System

Abstract

A system is provided.SOLUTION: A system comprising: a terminal configured to receive a travel reservation from a user; a server including a machine learning model configured to learn past reservation data; means for generating, by the server, an appropriate reservation candidate based on a user profile and presenting the reservation candidate to the terminal; and means for recording and confirming a reservation selected by the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Currently, planning a trip takes a lot of time and effort, and there are many uncertainties and uncertainties. Young people and remote workers in particular have diverse travel needs, so they need support to make quick and appropriate plans. Local tourism industry stakeholders also need efficient reservation management based on data to provide services that meet user needs. To solve these problems, there is a need for a system that automatically suggests reservation options based on past reservation data and allows users to easily complete reservations. [Means for solving the problem]

[0005] To solve this problem, the present invention provides the following configuration: a system including a terminal that accepts travel reservations from users, a server that includes a machine learning model that learns from past reservation data, a means for the server to generate appropriate reservation candidates based on a user profile and present them to the terminal, and a means for recording and confirming the reservations selected by the user. This system allows users to complete travel reservations more easily and quickly, and enables tourism industry personnel to provide services more efficiently. In addition, the system can learn features extracted from past data using the machine learning model, and suggest optimal reservation candidates based on the user's interests and age.

[0006] "Terminal" means a device operated by a user to make travel reservations and enter user profiles.

[0007] The "server" is the core of the system that stores past booking data and uses machine learning models to generate booking suggestions based on user profiles.

[0008] A "machine learning model" is an algorithm that learns features extracted from past reservation data and generates appropriate reservation candidates based on user profiles.

[0009] A "user profile" is a dataset containing information such as a user's age, interests, and past behavior that the machine learning model uses to generate appropriate booking suggestions.

[0010] "Appropriate booking suggestions" are suggestions of tourist attractions and activities that best suit a user's needs, generated by a machine learning model based on the user profile.

[0011] "Means to record and confirm a reservation" refers to the function that allows a user to save the reservation details selected by the user in the system and perform operations to officially complete the travel reservation.

[0012] "Historical booking data" is a dataset containing information about past travel bookings made by the user and other users, and serves as the foundation for machine learning models to learn from.

[0013] A "recommendation algorithm" is a method in which a machine learning model selects the best reservation candidates under certain conditions based on user profile and past reservation data. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0015] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0018] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0019] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0020] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0034] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0035] The present invention provides a system that uses a machine learning model that has learned from past reservation data to suggest suitable reservation options when a user plans a trip, allowing the user to easily complete a reservation. Specific embodiments of the invention are described below.

[0036] System configuration

[0037] 1. Devices that accept travel reservations from users:

[0038] A terminal is a device operated by a user, used to make travel reservations and enter user profiles. Examples include smartphones, PCs, and tablets.

[0039] 2. A server containing a machine learning model trained on past booking data:

[0040] The server is the heart of the system, storing past booking data and using machine learning models to generate booking suggestions based on user profiles.

[0041] 3. How to generate suitable booking candidates:

[0042] The server uses a machine learning model to learn features extracted from past booking data and runs an algorithm to generate optimal booking suggestions based on the user profile, taking into account the user's age, interests, past behavioral data, etc.

[0043] 4. Means for recording and confirming the reservation selected by the User:

[0044] The server saves the reservation details selected by the user in the system and provides a means to officially complete the reservation, allowing users to easily confirm their reservation and smoothly proceed with their travel planning.

[0045] System operation explanation

[0046] Device:

[0047] Users access online booking sites and applications from their devices.

[0048] Enter user profile information into the device, including age, interests, and past booking history.

[0049] server:

[0050] When a user enters profile information, the server receives it.

[0051] The server stores past reservation data and has trained the data using machine learning models.

[0052] Based on the received user profile, the server performs data analysis using machine learning models to generate appropriate reservation candidates.

[0053] Generate and present booking suggestions:

[0054] The server then presents the generated booking suggestions to the user's device, such as options like "a five-day trip to Paris" or "a seven-day trip to New York."

[0055] User selection and booking confirmation:

[0056] The user selects the desired reservation from the presented options.

[0057] Once the selection is complete, the terminal transmits the information to the server.

[0058] The server records the selected reservation and officially confirms the reservation. Once the reservation is confirmed, a confirmation message is sent to the user.

[0059] Specific examples

[0060] For example, if a user enters profile information such as "25 years old and interested in culture," the server can prioritize "culturally related tourist destinations" or "tourist destinations that have been booked by users with the same interests in the past" based on past reservation data. The server analyzes past reservation data (e.g., a 5-day trip to Paris or a 7-day trip to New York) and presents the most suitable reservation candidates.

[0061] As described above, by using this system, users can efficiently plan their trips, and those involved in the tourism industry can provide more efficient services.

[0062] The processing flow will be explained below.

[0063] Step 1:

[0064] The server collects past booking data and stores it in a database, including tourist destinations, length of stay, booking date and time, and user profile information (age, interests, etc.).

[0065] Step 2:

[0066] The server initializes the machine learning model and trains it on the stored historical reservation data. During this training phase, features of each reservation are extracted, forming the basis for the model to make future predictions and recommendations.

[0067] Step 3:

[0068] Users access an online booking site or application from their device and enter their profile information, which may include their age, interests, and past booking history.

[0069] Step 4:

[0070] The device transmits the user-entered profile information to the server, which receives it and processes it as input data for generating suitable reservation candidates.

[0071] Step 5:

[0072] Based on the received user profile, the server uses a trained machine learning model, which performs algorithmic processing to suggest the best sightseeing spots and activities according to the user's interests and age.

[0073] Step 6:

[0074] The server generates booking suggestions based on the machine learning model and sends them to the user's device, allowing the user to choose the optimal travel plan from multiple options.

[0075] Step 7:

[0076] The user selects the desired sightseeing spots and activities from the presented reservation options. Once the selection is complete, the device sends the information to the server.

[0077] Step 8:

[0078] The server records the reservation based on the user's selection and officially confirms the reservation. This information is stored in a database for future recommendations.

[0079] Step 9:

[0080] The server will send a confirmation message to the user's device to inform them that the reservation has been confirmed, allowing the user to confirm that the reservation has been completed.

[0081] Step 10:

[0082] The user will receive a confirmation message confirming that the booking has been confirmed, at which point the user's travel plans are complete.

[0083] Through these steps, the system helps users complete travel reservations efficiently and easily. The server also continuously collects data and learns from it, allowing it to make more accurate recommendations.

[0084] Example 1

[0085] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0086] When booking travel, users lack an efficient way to find the best options based on their interests and past behavior. They also need a way to quickly and accurately process and confirm their booking selections. This would enable users to plan their trips more efficiently and travel providers to provide more efficient services.

[0087] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0088] In this invention, the server includes a terminal that accepts travel reservations from users, a server that includes a machine learning model that learns from past reservation data, means for the server to generate appropriate reservation candidates based on a user profile and present them to the terminal, means for recording and confirming reservations selected by the user, means for presenting the generated reservation candidates to the user's terminal, and means for transmitting selected reservation information from the user's terminal to the server. This allows the user to be presented with optimal reservation candidates based on their own profile information, allowing them to efficiently plan their trip and quickly confirm the selected reservation.

[0089] "User" means an individual who makes a travel reservation and uses the System to enter their profile information and select reservation options.

[0090] "Device" means a device operated by a User to book a trip or enter profile information, including a smartphone, PC, or tablet.

[0091] "Server" refers to the central part of the system that stores past booking data and uses machine learning models to generate booking suggestions based on user profiles.

[0092] "Machine Learning Model" refers to a computational means that includes an algorithm that learns from past booking data to generate suitable booking suggestions based on user profile information.

[0093] "Profile Information" refers to personal information entered by a user, such as age, interests, and past booking history.

[0094] "Booking Suggestions" refers to a list of suggested travel itineraries and accommodations generated by machine learning models based on a user's profile.

[0095] "Recording" refers to the process of saving the reservation selections made by the user in a database.

[0096] "Confirmation" refers to the process of officially validating the recorded reservation and sending a confirmation message to the user.

[0097] "Database" refers to a software system for storing and managing user profile information and past reservation data.

[0098] This invention is a system that uses a machine learning model that has learned from past reservation data to suggest suitable reservation options when a user plans a trip, allowing the user to easily complete the reservation. This system presents the most suitable reservation options to the user based on profile information from the user. It also records the reservation details selected by the user and supports a series of processes to finalize the reservation.

[0099] System configuration

[0100] 1. Devices that accept travel reservations from users:

[0101] A terminal is a device operated by a user to make travel reservations and enter user profiles. Specific hardware examples include smartphones, PCs, and tablets. A terminal is used by a user to access online booking sites and dedicated applications.

[0102] 2. A server containing a machine learning model trained on past booking data:

[0103] The server uses past reservation data stored in a database to train a machine learning model using software such as Python's Scikit-learn and TensorFlow. The server then analyzes the data to generate optimal reservation suggestions based on user profile information.

[0104] 3. How to generate suitable booking candidates:

[0105] The server uses a machine learning model to learn features extracted from past booking data and then runs an algorithm to generate optimal booking suggestions based on the user profile, taking into account the user's age, interests, past behavioral data, etc.

[0106] 4. Means for recording and confirming the reservation selected by the User:

[0107] The server saves the reservation details selected by the user in a database and officially confirms the reservation. During this process, a confirmation message (e.g., a reservation confirmation email) is sent to the user.

[0108] Specific examples

[0109] For example, if a user enters profile information such as "I'm 25 years old and interested in culture," the system will operate as follows:

[0110] Device:

[0111] Users access online booking sites or applications from their devices and enter profile information, such as age (25 years old) and cultural interests.

[0112] server:

[0113] Based on the profile information received from the user, we look up past booking data of users with similar interests.

[0114] Use machine learning models (e.g., Scikit-learn or TensorFlow) to generate suitable appointment candidates.

[0115] Presenting generated booking suggestions:

[0116] It prioritizes cultural destinations, such as "5-day trip to Paris" or "7-day trip to New York."

[0117] Prompt Sentence Examples

[0118] 1. "Suggest the best travel destinations for a 25-year-old interested in culture. Explain why."

[0119] 2. "Generate a recommended travel plan for a couple in their 30s based on past data."

[0120] In this way, the system allows users to plan their trips quickly and effectively, and travel agents to provide efficient and personalized service.

[0121] The above is a specific embodiment of the present invention.

[0122] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0123] Step 1:

[0124] User access to booking site and profile information input

[0125] A user uses a terminal to access an online reservation site or dedicated application. This allows the user to connect to the system and access a profile information input screen. The terminal receives profile information entered by the user, such as age, interests, and past reservation history. Input: User profile information. Output: Profile information is saved in the terminal's input form.

[0126] Step 2:

[0127] Sending profile information to the server

[0128] The device sends the entered profile information to the server. This sending is performed using a protocol such as an HTTP POST request. Input: Profile information stored on the device. Output: Request to send profile information to the server.

[0129] Step 3:

[0130] Server receives and stores profile information

[0131] The server receives the profile information sent from the device. It then stores this information in a database. Input: Received profile information. Output: Profile information stored in the database.

[0132] Step 4:

[0133] Use machine learning models that learn from past booking data

[0134] The server uses the stored past reservation data to train a machine learning model. Specific software used is Scikit-learn and TensorFlow. The machine learning model extracts features from the past data and prepares to generate optimal reservation candidates based on the user profile. Input: Past reservation data. Output: Trained machine learning model.

[0135] Step 5:

[0136] Generate booking suggestions based on profile information

[0137] The server uses a machine learning model to analyze the profile information entered by the user and generate appropriate reservation suggestions. For example, it uses Python's Scikit-learn and TensorFlow libraries to suggest the best travel destinations and plans based on information such as the user's age and interests. Input: User profile information, trained machine learning model. Output: Generated reservation suggestion list.

[0138] Step 6:

[0139] Present the generated reservation candidates to the user's device

[0140] The server sends the generated reservation candidate list to the user's device. This is also usually done using an HTTP response. The device receives the response from the server and prepares to display the reservation candidates on the screen. Input: Generated reservation candidate list. Output: Response from the server, reservation candidate list.

[0141] Step 7:

[0142] User selection of reservation options

[0143] The user selects the desired reservation from the presented options. For example, they select "5-day trip to Paris." The selected information is temporarily saved on the user's device. Input: List of reservation options. Output: User's selection information.

[0144] Step 8:

[0145] Sending selected reservation information to the server

[0146] The terminal sends the selected reservation information to the server. This process is also performed using an HTTP POST request. Input: User's selected information. Output: Request to send selected information to the server.

[0147] Step 9:

[0148] The server records and confirms the reservation

[0149] The server receives the selection information and stores it in a database. It then officially confirms the reservation and sends a confirmation message to the user. The confirmation message is sent to the user via email or other means. Input: Selected reservation information. Output: Reservation information stored in the database, confirmation message to the user.

[0150] The above is the specific flow of processing in this system.

[0151] (Application example 1)

[0152] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0153] With traditional travel reservation systems, users had to spend a lot of time and effort to find the right travel plan. It was also difficult to find a plan that suited their individual needs. Furthermore, users were unable to virtually experience their chosen travel destination, which often led to uncertainty about choosing a travel destination.

[0154] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0155] In this invention, the server includes a terminal that accepts travel reservations from users, a server that includes a machine learning model that learns from past reservation data, means for the server to generate appropriate reservation candidates based on a user profile and present them to the terminal, means for providing a travel destination experience in a virtual space based on the provided profile information, means for analyzing data entered by the user through voice or manual input and proposing an optimal travel plan, and means for recording and confirming reservations selected by the user. This allows users to easily find efficient and personalized travel plans and further reduces anxiety about selecting a travel destination through the virtual experience.

[0156] "Terminal" means a device operated by a user to make travel reservations and enter user profiles.

[0157] A "machine learning model" is a model that includes an algorithm for learning from past reservation data and generating appropriate reservation candidates.

[0158] The "Server" is the central part of the system that stores past booking data and uses machine learning models to learn from the data to generate suitable booking suggestions based on user profiles.

[0159] A "user profile" is a profile that includes information such as a user's age, interests, and past behavioral data.

[0160] "Reservation candidates" are travel plan candidates that the server generates based on the user profile and that the user can select.

[0161] A "virtual space" is a virtual environment generated using computer technology in which users can have experiences.

[0162] "Virtual experience" refers to the visual and tactile reproduction of the scenery and environment of a travel destination within a virtual space, allowing users to experience it.

[0163] "Voice input" is an input method in which data input orally by the user is analyzed using voice recognition technology.

[0164] "Manual input" refers to data that is entered directly by the user using a keyboard or touch operation.

[0165] A "recommendation algorithm" is a computational method for suggesting optimal travel plans based on a user's interests and age.

[0166] The present invention provides a system for efficiently selecting an appropriate travel plan by utilizing a virtual space to provide a more realistic experience when users plan their trip. This system is realized by combining a terminal, a server, a user profile, a machine learning model, a virtual space, a virtual experience, voice input, manual input, and a recommendation algorithm.

[0167] System configuration

[0168] Device:

[0169] A device used by a user to make travel reservations and enter user profiles. Examples include smart glasses and head-mounted displays (HMDs).

[0170] server:

[0171] This is the central part of the system that stores past reservation data and uses machine learning models to learn from the data. The server is equipped with an advanced GPU, allowing for high-speed data analysis.

[0172] User profile:

[0173] A profile containing information such as a user's age, interests, and past behavioral data, which is used to generate travel plans.

[0174] Machine learning models:

[0175] The model includes an algorithm that learns from past reservation data and generates suitable reservation candidates based on user profiles. It uses machine learning frameworks such as TensorFlow and Scikit-learn.

[0176] Virtual Space:

[0177] It is a virtual environment generated using computer technology in which users can experience travel destinations, using Unity and Blender for 3D modeling.

[0178] Virtual Experience:

[0179] It refers to the visual and tactile reproduction of the scenery and environment of a travel destination in a virtual space, allowing users to experience it.

[0180] Audio Input:

[0181] It is an input method that uses speech recognition technology to analyze data entered orally by the user. It uses the NLP library spaCy.

[0182] Manual entry:

[0183] This refers to data that the user directly inputs using a keyboard or touch panel.

[0184] Recommendation algorithm:

[0185] It is a calculation method for suggesting optimal travel plans based on the user's interests and age.

[0186] System operation explanation

[0187] Enter your profile information:

[0188] Users put on smart glasses or an HMD, access the device, and then provide profile information (such as age, interests, and past travel history) via voice or manual input.

[0189] Parsing profile information:

[0190] Based on the received profile information, the server analyzes the data using a machine learning model that has learned from past booking data, and generates a travel plan that is suitable for the user.

[0191] Providing virtual experiences of your destination:

[0192] Based on the proposed itinerary, the destination environment is recreated in a virtual space, allowing users to experience the virtual world and visually confirm the atmosphere and tourist attractions of the destination.

[0193] Select and confirm your reservation:

[0194] The user selects their preferred travel plan through the virtual experience. Once the selection is complete, the server records the information and officially confirms the reservation.

[0195] Specific examples

[0196] For example, consider a system that allows users to create detailed travel plans and complete reservations through a virtual experience from the comfort of their own home, without having to visit a travel agency. When a user puts on smart glasses and voice-inputs, "I'm 25 years old and interested in culture," the system will refer to past data and suggest suitable tourist spots. For example, it will provide a virtual tour of the Louvre Museum in Paris or the MoMA in New York. Through this experience, users can select their travel destination and easily confirm their reservation.

[0197] Example prompt sentence:

[0198] "A 25-year-old culture-loving user has previously planned trips to Paris and New York. Suggest a new itinerary that best suits him / her."

[0199] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0200] Step 1:

[0201] Enter your profile information

[0202] A user accesses the reservation system wearing smart glasses or a head-mounted display (HMD), and provides profile information such as age, interests, and past travel history through voice or manual input.

[0203] Input: Information provided by the user such as age, interests, and past travel history.

[0204] Output: Sent to the server as profile data.

[0205] Step 2:

[0206] Analyzing profile information

[0207] The server parses the received profile information. First, if it is voice input, it converts it to text data using an NLP library (e.g., spaCy). Second, it formats the profile information so that it can be easily processed by machine learning models.

[0208] Input: Formatted profile data.

[0209] Output: Analysis results (user's age, interests, past travel history).

[0210] Step 3:

[0211] Generate optimal travel plans

[0212] The server then generates an appropriate itinerary based on the profile data analyzed, using a machine learning model (e.g., TensorFlow or Scikit-learn) trained on past booking data, which uses a recommendation algorithm based on the user's age and interests.

[0213] Input: Analyzed profile data, past booking data.

[0214] Output: A list of suggested itineraries suitable for the user.

[0215] Step 4:

[0216] Providing virtual experiences for travel plans

[0217] The server recreates the environment of the destination in a virtual space based on the generated travel plan. Using Unity or Blender, 3D modeling is performed, allowing users to experience the destination in the virtual space. Through the virtual experience, users can visually confirm the atmosphere and tourist spots of the destination.

[0218] Input: A list of itinerary suggestions.

[0219] Output: A travel destination experience in a virtual space.

[0220] Step 5:

[0221] Select and book your travel plan

[0222] The user selects their preferred travel plan through the virtual experience. Once the selection is complete, the device sends the information to the server, which records the selected plan and confirms the official reservation.

[0223] Input: The travel plan selected by the user.

[0224] Output: Recorded booking information, confirmed travel booking.

[0225] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0226] The present invention is a system that uses a machine learning model that learns from past reservation data when a user plans a trip and an emotion engine that recognizes the user's emotions to suggest appropriate reservation options, allowing the user to easily complete a reservation. Specific embodiments of the invention are described below.

[0227] System configuration

[0228] 1. Devices that accept travel reservations from users:

[0229] A terminal is a device operated by a user, used to make travel reservations and enter user profiles. Examples include smartphones, PCs, and tablets.

[0230] 2. A server containing a machine learning model trained on past booking data:

[0231] The server is the heart of the system, storing past booking data and using machine learning models to generate booking suggestions based on user profiles.

[0232] 3. How to generate suitable booking candidates:

[0233] The server uses a machine learning model to learn features extracted from past booking data and runs an algorithm to generate optimal booking suggestions based on the user profile, taking into account the user's age, interests, past behavioral data, etc.

[0234] 4. Means for recording and confirming the reservation selected by the User:

[0235] The server saves the reservation details selected by the user in the system and provides a means to officially complete the reservation, allowing users to easily confirm their reservation and smoothly proceed with their travel planning.

[0236] 5. Emotion engine that recognizes user emotions:

[0237] The emotion engine analyzes user input data, facial expressions, voice, and other information to assess the user's current emotions, which are then used to generate more appropriate reservation candidates.

[0238] System operation explanation

[0239] Device:

[0240] Users access online booking sites and applications from their devices.

[0241] The device inputs user profile information and current emotions, including age, interests, past booking history, etc. Facial expressions and voice data can also be input using the emotion engine.

[0242] server:

[0243] The user enters profile information and emotion data, which is received by the server.

[0244] The server stores past reservation data and has trained the data using machine learning models.

[0245] The server analyzes the data using a machine learning model based on the received user profile and emotion data to generate appropriate reservation candidates.

[0246] Generate and present booking suggestions:

[0247] The server then presents the generated booking suggestions to the user's device, such as options like "a five-day trip to Paris" or "a seven-day trip to New York."

[0248] The emotion engine can take into account the user's current emotions (e.g., excitement, tension, anxiety, etc.) to present reservation suggestions that better suit the user's mood.

[0249] User selection and booking confirmation:

[0250] The user selects the desired reservation from the presented options.

[0251] Once the selection is complete, the terminal transmits the information to the server.

[0252] The server records the selected reservation and officially confirms the reservation. Once the reservation is confirmed, a confirmation message is sent to the user.

[0253] Specific examples

[0254] For example, if a user enters profile information and emotion data such as "25 years old, interested in culture, and currently feeling excited," the server can prioritize "culturally related tourist spots" or "tourist spots previously booked by users with the same interests" based on past reservation data. Furthermore, the emotion engine can recognize the user's emotion of "excitement" and suggest reservation options that match that emotion (for example, "seeing a musical in New York" or "a wine tour in Paris").

[0255] As described above, by using this system, users can efficiently plan their trips and tourism industry personnel can provide more efficient services. In addition, the emotion engine enables more tailored suggestions to individual users, improving user satisfaction.

[0256] The processing flow will be explained below.

[0257] Step 1:

[0258] The server collects past booking data and stores it in a database, including tourist destinations, length of stay, booking date and time, and user profile information (age, interests, etc.).

[0259] Step 2:

[0260] The server initializes the machine learning model and trains it on the stored historical reservation data. During this training phase, features of each reservation are extracted, forming the basis for the model to make future predictions and recommendations.

[0261] Step 3:

[0262] Users access an online booking site or application from their device and enter their profile information and current emotions. Profile information includes age, interests, past booking history, etc. Facial expressions and voice data can also be input using the emotion engine.

[0263] Step 4:

[0264] The device transmits the user-entered profile information and emotion data to the server, which receives and processes this information as input data for generating suitable reservation candidates.

[0265] Step 5:

[0266] Based on the received user profile and emotion data, the server uses a trained machine learning model, which performs algorithmic processing to suggest the best sightseeing spots and activities according to the user's interests, age, and current emotions.

[0267] Step 6:

[0268] The server utilizes an emotion engine to assess the user's current emotion, using natural language processing algorithms and image and audio analysis techniques.

[0269] Step 7:

[0270] The server combines data from the machine learning model and emotion engine to generate optimal booking suggestions, which are best suited to the user's current emotions and profile.

[0271] Step 8:

[0272] The server sends the generated reservation candidates to the user's device, where the user can choose from multiple options (e.g., "a 5-day trip to Paris" or "a 7-day trip to New York").

[0273] Step 9:

[0274] The user selects the desired sightseeing spots and activities from the presented reservation options. Once the selection is complete, the device sends the information to the server.

[0275] Step 10:

[0276] The server records the reservation based on the user's selection and officially confirms the reservation. This information is stored in a database for future recommendations.

[0277] Step 11:

[0278] The server will send a confirmation message to the user's device to inform them that the reservation has been confirmed, allowing the user to confirm that the reservation has been completed.

[0279] Step 12:

[0280] The user will receive a confirmation message confirming that the booking has been confirmed, at which point the user's travel plans are complete.

[0281] Through these steps, the system helps users complete travel bookings efficiently and easily, and the emotion engine enables more personalized recommendations, improving user satisfaction.

[0282] Example 2

[0283] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0284] Conventional travel booking systems typically present booking suggestions based solely on past data or simple user profiles, which often fail to fully reflect the user's current emotions and interests. Furthermore, the manual booking confirmation process can be complex and degrade the user experience. In addition, there is a need for a method to analyze user emotions in real time and present more personalized booking suggestions.

[0285] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for generating appropriate reservation candidates based on the user profile and emotion data and presenting them to the terminal, means for analyzing the user's emotions using an emotion engine and using the data to generate reservation candidates, and means for recording and confirming the reservation selected by the user. This makes it possible to generate and present more appropriate reservation candidates that take into account not only the user's profile information but also their current emotional state, thereby realizing a more efficient reservation process and an improved user experience.

[0286] A "terminal" is a device that a user operates to make travel reservations and enter profile information, such as a smartphone, PC, or tablet.

[0287] "Server" means the central part of the system that stores past booking data, uses machine learning models to generate booking suggestions based on user profiles, and records and confirms user booking selections.

[0288] A "machine learning model" is an algorithm that learns features extracted from past reservation data and generates optimal reservation candidates based on the user's profile information.

[0289] A "user profile" includes information such as a user's age, interests, and past behavioral data, and is used to generate reservation candidates.

[0290] An "emotion engine" is software that analyzes information such as user input data, facial expressions, and voice to evaluate the user's current emotions.

[0291] "Booking Suggestions" are travel suggestions that users can select, generated using machine learning models and sentiment engines.

[0292] "Confirming a reservation" is the process of saving the reservation details selected by the user in the system and officially confirming them.

[0293] "Emotion Data" means data about a user's emotional state analyzed by the emotion engine and used to improve the accuracy of reservation suggestions.

[0294] This invention relates to a system that uses a machine learning model that learns from a user's past reservation data when planning a trip and an emotion engine that recognizes the user's emotions to suggest appropriate reservation options, allowing users to easily complete reservations.

[0295] 1. System Overview

[0296] The system consists of a terminal that accepts travel reservations from users and a server that contains a machine learning model that learns from past reservation data. The server then generates appropriate reservation suggestions based on the user profile and emotion data and presents them to the terminal. It also includes a function to record and confirm the reservation selected by the user.

[0297] 2. Use of the device

[0298] Users access online booking sites or applications from their smartphones, PCs, tablets, or other devices. They enter user profile information and emotional data using the emotion engine. Profile information includes age, interests, and past booking history. The emotion engine analyzes the user's facial expressions and voice to generate emotional data.

[0299] 3. Server Roles

[0300] The profile information and emotion data sent from the device are sent to the server. The server receives this data and analyzes past reservation data using a trained machine learning model. Based on the analyzed data, reservation candidates that best fit the user's profile and emotions are generated.

[0301] 4. Generating and presenting reservation candidates

[0302] The server uses a machine learning model and an emotion engine to generate suitable booking suggestions. These suggestions are then presented to the user's device. For example, specific options such as "a five-day trip to Paris" or "a seven-day trip to New York" are presented. The emotion engine takes into account the user's current emotions (e.g., excitement, nervousness, anxiety, etc.) to display more personalized booking suggestions.

[0303] 5. Select and confirm your reservation

[0304] The user selects the reservation option they want from the options presented, and the selection information is sent from the device to the server. The server records the selected reservation details and officially confirms the reservation. Once the reservation is confirmed, the server sends a confirmation message to the user, which is displayed on the device.

[0305] Specific examples

[0306] For example, if a user enters profile information and emotion data such as "25 years old, interested in culture, and currently feeling excited," the server can prioritize "culturally related tourist spots" or "tourist spots previously booked by users with the same interests" based on past reservation data. Furthermore, the emotion engine can recognize the user's emotion of "excitement" and suggest reservation options that match that emotion (for example, "seeing a musical in New York" or "a wine tour in Paris").

[0307] Example prompts to input to the generative AI model

[0308] "A user is 25 years old, interested in culture, and currently in a state of excitement. Suggest the best international trips for this user."

[0309] As described above, this system enables users to plan their trips efficiently and tourism industry personnel to provide more efficient services. The use of an emotion engine improves user satisfaction and provides a more personalized travel experience.

[0310] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0311] Step 1:

[0312] User information and emotion data input

[0313] input:

[0314] Users access online booking sites or applications from devices such as smartphones, PCs, or tablets, and enter profile information such as age, interests, and past booking history, as well as emotional data using an emotion engine.

[0315] Specific behavior:

[0316] The user enters "Age: 25" and "Hobbies: Culture," then takes a picture of their face using a webcam and enters "fun" as emotional data.

[0317] output:

[0318] Profile information and emotion data are acquired by the terminal and transmitted to the server.

[0319] ---

[0320] Step 2:

[0321] Server reception and data analysis

[0322] input:

[0323] The profile information and emotion data sent from the terminal are transmitted to the server.

[0324] Specific behavior:

[0325] The server receives data such as "Age: 25," "Hobbies: Culture," and "Current Emotion: Enjoyment."

[0326] Data processing:

[0327] Based on this data, the server analyzes past reservation data using a trained machine learning model.

[0328] output:

[0329] Based on the analyzed user profile and emotional data, features that best fit the user's hobbies and emotions are extracted.

[0330] ---

[0331] Step 3:

[0332] Generate and present reservation candidates to users

[0333] input:

[0334] Server-generated features.

[0335] Specific behavior:

[0336] The server uses machine learning models and an emotion engine to generate suitable reservation suggestions.

[0337] Data processing:

[0338] The machine learning model uses user profiles and sentiment data to generate travel suggestions, such as "seeing a musical in New York" or "a wine tour in Paris," based on data such as "cultural tourist destinations" and "tourist destinations previously booked by users with the same interests."

[0339] output:

[0340] The generated reservation candidates are presented on the user's terminal.

[0341] ---

[0342] Step 4:

[0343] User selection and confirmation

[0344] input:

[0345] The user selects the desired reservation from the presented options.

[0346] Specific behavior:

[0347] The user selects "See a musical in New York," and the terminal transmits the selected reservation information to the server.

[0348] Data Calculation:

[0349] The server officially records the reservation based on the selected reservation information.

[0350] output:

[0351] The selected reservation information is recorded and the reservation is confirmed.

[0352] ---

[0353] Step 5:

[0354] Confirmed booking notification

[0355] input:

[0356] Reservation information confirmed by the server.

[0357] Specific behavior:

[0358] The server verifies the confirmed reservation information and sends a confirmation message to the user.

[0359] output:

[0360] The device receives a confirmation message from the server and notifies the user.

[0361] ---

[0362] Through these steps, the system can present personalized travel suggestions based on the user's profile information and current emotional data, and efficiently confirm bookings.

[0363] (Application example 2)

[0364] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0365] Conventional travel reservation systems only suggest suitable reservation options based on the user's past reservation data, and are unable to consider the user's emotions or real-time changes in interests. Furthermore, they do not adequately provide means to improve the user experience in physical stores, making it difficult for users to have a satisfying purchasing experience. Therefore, the challenge is to provide more personalized product suggestions based on the user's emotional state and in-store purchasing behavior.

[0366] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0367] In this invention, the server includes a means for accepting travel reservations from users, a means for learning multiple features from past reservation data, a means for recognizing emotions and adjusting proposals based on the emotions, and a means for recording and confirming reservations selected by the user. This enables appropriate and personalized product proposals to be made based on the user's emotional state and purchasing behavior in physical stores.

[0368] "User" means a person who uses the system to make travel reservations or purchase products.

[0369] "Terminal" means a device operated by a user to make travel reservations and enter user profiles, and includes smartphones, PCs, tablets, etc.

[0370] The "server" is the core of the system that stores past booking data and uses machine learning models to generate booking suggestions based on user profiles.

[0371] A "machine learning model" is an algorithm that learns features extracted from past reservation data and makes appropriate predictions and suggestions for new data.

[0372] A "user profile" is a collection of data that includes information such as a user's age, interests, and past purchasing history.

[0373] "Reservation suggestions" are travel plans and product selection suggestions that are considered appropriate for the user.

[0374] "Emotion recognition means" is a technology that analyzes information such as user input data, facial expressions, and voice to evaluate current emotions.

[0375] The "recording means" is a function that saves the reservation details selected by the user in the system and officially confirms the reservation.

[0376] "Product selection in a physical store" refers to the behavior and selection process that users take when choosing products in a physical store.

[0377] A "recommendation algorithm" is an algorithm that suggests appropriate products and services based on past data and current user profile information.

[0378] Hereinafter, specific embodiments of the present invention will be described.

[0379] System configuration

[0380] The system of the present invention consists of the following major components:

[0381] 1. Terminal

[0382] A terminal is a device that a user uses to book a trip or enter profile information, and can include a smartphone, PC, tablet, smart glasses, etc. A user uses a terminal to access an online booking site or application.

[0383] 2. Server

[0384] The server is the core of the system, storing past reservation data and generating reservation suggestions based on user profiles using machine learning models. In addition to the machine learning models, the server is equipped with an emotion recognition engine.

[0385] 3. Emotion recognition means

[0386] The server is equipped with an emotion recognition engine that analyzes user input data, facial expressions, voice, and other information to evaluate the user's current emotion. This emotion recognition engine can use TensorFlow and Keras to evaluate emotions.

[0387] 4. Recording Method

[0388] The server has a function to save the reservation details selected by the user in the system and officially confirm the reservation. Once the reservation is confirmed, the server sends a confirmation message to the user.

[0389] Processing flow

[0390] User Actions

[0391] A user uses a device to access a travel booking website and enters profile information (such as age, interests, and past booking history) and emotional data, which may be captured through facial expressions or voice data.

[0392] Server Processing

[0393] The server analyzes the profile information and emotion data received from the user. It uses a machine learning model trained on past reservation data to generate reservation suggestions based on the user profile. It also analyzes the user's current emotions using an emotion recognition engine and tailors reservation suggestions accordingly. The server then presents the generated reservation suggestions to the user's device.

[0394] Confirmation of reservation

[0395] When the user selects the reservation they want from the presented options, the device sends that information to the server, which records the selected reservation and officially confirms it, allowing the user to complete the reservation smoothly.

[0396] Specific examples

[0397] For example, consider a 25-year-old user who is interested in fashion and electronics and whose purchase history includes "handbags," "smartphones," and "headphones." When this user puts on smart glasses and goes shopping, the emotion engine recognizes the user's emotion of "enjoyment." Based on this information, the server can suggest products such as "newly designed handbags" and "the latest smartphone accessories."

[0398] Prompt Sentence Examples

[0399] The user is 25 years old, interested in fashion and electronics, and their past purchases include handbags, smartphones, and headphones. Their current emotion is excitement. Please suggest the best products for the user.

[0400] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0401] Step 1:

[0402] Subject: User

[0403] Specific actions: A user uses a device to access an online booking site or application.

[0404] Input: Enter user profile information (age, interests, past booking history) and emotional data (facial expressions and voice).

[0405] Output: User profile information and emotional data are recorded on the device and sent to the next step.

[0406] Step 2:

[0407] Subject: Device

[0408] Specific operation: The device sends user profile information and emotion data to the server.

[0409] Input: User profile information and sentiment data.

[0410] Output: This data is sent to the server.

[0411] Step 3:

[0412] Subject: Server

[0413] Specific operations: The server analyzes the received user profile information and emotion data.

[0414] Input: User profile information and sentiment data.

[0415] Output: Analysis results are obtained, which include feature extraction based on user profiles using machine learning models and emotion assessment using an emotion recognition engine.

[0416] Step 4:

[0417] Subject: Server

[0418] What it does: The server uses past booking data to generate suitable booking suggestions using machine learning models, and then uses an emotion recognition engine to tailor the suggestions based on the user's current emotions.

[0419] Input: Historical booking data, user profile information, and sentiment analysis results.

[0420] Output: Generated reservation candidates are obtained.

[0421] Step 5:

[0422] Subject: Server

[0423] Specific operation: The server presents the generated reservation candidates to the user's terminal.

[0424] Input: Generated booking suggestions.

[0425] Output: The reservation proposal is sent to the terminal for the user to review.

[0426] Step 6:

[0427] Subject: User

[0428] Specific operation: The user selects the desired reservation from the presented options.

[0429] Input: Booking candidate.

[0430] Output: Selected reservation information.

[0431] Step 7:

[0432] Subject: Device

[0433] Specific operation: The terminal sends the reservation information selected by the user to the server.

[0434] Input: Selected reservation information.

[0435] Output: Reservation information is sent to the server.

[0436] Step 8:

[0437] Subject: Server

[0438] Specific operation: The server records the selected reservation information, officially confirms the reservation, and sends a confirmation message to the user.

[0439] Input: Selected reservation information.

[0440] Output: The booking is confirmed and a confirmation message is sent to the user.

[0441] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0442] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0443] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0444] [Second embodiment]

[0445] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0446] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0447] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0448] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0449] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0450] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0451] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0452] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0453] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0454] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0455] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0456] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0457] The present invention provides a system that uses a machine learning model that has learned from past reservation data to suggest suitable reservation options when a user plans a trip, allowing the user to easily complete a reservation. Specific embodiments of the invention are described below.

[0458] System configuration

[0459] 1. Devices that accept travel reservations from users:

[0460] A terminal is a device operated by a user, used to make travel reservations and enter user profiles. Examples include smartphones, PCs, and tablets.

[0461] 2. A server containing a machine learning model trained on past booking data:

[0462] The server is the heart of the system, storing past booking data and using machine learning models to generate booking suggestions based on user profiles.

[0463] 3. How to generate suitable booking candidates:

[0464] The server uses a machine learning model to learn features extracted from past booking data and runs an algorithm to generate optimal booking suggestions based on the user profile, taking into account the user's age, interests, past behavioral data, etc.

[0465] 4. Means for recording and confirming the reservation selected by the User:

[0466] The server saves the reservation details selected by the user in the system and provides a means to officially complete the reservation, allowing users to easily confirm their reservation and smoothly proceed with their travel planning.

[0467] System operation explanation

[0468] Device:

[0469] Users access online booking sites and applications from their devices.

[0470] Enter user profile information into the device, including age, interests, and past booking history.

[0471] server:

[0472] When a user enters profile information, the server receives it.

[0473] The server stores past reservation data and has trained the data using machine learning models.

[0474] Based on the received user profile, the server performs data analysis using machine learning models to generate appropriate reservation candidates.

[0475] Generate and present booking suggestions:

[0476] The server then presents the generated booking suggestions to the user's device, such as options like "a five-day trip to Paris" or "a seven-day trip to New York."

[0477] User selection and booking confirmation:

[0478] The user selects the desired reservation from the presented options.

[0479] Once the selection is complete, the terminal transmits the information to the server.

[0480] The server records the selected reservation and officially confirms the reservation. Once the reservation is confirmed, a confirmation message is sent to the user.

[0481] Specific examples

[0482] For example, if a user enters profile information such as "25 years old and interested in culture," the server can prioritize "culturally related tourist destinations" or "tourist destinations that have been booked by users with the same interests in the past" based on past reservation data. The server analyzes past reservation data (e.g., a 5-day trip to Paris or a 7-day trip to New York) and presents the most suitable reservation candidates.

[0483] As described above, by using this system, users can efficiently plan their trips, and those involved in the tourism industry can provide more efficient services.

[0484] The processing flow will be explained below.

[0485] Step 1:

[0486] The server collects past booking data and stores it in a database, including tourist destinations, length of stay, booking date and time, and user profile information (age, interests, etc.).

[0487] Step 2:

[0488] The server initializes the machine learning model and trains it on the stored historical reservation data. During this training phase, features of each reservation are extracted, forming the basis for the model to make future predictions and recommendations.

[0489] Step 3:

[0490] Users access an online booking site or application from their device and enter their profile information, which may include their age, interests, and past booking history.

[0491] Step 4:

[0492] The device transmits the user-entered profile information to the server, which receives it and processes it as input data for generating suitable reservation candidates.

[0493] Step 5:

[0494] Based on the received user profile, the server uses a trained machine learning model, which performs algorithmic processing to suggest the best sightseeing spots and activities according to the user's interests and age.

[0495] Step 6:

[0496] The server generates booking suggestions based on the machine learning model and sends them to the user's device, allowing the user to choose the optimal travel plan from multiple options.

[0497] Step 7:

[0498] The user selects the desired sightseeing spots and activities from the presented reservation options. Once the selection is complete, the device sends the information to the server.

[0499] Step 8:

[0500] The server records the reservation based on the user's selection and officially confirms the reservation. This information is stored in a database for future recommendations.

[0501] Step 9:

[0502] The server will send a confirmation message to the user's device to inform them that the reservation has been confirmed, allowing the user to confirm that the reservation has been completed.

[0503] Step 10:

[0504] The user will receive a confirmation message confirming that the booking has been confirmed, at which point the user's travel plans are complete.

[0505] Through these steps, the system helps users complete travel reservations efficiently and easily. The server also continuously collects data and learns from it, allowing it to make more accurate recommendations.

[0506] Example 1

[0507] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0508] When booking travel, users lack an efficient way to find the best options based on their interests and past behavior. They also need a way to quickly and accurately process and confirm their booking selections. This would enable users to plan their trips more efficiently and travel providers to provide more efficient services.

[0509] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0510] In this invention, the server includes a terminal that accepts travel reservations from users, a server that includes a machine learning model that learns from past reservation data, means for the server to generate appropriate reservation candidates based on a user profile and present them to the terminal, means for recording and confirming reservations selected by the user, means for presenting the generated reservation candidates to the user's terminal, and means for transmitting selected reservation information from the user's terminal to the server. This allows the user to be presented with optimal reservation candidates based on their own profile information, allowing them to efficiently plan their trip and quickly confirm the selected reservation.

[0511] "User" means an individual who makes a travel reservation and uses the System to enter their profile information and select reservation options.

[0512] "Device" means a device operated by a User to book a trip or enter profile information, including a smartphone, PC, or tablet.

[0513] "Server" refers to the central part of the system that stores past booking data and uses machine learning models to generate booking suggestions based on user profiles.

[0514] "Machine Learning Model" refers to a computational means that includes an algorithm that learns from past booking data to generate suitable booking suggestions based on user profile information.

[0515] "Profile Information" refers to personal information entered by a user, such as age, interests, and past booking history.

[0516] "Booking Suggestions" refers to a list of suggested travel itineraries and accommodations generated by machine learning models based on a user's profile.

[0517] "Recording" refers to the process of saving the reservation selections made by the user in a database.

[0518] "Confirmation" refers to the process of officially validating the recorded reservation and sending a confirmation message to the user.

[0519] "Database" refers to a software system for storing and managing user profile information and past reservation data.

[0520] This invention is a system that uses a machine learning model that has learned from past reservation data to suggest suitable reservation options when a user plans a trip, allowing the user to easily complete the reservation. This system presents the most suitable reservation options to the user based on profile information from the user. It also records the reservation details selected by the user and supports a series of processes to finalize the reservation.

[0521] System configuration

[0522] 1. Devices that accept travel reservations from users:

[0523] A terminal is a device operated by a user to make travel reservations and enter user profiles. Specific hardware examples include smartphones, PCs, and tablets. A terminal is used by a user to access online booking sites and dedicated applications.

[0524] 2. A server containing a machine learning model trained on past booking data:

[0525] The server uses past reservation data stored in a database to train a machine learning model using software such as Python's Scikit-learn and TensorFlow. The server then analyzes the data to generate optimal reservation suggestions based on user profile information.

[0526] 3. How to generate suitable booking candidates:

[0527] The server uses a machine learning model to learn features extracted from past booking data and then runs an algorithm to generate optimal booking suggestions based on the user profile, taking into account the user's age, interests, past behavioral data, etc.

[0528] 4. Means for recording and confirming the reservation selected by the User:

[0529] The server saves the reservation details selected by the user in a database and officially confirms the reservation. During this process, a confirmation message (e.g., a reservation confirmation email) is sent to the user.

[0530] Specific examples

[0531] For example, if a user enters profile information such as "I'm 25 years old and interested in culture," the system will operate as follows:

[0532] Device:

[0533] Users access online booking sites or applications from their devices and enter profile information, such as age (25 years old) and cultural interests.

[0534] server:

[0535] Based on the profile information received from the user, we look up past booking data of users with similar interests.

[0536] Use machine learning models (e.g., Scikit-learn or TensorFlow) to generate suitable appointment candidates.

[0537] Presenting generated booking suggestions:

[0538] It prioritizes cultural destinations, such as "5-day trip to Paris" or "7-day trip to New York."

[0539] Prompt Sentence Examples

[0540] 1. "Suggest the best travel destinations for a 25-year-old interested in culture. Explain why."

[0541] 2. "Generate a recommended travel plan for a couple in their 30s based on past data."

[0542] In this way, the system allows users to plan their trips quickly and effectively, and travel agents to provide efficient and personalized service.

[0543] The above is a specific embodiment of the present invention.

[0544] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0545] Step 1:

[0546] User access to booking site and profile information input

[0547] A user uses a terminal to access an online reservation site or dedicated application. This allows the user to connect to the system and access a profile information input screen. The terminal receives profile information entered by the user, such as age, interests, and past reservation history. Input: User profile information. Output: Profile information is saved in the terminal's input form.

[0548] Step 2:

[0549] Sending profile information to the server

[0550] The device sends the entered profile information to the server. This sending is performed using a protocol such as an HTTP POST request. Input: Profile information stored on the device. Output: Request to send profile information to the server.

[0551] Step 3:

[0552] Server receives and stores profile information

[0553] The server receives the profile information sent from the device. It then stores this information in a database. Input: Received profile information. Output: Profile information stored in the database.

[0554] Step 4:

[0555] Use machine learning models that learn from past booking data

[0556] The server uses the stored past reservation data to train a machine learning model. Specific software used is Scikit-learn and TensorFlow. The machine learning model extracts features from the past data and prepares to generate optimal reservation candidates based on the user profile. Input: Past reservation data. Output: Trained machine learning model.

[0557] Step 5:

[0558] Generate booking suggestions based on profile information

[0559] The server uses a machine learning model to analyze the profile information entered by the user and generate appropriate reservation suggestions. For example, it uses Python's Scikit-learn and TensorFlow libraries to suggest the best travel destinations and plans based on information such as the user's age and interests. Input: User profile information, trained machine learning model. Output: Generated reservation suggestion list.

[0560] Step 6:

[0561] Present the generated reservation candidates to the user's device

[0562] The server sends the generated reservation candidate list to the user's device. This is also usually done using an HTTP response. The device receives the response from the server and prepares to display the reservation candidates on the screen. Input: Generated reservation candidate list. Output: Response from the server, reservation candidate list.

[0563] Step 7:

[0564] User selection of reservation options

[0565] The user selects the desired reservation from the presented options. For example, they select "5-day trip to Paris." The selected information is temporarily saved on the user's device. Input: List of reservation options. Output: User's selection information.

[0566] Step 8:

[0567] Sending selected reservation information to the server

[0568] The terminal sends the selected reservation information to the server. This process is also performed using an HTTP POST request. Input: User's selected information. Output: Request to send selected information to the server.

[0569] Step 9:

[0570] The server records and confirms the reservation

[0571] The server receives the selection information and stores it in a database. It then officially confirms the reservation and sends a confirmation message to the user. The confirmation message is sent to the user via email or other means. Input: Selected reservation information. Output: Reservation information stored in the database, confirmation message to the user.

[0572] The above is the specific flow of processing in this system.

[0573] (Application example 1)

[0574] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0575] With traditional travel reservation systems, users had to spend a lot of time and effort to find the right travel plan. It was also difficult to find a plan that suited their individual needs. Furthermore, users were unable to virtually experience their chosen travel destination, which often led to uncertainty about choosing a travel destination.

[0576] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0577] In this invention, the server includes a terminal that accepts travel reservations from users, a server that includes a machine learning model that learns from past reservation data, means for the server to generate appropriate reservation candidates based on a user profile and present them to the terminal, means for providing a travel destination experience in a virtual space based on the provided profile information, means for analyzing data entered by the user through voice or manual input and proposing an optimal travel plan, and means for recording and confirming reservations selected by the user. This allows users to easily find efficient and personalized travel plans and further reduces anxiety about selecting a travel destination through the virtual experience.

[0578] "Terminal" means a device operated by a user to make travel reservations and enter user profiles.

[0579] A "machine learning model" is a model that includes an algorithm for learning from past reservation data and generating appropriate reservation candidates.

[0580] The "Server" is the central part of the system that stores past booking data and uses machine learning models to learn from the data to generate suitable booking suggestions based on user profiles.

[0581] A "user profile" is a profile that includes information such as a user's age, interests, and past behavioral data.

[0582] "Reservation candidates" are travel plan candidates that the server generates based on the user profile and that the user can select.

[0583] A "virtual space" is a virtual environment generated using computer technology in which users can have experiences.

[0584] "Virtual experience" refers to the visual and tactile reproduction of the scenery and environment of a travel destination within a virtual space, allowing users to experience it.

[0585] "Voice input" is an input method in which data input orally by the user is analyzed using voice recognition technology.

[0586] "Manual input" refers to data that is entered directly by the user using a keyboard or touch operation.

[0587] A "recommendation algorithm" is a computational method for suggesting optimal travel plans based on a user's interests and age.

[0588] The present invention provides a system for efficiently selecting an appropriate travel plan by utilizing a virtual space to provide a more realistic experience when users plan their trip. This system is realized by combining a terminal, a server, a user profile, a machine learning model, a virtual space, a virtual experience, voice input, manual input, and a recommendation algorithm.

[0589] System configuration

[0590] Device:

[0591] A device used by a user to make travel reservations and enter user profiles. Examples include smart glasses and head-mounted displays (HMDs).

[0592] server:

[0593] This is the central part of the system that stores past reservation data and uses machine learning models to learn from the data. The server is equipped with an advanced GPU, allowing for high-speed data analysis.

[0594] User profile:

[0595] A profile containing information such as a user's age, interests, and past behavioral data, which is used to generate travel plans.

[0596] Machine learning models:

[0597] The model includes an algorithm that learns from past reservation data and generates suitable reservation candidates based on user profiles. It uses machine learning frameworks such as TensorFlow and Scikit-learn.

[0598] Virtual Space:

[0599] It is a virtual environment generated using computer technology in which users can experience travel destinations, using Unity and Blender for 3D modeling.

[0600] Virtual Experience:

[0601] It refers to the visual and tactile reproduction of the scenery and environment of a travel destination in a virtual space, allowing users to experience it.

[0602] Audio Input:

[0603] It is an input method that uses speech recognition technology to analyze data entered orally by the user. It uses the NLP library spaCy.

[0604] Manual entry:

[0605] This refers to data that the user directly inputs using a keyboard or touch panel.

[0606] Recommendation algorithm:

[0607] It is a calculation method for suggesting optimal travel plans based on the user's interests and age.

[0608] System operation explanation

[0609] Enter your profile information:

[0610] Users put on smart glasses or an HMD, access the device, and then provide profile information (such as age, interests, and past travel history) via voice or manual input.

[0611] Parsing profile information:

[0612] Based on the received profile information, the server analyzes the data using a machine learning model that has learned from past booking data, and generates a travel plan that is suitable for the user.

[0613] Providing virtual experiences of your destination:

[0614] Based on the proposed itinerary, the destination environment is recreated in a virtual space, allowing users to experience the virtual world and visually confirm the atmosphere and tourist attractions of the destination.

[0615] Select and confirm your reservation:

[0616] The user selects their preferred travel plan through the virtual experience. Once the selection is complete, the server records the information and officially confirms the reservation.

[0617] Specific examples

[0618] For example, consider a system that allows users to create detailed travel plans and complete reservations through a virtual experience from the comfort of their own home, without having to visit a travel agency. When a user puts on smart glasses and voice-inputs, "I'm 25 years old and interested in culture," the system will refer to past data and suggest suitable tourist spots. For example, it will provide a virtual tour of the Louvre Museum in Paris or the MoMA in New York. Through this experience, users can select their travel destination and easily confirm their reservation.

[0619] Example prompt sentence:

[0620] "A 25-year-old culture-loving user has previously planned trips to Paris and New York. Suggest a new itinerary that best suits him / her."

[0621] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0622] Step 1:

[0623] Enter your profile information

[0624] A user accesses the reservation system wearing smart glasses or a head-mounted display (HMD), and provides profile information such as age, interests, and past travel history through voice or manual input.

[0625] Input: Information provided by the user such as age, interests, and past travel history.

[0626] Output: Sent to the server as profile data.

[0627] Step 2:

[0628] Analyzing profile information

[0629] The server parses the received profile information. First, if it is voice input, it converts it to text data using an NLP library (e.g., spaCy). Second, it formats the profile information so that it can be easily processed by machine learning models.

[0630] Input: Formatted profile data.

[0631] Output: Analysis results (user's age, interests, past travel history).

[0632] Step 3:

[0633] Generate optimal travel plans

[0634] The server then generates an appropriate itinerary based on the profile data analyzed, using a machine learning model (e.g., TensorFlow or Scikit-learn) trained on past booking data, which uses a recommendation algorithm based on the user's age and interests.

[0635] Input: Analyzed profile data, past booking data.

[0636] Output: A list of suggested itineraries suitable for the user.

[0637] Step 4:

[0638] Providing virtual experiences for travel plans

[0639] The server recreates the environment of the destination in a virtual space based on the generated travel plan. Using Unity or Blender, 3D modeling is performed, allowing users to experience the destination in the virtual space. Through the virtual experience, users can visually confirm the atmosphere and tourist spots of the destination.

[0640] Input: A list of itinerary suggestions.

[0641] Output: A travel destination experience in a virtual space.

[0642] Step 5:

[0643] Select and book your travel plan

[0644] The user selects their preferred travel plan through the virtual experience. Once the selection is complete, the device sends the information to the server, which records the selected plan and confirms the official reservation.

[0645] Input: The travel plan selected by the user.

[0646] Output: Recorded booking information, confirmed travel booking.

[0647] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0648] The present invention is a system that uses a machine learning model that learns from past reservation data when a user plans a trip and an emotion engine that recognizes the user's emotions to suggest appropriate reservation options, allowing the user to easily complete a reservation. Specific embodiments of the invention are described below.

[0649] System configuration

[0650] 1. Devices that accept travel reservations from users:

[0651] A terminal is a device operated by a user, used to make travel reservations and enter user profiles. Examples include smartphones, PCs, and tablets.

[0652] 2. A server containing a machine learning model trained on past booking data:

[0653] The server is the heart of the system, storing past booking data and using machine learning models to generate booking suggestions based on user profiles.

[0654] 3. How to generate suitable booking candidates:

[0655] The server uses a machine learning model to learn features extracted from past booking data and runs an algorithm to generate optimal booking suggestions based on the user profile, taking into account the user's age, interests, past behavioral data, etc.

[0656] 4. Means for recording and confirming the reservation selected by the User:

[0657] The server saves the reservation details selected by the user in the system and provides a means to officially complete the reservation, allowing users to easily confirm their reservation and smoothly proceed with their travel planning.

[0658] 5. Emotion engine that recognizes user emotions:

[0659] The emotion engine analyzes user input data, facial expressions, voice, and other information to assess the user's current emotions, which are then used to generate more appropriate reservation candidates.

[0660] System operation explanation

[0661] Device:

[0662] Users access online booking sites and applications from their devices.

[0663] The device inputs user profile information and current emotions, including age, interests, past booking history, etc. Facial expressions and voice data can also be input using the emotion engine.

[0664] server:

[0665] The user enters profile information and emotion data, which is received by the server.

[0666] The server stores past reservation data and has trained the data using machine learning models.

[0667] The server analyzes the data using a machine learning model based on the received user profile and emotion data to generate appropriate reservation candidates.

[0668] Generate and present booking suggestions:

[0669] The server then presents the generated booking suggestions to the user's device, such as options like "a five-day trip to Paris" or "a seven-day trip to New York."

[0670] The emotion engine can take into account the user's current emotions (e.g., excitement, tension, anxiety, etc.) to present reservation suggestions that better suit the user's mood.

[0671] User selection and booking confirmation:

[0672] The user selects the desired reservation from the presented options.

[0673] Once the selection is complete, the terminal transmits the information to the server.

[0674] The server records the selected reservation and officially confirms the reservation. Once the reservation is confirmed, a confirmation message is sent to the user.

[0675] Specific examples

[0676] For example, if a user enters profile information and emotion data such as "25 years old, interested in culture, and currently feeling excited," the server can prioritize "culturally related tourist spots" or "tourist spots previously booked by users with the same interests" based on past reservation data. Furthermore, the emotion engine can recognize the user's emotion of "excitement" and suggest reservation options that match that emotion (for example, "seeing a musical in New York" or "a wine tour in Paris").

[0677] As described above, by using this system, users can efficiently plan their trips and tourism industry personnel can provide more efficient services. In addition, the emotion engine enables more tailored suggestions to individual users, improving user satisfaction.

[0678] The processing flow will be explained below.

[0679] Step 1:

[0680] The server collects past booking data and stores it in a database, including tourist destinations, length of stay, booking date and time, and user profile information (age, interests, etc.).

[0681] Step 2:

[0682] The server initializes the machine learning model and trains it on the stored historical reservation data. During this training phase, features of each reservation are extracted, forming the basis for the model to make future predictions and recommendations.

[0683] Step 3:

[0684] Users access an online booking site or application from their device and enter their profile information and current emotions. Profile information includes age, interests, past booking history, etc. Facial expressions and voice data can also be input using the emotion engine.

[0685] Step 4:

[0686] The device transmits the user-entered profile information and emotion data to the server, which receives and processes this information as input data for generating suitable reservation candidates.

[0687] Step 5:

[0688] Based on the received user profile and emotion data, the server uses a trained machine learning model, which performs algorithmic processing to suggest the best sightseeing spots and activities according to the user's interests, age, and current emotions.

[0689] Step 6:

[0690] The server utilizes an emotion engine to assess the user's current emotion, using natural language processing algorithms and image and audio analysis techniques.

[0691] Step 7:

[0692] The server combines data from the machine learning model and emotion engine to generate optimal booking suggestions, which are best suited to the user's current emotions and profile.

[0693] Step 8:

[0694] The server sends the generated reservation candidates to the user's device, where the user can choose from multiple options (e.g., "a 5-day trip to Paris" or "a 7-day trip to New York").

[0695] Step 9:

[0696] The user selects the desired sightseeing spots and activities from the presented reservation options. Once the selection is complete, the device sends the information to the server.

[0697] Step 10:

[0698] The server records the reservation based on the user's selection and officially confirms the reservation. This information is stored in a database for future recommendations.

[0699] Step 11:

[0700] The server will send a confirmation message to the user's device to inform them that the reservation has been confirmed, allowing the user to confirm that the reservation has been completed.

[0701] Step 12:

[0702] The user will receive a confirmation message confirming that the booking has been confirmed, at which point the user's travel plans are complete.

[0703] Through these steps, the system helps users complete travel bookings efficiently and easily, and the emotion engine enables more personalized recommendations, improving user satisfaction.

[0704] Example 2

[0705] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0706] Conventional travel booking systems typically present booking suggestions based solely on past data or simple user profiles, which often fail to fully reflect the user's current emotions and interests. Furthermore, the manual booking confirmation process can be complex and degrade the user experience. In addition, there is a need for a method to analyze user emotions in real time and present more personalized booking suggestions.

[0707] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for generating appropriate reservation candidates based on the user profile and emotion data and presenting them to the terminal, means for analyzing the user's emotions using an emotion engine and using the data to generate reservation candidates, and means for recording and confirming the reservation selected by the user. This makes it possible to generate and present more appropriate reservation candidates that take into account not only the user's profile information but also their current emotional state, thereby realizing a more efficient reservation process and an improved user experience.

[0708] A "terminal" is a device that a user operates to make travel reservations and enter profile information, such as a smartphone, PC, or tablet.

[0709] "Server" means the central part of the system that stores past booking data, uses machine learning models to generate booking suggestions based on user profiles, and records and confirms user booking selections.

[0710] A "machine learning model" is an algorithm that learns features extracted from past reservation data and generates optimal reservation candidates based on the user's profile information.

[0711] A "user profile" includes information such as a user's age, interests, and past behavioral data, and is used to generate reservation candidates.

[0712] An "emotion engine" is software that analyzes information such as user input data, facial expressions, and voice to evaluate the user's current emotions.

[0713] "Booking Suggestions" are travel suggestions that users can select, generated using machine learning models and sentiment engines.

[0714] "Confirming a reservation" is the process of saving the reservation details selected by the user in the system and officially confirming them.

[0715] "Emotion Data" means data about a user's emotional state analyzed by the emotion engine and used to improve the accuracy of reservation suggestions.

[0716] This invention relates to a system that uses a machine learning model that learns from a user's past reservation data when planning a trip and an emotion engine that recognizes the user's emotions to suggest appropriate reservation options, allowing users to easily complete reservations.

[0717] 1. System Overview

[0718] The system consists of a terminal that accepts travel reservations from users and a server that contains a machine learning model that learns from past reservation data. The server then generates appropriate reservation suggestions based on the user profile and emotion data and presents them to the terminal. It also includes a function to record and confirm the reservation selected by the user.

[0719] 2. Use of the device

[0720] Users access online booking sites or applications from their smartphones, PCs, tablets, or other devices. They enter user profile information and emotional data using the emotion engine. Profile information includes age, interests, and past booking history. The emotion engine analyzes the user's facial expressions and voice to generate emotional data.

[0721] 3. Server Roles

[0722] The profile information and emotion data sent from the device are sent to the server. The server receives this data and analyzes past reservation data using a trained machine learning model. Based on the analyzed data, reservation candidates that best fit the user's profile and emotions are generated.

[0723] 4. Generating and presenting reservation candidates

[0724] The server uses a machine learning model and an emotion engine to generate suitable booking suggestions. These suggestions are then presented to the user's device. For example, specific options such as "a five-day trip to Paris" or "a seven-day trip to New York" are presented. The emotion engine takes into account the user's current emotions (e.g., excitement, nervousness, anxiety, etc.) to display more personalized booking suggestions.

[0725] 5. Select and confirm your reservation

[0726] The user selects the reservation option they want from the options presented, and the selection information is sent from the device to the server. The server records the selected reservation details and officially confirms the reservation. Once the reservation is confirmed, the server sends a confirmation message to the user, which is displayed on the device.

[0727] Specific examples

[0728] For example, if a user enters profile information and emotion data such as "25 years old, interested in culture, and currently feeling excited," the server can prioritize "culturally related tourist spots" or "tourist spots previously booked by users with the same interests" based on past reservation data. Furthermore, the emotion engine can recognize the user's emotion of "excitement" and suggest reservation options that match that emotion (for example, "seeing a musical in New York" or "a wine tour in Paris").

[0729] Example prompts to input to the generative AI model

[0730] "A user is 25 years old, interested in culture, and currently in a state of excitement. Suggest the best international trips for this user."

[0731] As described above, this system enables users to plan their trips efficiently and tourism industry personnel to provide more efficient services. The use of an emotion engine improves user satisfaction and provides a more personalized travel experience.

[0732] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0733] Step 1:

[0734] User information and emotion data input

[0735] input:

[0736] Users access online booking sites or applications from devices such as smartphones, PCs, or tablets, and enter profile information such as age, interests, and past booking history, as well as emotional data using an emotion engine.

[0737] Specific behavior:

[0738] The user enters "Age: 25" and "Hobbies: Culture," then takes a picture of their face using a webcam and enters "fun" as emotional data.

[0739] output:

[0740] Profile information and emotion data are acquired by the terminal and transmitted to the server.

[0741] ---

[0742] Step 2:

[0743] Server reception and data analysis

[0744] input:

[0745] The profile information and emotion data sent from the terminal are transmitted to the server.

[0746] Specific behavior:

[0747] The server receives data such as "Age: 25," "Hobbies: Culture," and "Current Emotion: Enjoyment."

[0748] Data processing:

[0749] Based on this data, the server analyzes past reservation data using a trained machine learning model.

[0750] output:

[0751] Based on the analyzed user profile and emotional data, features that best fit the user's hobbies and emotions are extracted.

[0752] ---

[0753] Step 3:

[0754] Generate and present reservation candidates to users

[0755] input:

[0756] Server-generated features.

[0757] Specific behavior:

[0758] The server uses machine learning models and an emotion engine to generate suitable reservation suggestions.

[0759] Data processing:

[0760] The machine learning model uses user profiles and sentiment data to generate travel suggestions, such as "seeing a musical in New York" or "a wine tour in Paris," based on data such as "cultural tourist destinations" and "tourist destinations previously booked by users with the same interests."

[0761] output:

[0762] The generated reservation candidates are presented on the user's terminal.

[0763] ---

[0764] Step 4:

[0765] User selection and confirmation

[0766] input:

[0767] The user selects the desired reservation from the presented options.

[0768] Specific behavior:

[0769] The user selects "See a musical in New York," and the terminal transmits the selected reservation information to the server.

[0770] Data Calculation:

[0771] The server officially records the reservation based on the selected reservation information.

[0772] output:

[0773] The selected reservation information is recorded and the reservation is confirmed.

[0774] ---

[0775] Step 5:

[0776] Confirmed booking notification

[0777] input:

[0778] Reservation information confirmed by the server.

[0779] Specific behavior:

[0780] The server verifies the confirmed reservation information and sends a confirmation message to the user.

[0781] output:

[0782] The device receives a confirmation message from the server and notifies the user.

[0783] ---

[0784] Through these steps, the system can present personalized travel suggestions based on the user's profile information and current emotional data, and efficiently confirm bookings.

[0785] (Application example 2)

[0786] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0787] Conventional travel reservation systems only suggest suitable reservation options based on the user's past reservation data, and are unable to consider the user's emotions or real-time changes in interests. Furthermore, they do not adequately provide means to improve the user experience in physical stores, making it difficult for users to have a satisfying purchasing experience. Therefore, the challenge is to provide more personalized product suggestions based on the user's emotional state and in-store purchasing behavior.

[0788] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0789] In this invention, the server includes a means for accepting travel reservations from users, a means for learning multiple features from past reservation data, a means for recognizing emotions and adjusting proposals based on the emotions, and a means for recording and confirming reservations selected by the user. This enables appropriate and personalized product proposals to be made based on the user's emotional state and purchasing behavior in physical stores.

[0790] "User" means a person who uses the system to make travel reservations or purchase products.

[0791] "Terminal" means a device operated by a user to make travel reservations and enter user profiles, and includes smartphones, PCs, tablets, etc.

[0792] The "server" is the core of the system that stores past booking data and uses machine learning models to generate booking suggestions based on user profiles.

[0793] A "machine learning model" is an algorithm that learns features extracted from past reservation data and makes appropriate predictions and suggestions for new data.

[0794] A "user profile" is a collection of data that includes information such as a user's age, interests, and past purchasing history.

[0795] "Reservation suggestions" are travel plans and product selection suggestions that are considered appropriate for the user.

[0796] "Emotion recognition means" is a technology that analyzes information such as user input data, facial expressions, and voice to evaluate current emotions.

[0797] The "recording means" is a function that saves the reservation details selected by the user in the system and officially confirms the reservation.

[0798] "Product selection in a physical store" refers to the behavior and selection process that users take when choosing products in a physical store.

[0799] A "recommendation algorithm" is an algorithm that suggests appropriate products and services based on past data and current user profile information.

[0800] Hereinafter, specific embodiments of the present invention will be described.

[0801] System configuration

[0802] The system of the present invention consists of the following major components:

[0803] 1. Terminal

[0804] A terminal is a device that a user uses to book a trip or enter profile information, and can include a smartphone, PC, tablet, smart glasses, etc. A user uses a terminal to access an online booking site or application.

[0805] 2. Server

[0806] The server is the core of the system, storing past reservation data and generating reservation suggestions based on user profiles using machine learning models. In addition to the machine learning models, the server is equipped with an emotion recognition engine.

[0807] 3. Emotion recognition means

[0808] The server is equipped with an emotion recognition engine that analyzes user input data, facial expressions, voice, and other information to evaluate the user's current emotion. This emotion recognition engine can use TensorFlow and Keras to evaluate emotions.

[0809] 4. Recording Method

[0810] The server has a function to save the reservation details selected by the user in the system and officially confirm the reservation. Once the reservation is confirmed, the server sends a confirmation message to the user.

[0811] Processing flow

[0812] User Actions

[0813] A user uses a device to access a travel booking website and enters profile information (such as age, interests, and past booking history) and emotional data, which may be captured through facial expressions or voice data.

[0814] Server Processing

[0815] The server analyzes the profile information and emotion data received from the user. It uses a machine learning model trained on past reservation data to generate reservation suggestions based on the user profile. It also analyzes the user's current emotions using an emotion recognition engine and tailors reservation suggestions accordingly. The server then presents the generated reservation suggestions to the user's device.

[0816] Confirmation of reservation

[0817] When the user selects the reservation they want from the presented options, the device sends that information to the server, which records the selected reservation and officially confirms it, allowing the user to complete the reservation smoothly.

[0818] Specific examples

[0819] For example, consider a 25-year-old user who is interested in fashion and electronics and whose purchase history includes "handbags," "smartphones," and "headphones." When this user puts on smart glasses and goes shopping, the emotion engine recognizes the user's emotion of "enjoyment." Based on this information, the server can suggest products such as "newly designed handbags" and "the latest smartphone accessories."

[0820] Prompt Sentence Examples

[0821] The user is 25 years old, interested in fashion and electronics, and their past purchases include handbags, smartphones, and headphones. Their current emotion is excitement. Please suggest the best products for the user.

[0822] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0823] Step 1:

[0824] Subject: User

[0825] Specific actions: A user uses a device to access an online booking site or application.

[0826] Input: Enter user profile information (age, interests, past booking history) and emotional data (facial expressions and voice).

[0827] Output: User profile information and emotional data are recorded on the device and sent to the next step.

[0828] Step 2:

[0829] Subject: Device

[0830] Specific operation: The device sends user profile information and emotion data to the server.

[0831] Input: User profile information and sentiment data.

[0832] Output: This data is sent to the server.

[0833] Step 3:

[0834] Subject: Server

[0835] Specific operations: The server analyzes the received user profile information and emotion data.

[0836] Input: User profile information and sentiment data.

[0837] Output: Analysis results are obtained, which include feature extraction based on user profiles using machine learning models and emotion assessment using an emotion recognition engine.

[0838] Step 4:

[0839] Subject: Server

[0840] What it does: The server uses past booking data to generate suitable booking suggestions using machine learning models, and then uses an emotion recognition engine to tailor the suggestions based on the user's current emotions.

[0841] Input: Historical booking data, user profile information, and sentiment analysis results.

[0842] Output: Generated reservation candidates are obtained.

[0843] Step 5:

[0844] Subject: Server

[0845] Specific operation: The server presents the generated reservation candidates to the user's terminal.

[0846] Input: Generated booking suggestions.

[0847] Output: The reservation proposal is sent to the terminal for the user to review.

[0848] Step 6:

[0849] Subject: User

[0850] Specific operation: The user selects the desired reservation from the presented options.

[0851] Input: Booking candidate.

[0852] Output: Selected reservation information.

[0853] Step 7:

[0854] Subject: Device

[0855] Specific operation: The terminal sends the reservation information selected by the user to the server.

[0856] Input: Selected reservation information.

[0857] Output: Reservation information is sent to the server.

[0858] Step 8:

[0859] Subject: Server

[0860] Specific operation: The server records the selected reservation information, officially confirms the reservation, and sends a confirmation message to the user.

[0861] Input: Selected reservation information.

[0862] Output: The booking is confirmed and a confirmation message is sent to the user.

[0863] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0864] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0865] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0866] [Third embodiment]

[0867] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0868] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0869] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0870] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0871] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0872] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0873] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0874] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0875] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0876] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0877] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0878] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0879] The present invention provides a system that uses a machine learning model that has learned from past reservation data to suggest suitable reservation options when a user plans a trip, allowing the user to easily complete a reservation. Specific embodiments of the invention are described below.

[0880] System configuration

[0881] 1. Devices that accept travel reservations from users:

[0882] A terminal is a device operated by a user, used to make travel reservations and enter user profiles. Examples include smartphones, PCs, and tablets.

[0883] 2. A server containing a machine learning model trained on past booking data:

[0884] The server is the heart of the system, storing past booking data and using machine learning models to generate booking suggestions based on user profiles.

[0885] 3. How to generate suitable booking candidates:

[0886] The server uses a machine learning model to learn features extracted from past booking data and runs an algorithm to generate optimal booking suggestions based on the user profile, taking into account the user's age, interests, past behavioral data, etc.

[0887] 4. Means for recording and confirming the reservation selected by the User:

[0888] The server saves the reservation details selected by the user in the system and provides a means to officially complete the reservation, allowing users to easily confirm their reservation and smoothly proceed with their travel planning.

[0889] System operation explanation

[0890] Device:

[0891] Users access online booking sites and applications from their devices.

[0892] Enter user profile information into the device, including age, interests, and past booking history.

[0893] server:

[0894] When a user enters profile information, the server receives it.

[0895] The server stores past reservation data and has trained the data using machine learning models.

[0896] Based on the received user profile, the server performs data analysis using machine learning models to generate appropriate reservation candidates.

[0897] Generate and present booking suggestions:

[0898] The server then presents the generated booking suggestions to the user's device, such as options like "a five-day trip to Paris" or "a seven-day trip to New York."

[0899] User selection and booking confirmation:

[0900] The user selects the desired reservation from the presented options.

[0901] Once the selection is complete, the terminal transmits the information to the server.

[0902] The server records the selected reservation and officially confirms the reservation. Once the reservation is confirmed, a confirmation message is sent to the user.

[0903] Specific examples

[0904] For example, if a user enters profile information such as "25 years old and interested in culture," the server can prioritize "culturally related tourist destinations" or "tourist destinations that have been booked by users with the same interests in the past" based on past reservation data. The server analyzes past reservation data (e.g., a 5-day trip to Paris or a 7-day trip to New York) and presents the most suitable reservation candidates.

[0905] As described above, by using this system, users can efficiently plan their trips, and those involved in the tourism industry can provide more efficient services.

[0906] The processing flow will be explained below.

[0907] Step 1:

[0908] The server collects past booking data and stores it in a database, including tourist destinations, length of stay, booking date and time, and user profile information (age, interests, etc.).

[0909] Step 2:

[0910] The server initializes the machine learning model and trains it on the stored historical reservation data. During this training phase, features of each reservation are extracted, forming the basis for the model to make future predictions and recommendations.

[0911] Step 3:

[0912] Users access an online booking site or application from their device and enter their profile information, which may include their age, interests, and past booking history.

[0913] Step 4:

[0914] The device transmits the user-entered profile information to the server, which receives it and processes it as input data for generating suitable reservation candidates.

[0915] Step 5:

[0916] Based on the received user profile, the server uses a trained machine learning model, which performs algorithmic processing to suggest the best sightseeing spots and activities according to the user's interests and age.

[0917] Step 6:

[0918] The server generates booking suggestions based on the machine learning model and sends them to the user's device, allowing the user to choose the optimal travel plan from multiple options.

[0919] Step 7:

[0920] The user selects the desired sightseeing spots and activities from the presented reservation options. Once the selection is complete, the device sends the information to the server.

[0921] Step 8:

[0922] The server records the reservation based on the user's selection and officially confirms the reservation. This information is stored in a database for future recommendations.

[0923] Step 9:

[0924] The server will send a confirmation message to the user's device to inform them that the reservation has been confirmed, allowing the user to confirm that the reservation has been completed.

[0925] Step 10:

[0926] The user will receive a confirmation message confirming that the booking has been confirmed, at which point the user's travel plans are complete.

[0927] Through these steps, the system helps users complete travel reservations efficiently and easily. The server also continuously collects data and learns from it, allowing it to make more accurate recommendations.

[0928] Example 1

[0929] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0930] When booking travel, users lack an efficient way to find the best options based on their interests and past behavior. They also need a way to quickly and accurately process and confirm their booking selections. This would enable users to plan their trips more efficiently and travel providers to provide more efficient services.

[0931] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0932] In this invention, the server includes a terminal that accepts travel reservations from users, a server that includes a machine learning model that learns from past reservation data, means for the server to generate appropriate reservation candidates based on a user profile and present them to the terminal, means for recording and confirming reservations selected by the user, means for presenting the generated reservation candidates to the user's terminal, and means for transmitting selected reservation information from the user's terminal to the server. This allows the user to be presented with optimal reservation candidates based on their own profile information, allowing them to efficiently plan their trip and quickly confirm the selected reservation.

[0933] "User" means an individual who makes a travel reservation and uses the System to enter their profile information and select reservation options.

[0934] "Device" means a device operated by a User to book a trip or enter profile information, including a smartphone, PC, or tablet.

[0935] "Server" refers to the central part of the system that stores past booking data and uses machine learning models to generate booking suggestions based on user profiles.

[0936] "Machine Learning Model" refers to a computational means that includes an algorithm that learns from past booking data to generate suitable booking suggestions based on user profile information.

[0937] "Profile Information" refers to personal information entered by a user, such as age, interests, and past booking history.

[0938] "Booking Suggestions" refers to a list of suggested travel itineraries and accommodations generated by machine learning models based on a user's profile.

[0939] "Recording" refers to the process of saving the reservation selections made by the user in a database.

[0940] "Confirmation" refers to the process of officially validating the recorded reservation and sending a confirmation message to the user.

[0941] "Database" refers to a software system for storing and managing user profile information and past reservation data.

[0942] This invention is a system that uses a machine learning model that has learned from past reservation data to suggest suitable reservation options when a user plans a trip, allowing the user to easily complete the reservation. This system presents the most suitable reservation options to the user based on profile information from the user. It also records the reservation details selected by the user and supports a series of processes to finalize the reservation.

[0943] System configuration

[0944] 1. Devices that accept travel reservations from users:

[0945] A terminal is a device operated by a user to make travel reservations and enter user profiles. Specific hardware examples include smartphones, PCs, and tablets. A terminal is used by a user to access online booking sites and dedicated applications.

[0946] 2. A server containing a machine learning model trained on past booking data:

[0947] The server uses past reservation data stored in a database to train a machine learning model using software such as Python's Scikit-learn and TensorFlow. The server then analyzes the data to generate optimal reservation suggestions based on user profile information.

[0948] 3. How to generate suitable booking candidates:

[0949] The server uses a machine learning model to learn features extracted from past booking data and then runs an algorithm to generate optimal booking suggestions based on the user profile, taking into account the user's age, interests, past behavioral data, etc.

[0950] 4. Means for recording and confirming the reservation selected by the User:

[0951] The server saves the reservation details selected by the user in a database and officially confirms the reservation. During this process, a confirmation message (e.g., a reservation confirmation email) is sent to the user.

[0952] Specific examples

[0953] For example, if a user enters profile information such as "I'm 25 years old and interested in culture," the system will operate as follows:

[0954] Device:

[0955] Users access online booking sites or applications from their devices and enter profile information, such as age (25 years old) and cultural interests.

[0956] server:

[0957] Based on the profile information received from the user, we look up past booking data of users with similar interests.

[0958] Use machine learning models (e.g., Scikit-learn or TensorFlow) to generate suitable appointment candidates.

[0959] Presenting generated booking suggestions:

[0960] It prioritizes cultural destinations, such as "5-day trip to Paris" or "7-day trip to New York."

[0961] Prompt Sentence Examples

[0962] 1. "Suggest the best travel destinations for a 25-year-old interested in culture. Explain why."

[0963] 2. "Generate a recommended travel plan for a couple in their 30s based on past data."

[0964] In this way, the system allows users to plan their trips quickly and effectively, and travel agents to provide efficient and personalized service.

[0965] The above is a specific embodiment of the present invention.

[0966] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0967] Step 1:

[0968] User access to booking site and profile information input

[0969] A user uses a terminal to access an online reservation site or dedicated application. This allows the user to connect to the system and access a profile information input screen. The terminal receives profile information entered by the user, such as age, interests, and past reservation history. Input: User profile information. Output: Profile information is saved in the terminal's input form.

[0970] Step 2:

[0971] Sending profile information to the server

[0972] The device sends the entered profile information to the server. This sending is performed using a protocol such as an HTTP POST request. Input: Profile information stored on the device. Output: Request to send profile information to the server.

[0973] Step 3:

[0974] Server receives and stores profile information

[0975] The server receives the profile information sent from the device. It then stores this information in a database. Input: Received profile information. Output: Profile information stored in the database.

[0976] Step 4:

[0977] Use machine learning models that learn from past booking data

[0978] The server uses the stored past reservation data to train a machine learning model. Specific software used is Scikit-learn and TensorFlow. The machine learning model extracts features from the past data and prepares to generate optimal reservation candidates based on the user profile. Input: Past reservation data. Output: Trained machine learning model.

[0979] Step 5:

[0980] Generate booking suggestions based on profile information

[0981] The server uses a machine learning model to analyze the profile information entered by the user and generate appropriate reservation suggestions. For example, it uses Python's Scikit-learn and TensorFlow libraries to suggest the best travel destinations and plans based on information such as the user's age and interests. Input: User profile information, trained machine learning model. Output: Generated reservation suggestion list.

[0982] Step 6:

[0983] Present the generated reservation candidates to the user's device

[0984] The server sends the generated reservation candidate list to the user's device. This is also usually done using an HTTP response. The device receives the response from the server and prepares to display the reservation candidates on the screen. Input: Generated reservation candidate list. Output: Response from the server, reservation candidate list.

[0985] Step 7:

[0986] User selection of reservation options

[0987] The user selects the desired reservation from the presented options. For example, they select "5-day trip to Paris." The selected information is temporarily saved on the user's device. Input: List of reservation options. Output: User's selection information.

[0988] Step 8:

[0989] Sending selected reservation information to the server

[0990] The terminal sends the selected reservation information to the server. This process is also performed using an HTTP POST request. Input: User's selected information. Output: Request to send selected information to the server.

[0991] Step 9:

[0992] The server records and confirms the reservation

[0993] The server receives the selection information and stores it in a database. It then officially confirms the reservation and sends a confirmation message to the user. The confirmation message is sent to the user via email or other means. Input: Selected reservation information. Output: Reservation information stored in the database, confirmation message to the user.

[0994] The above is the specific flow of processing in this system.

[0995] (Application example 1)

[0996] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0997] With traditional travel reservation systems, users had to spend a lot of time and effort to find the right travel plan. It was also difficult to find a plan that suited their individual needs. Furthermore, users were unable to virtually experience their chosen travel destination, which often led to uncertainty about choosing a travel destination.

[0998] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0999] In this invention, the server includes a terminal that accepts travel reservations from users, a server that includes a machine learning model that learns from past reservation data, means for the server to generate appropriate reservation candidates based on a user profile and present them to the terminal, means for providing a travel destination experience in a virtual space based on the provided profile information, means for analyzing data entered by the user through voice or manual input and proposing an optimal travel plan, and means for recording and confirming reservations selected by the user. This allows users to easily find efficient and personalized travel plans and further reduces anxiety about selecting a travel destination through the virtual experience.

[1000] "Terminal" means a device operated by a user to make travel reservations and enter user profiles.

[1001] A "machine learning model" is a model that includes an algorithm for learning from past reservation data and generating appropriate reservation candidates.

[1002] The "Server" is the central part of the system that stores past booking data and uses machine learning models to learn from the data to generate suitable booking suggestions based on user profiles.

[1003] A "user profile" is a profile that includes information such as a user's age, interests, and past behavioral data.

[1004] "Reservation candidates" are travel plan candidates that the server generates based on the user profile and that the user can select.

[1005] A "virtual space" is a virtual environment generated using computer technology in which users can have experiences.

[1006] "Virtual experience" refers to the visual and tactile reproduction of the scenery and environment of a travel destination within a virtual space, allowing users to experience it.

[1007] "Voice input" is an input method in which data input orally by the user is analyzed using voice recognition technology.

[1008] "Manual input" refers to data that is entered directly by the user using a keyboard or touch operation.

[1009] A "recommendation algorithm" is a computational method for suggesting optimal travel plans based on a user's interests and age.

[1010] The present invention provides a system for efficiently selecting an appropriate travel plan by utilizing a virtual space to provide a more realistic experience when users plan their trip. This system is realized by combining a terminal, a server, a user profile, a machine learning model, a virtual space, a virtual experience, voice input, manual input, and a recommendation algorithm.

[1011] System configuration

[1012] Device:

[1013] A device used by a user to make travel reservations and enter user profiles. Examples include smart glasses and head-mounted displays (HMDs).

[1014] server:

[1015] This is the central part of the system that stores past reservation data and uses machine learning models to learn from the data. The server is equipped with an advanced GPU, allowing for high-speed data analysis.

[1016] User profile:

[1017] A profile containing information such as a user's age, interests, and past behavioral data, which is used to generate travel plans.

[1018] Machine learning models:

[1019] The model includes an algorithm that learns from past reservation data and generates suitable reservation candidates based on user profiles. It uses machine learning frameworks such as TensorFlow and Scikit-learn.

[1020] Virtual Space:

[1021] It is a virtual environment generated using computer technology in which users can experience travel destinations, using Unity and Blender for 3D modeling.

[1022] Virtual Experience:

[1023] It refers to the visual and tactile reproduction of the scenery and environment of a travel destination in a virtual space, allowing users to experience it.

[1024] Audio Input:

[1025] It is an input method that uses speech recognition technology to analyze data entered orally by the user. It uses the NLP library spaCy.

[1026] Manual entry:

[1027] This refers to data that the user directly inputs using a keyboard or touch panel.

[1028] Recommendation algorithm:

[1029] It is a calculation method for suggesting optimal travel plans based on the user's interests and age.

[1030] System operation explanation

[1031] Enter your profile information:

[1032] Users put on smart glasses or an HMD, access the device, and then provide profile information (such as age, interests, and past travel history) via voice or manual input.

[1033] Parsing profile information:

[1034] Based on the received profile information, the server analyzes the data using a machine learning model that has learned from past booking data, and generates a travel plan that is suitable for the user.

[1035] Providing virtual experiences of your destination:

[1036] Based on the proposed itinerary, the destination environment is recreated in a virtual space, allowing users to experience the virtual world and visually confirm the atmosphere and tourist attractions of the destination.

[1037] Select and confirm your reservation:

[1038] The user selects their preferred travel plan through the virtual experience. Once the selection is complete, the server records the information and officially confirms the reservation.

[1039] Specific examples

[1040] For example, consider a system that allows users to create detailed travel plans and complete reservations through a virtual experience from the comfort of their own home, without having to visit a travel agency. When a user puts on smart glasses and voice-inputs, "I'm 25 years old and interested in culture," the system will refer to past data and suggest suitable tourist spots. For example, it will provide a virtual tour of the Louvre Museum in Paris or the MoMA in New York. Through this experience, users can select their travel destination and easily confirm their reservation.

[1041] Example prompt sentence:

[1042] "A 25-year-old culture-loving user has previously planned trips to Paris and New York. Suggest a new itinerary that best suits him / her."

[1043] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1044] Step 1:

[1045] Enter your profile information

[1046] A user accesses the reservation system wearing smart glasses or a head-mounted display (HMD), and provides profile information such as age, interests, and past travel history through voice or manual input.

[1047] Input: Information provided by the user such as age, interests, and past travel history.

[1048] Output: Sent to the server as profile data.

[1049] Step 2:

[1050] Analyzing profile information

[1051] The server parses the received profile information. First, if it is voice input, it converts it to text data using an NLP library (e.g., spaCy). Second, it formats the profile information so that it can be easily processed by machine learning models.

[1052] Input: Formatted profile data.

[1053] Output: Analysis results (user's age, interests, past travel history).

[1054] Step 3:

[1055] Generate optimal travel plans

[1056] The server then generates an appropriate itinerary based on the profile data analyzed, using a machine learning model (e.g., TensorFlow or Scikit-learn) trained on past booking data, which uses a recommendation algorithm based on the user's age and interests.

[1057] Input: Analyzed profile data, past booking data.

[1058] Output: A list of suggested itineraries suitable for the user.

[1059] Step 4:

[1060] Providing virtual experiences for travel plans

[1061] The server recreates the environment of the destination in a virtual space based on the generated travel plan. Using Unity or Blender, 3D modeling is performed, allowing users to experience the destination in the virtual space. Through the virtual experience, users can visually confirm the atmosphere and tourist spots of the destination.

[1062] Input: A list of itinerary suggestions.

[1063] Output: A travel destination experience in a virtual space.

[1064] Step 5:

[1065] Select and book your travel plan

[1066] The user selects their preferred travel plan through the virtual experience. Once the selection is complete, the device sends the information to the server, which records the selected plan and confirms the official reservation.

[1067] Input: The travel plan selected by the user.

[1068] Output: Recorded booking information, confirmed travel booking.

[1069] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1070] The present invention is a system that uses a machine learning model that learns from past reservation data when a user plans a trip and an emotion engine that recognizes the user's emotions to suggest appropriate reservation options, allowing the user to easily complete a reservation. Specific embodiments of the invention are described below.

[1071] System configuration

[1072] 1. Devices that accept travel reservations from users:

[1073] A terminal is a device operated by a user, used to make travel reservations and enter user profiles. Examples include smartphones, PCs, and tablets.

[1074] 2. A server containing a machine learning model trained on past booking data:

[1075] The server is the heart of the system, storing past booking data and using machine learning models to generate booking suggestions based on user profiles.

[1076] 3. How to generate suitable booking candidates:

[1077] The server uses a machine learning model to learn features extracted from past booking data and runs an algorithm to generate optimal booking suggestions based on the user profile, taking into account the user's age, interests, past behavioral data, etc.

[1078] 4. Means for recording and confirming the reservation selected by the User:

[1079] The server saves the reservation details selected by the user in the system and provides a means to officially complete the reservation, allowing users to easily confirm their reservation and smoothly proceed with their travel planning.

[1080] 5. Emotion engine that recognizes user emotions:

[1081] The emotion engine analyzes user input data, facial expressions, voice, and other information to assess the user's current emotions, which are then used to generate more appropriate reservation candidates.

[1082] System operation explanation

[1083] Device:

[1084] Users access online booking sites and applications from their devices.

[1085] The device inputs user profile information and current emotions, including age, interests, past booking history, etc. Facial expressions and voice data can also be input using the emotion engine.

[1086] server:

[1087] The user enters profile information and emotion data, which is received by the server.

[1088] The server stores past reservation data and has trained the data using machine learning models.

[1089] The server analyzes the data using a machine learning model based on the received user profile and emotion data to generate appropriate reservation candidates.

[1090] Generate and present booking suggestions:

[1091] The server then presents the generated booking suggestions to the user's device, such as options like "a five-day trip to Paris" or "a seven-day trip to New York."

[1092] The emotion engine can take into account the user's current emotions (e.g., excitement, tension, anxiety, etc.) to present reservation suggestions that better suit the user's mood.

[1093] User selection and booking confirmation:

[1094] The user selects the desired reservation from the presented options.

[1095] Once the selection is complete, the terminal transmits the information to the server.

[1096] The server records the selected reservation and officially confirms the reservation. Once the reservation is confirmed, a confirmation message is sent to the user.

[1097] Specific examples

[1098] For example, if a user enters profile information and emotion data such as "25 years old, interested in culture, and currently feeling excited," the server can prioritize "culturally related tourist spots" or "tourist spots previously booked by users with the same interests" based on past reservation data. Furthermore, the emotion engine can recognize the user's emotion of "excitement" and suggest reservation options that match that emotion (for example, "seeing a musical in New York" or "a wine tour in Paris").

[1099] As described above, by using this system, users can efficiently plan their trips and tourism industry personnel can provide more efficient services. In addition, the emotion engine enables more tailored suggestions to individual users, improving user satisfaction.

[1100] The processing flow will be explained below.

[1101] Step 1:

[1102] The server collects past booking data and stores it in a database, including tourist destinations, length of stay, booking date and time, and user profile information (age, interests, etc.).

[1103] Step 2:

[1104] The server initializes the machine learning model and trains it on the stored historical reservation data. During this training phase, features of each reservation are extracted, forming the basis for the model to make future predictions and recommendations.

[1105] Step 3:

[1106] Users access an online booking site or application from their device and enter their profile information and current emotions. Profile information includes age, interests, past booking history, etc. Facial expressions and voice data can also be input using the emotion engine.

[1107] Step 4:

[1108] The device transmits the user-entered profile information and emotion data to the server, which receives and processes this information as input data for generating suitable reservation candidates.

[1109] Step 5:

[1110] Based on the received user profile and emotion data, the server uses a trained machine learning model, which performs algorithmic processing to suggest the best sightseeing spots and activities according to the user's interests, age, and current emotions.

[1111] Step 6:

[1112] The server utilizes an emotion engine to assess the user's current emotion, using natural language processing algorithms and image and audio analysis techniques.

[1113] Step 7:

[1114] The server combines data from the machine learning model and emotion engine to generate optimal booking suggestions, which are best suited to the user's current emotions and profile.

[1115] Step 8:

[1116] The server sends the generated reservation candidates to the user's device, where the user can choose from multiple options (e.g., "a 5-day trip to Paris" or "a 7-day trip to New York").

[1117] Step 9:

[1118] The user selects the desired sightseeing spots and activities from the presented reservation options. Once the selection is complete, the device sends the information to the server.

[1119] Step 10:

[1120] The server records the reservation based on the user's selection and officially confirms the reservation. This information is stored in a database for future recommendations.

[1121] Step 11:

[1122] The server will send a confirmation message to the user's device to inform them that the reservation has been confirmed, allowing the user to confirm that the reservation has been completed.

[1123] Step 12:

[1124] The user will receive a confirmation message confirming that the booking has been confirmed, at which point the user's travel plans are complete.

[1125] Through these steps, the system helps users complete travel bookings efficiently and easily, and the emotion engine enables more personalized recommendations, improving user satisfaction.

[1126] Example 2

[1127] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1128] Conventional travel booking systems typically present booking suggestions based solely on past data or simple user profiles, which often fail to fully reflect the user's current emotions and interests. Furthermore, the manual booking confirmation process can be complex and degrade the user experience. In addition, there is a need for a method to analyze user emotions in real time and present more personalized booking suggestions.

[1129] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for generating appropriate reservation candidates based on the user profile and emotion data and presenting them to the terminal, means for analyzing the user's emotions using an emotion engine and using the data to generate reservation candidates, and means for recording and confirming the reservation selected by the user. This makes it possible to generate and present more appropriate reservation candidates that take into account not only the user's profile information but also their current emotional state, thereby realizing a more efficient reservation process and an improved user experience.

[1130] A "terminal" is a device that a user operates to make travel reservations and enter profile information, such as a smartphone, PC, or tablet.

[1131] "Server" means the central part of the system that stores past booking data, uses machine learning models to generate booking suggestions based on user profiles, and records and confirms user booking selections.

[1132] A "machine learning model" is an algorithm that learns features extracted from past reservation data and generates optimal reservation candidates based on the user's profile information.

[1133] A "user profile" includes information such as a user's age, interests, and past behavioral data, and is used to generate reservation candidates.

[1134] An "emotion engine" is software that analyzes information such as user input data, facial expressions, and voice to evaluate the user's current emotions.

[1135] "Booking Suggestions" are travel suggestions that users can select, generated using machine learning models and sentiment engines.

[1136] "Confirming a reservation" is the process of saving the reservation details selected by the user in the system and officially confirming them.

[1137] "Emotion Data" means data about a user's emotional state analyzed by the emotion engine and used to improve the accuracy of reservation suggestions.

[1138] This invention relates to a system that uses a machine learning model that learns from a user's past reservation data when planning a trip and an emotion engine that recognizes the user's emotions to suggest appropriate reservation options, allowing users to easily complete reservations.

[1139] 1. System Overview

[1140] The system consists of a terminal that accepts travel reservations from users and a server that contains a machine learning model that learns from past reservation data. The server then generates appropriate reservation suggestions based on the user profile and emotion data and presents them to the terminal. It also includes a function to record and confirm the reservation selected by the user.

[1141] 2. Use of the device

[1142] Users access online booking sites or applications from their smartphones, PCs, tablets, or other devices. They enter user profile information and emotional data using the emotion engine. Profile information includes age, interests, and past booking history. The emotion engine analyzes the user's facial expressions and voice to generate emotional data.

[1143] 3. Server Roles

[1144] The profile information and emotion data sent from the device are sent to the server. The server receives this data and analyzes past reservation data using a trained machine learning model. Based on the analyzed data, reservation candidates that best fit the user's profile and emotions are generated.

[1145] 4. Generating and presenting reservation candidates

[1146] The server uses a machine learning model and an emotion engine to generate suitable booking suggestions. These suggestions are then presented to the user's device. For example, specific options such as "a five-day trip to Paris" or "a seven-day trip to New York" are presented. The emotion engine takes into account the user's current emotions (e.g., excitement, nervousness, anxiety, etc.) to display more personalized booking suggestions.

[1147] 5. Select and confirm your reservation

[1148] The user selects the reservation option they want from the options presented, and the selection information is sent from the device to the server. The server records the selected reservation details and officially confirms the reservation. Once the reservation is confirmed, the server sends a confirmation message to the user, which is displayed on the device.

[1149] Specific examples

[1150] For example, if a user enters profile information and emotion data such as "25 years old, interested in culture, and currently feeling excited," the server can prioritize "culturally related tourist spots" or "tourist spots previously booked by users with the same interests" based on past reservation data. Furthermore, the emotion engine can recognize the user's emotion of "excitement" and suggest reservation options that match that emotion (for example, "seeing a musical in New York" or "a wine tour in Paris").

[1151] Example prompts to input to the generative AI model

[1152] "A user is 25 years old, interested in culture, and currently in a state of excitement. Suggest the best international trips for this user."

[1153] As described above, this system enables users to plan their trips efficiently and tourism industry personnel to provide more efficient services. The use of an emotion engine improves user satisfaction and provides a more personalized travel experience.

[1154] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1155] Step 1:

[1156] User information and emotion data input

[1157] input:

[1158] Users access online booking sites or applications from devices such as smartphones, PCs, or tablets, and enter profile information such as age, interests, and past booking history, as well as emotional data using an emotion engine.

[1159] Specific behavior:

[1160] The user enters "Age: 25" and "Hobbies: Culture," then takes a picture of their face using a webcam and enters "fun" as emotional data.

[1161] output:

[1162] Profile information and emotion data are acquired by the terminal and transmitted to the server.

[1163] ---

[1164] Step 2:

[1165] Server reception and data analysis

[1166] input:

[1167] The profile information and emotion data sent from the terminal are transmitted to the server.

[1168] Specific behavior:

[1169] The server receives data such as "Age: 25," "Hobbies: Culture," and "Current Emotion: Enjoyment."

[1170] Data processing:

[1171] Based on this data, the server analyzes past reservation data using a trained machine learning model.

[1172] output:

[1173] Based on the analyzed user profile and emotional data, features that best fit the user's hobbies and emotions are extracted.

[1174] ---

[1175] Step 3:

[1176] Generate and present reservation candidates to users

[1177] input:

[1178] Server-generated features.

[1179] Specific behavior:

[1180] The server uses machine learning models and an emotion engine to generate suitable reservation suggestions.

[1181] Data processing:

[1182] The machine learning model uses user profiles and sentiment data to generate travel suggestions, such as "seeing a musical in New York" or "a wine tour in Paris," based on data such as "cultural tourist destinations" and "tourist destinations previously booked by users with the same interests."

[1183] output:

[1184] The generated reservation candidates are presented on the user's terminal.

[1185] ---

[1186] Step 4:

[1187] User selection and confirmation

[1188] input:

[1189] The user selects the desired reservation from the presented options.

[1190] Specific behavior:

[1191] The user selects "See a musical in New York," and the terminal transmits the selected reservation information to the server.

[1192] Data Calculation:

[1193] The server officially records the reservation based on the selected reservation information.

[1194] output:

[1195] The selected reservation information is recorded and the reservation is confirmed.

[1196] ---

[1197] Step 5:

[1198] Confirmed booking notification

[1199] input:

[1200] Reservation information confirmed by the server.

[1201] Specific behavior:

[1202] The server verifies the confirmed reservation information and sends a confirmation message to the user.

[1203] output:

[1204] The device receives a confirmation message from the server and notifies the user.

[1205] ---

[1206] Through these steps, the system can present personalized travel suggestions based on the user's profile information and current emotional data, and efficiently confirm bookings.

[1207] (Application example 2)

[1208] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1209] Conventional travel reservation systems only suggest suitable reservation options based on the user's past reservation data, and are unable to consider the user's emotions or real-time changes in interests. Furthermore, they do not adequately provide means to improve the user experience in physical stores, making it difficult for users to have a satisfying purchasing experience. Therefore, the challenge is to provide more personalized product suggestions based on the user's emotional state and in-store purchasing behavior.

[1210] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1211] In this invention, the server includes a means for accepting travel reservations from users, a means for learning multiple features from past reservation data, a means for recognizing emotions and adjusting proposals based on the emotions, and a means for recording and confirming reservations selected by the user. This enables appropriate and personalized product proposals to be made based on the user's emotional state and purchasing behavior in physical stores.

[1212] "User" means a person who uses the system to make travel reservations or purchase products.

[1213] "Terminal" means a device operated by a user to make travel reservations and enter user profiles, and includes smartphones, PCs, tablets, etc.

[1214] The "server" is the core of the system that stores past booking data and uses machine learning models to generate booking suggestions based on user profiles.

[1215] A "machine learning model" is an algorithm that learns features extracted from past reservation data and makes appropriate predictions and suggestions for new data.

[1216] A "user profile" is a collection of data that includes information such as a user's age, interests, and past purchasing history.

[1217] "Reservation suggestions" are travel plans and product selection suggestions that are considered appropriate for the user.

[1218] "Emotion recognition means" is a technology that analyzes information such as user input data, facial expressions, and voice to evaluate current emotions.

[1219] The "recording means" is a function that saves the reservation details selected by the user in the system and officially confirms the reservation.

[1220] "Product selection in a physical store" refers to the behavior and selection process that users take when choosing products in a physical store.

[1221] A "recommendation algorithm" is an algorithm that suggests appropriate products and services based on past data and current user profile information.

[1222] Hereinafter, specific embodiments of the present invention will be described.

[1223] System configuration

[1224] The system of the present invention consists of the following major components:

[1225] 1. Terminal

[1226] A terminal is a device that a user uses to book a trip or enter profile information, and can include a smartphone, PC, tablet, smart glasses, etc. A user uses a terminal to access an online booking site or application.

[1227] 2. Server

[1228] The server is the core of the system, storing past reservation data and generating reservation suggestions based on user profiles using machine learning models. In addition to the machine learning models, the server is equipped with an emotion recognition engine.

[1229] 3. Emotion recognition means

[1230] The server is equipped with an emotion recognition engine that analyzes user input data, facial expressions, voice, and other information to evaluate the user's current emotion. This emotion recognition engine can use TensorFlow and Keras to evaluate emotions.

[1231] 4. Recording Method

[1232] The server has a function to save the reservation details selected by the user in the system and officially confirm the reservation. Once the reservation is confirmed, the server sends a confirmation message to the user.

[1233] Processing flow

[1234] User Actions

[1235] A user uses a device to access a travel booking website and enters profile information (such as age, interests, and past booking history) and emotional data, which may be captured through facial expressions or voice data.

[1236] Server Processing

[1237] The server analyzes the profile information and emotion data received from the user. It uses a machine learning model trained on past reservation data to generate reservation suggestions based on the user profile. It also analyzes the user's current emotions using an emotion recognition engine and tailors reservation suggestions accordingly. The server then presents the generated reservation suggestions to the user's device.

[1238] Confirmation of reservation

[1239] When the user selects the reservation they want from the presented options, the device sends that information to the server, which records the selected reservation and officially confirms it, allowing the user to complete the reservation smoothly.

[1240] Specific examples

[1241] For example, consider a 25-year-old user who is interested in fashion and electronics and whose purchase history includes "handbags," "smartphones," and "headphones." When this user puts on smart glasses and goes shopping, the emotion engine recognizes the user's emotion of "enjoyment." Based on this information, the server can suggest products such as "newly designed handbags" and "the latest smartphone accessories."

[1242] Prompt Sentence Examples

[1243] The user is 25 years old, interested in fashion and electronics, and their past purchases include handbags, smartphones, and headphones. Their current emotion is excitement. Please suggest the best products for the user.

[1244] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1245] Step 1:

[1246] Subject: User

[1247] Specific actions: A user uses a device to access an online booking site or application.

[1248] Input: Enter user profile information (age, interests, past booking history) and emotional data (facial expressions and voice).

[1249] Output: User profile information and emotional data are recorded on the device and sent to the next step.

[1250] Step 2:

[1251] Subject: Device

[1252] Specific operation: The device sends user profile information and emotion data to the server.

[1253] Input: User profile information and sentiment data.

[1254] Output: This data is sent to the server.

[1255] Step 3:

[1256] Subject: Server

[1257] Specific operations: The server analyzes the received user profile information and emotion data.

[1258] Input: User profile information and sentiment data.

[1259] Output: Analysis results are obtained, which include feature extraction based on user profiles using machine learning models and emotion assessment using an emotion recognition engine.

[1260] Step 4:

[1261] Subject: Server

[1262] What it does: The server uses past booking data to generate suitable booking suggestions using machine learning models, and then uses an emotion recognition engine to tailor the suggestions based on the user's current emotions.

[1263] Input: Historical booking data, user profile information, and sentiment analysis results.

[1264] Output: Generated reservation candidates are obtained.

[1265] Step 5:

[1266] Subject: Server

[1267] Specific operation: The server presents the generated reservation candidates to the user's terminal.

[1268] Input: Generated booking suggestions.

[1269] Output: The reservation proposal is sent to the terminal for the user to review.

[1270] Step 6:

[1271] Subject: User

[1272] Specific operation: The user selects the desired reservation from the presented options.

[1273] Input: Booking candidate.

[1274] Output: Selected reservation information.

[1275] Step 7:

[1276] Subject: Device

[1277] Specific operation: The terminal sends the reservation information selected by the user to the server.

[1278] Input: Selected reservation information.

[1279] Output: Reservation information is sent to the server.

[1280] Step 8:

[1281] Subject: Server

[1282] Specific operation: The server records the selected reservation information, officially confirms the reservation, and sends a confirmation message to the user.

[1283] Input: Selected reservation information.

[1284] Output: The booking is confirmed and a confirmation message is sent to the user.

[1285] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1286] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1287] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1288] [Fourth embodiment]

[1289] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1290] 7, a 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.

[1291] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1292] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1293] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1294] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1295] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1296] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1297] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1298] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1299] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1300] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1301] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1302] The present invention provides a system that uses a machine learning model that has learned from past reservation data to suggest suitable reservation options when a user plans a trip, allowing the user to easily complete a reservation. Specific embodiments of the invention are described below.

[1303] System configuration

[1304] 1. Devices that accept travel reservations from users:

[1305] A terminal is a device operated by a user, used to make travel reservations and enter user profiles. Examples include smartphones, PCs, and tablets.

[1306] 2. A server containing a machine learning model trained on past booking data:

[1307] The server is the heart of the system, storing past booking data and using machine learning models to generate booking suggestions based on user profiles.

[1308] 3. How to generate suitable booking candidates:

[1309] The server uses a machine learning model to learn features extracted from past booking data and runs an algorithm to generate optimal booking suggestions based on the user profile, taking into account the user's age, interests, past behavioral data, etc.

[1310] 4. Means for recording and confirming the reservation selected by the User:

[1311] The server saves the reservation details selected by the user in the system and provides a means to officially complete the reservation, allowing users to easily confirm their reservation and smoothly proceed with their travel planning.

[1312] System operation explanation

[1313] Device:

[1314] Users access online booking sites and applications from their devices.

[1315] Enter user profile information into the device, including age, interests, and past booking history.

[1316] server:

[1317] When a user enters profile information, the server receives it.

[1318] The server stores past reservation data and has trained the data using machine learning models.

[1319] Based on the received user profile, the server performs data analysis using machine learning models to generate appropriate reservation candidates.

[1320] Generate and present booking suggestions:

[1321] The server then presents the generated booking suggestions to the user's device, such as options like "a five-day trip to Paris" or "a seven-day trip to New York."

[1322] User selection and booking confirmation:

[1323] The user selects the desired reservation from the presented options.

[1324] Once the selection is complete, the terminal transmits the information to the server.

[1325] The server records the selected reservation and officially confirms the reservation. Once the reservation is confirmed, a confirmation message is sent to the user.

[1326] Specific examples

[1327] For example, if a user enters profile information such as "25 years old and interested in culture," the server can prioritize "culturally related tourist destinations" or "tourist destinations that have been booked by users with the same interests in the past" based on past reservation data. The server analyzes past reservation data (e.g., a 5-day trip to Paris or a 7-day trip to New York) and presents the most suitable reservation candidates.

[1328] As described above, by using this system, users can efficiently plan their trips, and those involved in the tourism industry can provide more efficient services.

[1329] The processing flow will be explained below.

[1330] Step 1:

[1331] The server collects past booking data and stores it in a database, including tourist destinations, length of stay, booking date and time, and user profile information (age, interests, etc.).

[1332] Step 2:

[1333] The server initializes the machine learning model and trains it on the stored historical reservation data. During this training phase, features of each reservation are extracted, forming the basis for the model to make future predictions and recommendations.

[1334] Step 3:

[1335] Users access an online booking site or application from their device and enter their profile information, which may include their age, interests, and past booking history.

[1336] Step 4:

[1337] The device transmits the user-entered profile information to the server, which receives it and processes it as input data for generating suitable reservation candidates.

[1338] Step 5:

[1339] Based on the received user profile, the server uses a trained machine learning model, which performs algorithmic processing to suggest the best sightseeing spots and activities according to the user's interests and age.

[1340] Step 6:

[1341] The server generates booking suggestions based on the machine learning model and sends them to the user's device, allowing the user to choose the optimal travel plan from multiple options.

[1342] Step 7:

[1343] The user selects the desired sightseeing spots and activities from the presented reservation options. Once the selection is complete, the device sends the information to the server.

[1344] Step 8:

[1345] The server records the reservation based on the user's selection and officially confirms the reservation. This information is stored in a database for future recommendations.

[1346] Step 9:

[1347] The server will send a confirmation message to the user's device to inform them that the reservation has been confirmed, allowing the user to confirm that the reservation has been completed.

[1348] Step 10:

[1349] The user will receive a confirmation message confirming that the booking has been confirmed, at which point the user's travel plans are complete.

[1350] Through these steps, the system helps users complete travel reservations efficiently and easily. The server also continuously collects data and learns from it, allowing it to make more accurate recommendations.

[1351] Example 1

[1352] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1353] When booking travel, users lack an efficient way to find the best options based on their interests and past behavior. They also need a way to quickly and accurately process and confirm their booking selections. This would enable users to plan their trips more efficiently and travel providers to provide more efficient services.

[1354] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1355] In this invention, the server includes a terminal that accepts travel reservations from users, a server that includes a machine learning model that learns from past reservation data, means for the server to generate appropriate reservation candidates based on a user profile and present them to the terminal, means for recording and confirming reservations selected by the user, means for presenting the generated reservation candidates to the user's terminal, and means for transmitting selected reservation information from the user's terminal to the server. This allows the user to be presented with optimal reservation candidates based on their own profile information, allowing them to efficiently plan their trip and quickly confirm the selected reservation.

[1356] "User" means an individual who makes a travel reservation and uses the System to enter their profile information and select reservation options.

[1357] "Device" means a device operated by a User to book a trip or enter profile information, including a smartphone, PC, or tablet.

[1358] "Server" refers to the central part of the system that stores past booking data and uses machine learning models to generate booking suggestions based on user profiles.

[1359] "Machine Learning Model" refers to a computational means that includes an algorithm that learns from past booking data to generate suitable booking suggestions based on user profile information.

[1360] "Profile Information" refers to personal information entered by a user, such as age, interests, and past booking history.

[1361] "Booking Suggestions" refers to a list of suggested travel itineraries and accommodations generated by machine learning models based on a user's profile.

[1362] "Recording" refers to the process of saving the reservation selections made by the user in a database.

[1363] "Confirmation" refers to the process of officially validating the recorded reservation and sending a confirmation message to the user.

[1364] "Database" refers to a software system for storing and managing user profile information and past reservation data.

[1365] This invention is a system that uses a machine learning model that has learned from past reservation data to suggest suitable reservation options when a user plans a trip, allowing the user to easily complete the reservation. This system presents the most suitable reservation options to the user based on profile information from the user. It also records the reservation details selected by the user and supports a series of processes to finalize the reservation.

[1366] System configuration

[1367] 1. Devices that accept travel reservations from users:

[1368] A terminal is a device operated by a user to make travel reservations and enter user profiles. Specific hardware examples include smartphones, PCs, and tablets. A terminal is used by a user to access online booking sites and dedicated applications.

[1369] 2. A server containing a machine learning model trained on past booking data:

[1370] The server uses past reservation data stored in a database to train a machine learning model using software such as Python's Scikit-learn and TensorFlow. The server then analyzes the data to generate optimal reservation suggestions based on user profile information.

[1371] 3. How to generate suitable booking candidates:

[1372] The server uses a machine learning model to learn features extracted from past booking data and then runs an algorithm to generate optimal booking suggestions based on the user profile, taking into account the user's age, interests, past behavioral data, etc.

[1373] 4. Means for recording and confirming the reservation selected by the User:

[1374] The server saves the reservation details selected by the user in a database and officially confirms the reservation. During this process, a confirmation message (e.g., a reservation confirmation email) is sent to the user.

[1375] Specific examples

[1376] For example, if a user enters profile information such as "I'm 25 years old and interested in culture," the system will operate as follows:

[1377] Device:

[1378] Users access online booking sites or applications from their devices and enter profile information, such as age (25 years old) and cultural interests.

[1379] server:

[1380] Based on the profile information received from the user, we look up past booking data of users with similar interests.

[1381] Use machine learning models (e.g., Scikit-learn or TensorFlow) to generate suitable appointment candidates.

[1382] Presenting generated booking suggestions:

[1383] It prioritizes cultural destinations, such as "5-day trip to Paris" or "7-day trip to New York."

[1384] Prompt Sentence Examples

[1385] 1. "Suggest the best travel destinations for a 25-year-old interested in culture. Explain why."

[1386] 2. "Generate a recommended travel plan for a couple in their 30s based on past data."

[1387] In this way, the system allows users to plan their trips quickly and effectively, and travel agents to provide efficient and personalized service.

[1388] The above is a specific embodiment of the present invention.

[1389] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1390] Step 1:

[1391] User access to booking site and profile information input

[1392] A user uses a terminal to access an online reservation site or dedicated application. This allows the user to connect to the system and access a profile information input screen. The terminal receives profile information entered by the user, such as age, interests, and past reservation history. Input: User profile information. Output: Profile information is saved in the terminal's input form.

[1393] Step 2:

[1394] Sending profile information to the server

[1395] The device sends the entered profile information to the server. This sending is performed using a protocol such as an HTTP POST request. Input: Profile information stored on the device. Output: Request to send profile information to the server.

[1396] Step 3:

[1397] Server receives and stores profile information

[1398] The server receives the profile information sent from the device. It then stores this information in a database. Input: Received profile information. Output: Profile information stored in the database.

[1399] Step 4:

[1400] Use machine learning models that learn from past booking data

[1401] The server uses the stored past reservation data to train a machine learning model. Specific software used is Scikit-learn and TensorFlow. The machine learning model extracts features from the past data and prepares to generate optimal reservation candidates based on the user profile. Input: Past reservation data. Output: Trained machine learning model.

[1402] Step 5:

[1403] Generate booking suggestions based on profile information

[1404] The server uses a machine learning model to analyze the profile information entered by the user and generate appropriate reservation suggestions. For example, it uses Python's Scikit-learn and TensorFlow libraries to suggest the best travel destinations and plans based on information such as the user's age and interests. Input: User profile information, trained machine learning model. Output: Generated reservation suggestion list.

[1405] Step 6:

[1406] Present the generated reservation candidates to the user's device

[1407] The server sends the generated reservation candidate list to the user's device. This is also usually done using an HTTP response. The device receives the response from the server and prepares to display the reservation candidates on the screen. Input: Generated reservation candidate list. Output: Response from the server, reservation candidate list.

[1408] Step 7:

[1409] User selection of reservation options

[1410] The user selects the desired reservation from the presented options. For example, they select "5-day trip to Paris." The selected information is temporarily saved on the user's device. Input: List of reservation options. Output: User's selection information.

[1411] Step 8:

[1412] Sending selected reservation information to the server

[1413] The terminal sends the selected reservation information to the server. This process is also performed using an HTTP POST request. Input: User's selected information. Output: Request to send selected information to the server.

[1414] Step 9:

[1415] The server records and confirms the reservation

[1416] The server receives the selection information and stores it in a database. It then officially confirms the reservation and sends a confirmation message to the user. The confirmation message is sent to the user via email or other means. Input: Selected reservation information. Output: Reservation information stored in the database, confirmation message to the user.

[1417] The above is the specific flow of processing in this system.

[1418] (Application example 1)

[1419] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1420] With traditional travel reservation systems, users had to spend a lot of time and effort to find the right travel plan. It was also difficult to find a plan that suited their individual needs. Furthermore, users were unable to virtually experience their chosen travel destination, which often led to uncertainty about choosing a travel destination.

[1421] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1422] In this invention, the server includes a terminal that accepts travel reservations from users, a server that includes a machine learning model that learns from past reservation data, means for the server to generate appropriate reservation candidates based on a user profile and present them to the terminal, means for providing a travel destination experience in a virtual space based on the provided profile information, means for analyzing data entered by the user through voice or manual input and proposing an optimal travel plan, and means for recording and confirming reservations selected by the user. This allows users to easily find efficient and personalized travel plans and further reduces anxiety about selecting a travel destination through the virtual experience.

[1423] "Terminal" means a device operated by a user to make travel reservations and enter user profiles.

[1424] A "machine learning model" is a model that includes an algorithm for learning from past reservation data and generating appropriate reservation candidates.

[1425] The "Server" is the central part of the system that stores past booking data and uses machine learning models to learn from the data to generate suitable booking suggestions based on user profiles.

[1426] A "user profile" is a profile that includes information such as a user's age, interests, and past behavioral data.

[1427] "Reservation candidates" are travel plan candidates that the server generates based on the user profile and that the user can select.

[1428] A "virtual space" is a virtual environment generated using computer technology in which users can have experiences.

[1429] "Virtual experience" refers to the visual and tactile reproduction of the scenery and environment of a travel destination within a virtual space, allowing users to experience it.

[1430] "Voice input" is an input method in which data input orally by the user is analyzed using voice recognition technology.

[1431] "Manual input" refers to data that is entered directly by the user using a keyboard or touch operation.

[1432] A "recommendation algorithm" is a computational method for suggesting optimal travel plans based on a user's interests and age.

[1433] The present invention provides a system for efficiently selecting an appropriate travel plan by utilizing a virtual space to provide a more realistic experience when users plan their trip. This system is realized by combining a terminal, a server, a user profile, a machine learning model, a virtual space, a virtual experience, voice input, manual input, and a recommendation algorithm.

[1434] System configuration

[1435] Device:

[1436] A device used by a user to make travel reservations and enter user profiles. Examples include smart glasses and head-mounted displays (HMDs).

[1437] server:

[1438] This is the central part of the system that stores past reservation data and uses machine learning models to learn from the data. The server is equipped with an advanced GPU, allowing for high-speed data analysis.

[1439] User profile:

[1440] A profile containing information such as a user's age, interests, and past behavioral data, which is used to generate travel plans.

[1441] Machine learning models:

[1442] The model includes an algorithm that learns from past reservation data and generates suitable reservation candidates based on user profiles. It uses machine learning frameworks such as TensorFlow and Scikit-learn.

[1443] Virtual Space:

[1444] It is a virtual environment generated using computer technology in which users can experience travel destinations, using Unity and Blender for 3D modeling.

[1445] Virtual Experience:

[1446] It refers to the visual and tactile reproduction of the scenery and environment of a travel destination in a virtual space, allowing users to experience it.

[1447] Audio Input:

[1448] It is an input method that uses speech recognition technology to analyze data entered orally by the user. It uses the NLP library spaCy.

[1449] Manual entry:

[1450] This refers to data that the user directly inputs using a keyboard or touch panel.

[1451] Recommendation algorithm:

[1452] It is a calculation method for suggesting optimal travel plans based on the user's interests and age.

[1453] System operation explanation

[1454] Enter your profile information:

[1455] Users put on smart glasses or an HMD, access the device, and then provide profile information (such as age, interests, and past travel history) via voice or manual input.

[1456] Parsing profile information:

[1457] Based on the received profile information, the server analyzes the data using a machine learning model that has learned from past booking data, and generates a travel plan that is suitable for the user.

[1458] Providing virtual experiences of your destination:

[1459] Based on the proposed itinerary, the destination environment is recreated in a virtual space, allowing users to experience the virtual world and visually confirm the atmosphere and tourist attractions of the destination.

[1460] Select and confirm your reservation:

[1461] The user selects their preferred travel plan through the virtual experience. Once the selection is complete, the server records the information and officially confirms the reservation.

[1462] Specific examples

[1463] For example, consider a system that allows users to create detailed travel plans and complete reservations through a virtual experience from the comfort of their own home, without having to visit a travel agency. When a user puts on smart glasses and voice-inputs, "I'm 25 years old and interested in culture," the system will refer to past data and suggest suitable tourist spots. For example, it will provide a virtual tour of the Louvre Museum in Paris or the MoMA in New York. Through this experience, users can select their travel destination and easily confirm their reservation.

[1464] Example prompt sentence:

[1465] "A 25-year-old culture-loving user has previously planned trips to Paris and New York. Suggest a new itinerary that best suits him / her."

[1466] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1467] Step 1:

[1468] Enter your profile information

[1469] A user accesses the reservation system wearing smart glasses or a head-mounted display (HMD), and provides profile information such as age, interests, and past travel history through voice or manual input.

[1470] Input: Information provided by the user such as age, interests, and past travel history.

[1471] Output: Sent to the server as profile data.

[1472] Step 2:

[1473] Analyzing profile information

[1474] The server parses the received profile information. First, if it is voice input, it converts it to text data using an NLP library (e.g., spaCy). Second, it formats the profile information so that it can be easily processed by machine learning models.

[1475] Input: Formatted profile data.

[1476] Output: Analysis results (user's age, interests, past travel history).

[1477] Step 3:

[1478] Generate optimal travel plans

[1479] The server then generates an appropriate itinerary based on the profile data analyzed, using a machine learning model (e.g., TensorFlow or Scikit-learn) trained on past booking data, which uses a recommendation algorithm based on the user's age and interests.

[1480] Input: Analyzed profile data, past booking data.

[1481] Output: A list of suggested itineraries suitable for the user.

[1482] Step 4:

[1483] Providing virtual experiences for travel plans

[1484] The server recreates the environment of the destination in a virtual space based on the generated travel plan. Using Unity or Blender, 3D modeling is performed, allowing users to experience the destination in the virtual space. Through the virtual experience, users can visually confirm the atmosphere and tourist spots of the destination.

[1485] Input: A list of itinerary suggestions.

[1486] Output: A travel destination experience in a virtual space.

[1487] Step 5:

[1488] Select and book your travel plan

[1489] The user selects their preferred travel plan through the virtual experience. Once the selection is complete, the device sends the information to the server, which records the selected plan and confirms the official reservation.

[1490] Input: The travel plan selected by the user.

[1491] Output: Recorded booking information, confirmed travel booking.

[1492] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1493] The present invention is a system that uses a machine learning model that learns from past reservation data when a user plans a trip and an emotion engine that recognizes the user's emotions to suggest appropriate reservation options, allowing the user to easily complete a reservation. Specific embodiments of the invention are described below.

[1494] System configuration

[1495] 1. Devices that accept travel reservations from users:

[1496] A terminal is a device operated by a user, used to make travel reservations and enter user profiles. Examples include smartphones, PCs, and tablets.

[1497] 2. A server containing a machine learning model trained on past booking data:

[1498] The server is the heart of the system, storing past booking data and using machine learning models to generate booking suggestions based on user profiles.

[1499] 3. How to generate suitable booking candidates:

[1500] The server uses a machine learning model to learn features extracted from past booking data and runs an algorithm to generate optimal booking suggestions based on the user profile, taking into account the user's age, interests, past behavioral data, etc.

[1501] 4. Means for recording and confirming the reservation selected by the User:

[1502] The server saves the reservation details selected by the user in the system and provides a means to officially complete the reservation, allowing users to easily confirm their reservation and smoothly proceed with their travel planning.

[1503] 5. Emotion engine that recognizes user emotions:

[1504] The emotion engine analyzes user input data, facial expressions, voice, and other information to assess the user's current emotions, which are then used to generate more appropriate reservation candidates.

[1505] System operation explanation

[1506] Device:

[1507] Users access online booking sites and applications from their devices.

[1508] The device inputs user profile information and current emotions, including age, interests, past booking history, etc. Facial expressions and voice data can also be input using the emotion engine.

[1509] server:

[1510] The user enters profile information and emotion data, which is received by the server.

[1511] The server stores past reservation data and has trained the data using machine learning models.

[1512] The server analyzes the data using a machine learning model based on the received user profile and emotion data to generate appropriate reservation candidates.

[1513] Generate and present booking suggestions:

[1514] The server then presents the generated booking suggestions to the user's device, such as options like "a five-day trip to Paris" or "a seven-day trip to New York."

[1515] The emotion engine can take into account the user's current emotions (e.g., excitement, tension, anxiety, etc.) to present reservation suggestions that better suit the user's mood.

[1516] User selection and booking confirmation:

[1517] The user selects the desired reservation from the presented options.

[1518] Once the selection is complete, the terminal transmits the information to the server.

[1519] The server records the selected reservation and officially confirms the reservation. Once the reservation is confirmed, a confirmation message is sent to the user.

[1520] Specific examples

[1521] For example, if a user enters profile information and emotion data such as "25 years old, interested in culture, and currently feeling excited," the server can prioritize "culturally related tourist spots" or "tourist spots previously booked by users with the same interests" based on past reservation data. Furthermore, the emotion engine can recognize the user's emotion of "excitement" and suggest reservation options that match that emotion (for example, "seeing a musical in New York" or "a wine tour in Paris").

[1522] As described above, by using this system, users can efficiently plan their trips and tourism industry personnel can provide more efficient services. In addition, the emotion engine enables more tailored suggestions to individual users, improving user satisfaction.

[1523] The processing flow will be explained below.

[1524] Step 1:

[1525] The server collects past booking data and stores it in a database, including tourist destinations, length of stay, booking date and time, and user profile information (age, interests, etc.).

[1526] Step 2:

[1527] The server initializes the machine learning model and trains it on the stored historical reservation data. During this training phase, features of each reservation are extracted, forming the basis for the model to make future predictions and recommendations.

[1528] Step 3:

[1529] Users access an online booking site or application from their device and enter their profile information and current emotions. Profile information includes age, interests, past booking history, etc. Facial expressions and voice data can also be input using the emotion engine.

[1530] Step 4:

[1531] The device transmits the user-entered profile information and emotion data to the server, which receives and processes this information as input data for generating suitable reservation candidates.

[1532] Step 5:

[1533] Based on the received user profile and emotion data, the server uses a trained machine learning model, which performs algorithmic processing to suggest the best sightseeing spots and activities according to the user's interests, age, and current emotions.

[1534] Step 6:

[1535] The server utilizes an emotion engine to assess the user's current emotion, using natural language processing algorithms and image and audio analysis techniques.

[1536] Step 7:

[1537] The server combines data from the machine learning model and emotion engine to generate optimal booking suggestions, which are best suited to the user's current emotions and profile.

[1538] Step 8:

[1539] The server sends the generated reservation candidates to the user's device, where the user can choose from multiple options (e.g., "a 5-day trip to Paris" or "a 7-day trip to New York").

[1540] Step 9:

[1541] The user selects the desired sightseeing spots and activities from the presented reservation options. Once the selection is complete, the device sends the information to the server.

[1542] Step 10:

[1543] The server records the reservation based on the user's selection and officially confirms the reservation. This information is stored in a database for future recommendations.

[1544] Step 11:

[1545] The server will send a confirmation message to the user's device to inform them that the reservation has been confirmed, allowing the user to confirm that the reservation has been completed.

[1546] Step 12:

[1547] The user will receive a confirmation message confirming that the booking has been confirmed, at which point the user's travel plans are complete.

[1548] Through these steps, the system helps users complete travel bookings efficiently and easily, and the emotion engine enables more personalized recommendations, improving user satisfaction.

[1549] Example 2

[1550] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1551] Conventional travel booking systems typically present booking suggestions based solely on past data or simple user profiles, which often fail to fully reflect the user's current emotions and interests. Furthermore, the manual booking confirmation process can be complex and degrade the user experience. In addition, there is a need for a method to analyze user emotions in real time and present more personalized booking suggestions.

[1552] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for generating appropriate reservation candidates based on the user profile and emotion data and presenting them to the terminal, means for analyzing the user's emotions using an emotion engine and using the data to generate reservation candidates, and means for recording and confirming the reservation selected by the user. This makes it possible to generate and present more appropriate reservation candidates that take into account not only the user's profile information but also their current emotional state, thereby realizing a more efficient reservation process and an improved user experience.

[1553] A "terminal" is a device that a user operates to make travel reservations and enter profile information, such as a smartphone, PC, or tablet.

[1554] "Server" means the central part of the system that stores past booking data, uses machine learning models to generate booking suggestions based on user profiles, and records and confirms user booking selections.

[1555] A "machine learning model" is an algorithm that learns features extracted from past reservation data and generates optimal reservation candidates based on the user's profile information.

[1556] A "user profile" includes information such as a user's age, interests, and past behavioral data, and is used to generate reservation candidates.

[1557] An "emotion engine" is software that analyzes information such as user input data, facial expressions, and voice to evaluate the user's current emotions.

[1558] "Booking Suggestions" are travel suggestions that users can select, generated using machine learning models and sentiment engines.

[1559] "Confirming a reservation" is the process of saving the reservation details selected by the user in the system and officially confirming them.

[1560] "Emotion Data" means data about a user's emotional state analyzed by the emotion engine and used to improve the accuracy of reservation suggestions.

[1561] This invention relates to a system that uses a machine learning model that learns from a user's past reservation data when planning a trip and an emotion engine that recognizes the user's emotions to suggest appropriate reservation options, allowing users to easily complete reservations.

[1562] 1. System Overview

[1563] The system consists of a terminal that accepts travel reservations from users and a server that contains a machine learning model that learns from past reservation data. The server then generates appropriate reservation suggestions based on the user profile and emotion data and presents them to the terminal. It also includes a function to record and confirm the reservation selected by the user.

[1564] 2. Use of the device

[1565] Users access online booking sites or applications from their smartphones, PCs, tablets, or other devices. They enter user profile information and emotional data using the emotion engine. Profile information includes age, interests, and past booking history. The emotion engine analyzes the user's facial expressions and voice to generate emotional data.

[1566] 3. Server Roles

[1567] The profile information and emotion data sent from the device are sent to the server. The server receives this data and analyzes past reservation data using a trained machine learning model. Based on the analyzed data, reservation candidates that best fit the user's profile and emotions are generated.

[1568] 4. Generating and presenting reservation candidates

[1569] The server uses a machine learning model and an emotion engine to generate suitable booking suggestions. These suggestions are then presented to the user's device. For example, specific options such as "a five-day trip to Paris" or "a seven-day trip to New York" are presented. The emotion engine takes into account the user's current emotions (e.g., excitement, nervousness, anxiety, etc.) to display more personalized booking suggestions.

[1570] 5. Select and confirm your reservation

[1571] The user selects the reservation option they want from the options presented, and the selection information is sent from the device to the server. The server records the selected reservation details and officially confirms the reservation. Once the reservation is confirmed, the server sends a confirmation message to the user, which is displayed on the device.

[1572] Specific examples

[1573] For example, if a user enters profile information and emotion data such as "25 years old, interested in culture, and currently feeling excited," the server can prioritize "culturally related tourist spots" or "tourist spots previously booked by users with the same interests" based on past reservation data. Furthermore, the emotion engine can recognize the user's emotion of "excitement" and suggest reservation options that match that emotion (for example, "seeing a musical in New York" or "a wine tour in Paris").

[1574] Example prompts to input to the generative AI model

[1575] "A user is 25 years old, interested in culture, and currently in a state of excitement. Suggest the best international trips for this user."

[1576] As described above, this system enables users to plan their trips efficiently and tourism industry personnel to provide more efficient services. The use of an emotion engine improves user satisfaction and provides a more personalized travel experience.

[1577] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1578] Step 1:

[1579] User information and emotion data input

[1580] input:

[1581] Users access online booking sites or applications from devices such as smartphones, PCs, or tablets, and enter profile information such as age, interests, and past booking history, as well as emotional data using an emotion engine.

[1582] Specific behavior:

[1583] The user enters "Age: 25" and "Hobbies: Culture," then takes a picture of their face using a webcam and enters "fun" as emotional data.

[1584] output:

[1585] Profile information and emotion data are acquired by the terminal and transmitted to the server.

[1586] ---

[1587] Step 2:

[1588] Server reception and data analysis

[1589] input:

[1590] The profile information and emotion data sent from the terminal are transmitted to the server.

[1591] Specific behavior:

[1592] The server receives data such as "Age: 25," "Hobbies: Culture," and "Current Emotion: Enjoyment."

[1593] Data processing:

[1594] Based on this data, the server analyzes past reservation data using a trained machine learning model.

[1595] output:

[1596] Based on the analyzed user profile and emotional data, features that best fit the user's hobbies and emotions are extracted.

[1597] ---

[1598] Step 3:

[1599] Generate and present reservation candidates to users

[1600] input:

[1601] Server-generated features.

[1602] Specific behavior:

[1603] The server uses machine learning models and an emotion engine to generate suitable reservation suggestions.

[1604] Data processing:

[1605] The machine learning model uses user profiles and sentiment data to generate travel suggestions, such as "seeing a musical in New York" or "a wine tour in Paris," based on data such as "cultural tourist destinations" and "tourist destinations previously booked by users with the same interests."

[1606] output:

[1607] The generated reservation candidates are presented on the user's terminal.

[1608] ---

[1609] Step 4:

[1610] User selection and confirmation

[1611] input:

[1612] The user selects the desired reservation from the presented options.

[1613] Specific behavior:

[1614] The user selects "See a musical in New York," and the terminal transmits the selected reservation information to the server.

[1615] Data Calculation:

[1616] The server officially records the reservation based on the selected reservation information.

[1617] output:

[1618] The selected reservation information is recorded and the reservation is confirmed.

[1619] ---

[1620] Step 5:

[1621] Confirmed booking notification

[1622] input:

[1623] Reservation information confirmed by the server.

[1624] Specific behavior:

[1625] The server verifies the confirmed reservation information and sends a confirmation message to the user.

[1626] output:

[1627] The device receives a confirmation message from the server and notifies the user.

[1628] ---

[1629] Through these steps, the system can present personalized travel suggestions based on the user's profile information and current emotional data, and efficiently confirm bookings.

[1630] (Application example 2)

[1631] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1632] Conventional travel reservation systems only suggest suitable reservation options based on the user's past reservation data, and are unable to consider the user's emotions or real-time changes in interests. Furthermore, they do not adequately provide means to improve the user experience in physical stores, making it difficult for users to have a satisfying purchasing experience. Therefore, the challenge is to provide more personalized product suggestions based on the user's emotional state and in-store purchasing behavior.

[1633] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1634] In this invention, the server includes a means for accepting travel reservations from users, a means for learning multiple features from past reservation data, a means for recognizing emotions and adjusting proposals based on the emotions, and a means for recording and confirming reservations selected by the user. This enables appropriate and personalized product proposals to be made based on the user's emotional state and purchasing behavior in physical stores.

[1635] "User" means a person who uses the system to make travel reservations or purchase products.

[1636] "Terminal" means a device operated by a user to make travel reservations and enter user profiles, and includes smartphones, PCs, tablets, etc.

[1637] The "server" is the core of the system that stores past booking data and uses machine learning models to generate booking suggestions based on user profiles.

[1638] A "machine learning model" is an algorithm that learns features extracted from past reservation data and makes appropriate predictions and suggestions for new data.

[1639] A "user profile" is a collection of data that includes information such as a user's age, interests, and past purchasing history.

[1640] "Reservation suggestions" are travel plans and product selection suggestions that are considered appropriate for the user.

[1641] "Emotion recognition means" is a technology that analyzes information such as user input data, facial expressions, and voice to evaluate current emotions.

[1642] The "recording means" is a function that saves the reservation details selected by the user in the system and officially confirms the reservation.

[1643] "Product selection in a physical store" refers to the behavior and selection process that users take when choosing products in a physical store.

[1644] A "recommendation algorithm" is an algorithm that suggests appropriate products and services based on past data and current user profile information.

[1645] Hereinafter, specific embodiments of the present invention will be described.

[1646] System configuration

[1647] The system of the present invention consists of the following major components:

[1648] 1. Terminal

[1649] A terminal is a device that a user uses to book a trip or enter profile information, and can include a smartphone, PC, tablet, smart glasses, etc. A user uses a terminal to access an online booking site or application.

[1650] 2. Server

[1651] The server is the core of the system, storing past reservation data and generating reservation suggestions based on user profiles using machine learning models. In addition to the machine learning models, the server is equipped with an emotion recognition engine.

[1652] 3. Emotion recognition means

[1653] The server is equipped with an emotion recognition engine that analyzes user input data, facial expressions, voice, and other information to evaluate the user's current emotion. This emotion recognition engine can use TensorFlow and Keras to evaluate emotions.

[1654] 4. Recording Method

[1655] The server has a function to save the reservation details selected by the user in the system and officially confirm the reservation. Once the reservation is confirmed, the server sends a confirmation message to the user.

[1656] Processing flow

[1657] User Actions

[1658] A user uses a device to access a travel booking website and enters profile information (such as age, interests, and past booking history) and emotional data, which may be captured through facial expressions or voice data.

[1659] Server Processing

[1660] The server analyzes the profile information and emotion data received from the user. It uses a machine learning model trained on past reservation data to generate reservation suggestions based on the user profile. It also analyzes the user's current emotions using an emotion recognition engine and tailors reservation suggestions accordingly. The server then presents the generated reservation suggestions to the user's device.

[1661] Confirmation of reservation

[1662] When the user selects the reservation they want from the presented options, the device sends that information to the server, which records the selected reservation and officially confirms it, allowing the user to complete the reservation smoothly.

[1663] Specific examples

[1664] For example, consider a 25-year-old user who is interested in fashion and electronics and whose purchase history includes "handbags," "smartphones," and "headphones." When this user puts on smart glasses and goes shopping, the emotion engine recognizes the user's emotion of "enjoyment." Based on this information, the server can suggest products such as "newly designed handbags" and "the latest smartphone accessories."

[1665] Prompt Sentence Examples

[1666] The user is 25 years old, interested in fashion and electronics, and their past purchases include handbags, smartphones, and headphones. Their current emotion is excitement. Please suggest the best products for the user.

[1667] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1668] Step 1:

[1669] Subject: User

[1670] Specific actions: A user uses a device to access an online booking site or application.

[1671] Input: Enter user profile information (age, interests, past booking history) and emotional data (facial expressions and voice).

[1672] Output: User profile information and emotional data are recorded on the device and sent to the next step.

[1673] Step 2:

[1674] Subject: Device

[1675] Specific operation: The device sends user profile information and emotion data to the server.

[1676] Input: User profile information and sentiment data.

[1677] Output: This data is sent to the server.

[1678] Step 3:

[1679] Subject: Server

[1680] Specific operations: The server analyzes the received user profile information and emotion data.

[1681] Input: User profile information and sentiment data.

[1682] Output: Analysis results are obtained, which include feature extraction based on user profiles using machine learning models and emotion assessment using an emotion recognition engine.

[1683] Step 4:

[1684] Subject: Server

[1685] What it does: The server uses past booking data to generate suitable booking suggestions using machine learning models, and then uses an emotion recognition engine to tailor the suggestions based on the user's current emotions.

[1686] Input: Historical booking data, user profile information, and sentiment analysis results.

[1687] Output: Generated reservation candidates are obtained.

[1688] Step 5:

[1689] Subject: Server

[1690] Specific operation: The server presents the generated reservation candidates to the user's terminal.

[1691] Input: Generated booking suggestions.

[1692] Output: The reservation proposal is sent to the terminal for the user to review.

[1693] Step 6:

[1694] Subject: User

[1695] Specific operation: The user selects the desired reservation from the presented options.

[1696] Input: Booking candidate.

[1697] Output: Selected reservation information.

[1698] Step 7:

[1699] Subject: Device

[1700] Specific operation: The terminal sends the reservation information selected by the user to the server.

[1701] Input: Selected reservation information.

[1702] Output: Reservation information is sent to the server.

[1703] Step 8:

[1704] Subject: Server

[1705] Specific operation: The server records the selected reservation information, officially confirms the reservation, and sends a confirmation message to the user.

[1706] Input: Selected reservation information.

[1707] Output: The booking is confirmed and a confirmation message is sent to the user.

[1708] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1709] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1710] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1711] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1712] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1713] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1714] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1715] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1716] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1717] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1718] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1719] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1720] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1721] 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.

[1722] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1723] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1724] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1725] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1726] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1727] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1728] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1729] The following is further disclosed regarding the above embodiment.

[1730] (Claim 1)

[1731] a terminal that accepts travel reservations from users;

[1732] a server including a machine learning model that learns from past reservation data;

[1733] means for the server to generate suitable reservation candidates based on the user profile and present them to the terminal;

[1734] a means for recording and confirming the reservation selected by the user;

[1735] A system including:

[1736] (Claim 2)

[1737] The system according to claim 1 , wherein the machine learning model learns a plurality of features extracted from past reservation data.

[1738] (Claim 3)

[1739] The system of claim 1 , wherein the system uses a recommendation algorithm based on the user's interests and age when generating the suitable appointment candidates.

[1740] "Example 1"

[1741] (Claim 1)

[1742] a terminal that accepts travel reservations from users;

[1743] a server including a machine learning model that learns from past reservation data;

[1744] means for the server to generate suitable reservation candidates based on the user profile and present them to the terminal;

[1745] a means for recording and confirming the reservation selected by the user;

[1746] A means for presenting the generated reservation candidates to a user's terminal;

[1747] means for transmitting the selected reservation information from the user's terminal to the server;

[1748] A system including:

[1749] (Claim 2)

[1750] The system according to claim 1 , wherein the machine learning model learns a plurality of features extracted from past reservation data.

[1751] (Claim 3)

[1752] The system of claim 1 , wherein the system uses a recommendation algorithm based on the user's interests and age when generating the suitable appointment candidates.

[1753] "Application Example 1"

[1754] (Claim 1)

[1755] a terminal that accepts travel reservations from users;

[1756] a server including a machine learning model that learns from past reservation data;

[1757] means for the server to generate suitable reservation candidates based on the user profile and present them to the terminal;

[1758] A means for providing a travel destination experience in a virtual space based on the provided profile information;

[1759] A means to analyze data entered by voice or manually by the user and propose optimal travel plans,

[1760] a means for recording and confirming the reservation selected by the user;

[1761] A system including:

[1762] (Claim 2)

[1763] The system according to claim 1 , wherein the machine learning model learns a plurality of features extracted from past reservation data.

[1764] (Claim 3)

[1765] The system of claim 1 , wherein the system uses a recommendation algorithm based on the user's interests and age when generating the suitable appointment candidates.

[1766] "Example 2: Combining Emotion Engines"

[1767] (Claim 1)

[1768] a terminal that accepts travel reservations from users;

[1769] a server including a machine learning model that learns from past reservation data;

[1770] a means for the server to generate appropriate reservation candidates based on the user profile and emotion data and present them to the terminal;

[1771] A means for analyzing user emotions using an emotion engine and utilizing the data to generate reservation candidates;

[1772] a means for recording and confirming the reservation selected by the user;

[1773] A system including:

[1774] (Claim 2)

[1775] The system according to claim 1 , wherein the machine learning model learns a plurality of features extracted from past reservation data.

[1776] (Claim 3)

[1777] The system of claim 1 , wherein the system uses a recommendation algorithm based on the user's interests and age when generating the suitable appointment candidates.

[1778] "Application example 2 when combining emotion engines"

[1779] (Claim 1)

[1780] a terminal that accepts travel reservations from users;

[1781] a server including a machine learning model that learns from past reservation data;

[1782] means for the server to generate suitable reservation candidates based on the user profile and present them to the terminal;

[1783] an emotion recognition means for recognizing an emotion of a user and adjusting suggestions based on the emotion;

[1784] a means for recording and confirming the reservation selected by the user;

[1785] A system including:

[1786] (Claim 2)

[1787] The system according to claim 1 , wherein the machine learning model learns a plurality of features extracted from past reservation data.

[1788] (Claim 3)

[1789] The system of claim 1, wherein the system uses a recommendation algorithm based on the user's interests and age and the user's product selection in a physical store when generating the suitable reservation candidates. [Explanation of symbols]

[1790] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a terminal that accepts travel reservations from users; a server including a machine learning model that learns from past reservation data; means for the server to generate suitable reservation candidates based on the user profile and present them to the terminal; a means for recording and confirming the reservation selected by the user; A system including:

2. The system according to claim 1 , wherein the machine learning model learns a plurality of feature quantities extracted from past reservation data.

3. The system of claim 1 , wherein the system uses a recommendation algorithm based on the user's interests and age when generating the suitable appointment candidates.

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