are handled as required.

[0745] This entire system's program processing allows travel plans to be flexibly and precisely adjusted based on the user's emotions and specific preferences.

[0746] (Application Example 2)

[0747] 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".

[0748] In modern travel planning, it's common for travel plans to be suggested based on the user's interests and schedule. However, suggesting plans that take into account the traveler's emotional state is extremely difficult, and it's rare to provide content and plans that adapt to the traveler's real-time emotions. As a result, there is a challenge in creating travel plans that enhance satisfaction because they cannot flexibly respond to changes in the traveler's emotions.

[0749] 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.

[0750] In this invention, the server includes information processing means that receive travel preferences from the user and generate a travel plan based on them; reservation means that automatically make reservations for related services based on the generated travel plan; learning means that analyze the user's past usage history and evaluation information and propose an optimized travel plan; and emotion recognition means that analyze the user's facial expressions and vocalizations and recommend content that matches their emotions. This makes it possible to provide travel plans and content optimized for the user's emotions.

[0751] "Information processing means" refers to a device that has the function of generating a travel plan based on the travel preferences entered by the user.

[0752] A "booking device" is a device that automatically makes reservations for related services based on a generated travel plan.

[0753] A "learning tool" is a device that analyzes a user's past usage history and evaluation information and has the function of proposing an optimized travel plan.

[0754] An "emotion recognition device" is a device that analyzes a user's facial expressions and vocalizations and recommends content that corresponds to their emotions.

[0755] "User preferences" refer to the conditions that users desire when traveling, such as the date and time of the trip, destination, budget, activities, and travel companions.

[0756] The system for carrying out this invention consists of a server and a terminal. The server is equipped with information processing means, reservation means, learning means, and emotion recognition means. The terminal is a device for the user to input their travel preferences and is equipped with a camera and a microphone.

[0757] First, the user uses a terminal to enter their travel preferences. These preferences include the date and time of the trip, destination, budget, activities, and travel companions. The terminal then sends the entered preferences to the server.

[0758] On the server, information processing tools analyze these conditions and generate a basic travel plan. During this process, learning tools analyze past user usage history and evaluation information to propose an optimized travel plan.

[0759] Furthermore, emotion recognition technology captures the user's facial expressions and voice through the camera and microphone, and analyzes their emotions. By analyzing emotions using software such as Google Cloud Vision API and Amazon Rekognition, content tailored to the user's emotions is recommended. For example, if the user is tired, a relaxing plan will be suggested, while if they are excited, an active activity will be suggested.

[0760] For example, if a user is reviewing travel plans on their device after returning home from work, and their facial expression is analyzed as indicating fatigue, various relaxation-oriented content can be provided. This allows the user to enjoy a more appropriate travel plan.

[0761] An example of a prompt message for a generative AI model is, "The user looks tired. Please recommend relaxing content." Based on this prompt message, the server flexibly adjusts and optimizes the plan based on the user's emotional state, thereby creating a highly satisfying travel experience.

[0762] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0763] Step 1:

[0764] The user uses a terminal to enter their travel preferences (dates and times, destination, budget, activities, and travel companions). This input is verified on the terminal and then sent to the server. The entered data is used directly in the next processing step.

[0765] Step 2:

[0766] The server's information processing system analyzes the travel preferences submitted by the user and generates a basic travel plan for the user. The data used here is the input data of the user's preferences, and the output is a travel plan based on those preferences.

[0767] Step 3:

[0768] The server's learning mechanism utilizes past user usage history and evaluation information to process data and optimize the generated basic travel plan. The input here is past usage data and the current basic plan, and the output is an optimized travel plan.

[0769] Step 4:

[0770] The server's emotion recognition system captures and analyzes the user's facial expressions and vocalizations in real time through the device's camera and microphone. The input data consists of real-time image and audio data acquired from the camera and microphone, while the output is the analyzed emotional state of the user. Google Cloud Vision API and Amazon Rekognition are used for this analysis.

[0771] Step 5:

[0772] The server readjusts content and travel plans to suit the user's emotions based on emotion data obtained from emotion recognition devices. In this step, the user's emotional state is taken as input, and a plan including more suitable content and activities is output. Using a generative AI model, the plan is adjusted based on the prompt message, "The user looks tired. Please recommend relaxing content."

[0773] 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.

[0774] 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.

[0775] In the above embodiment, an example was given in which the 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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."

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] The following is further disclosed regarding the embodiments described above.

[0795] (Claim 1)

[0796] Information processing means that receives travel preferences from a user and generates a travel plan based on them,

[0797] A booking mechanism that automatically executes reservations with related services based on the generated travel plan,

[0798] A learning method that analyzes the user's past transaction history and evaluation data to propose an optimized travel plan,

[0799] A system that includes this.

[0800] (Claim 2)

[0801] The system according to claim 1, further comprising a modification means for providing a new travel plan that is appropriate to the current situation when a user requests a change to their travel plan.

[0802] (Claim 3)

[0803] The system according to claim 1, further comprising a means for handling group requests, which integrates requests from multiple users and generates a travel plan that meets the preferences of each participant.

[0804] "Example 1"

[0805] (Claim 1)

[0806] A terminal device that collects travel-related information from users and performs automatic input verification,

[0807] An information processing means that extracts candidate transportation methods, accommodations, and tourist spots from a database based on collected information and generates a provisional travel plan,

[0808] A learning method that analyzes user preferences from past data and optimizes personalized travel plans based on machine learning,

[0809] A means for providing the generated travel plan to the user terminal and readjusting it to reflect any adjustments made by the user,

[0810] A booking method that automatically executes reservations through integration with external services based on the finalized travel plan,

[0811] A system that includes this.

[0812] (Claim 2)

[0813] The system according to claim 1, further comprising a modification means for presenting a properly modified travel plan in real time when a user requests a change to the travel plan.

[0814] (Claim 3)

[0815] The system according to claim 1, further comprising a means for integrating travel requests from multiple users and creating a customized travel plan that meets the needs of each user.

[0816] "Application Example 1"

[0817] (Claim 1)

[0818] Information processing means that receives travel preferences from a user and generates a travel plan based on them,

[0819] A booking mechanism that automatically executes reservations with related services based on the generated travel plan,

[0820] A learning method that analyzes the user's past transaction history and evaluation data to propose an optimized travel plan,

[0821] A virtual experience method that allows users to experience travel destinations and adjust travel plans using a virtual reality environment,

[0822] A system that includes this.

[0823] (Claim 2)

[0824] The system according to claim 1, further comprising a modification means for providing a new travel plan that is appropriate to the current situation when a user requests a change to their travel plan.

[0825] (Claim 3)

[0826] The system according to claim 1, further comprising a means for handling group requests, which integrates requests from multiple users and generates a travel plan that meets the preferences of each participant.

[0827] "Example 2 of combining an emotion engine"

[0828] (Claim 1)

[0829] A data processing means that receives travel preferences from a user and generates a travel plan based on those preferences,

[0830] An emotion analysis means that analyzes the user's emotions through user input data and interactions with network terminals, and adjusts the travel plan based on those emotions,

[0831] A data reservation means that automatically executes reservations for related services based on the generated travel plan,

[0832] A data learning method that analyzes users' past history data and evaluation data to propose an optimized travel plan,

[0833] A system that includes this.

[0834] (Claim 2)

[0835] The system according to claim 1, further comprising a modification means for providing a new travel plan that is appropriate to the current situation when a user requests a change to their travel plan, and for fine-tuning the plan to reflect the user's real-time emotions.

[0836] (Claim 3)

[0837] The system according to claim 1, further comprising a group response means for integrating requests from multiple users and generating a travel plan that takes into account the state of each participant through sentiment analysis.

[0838] "Application example 2 when combining with an emotional engine"

[0839] (Claim 1)

[0840] Information processing means that receives travel preferences from a user and generates a travel plan based on them,

[0841] A booking mechanism that automatically executes reservations for related services based on the generated travel plan,

[0842] A learning method that analyzes the user's past usage history and evaluation information to propose an optimized travel plan,

[0843] An emotion recognition method that analyzes the user's facial expressions and vocalizations and recommends content that matches their emotions,

[0844] A system that includes this.

[0845] (Claim 2)

[0846] The system according to claim 1, further comprising a modification means for providing a new travel plan that is appropriate to the current situation when a user requests a change to their travel plan.

[0847] (Claim 3)

[0848] The system according to claim 1, further comprising a means for integrating requests from multiple users and generating a travel plan that is tailored to the wishes and feelings of each participant. [Explanation of Symbols]

[0849] 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

1. Information processing means that receives travel preferences from a user and generates a travel plan based on them, A booking mechanism that automatically executes reservations with related services based on the generated travel plan, A learning method that analyzes the user's past transaction history and evaluation data to propose an optimized travel plan, A system that includes this.

2. The system according to claim 1, further comprising a modification means for providing a new travel plan that is appropriate to the current situation when a user requests a change to their travel plan.

3. The system according to claim 1, further comprising a means for handling group requests, which integrates requests from multiple users and generates a travel plan that meets the preferences of each participant.

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