System

Abstract

A system is provided.SOLUTION: A system comprising: input means for allowing a user to input a condition related to a travel; storage means for storing the condition received from the input means; generation means for generating a travel plan based on the stored condition; suggestion means for suggesting the generated travel plan to the user; and reservation means for collectively making reservations based on the travel plan 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] When planning a trip, many users are overwhelmed by the wide variety of options available, including destinations, accommodations, transportation options, restaurants, and tourist attractions. Furthermore, the process of researching, comparing, selecting, and booking options that match their budget and the needs of their travel companions can be time-consuming, leading many users to end up falling into the same old travel patterns. This situation can reduce the variety of travel experiences and potentially eliminate opportunities for new experiences and refreshment. To solve this problem, a system is needed that can reduce user effort and quickly provide optimal travel plans tailored to individual needs. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means: a system including an input means for a user to input travel-related conditions, a storage means for storing the conditions received from the input means, a generation means for generating a travel plan based on the stored conditions, a proposal means for proposing the generated travel plan to the user, and a reservation means for making reservations in bulk based on the travel plan selected by the user. The proposal means further includes a means for adjusting the travel plan according to the user's requests, allowing the user to adjust and select multiple proposed plans. Furthermore, the storage means includes a means for storing travel-related conditions in a database, allowing efficient management and analysis of the user's conditions. This configuration allows the user to easily obtain the optimal travel plan for themselves.

[0006] "Input means" refers to a device or function that allows a user to input travel-related conditions.

[0007] The "storage means" is a device or function for storing the conditions received from the input means.

[0008] The "generation means" is a device or function for generating a travel plan based on the stored conditions.

[0009] The "suggestion means" is a device or function for proposing the generated travel plan to the user.

[0010] A "reservation means" is a device or function for making reservations in bulk based on a travel plan selected by a user.

[0011] A "database" is a collection of information used to efficiently manage and analyze travel requirements. [Brief explanation of the drawings]

[0012] [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

[0013] 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.

[0014] First, the terms used in the following description will be explained.

[0015] 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).

[0016] 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.

[0017] 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.

[0018] 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.

[0019] 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."

[0020] [First embodiment]

[0021] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0027] 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.

[0028] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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."

[0033] This invention is a "travel concierge" system that utilizes generative AI to optimize users' travel plans. The system is implemented with the following components:

[0034] 1. Input method:

[0035] Users input travel-related information using a device (such as a smartphone or PC), including budget, travel companion information, travel purpose, preferred means of transportation, and specific requests. The input method collects this information and temporarily stores it on the device.

[0036] 2. Preservation means:

[0037] The device sends the collected conditions to the server, which then stores them in a database designed to efficiently manage the conditions for each user.

[0038] 3. Generation means:

[0039] The server generates a travel plan based on the saved conditions using a generation AI. The generation AI analyzes the input conditions, lists suitable travel destinations, accommodations, transportation options, tourist spots, restaurants, etc., and assembles multiple travel plans.

[0040] 4. Proposal method:

[0041] The server sends the generated itineraries to the user's device, where the user can review and evaluate the suggestions. The suggestion tool also has the ability to adjust the plans based on user feedback.

[0042] 5. Reservation Method:

[0043] Once the user has selected their final travel plan, the server will link with the reservation system to make all reservations for accommodation, transportation, restaurants, tourist attractions, etc. If the reservation is successful, confirmation information will be sent from the server to the terminal and the user will be notified.

[0044] Explanation of the program processing flow

[0045] This travel concierge system operates as follows.

[0046] Flow of specific example

[0047] Let us take a specific example where User A is planning a relaxing trip with her family (husband and two children).

[0048] 1. Enter the conditions:

[0049] User A enters the following conditions into the terminal:

[0050] Budget: 200,000 yen

[0051] Travelling with: Husband and two children

[0052] Purpose of trip: Relaxation

[0053] Preferred mode of transportation: Car

[0054] These conditions are temporarily stored on the terminal and then transmitted to the server.

[0055] 2. Save conditions:

[0056] The server stores the received conditions in a database and efficiently manages User A's travel conditions.

[0057] 3. Plan Generation:

[0058] The server calls the generation AI based on the saved conditions and performs analysis. The generation AI lists hot spring resorts, resorts, pensions, nearby tourist spots, etc., and generates multiple travel plans (for example, a 2-night, 3-day plan at a hot spring inn, a 2-night, 3-day plan at a resort hotel, etc.).

[0059] 4. Plan proposal:

[0060] The generated travel plans are sent to the device of user A. User A checks and evaluates these plans on the device.

[0061] 5. Plan Adjustment:

[0062] Person A selects the plan he or she desires and requests "dinner at a famous local restaurant" as an additional request. This information is sent to the server via the device, and the server uses AI to generate a new plan and propose it to Person A again.

[0063] 6. Final Planning and Booking:

[0064] Once A decides on the final plan, the server will make reservations for hot spring inns, resort hotels, restaurants, etc. in one go through the reservation system. If the reservation is successful, the server will send confirmation information to the terminal and notify A.

[0065] This series of processes allows users to easily find the best travel plan for themselves, and makes travel planning go smoothly.

[0066] The processing flow will be explained below.

[0067] Step 1:

[0068] Users enter their travel requirements into a form on the device, including budget, travel companion information, travel purpose, transportation preferences, and any special requests.

[0069] Step 2:

[0070] The terminal temporarily stores the input conditions locally and then transmits them to the server.

[0071] Step 3:

[0072] The server stores the received conditions in a database, which is designed to efficiently manage conditions for each user.

[0073] Step 4:

[0074] The server generates a travel plan based on the saved conditions by calling the generation AI, which analyzes the conditions and lists suitable travel destinations, accommodations, transportation options, tourist spots, restaurants, etc.

[0075] Step 5:

[0076] The generative AI evaluates the list of options and creates multiple itineraries, based on criteria such as whether they fit within budget and whether they are suitable for travel companions (e.g., children).

[0077] Step 6:

[0078] The server transmits the generated travel plans to the terminal.

[0079] Step 7:

[0080] The device displays the received travel plans to the user, who can then review and rate them.

[0081] Step 8:

[0082] Users can select a proposed plan and then enter any additional requests or changes into the device, such as requesting a change of accommodation or adding a specific tourist attraction.

[0083] Step 9:

[0084] The device sends the user's additional request to the server, which then calls the generation AI again and adjusts the plan to accommodate the additional request.

[0085] Step 10:

[0086] The adjusted plans are sent back to the device and presented to the user, who then selects the final plan.

[0087] Step 11:

[0088] Once the user has selected their final travel plan, the terminal sends this to the server.

[0089] Step 12:

[0090] Based on the selected plan, the server makes all reservations for accommodation, transportation, restaurants, tourist attractions, etc. The reservation process is carried out using the APIs of each service provider.

[0091] Step 13:

[0092] If a reservation is successful, the server collects the reservation confirmation information, which is then sent to the terminal and notified to the user.

[0093] Example 1

[0094] 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."

[0095] Conventional travel planning systems have difficulty effectively and quickly generating optimal travel plans based on user requirements. Furthermore, processes such as rearranging plans to meet user requests and bulk bookings are time-consuming and detract from the user experience. There was a need for a system that could solve these issues and allow users to easily obtain optimal travel plans.

[0096] 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.

[0097] In this invention, the server includes an input means for a user to input travel-related conditions, a means for temporarily saving the conditions received from the input means, a means for transmitting the temporarily saved conditions to the server, a means for saving the sent conditions in a database, a means for calling a generative AI model for generating a travel plan based on the saved conditions, a suggestion means for proposing the generated travel plan to the user, a means for confirming the proposed travel plan and inputting additional requests, a means for generating a readjusted plan using the generative AI model based on the additional requests, a reservation means for making reservations in one go based on the travel plan selected by the user, and a means for transmitting reservation confirmation information to the user's terminal. This allows the user to obtain the optimal travel plan without any hassle.

[0098] "Input means" refers to a device or interface that allows a user to input travel-related conditions.

[0099] The "means for temporary saving" is a mechanism for temporarily saving the conditions entered by the user.

[0100] The "means for transmitting to the server" is a communication means for transmitting the temporarily saved conditions to the server.

[0101] The "means for saving in a database" is a data storage for permanently saving and managing the transmitted conditions.

[0102] The "means for calling the generative AI model" is a mechanism for calling and executing an artificial intelligence model for generating a travel plan based on the stored conditions.

[0103] The "proposal means" is a means for presenting the generated travel plan to the user.

[0104] A "means for inputting additional requests" is an input interface through which a user can request adjustments to the proposed travel plan.

[0105] The "means for generating a readjusted plan" is a mechanism for generating a new travel plan using a regenerative AI model based on additional requests from the user.

[0106] A "reservation method" is a method for making reservations for accommodation, transportation, meals, etc. all at once based on the travel plan selected by the user.

[0107] The "means for transmitting reservation confirmation information to the terminal" refers to a communication means for transmitting the confirmation information to the user's terminal when the reservation is completed.

[0108] This invention is a "travel concierge" system that utilizes generative AI to optimize a user's travel plans. The system is specifically implemented using the following hardware and software components:

[0109] Hardware and Software Components

[0110] 1. Input method:

[0111] Users input travel requirements using a device (e.g., a smartphone or PC). The input interface is implemented as a web form or a mobile application.

[0112] Specifically, User A enters the following conditions for a family trip into the device: "Budget: 200,000 yen," "Travel companions: husband and two children," "Purpose of trip: relaxation," and "Preference for transportation: car."

[0113] 2. Temporary storage means:

[0114] The terminal temporarily stores the entered travel conditions in local storage.

[0115] 3. Send to server:

[0116] The device sends the temporarily saved conditions to the server using an HTTP request. The communication protocol is HTTPS.

[0117] 4. How to save to database:

[0118] The server persistently stores the received condition data in a database, which can be an SQL-based relational database or a NoSQL database.

[0119] 5. Means for invoking generative AI models:

[0120] The server calls a generative AI model (e.g., a model based on natural language processing) based on the stored condition information and generates an appropriate travel plan.

[0121] The generation AI analyzes the input conditions, lists possible travel destinations, accommodations, means of transportation, tourist spots, restaurants, etc., and creates multiple travel plans.

[0122] 6. Proposal method:

[0123] The server sends the generated travel plans to the user's terminal, allowing the user to view and evaluate the plans on the terminal.

[0124] Example: The generated plans, "Hot Spring Inn 2 Nights 3 Days Course" and "Resort Hotel 2 Nights 3 Days Course" will be displayed on the terminal.

[0125] 7. How to enter additional requests:

[0126] The user can review the proposed itinerary and enter any additional requests. The input interface allows the user to make a rearrangement request.

[0127] Example: User A requests "dinner at a famous local restaurant."

[0128] 8. How to generate a realignment plan:

[0129] The server then calls the generative AI model again based on additional requests and generates a new plan.

[0130] Example: A new plan "2 nights and 3 days at a hot spring inn + dinner at a famous restaurant" is generated.

[0131] 9. Reservation Method:

[0132] Once the user selects the final plan, the server works in conjunction with a bulk reservation system to make reservations for accommodation, transportation, restaurants, tourist attractions, etc. all at once.

[0133] 10. Means for sending reservation confirmation information to the terminal:

[0134] If the reservation is successful, the server sends a confirmation to the user's device, allowing the user to check the reservation details in real time.

[0135] Prompt Sentence Examples

[0136] For example, the prompt for the generated AI is set as follows:

[0137] text

[0138] Travel conditions: Budget - 200,000 yen, Travel companions - husband and two children, Purpose - Relaxation, Transportation - Car

[0139] Suggestion: Generate multiple itineraries for a family trip based on the following criteria: Include hot springs, resorts, pensions, tourist spots, restaurants, etc.

[0140] This system allows users to easily and quickly obtain optimal travel plans, significantly reducing the effort required for travel planning.

[0141] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0142] Step 1:

[0143] The user enters the travel requirements.

[0144] The user enters travel conditions (such as budget, companions, purpose of travel, means of transportation, etc.) through the device's input interface. The entered data is temporarily stored in the device.

[0145] Input: User's travel conditions (budget, companions, purpose, means of transportation, etc.)

[0146] Output: Temporarily saved travel condition data

[0147] Specific operation: User A enters information such as "budget: 200,000 yen," "traveling companions: husband and two children," "purpose of trip: relaxation," and "means of transportation: car" into the form on the device.

[0148] Step 2:

[0149] The terminal sends the conditions to the server.

[0150] The device sends the temporarily saved travel condition data to the server. The data is securely transferred using an HTTP POST request.

[0151] Input: Temporarily saved travel condition data

[0152] Output: Travel condition data sent to the server

[0153] Specific operation: The device sends JSON format data including "budget 200,000 yen," "traveling companions: husband and two children," "purpose of trip: relaxation," and "means of transportation: car" to the server.

[0154] Step 3:

[0155] The server stores the conditions in a database.

[0156] The server receives the travel condition data and stores it permanently in a database, allowing efficient management of user conditions.

[0157] Input: Travel condition data sent to the server

[0158] Output: Travel condition data stored in the database

[0159] Specific operation: The server parses the received JSON data and saves it in the database as records of "User ID," "Budget: 200,000 yen," "Traveling companions: husband and two children," "Purpose of trip: relaxation," and "Method of transportation: car."

[0160] Step 4:

[0161] The server calls the generative AI model to generate a travel plan.

[0162] The server calls up a generative AI model based on the condition information stored in the database to generate a travel plan. The generative AI model analyzes the conditions, lists suitable travel destinations, accommodations, transportation options, tourist spots, and restaurant options, and creates multiple travel plans.

[0163] Input: Travel condition data stored in the database

[0164] Output: Multiple generated itineraries

[0165] Specific operation: The generation AI generates plans such as "2 nights and 3 days at a hot spring inn," "2 nights and 3 days at a resort hotel," and "3 nights and 4 days at a pension."

[0166] Step 5:

[0167] The server sends the generated travel plan to the user's terminal.

[0168] The server sends the generated travel plans to the user's device as an HTTP response, allowing the user to view the plans on their device.

[0169] Input: Multiple itineraries generated by a generative AI model

[0170] Output: A list of itineraries sent to the user's device

[0171] Specific operation: The server responds with the generated travel plan in JSON format, and the device receives and displays it.

[0172] Step 6:

[0173] The user reviews the plan and enters any additional requests.

[0174] The user reviews the proposed travel plan and, if necessary, enters additional requests into the terminal's input interface.

[0175] Input: Travel plan displayed on the device, additional requests from the user

[0176] Output: A new dataset containing additional requests.

[0177] Specific operation: User A selects the "2 nights, 3 days at a hot spring inn" plan and enters "dinner at a famous local restaurant" as an additional request.

[0178] Step 7:

[0179] The terminal sends an additional request to the server.

[0180] The terminal sends the user's request for addition to the server using an HTTP request.

[0181] Input: User's request for addition

[0182] Output: Additional requested data sent to the server

[0183] Specific behavior: The device sends JSON data containing a request for "dinner at a famous local restaurant" to the server.

[0184] Step 8:

[0185] The server again uses the generated AI model to generate an adjustment plan.

[0186] The server analyzes the additional requests and generates a new travel plan using the generative AI model again, which reflects the additional requests.

[0187] Input: Additional requested data sent to the server

[0188] Output: Reworked itinerary

[0189] Specific operation: Based on additional requests, the generation AI generates new plans such as "2 nights, 3 days at a hot spring inn + dinner at a famous restaurant."

[0190] Step 9:

[0191] The server sends the recalibration plan to the user's device.

[0192] The server then sends the adjusted travel plan back to the user's terminal.

[0193] Input: rearranged travel plans

[0194] Output: The adjusted itinerary sent to the user's device

[0195] Specific operation: The server responds in JSON format with a plan for "2 nights and 3 days at a hot spring inn + dinner at a famous restaurant," which the device receives and displays.

[0196] Step 10:

[0197] The user selects the final plan and confirms the reservation.

[0198] The user finally selects the desired travel plan and confirms the reservation through the terminal.

[0199] Input: User's final plan selection

[0200] Output: Reservation confirmation data

[0201] Specific operation: User A selects the "2 nights, 3 days at a hot spring inn + dinner at a famous restaurant" plan and presses the button to confirm the reservation.

[0202] Step 11:

[0203] The server works in conjunction with the reservation system to make bulk reservations.

[0204] The server works in conjunction with the reservation system to make bulk reservations for accommodation, transportation, restaurants, tourist attractions, etc. based on the selected plan.

[0205] Input: User's final plan selection data

[0206] Output: Bulk booking completed

[0207] Specific operation: The server calls reservation APIs for hotels, car rentals, restaurants, etc. to make bulk reservations.

[0208] Step 12:

[0209] The server sends the reservation confirmation information to the terminal and notifies the user.

[0210] If the reservation is successful, the server will send a confirmation to the user's terminal and notify the user.

[0211] Input: Completed bulk reservation data

[0212] Output: Booking confirmation sent to the user's device

[0213] Specific operation: The server responds with reservation confirmation information in JSON format, and the device displays a notification.

[0214] This allows users to smoothly navigate through a series of steps to find the best travel plan.

[0215] (Application example 1)

[0216] 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."

[0217] Conventional travel planning systems have the problem that it takes a lot of time and effort for users to select a travel plan. In addition, the travel plans generated based on the user's input criteria do not always perfectly match the user's needs, and there is a lack of means to compare multiple plans or visually confirm them.

[0218] 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.

[0219] In this invention, the server includes an input means for a user to input travel conditions, a storage means for storing the conditions received from the input means, a generation means for generating a travel plan based on the stored conditions, a 3D visualization means for proposing and visualizing the generated travel plan to the user, and a reservation means for making reservations in bulk based on the travel plan selected by the user. This allows the user to visually check the travel plans, quickly select the most suitable plan, and make a bulk reservation.

[0220] "Input means" refers to a device or interface that allows a user to input travel-related conditions.

[0221] The "storage means" is a database or other storage device for storing the conditions received from the input means.

[0222] The "generation means" refers to an algorithm or program for generating a travel plan based on the stored conditions.

[0223] "3D visualization means" refers to a three-dimensional display device or software that visually displays and allows users to check the generated travel plan.

[0224] A "reservation method" is a system or service that allows users to make reservations in bulk based on a travel plan selected by the user.

[0225] This invention is a system for enabling users to efficiently and effectively create, confirm, modify and book travel plans, and includes as its main components an input means, a storage means, a generation means, a 3D visualization means and a reservation means.

[0226] Users use input methods such as smartphones or computers to enter travel conditions such as budget, travel companions, purpose of trip, and preferred means of transportation. For example, they might enter "budget 200,000 yen, husband and two children, purpose is relaxation, and preferred car for transportation." These conditions are temporarily saved on the device and then sent to the server.

[0227] The server saves the received conditions in a database (storage means). Based on the saved conditions, the server generates a travel plan using a generative AI model (such as the OpenAI API) (generation means). Here, the generative AI analyzes the user's input conditions and assembles multiple travel plans by listing suitable travel destinations, accommodations, means of transportation, tourist spots, restaurants, etc. This plan generation process is carried out using prompt sentences. Examples of prompt sentences are as follows:

[0228] "Optimize your users' travel plans, based on the following criteria:

[0229] Budget: 200,000 yen

[0230] Travelling with: Husband and two children

[0231] Purpose of trip: Relaxation

[0232] Transportation: Car

[0233] Hope: Especially including dinner at a restaurant.”

[0234] The generated travel plans are provided to the user using 3D visualization. Using an HMD (head-mounted display) or smartphone, the user can visually check and evaluate the multiple generated travel plans. For example, a 3-day, 2-night plan at a hot spring inn or a 3-day, 2-night plan at a resort hotel can be displayed in 3D, allowing users to check the scenery and details of the facilities.

[0235] If the user selects the desired plan and makes any additional requests (e.g., "dinner at a local famous restaurant"), this information is sent to the server via the storage means. The server then calls the generation AI again to generate a new plan and propose it to the user again.

[0236] Once the user has finally selected the optimal travel plan, the server will make all reservations for accommodation, transportation, restaurants, etc. via the reservation tool. If the reservation is successful, confirmation information will be sent from the server to the terminal and the user will be notified.

[0237] This configuration allows users to create, check, change and book travel plans smoothly and efficiently, and as the entire process is automated, it significantly reduces the amount of work required.

[0238] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0239] Step 1:

[0240] The device receives travel-related conditions (e.g., budget, travel companions, travel purpose, preferred means of transportation, etc.) from the user through input means. The input conditions are temporarily stored in the device and then sent to the server. The input here is done by the user through the interface, specifically using text boxes and options to enter conditions.

[0241] Step 2:

[0242] The server stores the travel conditions sent from the device in a database. The received data is organized so that it can be managed for each user. The database serves to efficiently store the information needed to generate travel plans and make reservations.

[0243] Step 3:

[0244] Based on the saved conditions, the server calls the generation means (generative AI model) to generate a travel plan. At this time, the generative AI model is given a prompt sentence that includes the conditions. Specifically, the following prompt sentence is used:

[0245] "Optimize your users' travel plans, based on the following criteria:

[0246] Budget: 200,000 yen

[0247] Travelling with: Husband and two children

[0248] Purpose of trip: Relaxation

[0249] Transportation: Car

[0250] Hope: Especially including dinner at a restaurant.”

[0251] The generative AI model analyzes this prompt and generates a travel plan, which is then returned to the server as text data.

[0252] Step 4:

[0253] The generated itinerary is sent from the server to the terminal. The terminal then visualizes the itinerary using 3D visualization tools and displays it to the user. The input here is the text data of the generated itinerary, and the output is a visualized 3D model. Specifically, information such as the itinerary outline, accommodations, tourist spots, and transportation options is modeled in 3D.

[0254] Step 5:

[0255] The user can use the device to view and evaluate the visualized travel plans, select which plan is most appealing, and enter additional requests (e.g., "dinner at a famous local restaurant"). This additional information is sent to the server via the device.

[0256] Step 6:

[0257] The server receives the additional request and generates a new itinerary using the generation method (generative AI model), again using the same prompt as the first time, to further optimize the new itinerary for the user's requirements.

[0258] Step 7:

[0259] Once the user has finally selected the optimal travel plan, the server will make all reservations for accommodation, transportation, restaurants, etc. through the reservation tool. The input here is the final plan based on the user's selection, and the output is reservation confirmation information. If the reservation is successful, this confirmation information is sent from the server to the terminal and the user is notified.

[0260] These processing steps allow users to create, review, modify, and book travel plans efficiently and effectively.

[0261] 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.

[0262] This invention is a "travel concierge" system that utilizes generative AI to optimize users' travel plans, and in particular, incorporates an emotion engine that recognizes users' emotions and reflects them in the travel plan. Each component and program process will be explained in detail below.

[0263] 1. Input method:

[0264] Users input travel-related information using a device (such as a smartphone or PC), including budget, travel companion information, travel purpose, preferred means of transportation, and specific requests. Furthermore, the device is equipped with an emotion engine that recognizes the user's emotions.

[0265] 2. Emotion Engine:

[0266] The emotion engine has the ability to recognize emotions by analyzing the user's voice, facial expressions, or text. This allows it to analyze in real time what emotions the user is feeling while typing. For example, if a user types "I want to relax," it will recognize from their voice and facial expressions that they are feeling stressed.

[0267] 3. Preservation means:

[0268] Once the conditions are entered, the device temporarily stores the conditions and emotion data and sends them to the server, which then stores the received data in a database to efficiently manage the conditions and emotions for each user.

[0269] 4. Generation means:

[0270] The server uses a generative AI to generate a travel plan based on the saved conditions and emotion data. The generative AI comprehensively analyzes the input conditions and the user's emotions, and lists suitable travel destinations, accommodations, transportation options, tourist spots, restaurants, etc.

[0271] 5. Proposal method:

[0272] The server then sends the generated itineraries to the user's device. The suggestion method considers the emotional data and makes suggestions tailored to the user's mood and emotions. For example, if the user is feeling stressed, it will prioritize suggestions for relaxing hot springs and places rich in nature.

[0273] 6. Reservation Method:

[0274] Once the user has selected their final travel plan, the server will link with the reservation system to make all reservations for accommodation, transportation, restaurants, tourist attractions, etc. If the reservation is successful, confirmation information will be sent from the server to the terminal and the user will be notified.

[0275] Explanation of the program processing flow

[0276] This travel concierge system operates as follows.

[0277] Flow of specific example

[0278] Let's use the example of User B, who is planning a solo trip to refresh himself.

[0279] 1. Enter the conditions:

[0280] User B enters the following conditions into the terminal:

[0281] Budget: 100,000 yen

[0282] Accompanying person: None

[0283] Purpose of the trip: Refreshment

[0284] Preferred mode of transportation: Train

[0285] While this input is being made, the device's emotion engine recognizes from Mr. B's tone of voice and facial expression that he is feeling stressed.

[0286] 2. Save condition:

[0287] The device sends these conditions and emotion data to the server, which stores them in a database.

[0288] 3. Plan Generation:

[0289] The server then calls up the AI ​​generator based on the stored conditions and emotion data, and performs analysis. The AI ​​generator then creates a list of relaxing hot spring resorts and nature-rich resorts, and generates multiple travel plans (for example, a 2-night, 3-day plan at a hot spring inn, a 2-night, 3-day plan at a highland resort hotel, etc.).

[0290] 4. Plan proposal:

[0291] The generated travel plans are sent to User B's device. User B checks these plans on his / her device. Using data from the emotion engine, hot spring inns and resort hotels with particularly relaxing effects are prioritized in suggestions.

[0292] 5. Plan Adjustment:

[0293] Person B selects a "hot spring inn" and requests an additional "local spa experience." This information is sent to the server via the device, and the server uses the generation AI to generate a new plan and propose it to Person B again.

[0294] 6. Final Planning and Booking:

[0295] Once B has finalized the plan, the server will make reservations for the hot spring inn and spa experience all at once through the reservation system. If the reservation is successful, the server will send confirmation information to the terminal and notify B.

[0296] This series of processes allows users to easily find the best travel plan for them, and the travel planning process proceeds smoothly. In addition, the emotion engine enables customization based on the user's emotions, resulting in a highly satisfying travel experience.

[0297] The processing flow will be explained below.

[0298] Step 1:

[0299] Users enter their travel requirements into a form on the device, including budget, travel companion information, travel purpose, transportation preferences, and any special requests.

[0300] Step 2:

[0301] The device sends the input conditions to the emotion engine to analyze the user's emotions. For example, in the case of voice input, it analyzes the tone of the voice, and in the case of text input, it infers emotions from the words used.

[0302] Step 3:

[0303] The emotion engine recognizes the user's emotion, temporarily stores it along with the condition data, and sends it to the server.

[0304] Step 4:

[0305] The server stores the received conditions and emotion data in a database designed to efficiently manage each user's conditions and emotion data.

[0306] Step 5:

[0307] The server generates a travel plan based on the stored conditions and emotion data by calling a generation AI. The generation AI comprehensively analyzes the conditions and emotions and lists suitable travel destinations, accommodations, transportation options, tourist spots, restaurants, etc.

[0308] Step 6:

[0309] The generative AI evaluates the list of options and creates multiple itineraries, based on criteria such as whether they fit within the budget, suitability for the travel companions, and consideration for the user's emotions.

[0310] Step 7:

[0311] The server transmits the generated travel plans to the terminal.

[0312] Step 8:

[0313] The device displays the received travel plans to the user, who can then review and rate them. The plan that reflects the emotion data is presented as the one that best suits the user's emotions.

[0314] Step 9:

[0315] Users can select a proposed plan and then enter any additional requests or changes into the device, such as requesting a change of accommodation or adding a specific tourist attraction.

[0316] Step 10:

[0317] The device sends the user's additional request to the server, which then calls the generation AI again and adjusts the plan to accommodate the additional request.

[0318] Step 11:

[0319] The adjusted plans are sent back to the device and presented to the user, who then selects the final plan.

[0320] Step 12:

[0321] Once the user has selected their final travel plan, the terminal sends this to the server.

[0322] Step 13:

[0323] Based on the selected plan, the server makes all reservations for accommodation, transportation, restaurants, tourist attractions, etc. The reservation process is carried out using the APIs of each service provider.

[0324] Step 14:

[0325] If a reservation is successful, the server collects the reservation confirmation information, which is then sent to the terminal and notified to the user.

[0326] Example 2

[0327] 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."

[0328] Conventional travel planning systems generate travel plans based solely on user input, making it impossible to consider the user's emotions or psychological state. This means that they are inadequate in proposing relaxing travel plans to stressed users. Furthermore, if the generated plan does not match the user's emotions, there is a high possibility that the travel plan will leave the user unsatisfied.

[0329] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0330] In this invention, the server includes input means for a user to input travel-related conditions, storage means for storing the conditions received from the input means, means including an emotion engine for analyzing the user's emotions, generation means for generating a travel plan based on the stored conditions and the user's emotion data, proposal means for proposing the generated travel plan to the user, and reservation means for making reservations all at once based on the travel plan selected by the user. This makes it possible to propose a travel plan that suits the user's emotions, thereby increasing user satisfaction.

[0331] "Input means" refers to a hardware or software interface that allows a user to input travel-related conditions into a terminal.

[0332] "Storage Means" means a data storage means for temporarily or permanently storing the conditions entered by the User.

[0333] An "emotion engine" is software or algorithms that analyze a user's voice, facial expressions, or text to recognize their emotions.

[0334] "Generative means" means software means, such as a generative AI model, for generating a travel plan based on the stored conditions and user emotional data.

[0335] The "proposal means" is an interface means for presenting the travel plan generated by the generation means to the user.

[0336] A "booking method" is a method by which a user makes a single reservation for accommodation, transportation, and other services based on the travel plan selected by the user.

[0337] A "database" is a storage system for managing and saving information such as conditions and emotional data entered by users.

[0338] This invention is a "travel concierge" system that utilizes generative AI models to optimize users' travel plans. In particular, it incorporates an emotion engine that recognizes users' emotions and reflects them in the travel plan. Each component and program process is explained in detail below.

[0339] Input methods:

[0340] A user inputs travel-related information using their device (such as a smartphone or PC). These information includes budget, travel companion information, travel purpose, preferred means of transportation, and specific requests. Furthermore, the device is equipped with an emotion engine that analyzes the user's voice, facial expression, or text to recognize emotions. For example, if a user inputs, "I'm planning a solo trip to refresh myself," the emotion engine recognizes that the user is stressed from his tone of voice and facial expression.

[0341] Storage method:

[0342] Once the conditions are entered, the device temporarily stores these conditions and emotion data and sends them to the server. The server then stores the received data in a database, efficiently managing each user's conditions and emotions. This database stores the various conditions entered by the user and the results of emotion analysis.

[0343] Generation means:

[0344] The server generates a travel plan using a generative AI model based on the stored conditions and emotion data. At this time, the server inputs a prompt statement into the generative AI model, which analyzes and generates it. For example, the prompt statement could be "budget under 100,000 yen, solo trip, purpose of relaxation, means of transportation by train, stressful." Based on this prompt, the generative AI model generates multiple travel plans, such as relaxing hot springs and resorts rich in nature.

[0345] Suggested means:

[0346] The generated travel plan is sent from the server to the user's device. When presenting it to the user, the device takes into account the emotional data and prioritizes the plan that best suits the user's emotions. For example, it may prioritize suggestions for relaxing hot spring inns or resort hotels surrounded by nature.

[0347] Reservation method:

[0348] Once the user has selected their final travel plan, the server works with the reservation system to complete the reservation in one go. Specifically, the server makes reservations for hot spring inns and spa experiences. Once the reservation is successful, the server sends confirmation information to the user's terminal and notifies them. In this way, users can skip the complicated reservation process and easily find the travel plan that best suits their feelings and desires.

[0349] Example flow:

[0350] For example, consider the case where User B plans a solo trip to refresh himself and enters the following conditions:

[0351] Budget: 100,000 yen

[0352] Accompanying person: None

[0353] Purpose of the trip: Refreshment

[0354] Preferred mode of transportation: Train

[0355] While inputting, the emotion engine recognizes that B is feeling stressed from his tone of voice and facial expression. The device sends this information to the server, which then inputs the following prompt sentence into the generation AI:

[0356] "Budget: 100,000 yen or less, solo trip, purpose: to refresh, transportation: train, stressful"

[0357] Based on this prompt, the generative AI model lists relaxing hot spring resorts and nature-rich resorts, and generates multiple travel plans (for example, a 2-night, 3-day plan at a hot spring inn, or a 2-night, 3-day plan at a highland resort hotel). The generated plans are then sent to the user's device and displayed preferentially, taking into account emotional data. If User B selects a "hot spring inn" and requests a "local spa experience," the plan is adjusted again.

[0358] In this way, a customized travel plan is proposed that takes the user's emotions into consideration, allowing the user to make a travel plan that is more satisfying.

[0359] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0360] Step 1:

[0361] The user inputs travel-related conditions using a device (smartphone or PC). Specifically, they input budget, travel companion information, travel purpose, preferred means of transportation, and specific requests. At this time, an emotion engine built into the device analyzes the user's voice and facial expressions and collects emotional data. The input data, consisting of conditions and emotional data, becomes the input for the next step.

[0362] Step 2:

[0363] The device temporarily stores the conditions and emotion data entered by the user and sends it to the server. Specifically, the input data is sent to the server in a data format such as JSON. The server then receives the user's conditions and emotion data and stores them in a database. Once the data has been saved to data storage, the server starts the next step based on this data.

[0364] Step 3:

[0365] The server converts the stored conditions and emotional data into a prompt sentence to be input into the generative AI model. Specifically, it generates a prompt sentence that combines the conditions and emotional data. For example, it creates a prompt sentence such as "budget within 100,000 yen, solo trip, purpose of relaxation, means of transportation by train, stressful." This prompt sentence becomes the input to the generative AI model.

[0366] Step 4:

[0367] The server calls the generative AI model, inputs a prompt, and generates a travel plan. Based on the prompt, the generative AI model generates multiple travel plans (for example, a 2-night, 3-day plan at a hot spring inn, a 2-night, 3-day plan at a highland resort hotel, etc.). The generated plan is output to the server, and the process proceeds to the next step.

[0368] Step 5:

[0369] The server sends the generated travel plan to the user's device. Specifically, the generated plan is sent to the device in JSON or XML format. The device receives it and presents it to the user. Based on the emotional data, the plan that is most suitable for the user (for example, a hot spring inn with a high relaxing effect) is displayed in a list with priority.

[0370] Step 6:

[0371] The user reviews the travel plans on the device and selects the one they are most interested in. The user then inputs additional requests (e.g., "local spa experience"). This input data becomes the input for the next step.

[0372] Step 7:

[0373] The device sends the user's selection and additional requests to the server. The server receives this and reflects it in the prompt text again. For example, the prompt text is updated to something like "budget under 100,000 yen, solo travel, purpose of relaxation, means of transportation by train, stressful, local spa experience." This updated prompt text becomes the input to the generative AI model.

[0374] Step 8:

[0375] The server calls the generative AI model again, inputs the updated prompt text, and generates a new travel plan. The generated plan is output to the server again and proposed to the user. The user reviews this new plan and makes a final decision.

[0376] Step 9:

[0377] Once the user has selected their final travel plan, the device sends it to the server. The server then calls the reservation system based on the final plan received. The server then makes all reservations for accommodations and various services. If the reservation is successful, confirmation information is sent from the server to the device and the user is notified.

[0378] As described above, this system generates optimal travel plans from user input, and then proposes and adjusts the plans taking the user's emotions into consideration, seamlessly handling the final booking process, thereby optimizing the user's travel plans.

[0379] (Application example 2)

[0380] 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."

[0381] Traditional travel planning systems only propose travel plans based on user requirements, making it difficult to customize them to take into account user emotions and real-time feedback. This lacks the ability to provide plans tailored to users' specific needs, making it difficult to achieve a satisfying travel experience. Furthermore, the ability of travel agency staff in brick-and-mortar stores to quickly propose appropriate plans to customers is also limited.

[0382] 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.

[0383] In this invention, the server includes an input means for a user to input travel requirements, a storage means for storing the requirements received from the input means, a generation means for generating a travel plan based on the stored requirements, a proposal means for proposing the generated travel plan to the user, a sentiment analysis means for analyzing the user's sentiment, a means for adjusting the generated travel plan based on the user's sentiment, and a reservation means for making reservations in bulk based on the travel plan selected by the user. This enables customization that takes into account the user's sentiment and real-time feedback, providing a highly satisfying travel experience. It also enables brick-and-mortar travel agencies to quickly propose appropriate plans to customers.

[0384] "Input means" refers to devices or software that allow users to input travel-related requirements into the system.

[0385] The "storage means" refers to a device or software that temporarily or permanently stores the travel-related conditions and emotion data received from the input means.

[0386] The "generation means" refers to a device or software for generating an optimal travel plan based on the stored conditions and emotion data.

[0387] The "suggestion means" is a device or software that presents the generated travel plan to the user and provides appropriate feedback.

[0388] "Emotion analysis means" refers to a device or software for analyzing emotions from a user's voice, facial expression, or text.

[0389] The "adjustment means" is a device or software for reconstructing or correcting the generated travel plan based on the user's emotional data obtained by the emotion analysis means.

[0390] "Reservation Facility" means a device or software that allows a user to make a single booking of the accommodation, transportation and other services required based on the final travel plan selected by the user.

[0391] This invention is a system for optimizing a user's travel plan, specifically a "travel concierge" system equipped with an emotion engine that analyzes the user's emotions and reflects them in the travel plan. This system can also be effectively used in brick-and-mortar travel agencies.

[0392] System configuration

[0393] 1. Input Method

[0394] Using smart glasses or a head-mounted display, users input travel-related information by voice, including budget, destination, travel companions, and purpose of trip. The device also reads the user's facial expressions and voice in real time to collect data for sentiment analysis.

[0395] 2. Preservation means

[0396] The travel conditions and emotion data collected by the device are sent to a cloud server and temporarily stored in a database, making it possible to efficiently manage the data of multiple users.

[0397] 3. Generation means

[0398] The server generates a travel plan based on the stored conditions and emotion data using a generative AI model (e.g., OpenAI GPT-4). The generative AI model analyzes past data and current conditions to propose the optimal travel plan.

[0399] 4. Proposal method

[0400] The generated itinerary is sent to the user's device in real time, and based on the results of sentiment analysis, it prioritizes the itinerary that best matches the user's sentiment.

[0401] 5. Emotion analysis method

[0402] Emotion analysis uses IBM Watson Visual Recognition and other tools to analyze collected voice and facial expression data, and adjusts plans based on the results.

[0403] 6. Adjustment means

[0404] If the user makes additional requests regarding the proposed plan, the system will again use the sentiment analysis method and call the generative AI model to regenerate and adjust the plan.

[0405] 7. Reservation Methods

[0406] Once the user selects the final plan, the server will make all the reservations for accommodation and transportation through the reservation system, and the results will be sent to the user's device.

[0407] Specific examples

[0408] Let's say User A visits a brick-and-mortar travel agency to plan a solo trip for relaxation. User A is given a head-mounted display by the agency staff and asked to enter the following information by voice:

[0409] Budget: 100,000 yen

[0410] Accompanying person: None

[0411] Purpose of the trip: Refreshment

[0412] Preferred mode of transportation: Train

[0413] The microphone and camera in the head-mounted display collect user A's tone of voice and facial expressions, which are then analyzed by the emotion analysis means. The emotion analysis means, recognizing that user A is feeling stressed, sends this data along with the condition to the server.

[0414] The server generates multiple travel plans using a generative AI model (OpenAI GPT-4) based on the conditions and emotion data. The generated plans are then proposed to User A via a head-mounted display. User A is presented with a three-day, two-night plan to refresh at a hot spring inn, and if he or she wishes to add a spa experience, the plan is instantly adjusted.

[0415] The plan selected by User A is automatically reserved through the reservation system. Reservation confirmation information is sent to the head-mounted display and User A is informed.

[0416] Prompt Sentence Examples

[0417] User Emotional Data: Feeling Stressed

[0418] Travel conditions: Budget: 100,000 yen, Travel purpose: Refreshment, Transportation: Train

[0419] Desired output from the generative AI: Provide recommended itineraries for relaxation, taking into account hot springs and nature resorts.

[0420] In this way, it becomes possible to generate, suggest, adjust and book optimal travel plans in real time based on the user's emotions.

[0421] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0422] Step 1:

[0423] Collecting user input conditions

[0424] Users input travel information (budget, destination, travel companions, purpose of trip, etc.) by voice through smart glasses or a head-mounted display, while the device's built-in microphone and camera also collect the user's facial expressions and voice tone.

[0425] Input: Travel conditions (voice data), user emotional data (facial expressions, voice tone)

[0426] Data processing: speech-to-text conversion, facial expression and tone analysis

[0427] Output: Textualized travel conditions, analyzed emotion data

[0428] Step 2:

[0429] Data storage

[0430] The collected travel conditions and emotion data are sent from the device to a cloud server and stored in a database, allowing for effective management of the conditions and emotion data.

[0431] Input: Texted travel conditions, analyzed emotion data

[0432] Data processing: sending data and storing it in a database

[0433] Output: Saved travel conditions and emotion data

[0434] Step 3:

[0435] Travel plan generation

[0436] The server generates an optimal travel plan using a generative AI model (e.g., OpenAI GPT-4) based on the stored conditions and emotion data. The generative AI generates multiple travel plans based on the provided prompt sentences.

[0437] Input: saved travel conditions, emotion data, prompt sentence

[0438] Data processing: Generating travel plans using generative AI models

[0439] Output: Generated itinerary

[0440] Step 4:

[0441] Travel plan suggestions

[0442] The generated travel plan is sent to the user's smart glasses or head-mounted display in real time, and the plan that best suits the user's emotions is displayed first, taking into account the user's emotional data.

[0443] Input: Generated itinerary

[0444] Data processing: sending and displaying travel plans

[0445] Output: A suggested itinerary for the user

[0446] Step 5:

[0447] Adjusting travel plans

[0448] If the user makes additional requests, the device will re-analyze the request using the sentiment analysis method and call the generative AI model to re-generate the plan, incorporating the additional requests into the prompt text.

[0449] Input: User requests for additions, emotional data

[0450] Data processing: Incorporating additional requests into prompt sentences and regenerating them using a generative AI model

[0451] Output: Regenerated itinerary

[0452] Step 6:

[0453] Bulk booking

[0454] Once the user selects the final plan, the server connects to the reservation system and makes all necessary reservations for accommodation, transportation, etc. The reservation results are then sent to the terminal.

[0455] Input: Finalized travel plans

[0456] Data processing: Linking with reservation systems and reservation processing

[0457] Output: Reservation confirmation information

[0458] Through this process, users can easily obtain the optimal travel plan that reflects their emotions and specific requirements.

[0459] 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.

[0460] 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.

[0461] 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.

[0462] [Second embodiment]

[0463] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0464] 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.

[0465] 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).

[0466] 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.

[0467] 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.

[0468] 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).

[0469] 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.

[0470] 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.

[0471] 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.

[0472] 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.

[0473] 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.

[0474] 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."

[0475] This invention is a "travel concierge" system that utilizes generative AI to optimize users' travel plans. The system is implemented with the following components:

[0476] 1. Input method:

[0477] Users input travel-related information using a device (such as a smartphone or PC), including budget, travel companion information, travel purpose, preferred means of transportation, and specific requests. The input method collects this information and temporarily stores it on the device.

[0478] 2. Preservation means:

[0479] The device sends the collected conditions to the server, which then stores them in a database designed to efficiently manage the conditions for each user.

[0480] 3. Generation means:

[0481] The server generates a travel plan based on the saved conditions using a generation AI. The generation AI analyzes the input conditions, lists suitable travel destinations, accommodations, transportation options, tourist spots, restaurants, etc., and assembles multiple travel plans.

[0482] 4. Proposal method:

[0483] The server sends the generated itineraries to the user's device, where the user can review and evaluate the suggestions. The suggestion tool also has the ability to adjust the plans based on user feedback.

[0484] 5. Reservation Method:

[0485] Once the user has selected their final travel plan, the server will link with the reservation system to make all reservations for accommodation, transportation, restaurants, tourist attractions, etc. If the reservation is successful, confirmation information will be sent from the server to the terminal and the user will be notified.

[0486] Explanation of the program processing flow

[0487] This travel concierge system operates as follows.

[0488] Flow of specific example

[0489] Let us take a specific example where User A is planning a relaxing trip with her family (husband and two children).

[0490] 1. Enter the conditions:

[0491] User A enters the following conditions into the terminal:

[0492] Budget: 200,000 yen

[0493] Travelling with: Husband and two children

[0494] Purpose of trip: Relaxation

[0495] Preferred mode of transportation: Car

[0496] These conditions are temporarily stored on the terminal and then transmitted to the server.

[0497] 2. Save conditions:

[0498] The server stores the received conditions in a database and efficiently manages User A's travel conditions.

[0499] 3. Plan Generation:

[0500] The server calls the generation AI based on the saved conditions and performs analysis. The generation AI lists hot spring resorts, resorts, pensions, nearby tourist spots, etc., and generates multiple travel plans (for example, a 2-night, 3-day plan at a hot spring inn, a 2-night, 3-day plan at a resort hotel, etc.).

[0501] 4. Plan proposal:

[0502] The generated travel plans are sent to the device of user A. User A checks and evaluates these plans on the device.

[0503] 5. Plan Adjustment:

[0504] Person A selects the plan he or she desires and requests "dinner at a famous local restaurant" as an additional request. This information is sent to the server via the device, and the server uses AI to generate a new plan and propose it to Person A again.

[0505] 6. Final Planning and Booking:

[0506] Once A decides on the final plan, the server will make reservations for hot spring inns, resort hotels, restaurants, etc. in one go through the reservation system. If the reservation is successful, the server will send confirmation information to the terminal and notify A.

[0507] This series of processes allows users to easily find the best travel plan for themselves, and makes travel planning go smoothly.

[0508] The processing flow will be explained below.

[0509] Step 1:

[0510] Users enter their travel requirements into a form on the device, including budget, travel companion information, travel purpose, transportation preferences, and any special requests.

[0511] Step 2:

[0512] The terminal temporarily stores the input conditions locally and then transmits them to the server.

[0513] Step 3:

[0514] The server stores the received conditions in a database, which is designed to efficiently manage conditions for each user.

[0515] Step 4:

[0516] The server generates a travel plan based on the saved conditions by calling the generation AI, which analyzes the conditions and lists suitable travel destinations, accommodations, transportation options, tourist spots, restaurants, etc.

[0517] Step 5:

[0518] The generative AI evaluates the list of options and creates multiple itineraries, based on criteria such as whether they fit within budget and whether they are suitable for travel companions (e.g., children).

[0519] Step 6:

[0520] The server transmits the generated travel plans to the terminal.

[0521] Step 7:

[0522] The device displays the received travel plans to the user, who can then review and rate them.

[0523] Step 8:

[0524] Users can select a proposed plan and then enter any additional requests or changes into the device, such as requesting a change of accommodation or adding a specific tourist attraction.

[0525] Step 9:

[0526] The device sends the user's additional request to the server, which then calls the generation AI again and adjusts the plan to accommodate the additional request.

[0527] Step 10:

[0528] The adjusted plans are sent back to the device and presented to the user, who then selects the final plan.

[0529] Step 11:

[0530] Once the user has selected their final travel plan, the terminal sends this to the server.

[0531] Step 12:

[0532] Based on the selected plan, the server makes all reservations for accommodation, transportation, restaurants, tourist attractions, etc. The reservation process is carried out using the APIs of each service provider.

[0533] Step 13:

[0534] If a reservation is successful, the server collects the reservation confirmation information, which is then sent to the terminal and notified to the user.

[0535] Example 1

[0536] 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."

[0537] Conventional travel planning systems have difficulty effectively and quickly generating optimal travel plans based on user requirements. Furthermore, processes such as rearranging plans to meet user requests and bulk bookings are time-consuming and detract from the user experience. There was a need for a system that could solve these issues and allow users to easily obtain optimal travel plans.

[0538] 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.

[0539] In this invention, the server includes an input means for a user to input travel-related conditions, a means for temporarily saving the conditions received from the input means, a means for transmitting the temporarily saved conditions to the server, a means for saving the sent conditions in a database, a means for calling a generative AI model for generating a travel plan based on the saved conditions, a suggestion means for proposing the generated travel plan to the user, a means for confirming the proposed travel plan and inputting additional requests, a means for generating a readjusted plan using the generative AI model based on the additional requests, a reservation means for making reservations in one go based on the travel plan selected by the user, and a means for transmitting reservation confirmation information to the user's terminal. This allows the user to obtain the optimal travel plan without any hassle.

[0540] "Input means" refers to a device or interface that allows a user to input travel-related conditions.

[0541] The "means for temporary saving" is a mechanism for temporarily saving the conditions entered by the user.

[0542] The "means for transmitting to the server" is a communication means for transmitting the temporarily saved conditions to the server.

[0543] The "means for saving in a database" is a data storage for permanently saving and managing the transmitted conditions.

[0544] The "means for calling the generative AI model" is a mechanism for calling and executing an artificial intelligence model for generating a travel plan based on the stored conditions.

[0545] The "proposal means" is a means for presenting the generated travel plan to the user.

[0546] A "means for inputting additional requests" is an input interface through which a user can request adjustments to the proposed travel plan.

[0547] The "means for generating a readjusted plan" is a mechanism for generating a new travel plan using a regenerative AI model based on additional requests from the user.

[0548] A "reservation method" is a method for making reservations for accommodation, transportation, meals, etc. all at once based on the travel plan selected by the user.

[0549] The "means for transmitting reservation confirmation information to the terminal" refers to a communication means for transmitting the confirmation information to the user's terminal when the reservation is completed.

[0550] This invention is a "travel concierge" system that utilizes generative AI to optimize a user's travel plans. The system is specifically implemented using the following hardware and software components:

[0551] Hardware and Software Components

[0552] 1. Input method:

[0553] Users input travel requirements using a device (e.g., a smartphone or PC). The input interface is implemented as a web form or a mobile application.

[0554] Specifically, User A enters the following conditions for a family trip into the device: "Budget: 200,000 yen," "Travel companions: husband and two children," "Purpose of trip: relaxation," and "Preference for transportation: car."

[0555] 2. Temporary storage means:

[0556] The terminal temporarily stores the entered travel conditions in local storage.

[0557] 3. Send to server:

[0558] The device sends the temporarily saved conditions to the server using an HTTP request. The communication protocol is HTTPS.

[0559] 4. How to save to database:

[0560] The server persistently stores the received condition data in a database, which can be an SQL-based relational database or a NoSQL database.

[0561] 5. Means for invoking generative AI models:

[0562] The server calls a generative AI model (e.g., a model based on natural language processing) based on the stored condition information and generates an appropriate travel plan.

[0563] The generation AI analyzes the input conditions, lists possible travel destinations, accommodations, means of transportation, tourist spots, restaurants, etc., and creates multiple travel plans.

[0564] 6. Proposal method:

[0565] The server sends the generated travel plans to the user's terminal, allowing the user to view and evaluate the plans on the terminal.

[0566] Example: The generated plans, "Hot Spring Inn 2 Nights 3 Days Course" and "Resort Hotel 2 Nights 3 Days Course" will be displayed on the terminal.

[0567] 7. How to enter additional requests:

[0568] The user can review the proposed itinerary and enter any additional requests. The input interface allows the user to make a rearrangement request.

[0569] Example: User A requests "dinner at a famous local restaurant."

[0570] 8. How to generate a realignment plan:

[0571] The server then calls the generative AI model again based on additional requests and generates a new plan.

[0572] Example: A new plan "2 nights and 3 days at a hot spring inn + dinner at a famous restaurant" is generated.

[0573] 9. Reservation Method:

[0574] Once the user selects the final plan, the server works in conjunction with a bulk reservation system to make reservations for accommodation, transportation, restaurants, tourist attractions, etc. all at once.

[0575] 10. Means for sending reservation confirmation information to the terminal:

[0576] If the reservation is successful, the server sends a confirmation to the user's device, allowing the user to check the reservation details in real time.

[0577] Prompt Sentence Examples

[0578] For example, the prompt for the generated AI is set as follows:

[0579] text

[0580] Travel conditions: Budget - 200,000 yen, Travel companions - husband and two children, Purpose - Relaxation, Transportation - Car

[0581] Suggestion: Generate multiple itineraries for a family trip based on the following criteria: Include hot springs, resorts, pensions, tourist spots, restaurants, etc.

[0582] This system allows users to easily and quickly obtain optimal travel plans, significantly reducing the effort required for travel planning.

[0583] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0584] Step 1:

[0585] The user enters the travel requirements.

[0586] The user enters travel conditions (such as budget, companions, purpose of travel, means of transportation, etc.) through the device's input interface. The entered data is temporarily stored in the device.

[0587] Input: User's travel conditions (budget, companions, purpose, means of transportation, etc.)

[0588] Output: Temporarily saved travel condition data

[0589] Specific operation: User A enters information such as "budget: 200,000 yen," "traveling companions: husband and two children," "purpose of trip: relaxation," and "means of transportation: car" into the form on the device.

[0590] Step 2:

[0591] The terminal sends the conditions to the server.

[0592] The device sends the temporarily saved travel condition data to the server. The data is securely transferred using an HTTP POST request.

[0593] Input: Temporarily saved travel condition data

[0594] Output: Travel condition data sent to the server

[0595] Specific operation: The device sends JSON format data including "budget 200,000 yen," "traveling companions: husband and two children," "purpose of trip: relaxation," and "means of transportation: car" to the server.

[0596] Step 3:

[0597] The server stores the conditions in a database.

[0598] The server receives the travel condition data and stores it permanently in a database, allowing efficient management of user conditions.

[0599] Input: Travel condition data sent to the server

[0600] Output: Travel condition data stored in the database

[0601] Specific operation: The server parses the received JSON data and saves it in the database as records of "User ID," "Budget: 200,000 yen," "Traveling companions: husband and two children," "Purpose of trip: relaxation," and "Method of transportation: car."

[0602] Step 4:

[0603] The server calls the generative AI model to generate a travel plan.

[0604] The server calls up a generative AI model based on the condition information stored in the database to generate a travel plan. The generative AI model analyzes the conditions, lists suitable travel destinations, accommodations, transportation options, tourist spots, and restaurant options, and creates multiple travel plans.

[0605] Input: Travel condition data stored in the database

[0606] Output: Multiple generated itineraries

[0607] Specific operation: The generation AI generates plans such as "2 nights and 3 days at a hot spring inn," "2 nights and 3 days at a resort hotel," and "3 nights and 4 days at a pension."

[0608] Step 5:

[0609] The server sends the generated travel plan to the user's terminal.

[0610] The server sends the generated travel plans to the user's device as an HTTP response, allowing the user to view the plans on their device.

[0611] Input: Multiple itineraries generated by a generative AI model

[0612] Output: A list of itineraries sent to the user's device

[0613] Specific operation: The server responds with the generated travel plan in JSON format, and the device receives and displays it.

[0614] Step 6:

[0615] The user reviews the plan and enters any additional requests.

[0616] The user reviews the proposed travel plan and, if necessary, enters additional requests into the terminal's input interface.

[0617] Input: Travel plan displayed on the device, additional requests from the user

[0618] Output: A new dataset containing additional requests.

[0619] Specific operation: User A selects the "2 nights, 3 days at a hot spring inn" plan and enters "dinner at a famous local restaurant" as an additional request.

[0620] Step 7:

[0621] The terminal sends an additional request to the server.

[0622] The terminal sends the user's request for addition to the server using an HTTP request.

[0623] Input: User's request for addition

[0624] Output: Additional requested data sent to the server

[0625] Specific behavior: The device sends JSON data containing a request for "dinner at a famous local restaurant" to the server.

[0626] Step 8:

[0627] The server again uses the generated AI model to generate an adjustment plan.

[0628] The server analyzes the additional requests and generates a new travel plan using the generative AI model again, which reflects the additional requests.

[0629] Input: Additional requested data sent to the server

[0630] Output: Reworked itinerary

[0631] Specific operation: Based on additional requests, the generation AI generates new plans such as "2 nights, 3 days at a hot spring inn + dinner at a famous restaurant."

[0632] Step 9:

[0633] The server sends the recalibration plan to the user's device.

[0634] The server then sends the adjusted travel plan back to the user's terminal.

[0635] Input: rearranged travel plans

[0636] Output: The adjusted itinerary sent to the user's device

[0637] Specific operation: The server responds in JSON format with a plan for "2 nights and 3 days at a hot spring inn + dinner at a famous restaurant," which the device receives and displays.

[0638] Step 10:

[0639] The user selects the final plan and confirms the reservation.

[0640] The user finally selects the desired travel plan and confirms the reservation through the terminal.

[0641] Input: User's final plan selection

[0642] Output: Reservation confirmation data

[0643] Specific operation: User A selects the "2 nights, 3 days at a hot spring inn + dinner at a famous restaurant" plan and presses the button to confirm the reservation.

[0644] Step 11:

[0645] The server works in conjunction with the reservation system to make bulk reservations.

[0646] The server works in conjunction with the reservation system to make bulk reservations for accommodation, transportation, restaurants, tourist attractions, etc. based on the selected plan.

[0647] Input: User's final plan selection data

[0648] Output: Bulk booking completed

[0649] Specific operation: The server calls reservation APIs for hotels, car rentals, restaurants, etc. to make bulk reservations.

[0650] Step 12:

[0651] The server sends the reservation confirmation information to the terminal and notifies the user.

[0652] If the reservation is successful, the server will send a confirmation to the user's terminal and notify the user.

[0653] Input: Completed bulk reservation data

[0654] Output: Booking confirmation sent to the user's device

[0655] Specific operation: The server responds with reservation confirmation information in JSON format, and the device displays a notification.

[0656] This allows users to smoothly navigate through a series of steps to find the best travel plan.

[0657] (Application example 1)

[0658] 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."

[0659] Conventional travel planning systems have the problem that it takes a lot of time and effort for users to select a travel plan. In addition, the travel plans generated based on the user's input criteria do not always perfectly match the user's needs, and there is a lack of means to compare multiple plans or visually confirm them.

[0660] 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.

[0661] In this invention, the server includes an input means for a user to input travel conditions, a storage means for storing the conditions received from the input means, a generation means for generating a travel plan based on the stored conditions, a 3D visualization means for proposing and visualizing the generated travel plan to the user, and a reservation means for making reservations in bulk based on the travel plan selected by the user. This allows the user to visually check the travel plans, quickly select the most suitable plan, and make a bulk reservation.

[0662] "Input means" refers to a device or interface that allows a user to input travel-related conditions.

[0663] The "storage means" is a database or other storage device for storing the conditions received from the input means.

[0664] The "generation means" refers to an algorithm or program for generating a travel plan based on the stored conditions.

[0665] "3D visualization means" refers to a three-dimensional display device or software that visually displays and allows users to check the generated travel plan.

[0666] A "reservation method" is a system or service that allows users to make reservations in bulk based on a travel plan selected by the user.

[0667] This invention is a system for enabling users to efficiently and effectively create, confirm, modify and book travel plans, and includes as its main components an input means, a storage means, a generation means, a 3D visualization means and a reservation means.

[0668] Users use input methods such as smartphones or computers to enter travel conditions such as budget, travel companions, purpose of trip, and preferred means of transportation. For example, they might enter "budget 200,000 yen, husband and two children, purpose is relaxation, and preferred car for transportation." These conditions are temporarily saved on the device and then sent to the server.

[0669] The server saves the received conditions in a database (storage means). Based on the saved conditions, the server generates a travel plan using a generative AI model (such as the OpenAI API) (generation means). Here, the generative AI analyzes the user's input conditions and assembles multiple travel plans by listing suitable travel destinations, accommodations, means of transportation, tourist spots, restaurants, etc. This plan generation process is carried out using prompt sentences. Examples of prompt sentences are as follows:

[0670] "Optimize your users' travel plans, based on the following criteria:

[0671] Budget: 200,000 yen

[0672] Travelling with: Husband and two children

[0673] Purpose of trip: Relaxation

[0674] Transportation: Car

[0675] Hope: Especially including dinner at a restaurant.”

[0676] The generated travel plans are provided to the user using 3D visualization. Using an HMD (head-mounted display) or smartphone, the user can visually check and evaluate the multiple generated travel plans. For example, a 3-day, 2-night plan at a hot spring inn or a 3-day, 2-night plan at a resort hotel can be displayed in 3D, allowing users to check the scenery and details of the facilities.

[0677] If the user selects the desired plan and makes any additional requests (e.g., "dinner at a local famous restaurant"), this information is sent to the server via the storage means. The server then calls the generation AI again to generate a new plan and propose it to the user again.

[0678] Once the user has finally selected the optimal travel plan, the server will make all reservations for accommodation, transportation, restaurants, etc. via the reservation tool. If the reservation is successful, confirmation information will be sent from the server to the terminal and the user will be notified.

[0679] This configuration allows users to create, check, change and book travel plans smoothly and efficiently, and as the entire process is automated, it significantly reduces the amount of work required.

[0680] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0681] Step 1:

[0682] The device receives travel-related conditions (e.g., budget, travel companions, travel purpose, preferred means of transportation, etc.) from the user through input means. The input conditions are temporarily stored in the device and then sent to the server. The input here is done by the user through the interface, specifically using text boxes and options to enter conditions.

[0683] Step 2:

[0684] The server stores the travel conditions sent from the device in a database. The received data is organized so that it can be managed for each user. The database serves to efficiently store the information needed to generate travel plans and make reservations.

[0685] Step 3:

[0686] Based on the saved conditions, the server calls the generation means (generative AI model) to generate a travel plan. At this time, the generative AI model is given a prompt sentence that includes the conditions. Specifically, the following prompt sentence is used:

[0687] "Optimize your users' travel plans, based on the following criteria:

[0688] Budget: 200,000 yen

[0689] Travelling with: Husband and two children

[0690] Purpose of trip: Relaxation

[0691] Transportation: Car

[0692] Hope: Especially including dinner at a restaurant.”

[0693] The generative AI model analyzes this prompt and generates a travel plan, which is then returned to the server as text data.

[0694] Step 4:

[0695] The generated itinerary is sent from the server to the terminal. The terminal then visualizes the itinerary using 3D visualization tools and displays it to the user. The input here is the text data of the generated itinerary, and the output is a visualized 3D model. Specifically, information such as the itinerary outline, accommodations, tourist spots, and transportation options is modeled in 3D.

[0696] Step 5:

[0697] The user can use the device to view and evaluate the visualized travel plans, select which plan is most appealing, and enter additional requests (e.g., "dinner at a famous local restaurant"). This additional information is sent to the server via the device.

[0698] Step 6:

[0699] The server receives the additional request and generates a new itinerary using the generation method (generative AI model), again using the same prompt as the first time, to further optimize the new itinerary for the user's requirements.

[0700] Step 7:

[0701] Once the user has finally selected the optimal travel plan, the server will make all reservations for accommodation, transportation, restaurants, etc. through the reservation tool. The input here is the final plan based on the user's selection, and the output is reservation confirmation information. If the reservation is successful, this confirmation information is sent from the server to the terminal and the user is notified.

[0702] These processing steps allow users to create, review, modify, and book travel plans efficiently and effectively.

[0703] 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.

[0704] This invention is a "travel concierge" system that utilizes generative AI to optimize users' travel plans, and in particular, incorporates an emotion engine that recognizes users' emotions and reflects them in the travel plan. Each component and program process will be explained in detail below.

[0705] 1. Input method:

[0706] Users input travel-related information using a device (such as a smartphone or PC), including budget, travel companion information, travel purpose, preferred means of transportation, and specific requests. Furthermore, the device is equipped with an emotion engine that recognizes the user's emotions.

[0707] 2. Emotion Engine:

[0708] The emotion engine has the ability to recognize emotions by analyzing the user's voice, facial expressions, or text. This allows it to analyze in real time what emotions the user is feeling while typing. For example, if a user types "I want to relax," it will recognize from their voice and facial expressions that they are feeling stressed.

[0709] 3. Preservation means:

[0710] Once the conditions are entered, the device temporarily stores the conditions and emotion data and sends them to the server, which then stores the received data in a database to efficiently manage the conditions and emotions for each user.

[0711] 4. Generation means:

[0712] The server uses a generative AI to generate a travel plan based on the saved conditions and emotion data. The generative AI comprehensively analyzes the input conditions and the user's emotions, and lists suitable travel destinations, accommodations, transportation options, tourist spots, restaurants, etc.

[0713] 5. Proposal method:

[0714] The server then sends the generated itineraries to the user's device. The suggestion method considers the emotional data and makes suggestions tailored to the user's mood and emotions. For example, if the user is feeling stressed, it will prioritize suggestions for relaxing hot springs and places rich in nature.

[0715] 6. Reservation Method:

[0716] Once the user has selected their final travel plan, the server will link with the reservation system to make all reservations for accommodation, transportation, restaurants, tourist attractions, etc. If the reservation is successful, confirmation information will be sent from the server to the terminal and the user will be notified.

[0717] Explanation of the program processing flow

[0718] This travel concierge system operates as follows.

[0719] Flow of specific example

[0720] Let's use the example of User B, who is planning a solo trip to refresh himself.

[0721] 1. Enter the conditions:

[0722] User B enters the following conditions into the terminal:

[0723] Budget: 100,000 yen

[0724] Accompanying person: None

[0725] Purpose of the trip: Refreshment

[0726] Preferred mode of transportation: Train

[0727] While this input is being made, the device's emotion engine recognizes from Mr. B's tone of voice and facial expression that he is feeling stressed.

[0728] 2. Save condition:

[0729] The device sends these conditions and emotion data to the server, which stores them in a database.

[0730] 3. Plan Generation:

[0731] The server then calls up the AI ​​generator based on the stored conditions and emotion data, and performs analysis. The AI ​​generator then creates a list of relaxing hot spring resorts and nature-rich resorts, and generates multiple travel plans (for example, a 2-night, 3-day plan at a hot spring inn, a 2-night, 3-day plan at a highland resort hotel, etc.).

[0732] 4. Plan proposal:

[0733] The generated travel plans are sent to User B's device. User B checks these plans on his / her device. Using data from the emotion engine, hot spring inns and resort hotels with particularly relaxing effects are prioritized in suggestions.

[0734] 5. Plan Adjustment:

[0735] Person B selects a "hot spring inn" and requests an additional "local spa experience." This information is sent to the server via the device, and the server uses the generation AI to generate a new plan and propose it to Person B again.

[0736] 6. Final Planning and Booking:

[0737] Once B has finalized the plan, the server will make reservations for the hot spring inn and spa experience all at once through the reservation system. If the reservation is successful, the server will send confirmation information to the terminal and notify B.

[0738] This series of processes allows users to easily find the best travel plan for them, and the travel planning process proceeds smoothly. In addition, the emotion engine enables customization based on the user's emotions, resulting in a highly satisfying travel experience.

[0739] The processing flow will be explained below.

[0740] Step 1:

[0741] Users enter their travel requirements into a form on the device, including budget, travel companion information, travel purpose, transportation preferences, and any special requests.

[0742] Step 2:

[0743] The device sends the input conditions to the emotion engine to analyze the user's emotions. For example, in the case of voice input, it analyzes the tone of the voice, and in the case of text input, it infers emotions from the words used.

[0744] Step 3:

[0745] The emotion engine recognizes the user's emotion, temporarily stores it along with the condition data, and sends it to the server.

[0746] Step 4:

[0747] The server stores the received conditions and emotion data in a database designed to efficiently manage each user's conditions and emotion data.

[0748] Step 5:

[0749] The server generates a travel plan based on the stored conditions and emotion data by calling a generation AI. The generation AI comprehensively analyzes the conditions and emotions and lists suitable travel destinations, accommodations, transportation options, tourist spots, restaurants, etc.

[0750] Step 6:

[0751] The generative AI evaluates the list of options and creates multiple itineraries, based on criteria such as whether they fit within the budget, suitability for the travel companions, and consideration for the user's emotions.

[0752] Step 7:

[0753] The server transmits the generated travel plans to the terminal.

[0754] Step 8:

[0755] The device displays the received travel plans to the user, who can then review and rate them. The plan that reflects the emotion data is presented as the one that best suits the user's emotions.

[0756] Step 9:

[0757] Users can select a proposed plan and then enter any additional requests or changes into the device, such as requesting a change of accommodation or adding a specific tourist attraction.

[0758] Step 10:

[0759] The device sends the user's additional request to the server, which then calls the generation AI again and adjusts the plan to accommodate the additional request.

[0760] Step 11:

[0761] The adjusted plans are sent back to the device and presented to the user, who then selects the final plan.

[0762] Step 12:

[0763] Once the user has selected their final travel plan, the terminal sends this to the server.

[0764] Step 13:

[0765] Based on the selected plan, the server makes all reservations for accommodation, transportation, restaurants, tourist attractions, etc. The reservation process is carried out using the APIs of each service provider.

[0766] Step 14:

[0767] If a reservation is successful, the server collects the reservation confirmation information, which is then sent to the terminal and notified to the user.

[0768] Example 2

[0769] 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."

[0770] Conventional travel planning systems generate travel plans based solely on user input, making it impossible to consider the user's emotions or psychological state. This means that they are inadequate in proposing relaxing travel plans to stressed users. Furthermore, if the generated plan does not match the user's emotions, there is a high possibility that the travel plan will leave the user unsatisfied.

[0771] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0772] In this invention, the server includes input means for a user to input travel-related conditions, storage means for storing the conditions received from the input means, means including an emotion engine for analyzing the user's emotions, generation means for generating a travel plan based on the stored conditions and the user's emotion data, proposal means for proposing the generated travel plan to the user, and reservation means for making reservations all at once based on the travel plan selected by the user. This makes it possible to propose a travel plan that suits the user's emotions, thereby increasing user satisfaction.

[0773] "Input means" refers to a hardware or software interface that allows a user to input travel-related conditions into a terminal.

[0774] "Storage Means" means a data storage means for temporarily or permanently storing the conditions entered by the User.

[0775] An "emotion engine" is software or algorithms that analyze a user's voice, facial expressions, or text to recognize their emotions.

[0776] "Generative means" means software means, such as a generative AI model, for generating a travel plan based on the stored conditions and user emotional data.

[0777] The "proposal means" is an interface means for presenting the travel plan generated by the generation means to the user.

[0778] A "booking method" is a method by which a user makes a single reservation for accommodation, transportation, and other services based on the travel plan selected by the user.

[0779] A "database" is a storage system for managing and saving information such as conditions and emotional data entered by users.

[0780] This invention is a "travel concierge" system that utilizes generative AI models to optimize users' travel plans. In particular, it incorporates an emotion engine that recognizes users' emotions and reflects them in the travel plan. Each component and program process is explained in detail below.

[0781] Input methods:

[0782] A user inputs travel-related information using their device (such as a smartphone or PC). These information includes budget, travel companion information, travel purpose, preferred means of transportation, and specific requests. Furthermore, the device is equipped with an emotion engine that analyzes the user's voice, facial expression, or text to recognize emotions. For example, if a user inputs, "I'm planning a solo trip to refresh myself," the emotion engine recognizes that the user is stressed from his tone of voice and facial expression.

[0783] Storage method:

[0784] Once the conditions are entered, the device temporarily stores these conditions and emotion data and sends them to the server. The server then stores the received data in a database, efficiently managing each user's conditions and emotions. This database stores the various conditions entered by the user and the results of emotion analysis.

[0785] Generation means:

[0786] The server generates a travel plan using a generative AI model based on the stored conditions and emotion data. At this time, the server inputs a prompt statement into the generative AI model, which analyzes and generates it. For example, the prompt statement could be "budget under 100,000 yen, solo trip, purpose of relaxation, means of transportation by train, stressful." Based on this prompt, the generative AI model generates multiple travel plans, such as relaxing hot springs and resorts rich in nature.

[0787] Suggested means:

[0788] The generated travel plan is sent from the server to the user's device. When presenting it to the user, the device takes into account the emotional data and prioritizes the plan that best suits the user's emotions. For example, it may prioritize suggestions for relaxing hot spring inns or resort hotels surrounded by nature.

[0789] Reservation method:

[0790] Once the user has selected their final travel plan, the server works with the reservation system to complete the reservation in one go. Specifically, the server makes reservations for hot spring inns and spa experiences. Once the reservation is successful, the server sends confirmation information to the user's terminal and notifies them. In this way, users can skip the complicated reservation process and easily find the travel plan that best suits their feelings and desires.

[0791] Example flow:

[0792] For example, consider the case where User B plans a solo trip to refresh himself and enters the following conditions:

[0793] Budget: 100,000 yen

[0794] Accompanying person: None

[0795] Purpose of the trip: Refreshment

[0796] Preferred mode of transportation: Train

[0797] While inputting, the emotion engine recognizes that B is feeling stressed from his tone of voice and facial expression. The device sends this information to the server, which then inputs the following prompt sentence into the generation AI:

[0798] "Budget: 100,000 yen or less, solo trip, purpose: to refresh, transportation: train, stressful"

[0799] Based on this prompt, the generative AI model lists relaxing hot spring resorts and nature-rich resorts, and generates multiple travel plans (for example, a 2-night, 3-day plan at a hot spring inn, or a 2-night, 3-day plan at a highland resort hotel). The generated plans are then sent to the user's device and displayed preferentially, taking into account emotional data. If User B selects a "hot spring inn" and requests a "local spa experience," the plan is adjusted again.

[0800] In this way, a customized travel plan is proposed that takes the user's emotions into consideration, allowing the user to make a travel plan that is more satisfying.

[0801] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0802] Step 1:

[0803] The user inputs travel-related conditions using a device (smartphone or PC). Specifically, they input budget, travel companion information, travel purpose, preferred means of transportation, and specific requests. At this time, an emotion engine built into the device analyzes the user's voice and facial expressions and collects emotional data. The input data, consisting of conditions and emotional data, becomes the input for the next step.

[0804] Step 2:

[0805] The device temporarily stores the conditions and emotion data entered by the user and sends it to the server. Specifically, the input data is sent to the server in a data format such as JSON. The server then receives the user's conditions and emotion data and stores them in a database. Once the data has been saved to data storage, the server starts the next step based on this data.

[0806] Step 3:

[0807] The server converts the stored conditions and emotional data into a prompt sentence to be input into the generative AI model. Specifically, it generates a prompt sentence that combines the conditions and emotional data. For example, it creates a prompt sentence such as "budget within 100,000 yen, solo trip, purpose of relaxation, means of transportation by train, stressful." This prompt sentence becomes the input to the generative AI model.

[0808] Step 4:

[0809] The server calls the generative AI model, inputs a prompt, and generates a travel plan. Based on the prompt, the generative AI model generates multiple travel plans (for example, a 2-night, 3-day plan at a hot spring inn, a 2-night, 3-day plan at a highland resort hotel, etc.). The generated plan is output to the server, and the process proceeds to the next step.

[0810] Step 5:

[0811] The server sends the generated travel plan to the user's device. Specifically, the generated plan is sent to the device in JSON or XML format. The device receives it and presents it to the user. Based on the emotional data, the plan that is most suitable for the user (for example, a hot spring inn with a high relaxing effect) is displayed in a list with priority.

[0812] Step 6:

[0813] The user reviews the travel plans on the device and selects the one they are most interested in. The user then inputs additional requests (e.g., "local spa experience"). This input data becomes the input for the next step.

[0814] Step 7:

[0815] The device sends the user's selection and additional requests to the server. The server receives this and reflects it in the prompt text again. For example, the prompt text is updated to something like "budget under 100,000 yen, solo travel, purpose of relaxation, means of transportation by train, stressful, local spa experience." This updated prompt text becomes the input to the generative AI model.

[0816] Step 8:

[0817] The server calls the generative AI model again, inputs the updated prompt text, and generates a new travel plan. The generated plan is output to the server again and proposed to the user. The user reviews this new plan and makes a final decision.

[0818] Step 9:

[0819] Once the user has selected their final travel plan, the device sends it to the server. The server then calls the reservation system based on the final plan received. The server then makes all reservations for accommodations and various services. If the reservation is successful, confirmation information is sent from the server to the device and the user is notified.

[0820] As described above, this system generates optimal travel plans from user input, and then proposes and adjusts the plans taking the user's emotions into consideration, seamlessly handling the final booking process, thereby optimizing the user's travel plans.

[0821] (Application example 2)

[0822] 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."

[0823] Traditional travel planning systems only propose travel plans based on user requirements, making it difficult to customize them to take into account user emotions and real-time feedback. This lacks the ability to provide plans tailored to users' specific needs, making it difficult to achieve a satisfying travel experience. Furthermore, the ability of travel agency staff in brick-and-mortar stores to quickly propose appropriate plans to customers is also limited.

[0824] 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.

[0825] In this invention, the server includes an input means for a user to input travel requirements, a storage means for storing the requirements received from the input means, a generation means for generating a travel plan based on the stored requirements, a proposal means for proposing the generated travel plan to the user, a sentiment analysis means for analyzing the user's sentiment, a means for adjusting the generated travel plan based on the user's sentiment, and a reservation means for making reservations in bulk based on the travel plan selected by the user. This enables customization that takes into account the user's sentiment and real-time feedback, providing a highly satisfying travel experience. It also enables brick-and-mortar travel agencies to quickly propose appropriate plans to customers.

[0826] "Input means" refers to devices or software that allow users to input travel-related requirements into the system.

[0827] The "storage means" refers to a device or software that temporarily or permanently stores the travel-related conditions and emotion data received from the input means.

[0828] The "generation means" refers to a device or software for generating an optimal travel plan based on the stored conditions and emotion data.

[0829] The "suggestion means" is a device or software that presents the generated travel plan to the user and provides appropriate feedback.

[0830] "Emotion analysis means" refers to a device or software for analyzing emotions from a user's voice, facial expression, or text.

[0831] The "adjustment means" is a device or software for reconstructing or correcting the generated travel plan based on the user's emotional data obtained by the emotion analysis means.

[0832] "Reservation Facility" means a device or software that allows a user to make a single booking of the accommodation, transportation and other services required based on the final travel plan selected by the user.

[0833] This invention is a system for optimizing a user's travel plan, specifically a "travel concierge" system equipped with an emotion engine that analyzes the user's emotions and reflects them in the travel plan. This system can also be effectively used in brick-and-mortar travel agencies.

[0834] System configuration

[0835] 1. Input Method

[0836] Using smart glasses or a head-mounted display, users input travel-related information by voice, including budget, destination, travel companions, and purpose of trip. The device also reads the user's facial expressions and voice in real time to collect data for sentiment analysis.

[0837] 2. Preservation means

[0838] The travel conditions and emotion data collected by the device are sent to a cloud server and temporarily stored in a database, making it possible to efficiently manage the data of multiple users.

[0839] 3. Generation means

[0840] The server generates a travel plan based on the stored conditions and emotion data using a generative AI model (e.g., OpenAI GPT-4). The generative AI model analyzes past data and current conditions to propose the optimal travel plan.

[0841] 4. Proposal method

[0842] The generated itinerary is sent to the user's device in real time, and based on the results of sentiment analysis, it prioritizes the itinerary that best matches the user's sentiment.

[0843] 5. Emotion analysis method

[0844] Emotion analysis uses IBM Watson Visual Recognition and other tools to analyze collected voice and facial expression data, and adjusts plans based on the results.

[0845] 6. Adjustment means

[0846] If the user makes additional requests regarding the proposed plan, the system will again use the sentiment analysis method and call the generative AI model to regenerate and adjust the plan.

[0847] 7. Reservation Methods

[0848] Once the user selects the final plan, the server will make all the reservations for accommodation and transportation through the reservation system, and the results will be sent to the user's device.

[0849] Specific examples

[0850] Let's say User A visits a brick-and-mortar travel agency to plan a solo trip for relaxation. User A is given a head-mounted display by the agency staff and asked to enter the following information by voice:

[0851] Budget: 100,000 yen

[0852] Accompanying person: None

[0853] Purpose of the trip: Refreshment

[0854] Preferred mode of transportation: Train

[0855] The microphone and camera in the head-mounted display collect user A's tone of voice and facial expressions, which are then analyzed by the emotion analysis means. The emotion analysis means, recognizing that user A is feeling stressed, sends this data along with the condition to the server.

[0856] The server generates multiple travel plans using a generative AI model (OpenAI GPT-4) based on the conditions and emotion data. The generated plans are then proposed to User A via a head-mounted display. User A is presented with a three-day, two-night plan to refresh at a hot spring inn, and if he or she wishes to add a spa experience, the plan is instantly adjusted.

[0857] The plan selected by User A is automatically reserved through the reservation system. Reservation confirmation information is sent to the head-mounted display and User A is informed.

[0858] Prompt Sentence Examples

[0859] User Emotional Data: Feeling Stressed

[0860] Travel conditions: Budget: 100,000 yen, Travel purpose: Refreshment, Transportation: Train

[0861] Desired output from the generative AI: Provide recommended itineraries for relaxation, taking into account hot springs and nature resorts.

[0862] In this way, it becomes possible to generate, suggest, adjust and book optimal travel plans in real time based on the user's emotions.

[0863] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0864] Step 1:

[0865] Collecting user input conditions

[0866] Users input travel information (budget, destination, travel companions, purpose of trip, etc.) by voice through smart glasses or a head-mounted display, while the device's built-in microphone and camera also collect the user's facial expressions and voice tone.

[0867] Input: Travel conditions (voice data), user emotional data (facial expressions, voice tone)

[0868] Data processing: speech-to-text conversion, facial expression and tone analysis

[0869] Output: Textualized travel conditions, analyzed emotion data

[0870] Step 2:

[0871] Data storage

[0872] The collected travel conditions and emotion data are sent from the device to a cloud server and stored in a database, allowing for effective management of the conditions and emotion data.

[0873] Input: Texted travel conditions, analyzed emotion data

[0874] Data processing: sending data and storing it in a database

[0875] Output: Saved travel conditions and emotion data

[0876] Step 3:

[0877] Travel plan generation

[0878] The server generates an optimal travel plan using a generative AI model (e.g., OpenAI GPT-4) based on the stored conditions and emotion data. The generative AI generates multiple travel plans based on the provided prompt sentences.

[0879] Input: saved travel conditions, emotion data, prompt sentence

[0880] Data processing: Generating travel plans using generative AI models

[0881] Output: Generated itinerary

[0882] Step 4:

[0883] Travel plan suggestions

[0884] The generated travel plan is sent to the user's smart glasses or head-mounted display in real time, and the plan that best suits the user's emotions is displayed first, taking into account the user's emotional data.

[0885] Input: Generated itinerary

[0886] Data processing: sending and displaying travel plans

[0887] Output: A suggested itinerary for the user

[0888] Step 5:

[0889] Adjusting travel plans

[0890] If the user makes additional requests, the device will re-analyze the request using the sentiment analysis method and call the generative AI model to re-generate the plan, incorporating the additional requests into the prompt text.

[0891] Input: User requests for additions, emotional data

[0892] Data processing: Incorporating additional requests into prompt sentences and regenerating them using a generative AI model

[0893] Output: Regenerated itinerary

[0894] Step 6:

[0895] Bulk booking

[0896] Once the user selects the final plan, the server connects to the reservation system and makes all necessary reservations for accommodation, transportation, etc. The reservation results are then sent to the terminal.

[0897] Input: Finalized travel plans

[0898] Data processing: Linking with reservation systems and reservation processing

[0899] Output: Reservation confirmation information

[0900] Through this process, users can easily obtain the optimal travel plan that reflects their emotions and specific requirements.

[0901] 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.

[0902] 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.

[0903] 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.

[0904] [Third embodiment]

[0905] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0906] 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.

[0907] 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).

[0908] 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.

[0909] 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.

[0910] 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).

[0911] 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.

[0912] 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.

[0913] 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.

[0914] 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.

[0915] 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.

[0916] 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."

[0917] This invention is a "travel concierge" system that utilizes generative AI to optimize users' travel plans. The system is implemented with the following components:

[0918] 1. Input method:

[0919] Users input travel-related information using a device (such as a smartphone or PC), including budget, travel companion information, travel purpose, preferred means of transportation, and specific requests. The input method collects this information and temporarily stores it on the device.

[0920] 2. Preservation means:

[0921] The device sends the collected conditions to the server, which then stores them in a database designed to efficiently manage the conditions for each user.

[0922] 3. Generation means:

[0923] The server generates a travel plan based on the saved conditions using a generation AI. The generation AI analyzes the input conditions, lists suitable travel destinations, accommodations, transportation options, tourist spots, restaurants, etc., and assembles multiple travel plans.

[0924] 4. Proposal method:

[0925] The server sends the generated itineraries to the user's device, where the user can review and evaluate the suggestions. The suggestion tool also has the ability to adjust the plans based on user feedback.

[0926] 5. Reservation Method:

[0927] Once the user has selected their final travel plan, the server will link with the reservation system to make all reservations for accommodation, transportation, restaurants, tourist attractions, etc. If the reservation is successful, confirmation information will be sent from the server to the terminal and the user will be notified.

[0928] Explanation of the program processing flow

[0929] This travel concierge system operates as follows.

[0930] Flow of specific example

[0931] Let us take a specific example where User A is planning a relaxing trip with her family (husband and two children).

[0932] 1. Enter the conditions:

[0933] User A enters the following conditions into the terminal:

[0934] Budget: 200,000 yen

[0935] Travelling with: Husband and two children

[0936] Purpose of trip: Relaxation

[0937] Preferred mode of transportation: Car

[0938] These conditions are temporarily stored on the terminal and then transmitted to the server.

[0939] 2. Save conditions:

[0940] The server stores the received conditions in a database and efficiently manages User A's travel conditions.

[0941] 3. Plan Generation:

[0942] The server calls the generation AI based on the saved conditions and performs analysis. The generation AI lists hot spring resorts, resorts, pensions, nearby tourist spots, etc., and generates multiple travel plans (for example, a 2-night, 3-day plan at a hot spring inn, a 2-night, 3-day plan at a resort hotel, etc.).

[0943] 4. Plan proposal:

[0944] The generated travel plans are sent to the device of user A. User A checks and evaluates these plans on the device.

[0945] 5. Plan Adjustment:

[0946] Person A selects the plan he or she desires and requests "dinner at a famous local restaurant" as an additional request. This information is sent to the server via the device, and the server uses AI to generate a new plan and propose it to Person A again.

[0947] 6. Final Planning and Booking:

[0948] Once A decides on the final plan, the server will make reservations for hot spring inns, resort hotels, restaurants, etc. in one go through the reservation system. If the reservation is successful, the server will send confirmation information to the terminal and notify A.

[0949] This series of processes allows users to easily find the best travel plan for themselves, and makes travel planning go smoothly.

[0950] The processing flow will be explained below.

[0951] Step 1:

[0952] Users enter their travel requirements into a form on the device, including budget, travel companion information, travel purpose, transportation preferences, and any special requests.

[0953] Step 2:

[0954] The terminal temporarily stores the input conditions locally and then transmits them to the server.

[0955] Step 3:

[0956] The server stores the received conditions in a database, which is designed to efficiently manage conditions for each user.

[0957] Step 4:

[0958] The server generates a travel plan based on the saved conditions by calling the generation AI, which analyzes the conditions and lists suitable travel destinations, accommodations, transportation options, tourist spots, restaurants, etc.

[0959] Step 5:

[0960] The generative AI evaluates the list of options and creates multiple itineraries, based on criteria such as whether they fit within budget and whether they are suitable for travel companions (e.g., children).

[0961] Step 6:

[0962] The server transmits the generated travel plans to the terminal.

[0963] Step 7:

[0964] The device displays the received travel plans to the user, who can then review and rate them.

[0965] Step 8:

[0966] Users can select a proposed plan and then enter any additional requests or changes into the device, such as requesting a change of accommodation or adding a specific tourist attraction.

[0967] Step 9:

[0968] The device sends the user's additional request to the server, which then calls the generation AI again and adjusts the plan to accommodate the additional request.

[0969] Step 10:

[0970] The adjusted plans are sent back to the device and presented to the user, who then selects the final plan.

[0971] Step 11:

[0972] Once the user has selected their final travel plan, the terminal sends this to the server.

[0973] Step 12:

[0974] Based on the selected plan, the server makes all reservations for accommodation, transportation, restaurants, tourist attractions, etc. The reservation process is carried out using the APIs of each service provider.

[0975] Step 13:

[0976] If a reservation is successful, the server collects the reservation confirmation information, which is then sent to the terminal and notified to the user.

[0977] Example 1

[0978] 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."

[0979] Conventional travel planning systems have difficulty effectively and quickly generating optimal travel plans based on user requirements. Furthermore, processes such as rearranging plans to meet user requests and bulk bookings are time-consuming and detract from the user experience. There was a need for a system that could solve these issues and allow users to easily obtain optimal travel plans.

[0980] 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.

[0981] In this invention, the server includes an input means for a user to input travel-related conditions, a means for temporarily saving the conditions received from the input means, a means for transmitting the temporarily saved conditions to the server, a means for saving the sent conditions in a database, a means for calling a generative AI model for generating a travel plan based on the saved conditions, a suggestion means for proposing the generated travel plan to the user, a means for confirming the proposed travel plan and inputting additional requests, a means for generating a readjusted plan using the generative AI model based on the additional requests, a reservation means for making reservations in one go based on the travel plan selected by the user, and a means for transmitting reservation confirmation information to the user's terminal. This allows the user to obtain the optimal travel plan without any hassle.

[0982] "Input means" refers to a device or interface that allows a user to input travel-related conditions.

[0983] The "means for temporary saving" is a mechanism for temporarily saving the conditions entered by the user.

[0984] The "means for transmitting to the server" is a communication means for transmitting the temporarily saved conditions to the server.

[0985] The "means for saving in a database" is a data storage for permanently saving and managing the transmitted conditions.

[0986] The "means for calling the generative AI model" is a mechanism for calling and executing an artificial intelligence model for generating a travel plan based on the stored conditions.

[0987] The "proposal means" is a means for presenting the generated travel plan to the user.

[0988] A "means for inputting additional requests" is an input interface through which a user can request adjustments to the proposed travel plan.

[0989] The "means for generating a readjusted plan" is a mechanism for generating a new travel plan using a regenerative AI model based on additional requests from the user.

[0990] A "reservation method" is a method for making reservations for accommodation, transportation, meals, etc. all at once based on the travel plan selected by the user.

[0991] The "means for transmitting reservation confirmation information to the terminal" refers to a communication means for transmitting the confirmation information to the user's terminal when the reservation is completed.

[0992] This invention is a "travel concierge" system that utilizes generative AI to optimize a user's travel plans. The system is specifically implemented using the following hardware and software components:

[0993] Hardware and Software Components

[0994] 1. Input method:

[0995] Users input travel requirements using a device (e.g., a smartphone or PC). The input interface is implemented as a web form or a mobile application.

[0996] Specifically, User A enters the following conditions for a family trip into the device: "Budget: 200,000 yen," "Travel companions: husband and two children," "Purpose of trip: relaxation," and "Preference for transportation: car."

[0997] 2. Temporary storage means:

[0998] The terminal temporarily stores the entered travel conditions in local storage.

[0999] 3. Send to server:

[1000] The device sends the temporarily saved conditions to the server using an HTTP request. The communication protocol is HTTPS.

[1001] 4. How to save to database:

[1002] The server persistently stores the received condition data in a database, which can be an SQL-based relational database or a NoSQL database.

[1003] 5. Means for invoking generative AI models:

[1004] The server calls a generative AI model (e.g., a model based on natural language processing) based on the stored condition information and generates an appropriate travel plan.

[1005] The generation AI analyzes the input conditions, lists possible travel destinations, accommodations, means of transportation, tourist spots, restaurants, etc., and creates multiple travel plans.

[1006] 6. Proposal method:

[1007] The server sends the generated travel plans to the user's terminal, allowing the user to view and evaluate the plans on the terminal.

[1008] Example: The generated plans, "Hot Spring Inn 2 Nights 3 Days Course" and "Resort Hotel 2 Nights 3 Days Course" will be displayed on the terminal.

[1009] 7. How to enter additional requests:

[1010] The user can review the proposed itinerary and enter any additional requests. The input interface allows the user to make a rearrangement request.

[1011] Example: User A requests "dinner at a famous local restaurant."

[1012] 8. How to generate a realignment plan:

[1013] The server then calls the generative AI model again based on additional requests and generates a new plan.

[1014] Example: A new plan "2 nights and 3 days at a hot spring inn + dinner at a famous restaurant" is generated.

[1015] 9. Reservation Method:

[1016] Once the user selects the final plan, the server works in conjunction with a bulk reservation system to make reservations for accommodation, transportation, restaurants, tourist attractions, etc. all at once.

[1017] 10. Means for sending reservation confirmation information to the terminal:

[1018] If the reservation is successful, the server sends a confirmation to the user's device, allowing the user to check the reservation details in real time.

[1019] Prompt Sentence Examples

[1020] For example, the prompt for the generated AI is set as follows:

[1021] text

[1022] Travel conditions: Budget - 200,000 yen, Travel companions - husband and two children, Purpose - Relaxation, Transportation - Car

[1023] Suggestion: Generate multiple itineraries for a family trip based on the following criteria: Include hot springs, resorts, pensions, tourist spots, restaurants, etc.

[1024] This system allows users to easily and quickly obtain optimal travel plans, significantly reducing the effort required for travel planning.

[1025] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1026] Step 1:

[1027] The user enters the travel requirements.

[1028] The user enters travel conditions (such as budget, companions, purpose of travel, means of transportation, etc.) through the device's input interface. The entered data is temporarily stored in the device.

[1029] Input: User's travel conditions (budget, companions, purpose, means of transportation, etc.)

[1030] Output: Temporarily saved travel condition data

[1031] Specific operation: User A enters information such as "budget: 200,000 yen," "traveling companions: husband and two children," "purpose of trip: relaxation," and "means of transportation: car" into the form on the device.

[1032] Step 2:

[1033] The terminal sends the conditions to the server.

[1034] The device sends the temporarily saved travel condition data to the server. The data is securely transferred using an HTTP POST request.

[1035] Input: Temporarily saved travel condition data

[1036] Output: Travel condition data sent to the server

[1037] Specific operation: The device sends JSON format data including "budget 200,000 yen," "traveling companions: husband and two children," "purpose of trip: relaxation," and "means of transportation: car" to the server.

[1038] Step 3:

[1039] The server stores the conditions in a database.

[1040] The server receives the travel condition data and stores it permanently in a database, allowing efficient management of user conditions.

[1041] Input: Travel condition data sent to the server

[1042] Output: Travel condition data stored in the database

[1043] Specific operation: The server parses the received JSON data and saves it in the database as records of "User ID," "Budget: 200,000 yen," "Traveling companions: husband and two children," "Purpose of trip: relaxation," and "Method of transportation: car."

[1044] Step 4:

[1045] The server calls the generative AI model to generate a travel plan.

[1046] The server calls up a generative AI model based on the condition information stored in the database to generate a travel plan. The generative AI model analyzes the conditions, lists suitable travel destinations, accommodations, transportation options, tourist spots, and restaurant options, and creates multiple travel plans.

[1047] Input: Travel condition data stored in the database

[1048] Output: Multiple generated itineraries

[1049] Specific operation: The generation AI generates plans such as "2 nights and 3 days at a hot spring inn," "2 nights and 3 days at a resort hotel," and "3 nights and 4 days at a pension."

[1050] Step 5:

[1051] The server sends the generated travel plan to the user's terminal.

[1052] The server sends the generated travel plans to the user's device as an HTTP response, allowing the user to view the plans on their device.

[1053] Input: Multiple itineraries generated by a generative AI model

[1054] Output: A list of itineraries sent to the user's device

[1055] Specific operation: The server responds with the generated travel plan in JSON format, and the device receives and displays it.

[1056] Step 6:

[1057] The user reviews the plan and enters any additional requests.

[1058] The user reviews the proposed travel plan and, if necessary, enters additional requests into the terminal's input interface.

[1059] Input: Travel plan displayed on the device, additional requests from the user

[1060] Output: A new dataset containing additional requests.

[1061] Specific operation: User A selects the "2 nights, 3 days at a hot spring inn" plan and enters "dinner at a famous local restaurant" as an additional request.

[1062] Step 7:

[1063] The terminal sends an additional request to the server.

[1064] The terminal sends the user's request for addition to the server using an HTTP request.

[1065] Input: User's request for addition

[1066] Output: Additional requested data sent to the server

[1067] Specific behavior: The device sends JSON data containing a request for "dinner at a famous local restaurant" to the server.

[1068] Step 8:

[1069] The server again uses the generated AI model to generate an adjustment plan.

[1070] The server analyzes the additional requests and generates a new travel plan using the generative AI model again, which reflects the additional requests.

[1071] Input: Additional requested data sent to the server

[1072] Output: Reworked itinerary

[1073] Specific operation: Based on additional requests, the generation AI generates new plans such as "2 nights, 3 days at a hot spring inn + dinner at a famous restaurant."

[1074] Step 9:

[1075] The server sends the recalibration plan to the user's device.

[1076] The server then sends the adjusted travel plan back to the user's terminal.

[1077] Input: rearranged travel plans

[1078] Output: The adjusted itinerary sent to the user's device

[1079] Specific operation: The server responds in JSON format with a plan for "2 nights and 3 days at a hot spring inn + dinner at a famous restaurant," which the device receives and displays.

[1080] Step 10:

[1081] The user selects the final plan and confirms the reservation.

[1082] The user finally selects the desired travel plan and confirms the reservation through the terminal.

[1083] Input: User's final plan selection

[1084] Output: Reservation confirmation data

[1085] Specific operation: User A selects the "2 nights, 3 days at a hot spring inn + dinner at a famous restaurant" plan and presses the button to confirm the reservation.

[1086] Step 11:

[1087] The server works in conjunction with the reservation system to make bulk reservations.

[1088] The server works in conjunction with the reservation system to make bulk reservations for accommodation, transportation, restaurants, tourist attractions, etc. based on the selected plan.

[1089] Input: User's final plan selection data

[1090] Output: Bulk booking completed

[1091] Specific operation: The server calls reservation APIs for hotels, car rentals, restaurants, etc. to make bulk reservations.

[1092] Step 12:

[1093] The server sends the reservation confirmation information to the terminal and notifies the user.

[1094] If the reservation is successful, the server will send a confirmation to the user's terminal and notify the user.

[1095] Input: Completed bulk reservation data

[1096] Output: Booking confirmation sent to the user's device

[1097] Specific operation: The server responds with reservation confirmation information in JSON format, and the device displays a notification.

[1098] This allows users to smoothly navigate through a series of steps to find the best travel plan.

[1099] (Application example 1)

[1100] 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."

[1101] Conventional travel planning systems have the problem that it takes a lot of time and effort for users to select a travel plan. In addition, the travel plans generated based on the user's input criteria do not always perfectly match the user's needs, and there is a lack of means to compare multiple plans or visually confirm them.

[1102] 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.

[1103] In this invention, the server includes an input means for a user to input travel conditions, a storage means for storing the conditions received from the input means, a generation means for generating a travel plan based on the stored conditions, a 3D visualization means for proposing and visualizing the generated travel plan to the user, and a reservation means for making reservations in bulk based on the travel plan selected by the user. This allows the user to visually check the travel plans, quickly select the most suitable plan, and make a bulk reservation.

[1104] "Input means" refers to a device or interface that allows a user to input travel-related conditions.

[1105] The "storage means" is a database or other storage device for storing the conditions received from the input means.

[1106] The "generation means" refers to an algorithm or program for generating a travel plan based on the stored conditions.

[1107] "3D visualization means" refers to a three-dimensional display device or software that visually displays and allows users to check the generated travel plan.

[1108] A "reservation method" is a system or service that allows users to make reservations in bulk based on a travel plan selected by the user.

[1109] This invention is a system for enabling users to efficiently and effectively create, confirm, modify and book travel plans, and includes as its main components an input means, a storage means, a generation means, a 3D visualization means and a reservation means.

[1110] Users use input methods such as smartphones or computers to enter travel conditions such as budget, travel companions, purpose of trip, and preferred means of transportation. For example, they might enter "budget 200,000 yen, husband and two children, purpose is relaxation, and preferred car for transportation." These conditions are temporarily saved on the device and then sent to the server.

[1111] The server saves the received conditions in a database (storage means). Based on the saved conditions, the server generates a travel plan using a generative AI model (such as the OpenAI API) (generation means). Here, the generative AI analyzes the user's input conditions and assembles multiple travel plans by listing suitable travel destinations, accommodations, means of transportation, tourist spots, restaurants, etc. This plan generation process is carried out using prompt sentences. Examples of prompt sentences are as follows:

[1112] "Optimize your users' travel plans, based on the following criteria:

[1113] Budget: 200,000 yen

[1114] Travelling with: Husband and two children

[1115] Purpose of trip: Relaxation

[1116] Transportation: Car

[1117] Hope: Especially including dinner at a restaurant.”

[1118] The generated travel plans are provided to the user using 3D visualization. Using an HMD (head-mounted display) or smartphone, the user can visually check and evaluate the multiple generated travel plans. For example, a 3-day, 2-night plan at a hot spring inn or a 3-day, 2-night plan at a resort hotel can be displayed in 3D, allowing users to check the scenery and details of the facilities.

[1119] If the user selects the desired plan and makes any additional requests (e.g., "dinner at a local famous restaurant"), this information is sent to the server via the storage means. The server then calls the generation AI again to generate a new plan and propose it to the user again.

[1120] Once the user has finally selected the optimal travel plan, the server will make all reservations for accommodation, transportation, restaurants, etc. via the reservation tool. If the reservation is successful, confirmation information will be sent from the server to the terminal and the user will be notified.

[1121] This configuration allows users to create, check, change and book travel plans smoothly and efficiently, and as the entire process is automated, it significantly reduces the amount of work required.

[1122] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1123] Step 1:

[1124] The device receives travel-related conditions (e.g., budget, travel companions, travel purpose, preferred means of transportation, etc.) from the user through input means. The input conditions are temporarily stored in the device and then sent to the server. The input here is done by the user through the interface, specifically using text boxes and options to enter conditions.

[1125] Step 2:

[1126] The server stores the travel conditions sent from the device in a database. The received data is organized so that it can be managed for each user. The database serves to efficiently store the information needed to generate travel plans and make reservations.

[1127] Step 3:

[1128] Based on the saved conditions, the server calls the generation means (generative AI model) to generate a travel plan. At this time, the generative AI model is given a prompt sentence that includes the conditions. Specifically, the following prompt sentence is used:

[1129] "Optimize your users' travel plans, based on the following criteria:

[1130] Budget: 200,000 yen

[1131] Travelling with: Husband and two children

[1132] Purpose of trip: Relaxation

[1133] Transportation: Car

[1134] Hope: Especially including dinner at a restaurant.”

[1135] The generative AI model analyzes this prompt and generates a travel plan, which is then returned to the server as text data.

[1136] Step 4:

[1137] The generated itinerary is sent from the server to the terminal. The terminal then visualizes the itinerary using 3D visualization tools and displays it to the user. The input here is the text data of the generated itinerary, and the output is a visualized 3D model. Specifically, information such as the itinerary outline, accommodations, tourist spots, and transportation options is modeled in 3D.

[1138] Step 5:

[1139] The user can use the device to view and evaluate the visualized travel plans, select which plan is most appealing, and enter additional requests (e.g., "dinner at a famous local restaurant"). This additional information is sent to the server via the device.

[1140] Step 6:

[1141] The server receives the additional request and generates a new itinerary using the generation method (generative AI model), again using the same prompt as the first time, to further optimize the new itinerary for the user's requirements.

[1142] Step 7:

[1143] Once the user has finally selected the optimal travel plan, the server will make all reservations for accommodation, transportation, restaurants, etc. through the reservation tool. The input here is the final plan based on the user's selection, and the output is reservation confirmation information. If the reservation is successful, this confirmation information is sent from the server to the terminal and the user is notified.

[1144] These processing steps allow users to create, review, modify, and book travel plans efficiently and effectively.

[1145] 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.

[1146] This invention is a "travel concierge" system that utilizes generative AI to optimize users' travel plans, and in particular, incorporates an emotion engine that recognizes users' emotions and reflects them in the travel plan. Each component and program process will be explained in detail below.

[1147] 1. Input method:

[1148] Users input travel-related information using a device (such as a smartphone or PC), including budget, travel companion information, travel purpose, preferred means of transportation, and specific requests. Furthermore, the device is equipped with an emotion engine that recognizes the user's emotions.

[1149] 2. Emotion Engine:

[1150] The emotion engine has the ability to recognize emotions by analyzing the user's voice, facial expressions, or text. This allows it to analyze in real time what emotions the user is feeling while typing. For example, if a user types "I want to relax," it will recognize from their voice and facial expressions that they are feeling stressed.

[1151] 3. Preservation means:

[1152] Once the conditions are entered, the device temporarily stores the conditions and emotion data and sends them to the server, which then stores the received data in a database to efficiently manage the conditions and emotions for each user.

[1153] 4. Generation means:

[1154] The server uses a generative AI to generate a travel plan based on the saved conditions and emotion data. The generative AI comprehensively analyzes the input conditions and the user's emotions, and lists suitable travel destinations, accommodations, transportation options, tourist spots, restaurants, etc.

[1155] 5. Proposal method:

[1156] The server then sends the generated itineraries to the user's device. The suggestion method considers the emotional data and makes suggestions tailored to the user's mood and emotions. For example, if the user is feeling stressed, it will prioritize suggestions for relaxing hot springs and places rich in nature.

[1157] 6. Reservation Method:

[1158] Once the user has selected their final travel plan, the server will link with the reservation system to make all reservations for accommodation, transportation, restaurants, tourist attractions, etc. If the reservation is successful, confirmation information will be sent from the server to the terminal and the user will be notified.

[1159] Explanation of the program processing flow

[1160] This travel concierge system operates as follows.

[1161] Flow of specific example

[1162] Let's use the example of User B, who is planning a solo trip to refresh himself.

[1163] 1. Enter the conditions:

[1164] User B enters the following conditions into the terminal:

[1165] Budget: 100,000 yen

[1166] Accompanying person: None

[1167] Purpose of the trip: Refreshment

[1168] Preferred mode of transportation: Train

[1169] While this input is being made, the device's emotion engine recognizes from Mr. B's tone of voice and facial expression that he is feeling stressed.

[1170] 2. Save condition:

[1171] The device sends these conditions and emotion data to the server, which stores them in a database.

[1172] 3. Plan Generation:

[1173] The server then calls up the AI ​​generator based on the stored conditions and emotion data, and performs analysis. The AI ​​generator then creates a list of relaxing hot spring resorts and nature-rich resorts, and generates multiple travel plans (for example, a 2-night, 3-day plan at a hot spring inn, a 2-night, 3-day plan at a highland resort hotel, etc.).

[1174] 4. Plan proposal:

[1175] The generated travel plans are sent to User B's device. User B checks these plans on his / her device. Using data from the emotion engine, hot spring inns and resort hotels with particularly relaxing effects are prioritized in suggestions.

[1176] 5. Plan Adjustment:

[1177] Person B selects a "hot spring inn" and requests an additional "local spa experience." This information is sent to the server via the device, and the server uses the generation AI to generate a new plan and propose it to Person B again.

[1178] 6. Final Planning and Booking:

[1179] Once B has finalized the plan, the server will make reservations for the hot spring inn and spa experience all at once through the reservation system. If the reservation is successful, the server will send confirmation information to the terminal and notify B.

[1180] This series of processes allows users to easily find the best travel plan for them, and the travel planning process proceeds smoothly. In addition, the emotion engine enables customization based on the user's emotions, resulting in a highly satisfying travel experience.

[1181] The processing flow will be explained below.

[1182] Step 1:

[1183] Users enter their travel requirements into a form on the device, including budget, travel companion information, travel purpose, transportation preferences, and any special requests.

[1184] Step 2:

[1185] The device sends the input conditions to the emotion engine to analyze the user's emotions. For example, in the case of voice input, it analyzes the tone of the voice, and in the case of text input, it infers emotions from the words used.

[1186] Step 3:

[1187] The emotion engine recognizes the user's emotion, temporarily stores it along with the condition data, and sends it to the server.

[1188] Step 4:

[1189] The server stores the received conditions and emotion data in a database designed to efficiently manage each user's conditions and emotion data.

[1190] Step 5:

[1191] The server generates a travel plan based on the stored conditions and emotion data by calling a generation AI. The generation AI comprehensively analyzes the conditions and emotions and lists suitable travel destinations, accommodations, transportation options, tourist spots, restaurants, etc.

[1192] Step 6:

[1193] The generative AI evaluates the list of options and creates multiple itineraries, based on criteria such as whether they fit within the budget, suitability for the travel companions, and consideration for the user's emotions.

[1194] Step 7:

[1195] The server transmits the generated travel plans to the terminal.

[1196] Step 8:

[1197] The device displays the received travel plans to the user, who can then review and rate them. The plan that reflects the emotion data is presented as the one that best suits the user's emotions.

[1198] Step 9:

[1199] Users can select a proposed plan and then enter any additional requests or changes into the device, such as requesting a change of accommodation or adding a specific tourist attraction.

[1200] Step 10:

[1201] The device sends the user's additional request to the server, which then calls the generation AI again and adjusts the plan to accommodate the additional request.

[1202] Step 11:

[1203] The adjusted plans are sent back to the device and presented to the user, who then selects the final plan.

[1204] Step 12:

[1205] Once the user has selected their final travel plan, the terminal sends this to the server.

[1206] Step 13:

[1207] Based on the selected plan, the server makes all reservations for accommodation, transportation, restaurants, tourist attractions, etc. The reservation process is carried out using the APIs of each service provider.

[1208] Step 14:

[1209] If a reservation is successful, the server collects the reservation confirmation information, which is then sent to the terminal and notified to the user.

[1210] Example 2

[1211] 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."

[1212] Conventional travel planning systems generate travel plans based solely on user input, making it impossible to consider the user's emotions or psychological state. This means that they are inadequate in proposing relaxing travel plans to stressed users. Furthermore, if the generated plan does not match the user's emotions, there is a high possibility that the travel plan will leave the user unsatisfied.

[1213] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1214] In this invention, the server includes input means for a user to input travel-related conditions, storage means for storing the conditions received from the input means, means including an emotion engine for analyzing the user's emotions, generation means for generating a travel plan based on the stored conditions and the user's emotion data, proposal means for proposing the generated travel plan to the user, and reservation means for making reservations all at once based on the travel plan selected by the user. This makes it possible to propose a travel plan that suits the user's emotions, thereby increasing user satisfaction.

[1215] "Input means" refers to a hardware or software interface that allows a user to input travel-related conditions into a terminal.

[1216] "Storage Means" means a data storage means for temporarily or permanently storing the conditions entered by the User.

[1217] An "emotion engine" is software or algorithms that analyze a user's voice, facial expressions, or text to recognize their emotions.

[1218] "Generative means" means software means, such as a generative AI model, for generating a travel plan based on the stored conditions and user emotional data.

[1219] The "proposal means" is an interface means for presenting the travel plan generated by the generation means to the user.

[1220] A "booking method" is a method by which a user makes a single reservation for accommodation, transportation, and other services based on the travel plan selected by the user.

[1221] A "database" is a storage system for managing and saving information such as conditions and emotional data entered by users.

[1222] This invention is a "travel concierge" system that utilizes generative AI models to optimize users' travel plans. In particular, it incorporates an emotion engine that recognizes users' emotions and reflects them in the travel plan. Each component and program process is explained in detail below.

[1223] Input methods:

[1224] A user inputs travel-related information using their device (such as a smartphone or PC). These information includes budget, travel companion information, travel purpose, preferred means of transportation, and specific requests. Furthermore, the device is equipped with an emotion engine that analyzes the user's voice, facial expression, or text to recognize emotions. For example, if a user inputs, "I'm planning a solo trip to refresh myself," the emotion engine recognizes that the user is stressed from his tone of voice and facial expression.

[1225] Storage method:

[1226] Once the conditions are entered, the device temporarily stores these conditions and emotion data and sends them to the server. The server then stores the received data in a database, efficiently managing each user's conditions and emotions. This database stores the various conditions entered by the user and the results of emotion analysis.

[1227] Generation means:

[1228] The server generates a travel plan using a generative AI model based on the stored conditions and emotion data. At this time, the server inputs a prompt statement into the generative AI model, which analyzes and generates it. For example, the prompt statement could be "budget under 100,000 yen, solo trip, purpose of relaxation, means of transportation by train, stressful." Based on this prompt, the generative AI model generates multiple travel plans, such as relaxing hot springs and resorts rich in nature.

[1229] Suggested means:

[1230] The generated travel plan is sent from the server to the user's device. When presenting it to the user, the device takes into account the emotional data and prioritizes the plan that best suits the user's emotions. For example, it may prioritize suggestions for relaxing hot spring inns or resort hotels surrounded by nature.

[1231] Reservation method:

[1232] Once the user has selected their final travel plan, the server works with the reservation system to complete the reservation in one go. Specifically, the server makes reservations for hot spring inns and spa experiences. Once the reservation is successful, the server sends confirmation information to the user's terminal and notifies them. In this way, users can skip the complicated reservation process and easily find the travel plan that best suits their feelings and desires.

[1233] Example flow:

[1234] For example, consider the case where User B plans a solo trip to refresh himself and enters the following conditions:

[1235] Budget: 100,000 yen

[1236] Accompanying person: None

[1237] Purpose of the trip: Refreshment

[1238] Preferred mode of transportation: Train

[1239] While inputting, the emotion engine recognizes that B is feeling stressed from his tone of voice and facial expression. The device sends this information to the server, which then inputs the following prompt sentence into the generation AI:

[1240] "Budget: 100,000 yen or less, solo trip, purpose: to refresh, transportation: train, stressful"

[1241] Based on this prompt, the generative AI model lists relaxing hot spring resorts and nature-rich resorts, and generates multiple travel plans (for example, a 2-night, 3-day plan at a hot spring inn, or a 2-night, 3-day plan at a highland resort hotel). The generated plans are then sent to the user's device and displayed preferentially, taking into account emotional data. If User B selects a "hot spring inn" and requests a "local spa experience," the plan is adjusted again.

[1242] In this way, a customized travel plan is proposed that takes the user's emotions into consideration, allowing the user to make a travel plan that is more satisfying.

[1243] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1244] Step 1:

[1245] The user inputs travel-related conditions using a device (smartphone or PC). Specifically, they input budget, travel companion information, travel purpose, preferred means of transportation, and specific requests. At this time, an emotion engine built into the device analyzes the user's voice and facial expressions and collects emotional data. The input data, consisting of conditions and emotional data, becomes the input for the next step.

[1246] Step 2:

[1247] The device temporarily stores the conditions and emotion data entered by the user and sends it to the server. Specifically, the input data is sent to the server in a data format such as JSON. The server then receives the user's conditions and emotion data and stores them in a database. Once the data has been saved to data storage, the server starts the next step based on this data.

[1248] Step 3:

[1249] The server converts the stored conditions and emotional data into a prompt sentence to be input into the generative AI model. Specifically, it generates a prompt sentence that combines the conditions and emotional data. For example, it creates a prompt sentence such as "budget within 100,000 yen, solo trip, purpose of relaxation, means of transportation by train, stressful." This prompt sentence becomes the input to the generative AI model.

[1250] Step 4:

[1251] The server calls the generative AI model, inputs a prompt, and generates a travel plan. Based on the prompt, the generative AI model generates multiple travel plans (for example, a 2-night, 3-day plan at a hot spring inn, a 2-night, 3-day plan at a highland resort hotel, etc.). The generated plan is output to the server, and the process proceeds to the next step.

[1252] Step 5:

[1253] The server sends the generated travel plan to the user's device. Specifically, the generated plan is sent to the device in JSON or XML format. The device receives it and presents it to the user. Based on the emotional data, the plan that is most suitable for the user (for example, a hot spring inn with a high relaxing effect) is displayed in a list with priority.

[1254] Step 6:

[1255] The user reviews the travel plans on the device and selects the one they are most interested in. The user then inputs additional requests (e.g., "local spa experience"). This input data becomes the input for the next step.

[1256] Step 7:

[1257] The device sends the user's selection and additional requests to the server. The server receives this and reflects it in the prompt text again. For example, the prompt text is updated to something like "budget under 100,000 yen, solo travel, purpose of relaxation, means of transportation by train, stressful, local spa experience." This updated prompt text becomes the input to the generative AI model.

[1258] Step 8:

[1259] The server calls the generative AI model again, inputs the updated prompt text, and generates a new travel plan. The generated plan is output to the server again and proposed to the user. The user reviews this new plan and makes a final decision.

[1260] Step 9:

[1261] Once the user has selected their final travel plan, the device sends it to the server. The server then calls the reservation system based on the final plan received. The server then makes all reservations for accommodations and various services. If the reservation is successful, confirmation information is sent from the server to the device and the user is notified.

[1262] As described above, this system generates optimal travel plans from user input, and then proposes and adjusts the plans taking the user's emotions into consideration, seamlessly handling the final booking process, thereby optimizing the user's travel plans.

[1263] (Application example 2)

[1264] 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."

[1265] Traditional travel planning systems only propose travel plans based on user requirements, making it difficult to customize them to take into account user emotions and real-time feedback. This lacks the ability to provide plans tailored to users' specific needs, making it difficult to achieve a satisfying travel experience. Furthermore, the ability of travel agency staff in brick-and-mortar stores to quickly propose appropriate plans to customers is also limited.

[1266] 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.

[1267] In this invention, the server includes an input means for a user to input travel requirements, a storage means for storing the requirements received from the input means, a generation means for generating a travel plan based on the stored requirements, a proposal means for proposing the generated travel plan to the user, a sentiment analysis means for analyzing the user's sentiment, a means for adjusting the generated travel plan based on the user's sentiment, and a reservation means for making reservations in bulk based on the travel plan selected by the user. This enables customization that takes into account the user's sentiment and real-time feedback, providing a highly satisfying travel experience. It also enables brick-and-mortar travel agencies to quickly propose appropriate plans to customers.

[1268] "Input means" refers to devices or software that allow users to input travel-related requirements into the system.

[1269] The "storage means" refers to a device or software that temporarily or permanently stores the travel-related conditions and emotion data received from the input means.

[1270] The "generation means" refers to a device or software for generating an optimal travel plan based on the stored conditions and emotion data.

[1271] The "suggestion means" is a device or software that presents the generated travel plan to the user and provides appropriate feedback.

[1272] "Emotion analysis means" refers to a device or software for analyzing emotions from a user's voice, facial expression, or text.

[1273] The "adjustment means" is a device or software for reconstructing or correcting the generated travel plan based on the user's emotional data obtained by the emotion analysis means.

[1274] "Reservation Facility" means a device or software that allows a user to make a single booking of the accommodation, transportation and other services required based on the final travel plan selected by the user.

[1275] This invention is a system for optimizing a user's travel plan, specifically a "travel concierge" system equipped with an emotion engine that analyzes the user's emotions and reflects them in the travel plan. This system can also be effectively used in brick-and-mortar travel agencies.

[1276] System configuration

[1277] 1. Input Method

[1278] Using smart glasses or a head-mounted display, users input travel-related information by voice, including budget, destination, travel companions, and purpose of trip. The device also reads the user's facial expressions and voice in real time to collect data for sentiment analysis.

[1279] 2. Preservation means

[1280] The travel conditions and emotion data collected by the device are sent to a cloud server and temporarily stored in a database, making it possible to efficiently manage the data of multiple users.

[1281] 3. Generation means

[1282] The server generates a travel plan based on the stored conditions and emotion data using a generative AI model (e.g., OpenAI GPT-4). The generative AI model analyzes past data and current conditions to propose the optimal travel plan.

[1283] 4. Proposal method

[1284] The generated itinerary is sent to the user's device in real time, and based on the results of sentiment analysis, it prioritizes the itinerary that best matches the user's sentiment.

[1285] 5. Emotion analysis method

[1286] Emotion analysis uses IBM Watson Visual Recognition and other tools to analyze collected voice and facial expression data, and adjusts plans based on the results.

[1287] 6. Adjustment means

[1288] If the user makes additional requests regarding the proposed plan, the system will again use the sentiment analysis method and call the generative AI model to regenerate and adjust the plan.

[1289] 7. Reservation Methods

[1290] Once the user selects the final plan, the server will make all the reservations for accommodation and transportation through the reservation system, and the results will be sent to the user's device.

[1291] Specific examples

[1292] Let's say User A visits a brick-and-mortar travel agency to plan a solo trip for relaxation. User A is given a head-mounted display by the agency staff and asked to enter the following information by voice:

[1293] Budget: 100,000 yen

[1294] Accompanying person: None

[1295] Purpose of the trip: Refreshment

[1296] Preferred mode of transportation: Train

[1297] The microphone and camera in the head-mounted display collect user A's tone of voice and facial expressions, which are then analyzed by the emotion analysis means. The emotion analysis means, recognizing that user A is feeling stressed, sends this data along with the condition to the server.

[1298] The server generates multiple travel plans using a generative AI model (OpenAI GPT-4) based on the conditions and emotion data. The generated plans are then proposed to User A via a head-mounted display. User A is presented with a three-day, two-night plan to refresh at a hot spring inn, and if he or she wishes to add a spa experience, the plan is instantly adjusted.

[1299] The plan selected by User A is automatically reserved through the reservation system. Reservation confirmation information is sent to the head-mounted display and User A is informed.

[1300] Prompt Sentence Examples

[1301] User Emotional Data: Feeling Stressed

[1302] Travel conditions: Budget: 100,000 yen, Travel purpose: Refreshment, Transportation: Train

[1303] Desired output from the generative AI: Provide recommended itineraries for relaxation, taking into account hot springs and nature resorts.

[1304] In this way, it becomes possible to generate, suggest, adjust and book optimal travel plans in real time based on the user's emotions.

[1305] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1306] Step 1:

[1307] Collecting user input conditions

[1308] Users input travel information (budget, destination, travel companions, purpose of trip, etc.) by voice through smart glasses or a head-mounted display, while the device's built-in microphone and camera also collect the user's facial expressions and voice tone.

[1309] Input: Travel conditions (voice data), user emotional data (facial expressions, voice tone)

[1310] Data processing: speech-to-text conversion, facial expression and tone analysis

[1311] Output: Textualized travel conditions, analyzed emotion data

[1312] Step 2:

[1313] Data storage

[1314] The collected travel conditions and emotion data are sent from the device to a cloud server and stored in a database, allowing for effective management of the conditions and emotion data.

[1315] Input: Texted travel conditions, analyzed emotion data

[1316] Data processing: sending data and storing it in a database

[1317] Output: Saved travel conditions and emotion data

[1318] Step 3:

[1319] Travel plan generation

[1320] The server generates an optimal travel plan using a generative AI model (e.g., OpenAI GPT-4) based on the stored conditions and emotion data. The generative AI generates multiple travel plans based on the provided prompt sentences.

[1321] Input: saved travel conditions, emotion data, prompt sentence

[1322] Data processing: Generating travel plans using generative AI models

[1323] Output: Generated itinerary

[1324] Step 4:

[1325] Travel plan suggestions

[1326] The generated travel plan is sent to the user's smart glasses or head-mounted display in real time, and the plan that best suits the user's emotions is displayed first, taking into account the user's emotional data.

[1327] Input: Generated itinerary

[1328] Data processing: sending and displaying travel plans

[1329] Output: A suggested itinerary for the user

[1330] Step 5:

[1331] Adjusting travel plans

[1332] If the user makes additional requests, the device will re-analyze the request using the sentiment analysis method and call the generative AI model to re-generate the plan, incorporating the additional requests into the prompt text.

[1333] Input: User requests for additions, emotional data

[1334] Data processing: Incorporating additional requests into prompt sentences and regenerating them using a generative AI model

[1335] Output: Regenerated itinerary

[1336] Step 6:

[1337] Bulk booking

[1338] Once the user selects the final plan, the server connects to the reservation system and makes all necessary reservations for accommodation, transportation, etc. The reservation results are then sent to the terminal.

[1339] Input: Finalized travel plans

[1340] Data processing: Linking with reservation systems and reservation processing

[1341] Output: Reservation confirmation information

[1342] Through this process, users can easily obtain the optimal travel plan that reflects their emotions and specific requirements.

[1343] 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.

[1344] 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.

[1345] 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.

[1346] [Fourth embodiment]

[1347] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1348] 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.

[1349] 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).

[1350] 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.

[1351] 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.

[1352] 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).

[1353] 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.

[1354] 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.

[1355] 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.

[1356] 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.

[1357] 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.

[1358] 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.

[1359] 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."

[1360] This invention is a "travel concierge" system that utilizes generative AI to optimize users' travel plans. The system is implemented with the following components:

[1361] 1. Input method:

[1362] Users input travel-related information using a device (such as a smartphone or PC), including budget, travel companion information, travel purpose, preferred means of transportation, and specific requests. The input method collects this information and temporarily stores it on the device.

[1363] 2. Preservation means:

[1364] The device sends the collected conditions to the server, which then stores them in a database designed to efficiently manage the conditions for each user.

[1365] 3. Generation means:

[1366] The server generates a travel plan based on the saved conditions using a generation AI. The generation AI analyzes the input conditions, lists suitable travel destinations, accommodations, transportation options, tourist spots, restaurants, etc., and assembles multiple travel plans.

[1367] 4. Proposal method:

[1368] The server sends the generated itineraries to the user's device, where the user can review and evaluate the suggestions. The suggestion tool also has the ability to adjust the plans based on user feedback.

[1369] 5. Reservation Method:

[1370] Once the user has selected their final travel plan, the server will link with the reservation system to make all reservations for accommodation, transportation, restaurants, tourist attractions, etc. If the reservation is successful, confirmation information will be sent from the server to the terminal and the user will be notified.

[1371] Explanation of the program processing flow

[1372] This travel concierge system operates as follows.

[1373] Flow of specific example

[1374] Let us take a specific example where User A is planning a relaxing trip with her family (husband and two children).

[1375] 1. Enter the conditions:

[1376] User A enters the following conditions into the terminal:

[1377] Budget: 200,000 yen

[1378] Travelling with: Husband and two children

[1379] Purpose of trip: Relaxation

[1380] Preferred mode of transportation: Car

[1381] These conditions are temporarily stored on the terminal and then transmitted to the server.

[1382] 2. Save conditions:

[1383] The server stores the received conditions in a database and efficiently manages User A's travel conditions.

[1384] 3. Plan Generation:

[1385] The server calls the generation AI based on the saved conditions and performs analysis. The generation AI lists hot spring resorts, resorts, pensions, nearby tourist spots, etc., and generates multiple travel plans (for example, a 2-night, 3-day plan at a hot spring inn, a 2-night, 3-day plan at a resort hotel, etc.).

[1386] 4. Plan proposal:

[1387] The generated travel plans are sent to the device of user A. User A checks and evaluates these plans on the device.

[1388] 5. Plan Adjustment:

[1389] Person A selects the plan he or she desires and requests "dinner at a famous local restaurant" as an additional request. This information is sent to the server via the device, and the server uses AI to generate a new plan and propose it to Person A again.

[1390] 6. Final Planning and Booking:

[1391] Once A decides on the final plan, the server will make reservations for hot spring inns, resort hotels, restaurants, etc. in one go through the reservation system. If the reservation is successful, the server will send confirmation information to the terminal and notify A.

[1392] This series of processes allows users to easily find the best travel plan for themselves, and makes travel planning go smoothly.

[1393] The processing flow will be explained below.

[1394] Step 1:

[1395] Users enter their travel requirements into a form on the device, including budget, travel companion information, travel purpose, transportation preferences, and any special requests.

[1396] Step 2:

[1397] The terminal temporarily stores the input conditions locally and then transmits them to the server.

[1398] Step 3:

[1399] The server stores the received conditions in a database, which is designed to efficiently manage conditions for each user.

[1400] Step 4:

[1401] The server generates a travel plan based on the saved conditions by calling the generation AI, which analyzes the conditions and lists suitable travel destinations, accommodations, transportation options, tourist spots, restaurants, etc.

[1402] Step 5:

[1403] The generative AI evaluates the list of options and creates multiple itineraries, based on criteria such as whether they fit within budget and whether they are suitable for travel companions (e.g., children).

[1404] Step 6:

[1405] The server transmits the generated travel plans to the terminal.

[1406] Step 7:

[1407] The device displays the received travel plans to the user, who can then review and rate them.

[1408] Step 8:

[1409] Users can select a proposed plan and then enter any additional requests or changes into the device, such as requesting a change of accommodation or adding a specific tourist attraction.

[1410] Step 9:

[1411] The device sends the user's additional request to the server, which then calls the generation AI again and adjusts the plan to accommodate the additional request.

[1412] Step 10:

[1413] The adjusted plans are sent back to the device and presented to the user, who then selects the final plan.

[1414] Step 11:

[1415] Once the user has selected their final travel plan, the terminal sends this to the server.

[1416] Step 12:

[1417] Based on the selected plan, the server makes all reservations for accommodation, transportation, restaurants, tourist attractions, etc. The reservation process is carried out using the APIs of each service provider.

[1418] Step 13:

[1419] If a reservation is successful, the server collects the reservation confirmation information, which is then sent to the terminal and notified to the user.

[1420] Example 1

[1421] 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."

[1422] Conventional travel planning systems have difficulty effectively and quickly generating optimal travel plans based on user requirements. Furthermore, processes such as rearranging plans to meet user requests and bulk bookings are time-consuming and detract from the user experience. There was a need for a system that could solve these issues and allow users to easily obtain optimal travel plans.

[1423] 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.

[1424] In this invention, the server includes an input means for a user to input travel-related conditions, a means for temporarily saving the conditions received from the input means, a means for transmitting the temporarily saved conditions to the server, a means for saving the sent conditions in a database, a means for calling a generative AI model for generating a travel plan based on the saved conditions, a suggestion means for proposing the generated travel plan to the user, a means for confirming the proposed travel plan and inputting additional requests, a means for generating a readjusted plan using the generative AI model based on the additional requests, a reservation means for making reservations in one go based on the travel plan selected by the user, and a means for transmitting reservation confirmation information to the user's terminal. This allows the user to obtain the optimal travel plan without any hassle.

[1425] "Input means" refers to a device or interface that allows a user to input travel-related conditions.

[1426] The "means for temporary saving" is a mechanism for temporarily saving the conditions entered by the user.

[1427] The "means for transmitting to the server" is a communication means for transmitting the temporarily saved conditions to the server.

[1428] The "means for saving in a database" is a data storage for permanently saving and managing the transmitted conditions.

[1429] The "means for calling the generative AI model" is a mechanism for calling and executing an artificial intelligence model for generating a travel plan based on the stored conditions.

[1430] The "proposal means" is a means for presenting the generated travel plan to the user.

[1431] A "means for inputting additional requests" is an input interface through which a user can request adjustments to the proposed travel plan.

[1432] The "means for generating a readjusted plan" is a mechanism for generating a new travel plan using a regenerative AI model based on additional requests from the user.

[1433] A "reservation method" is a method for making reservations for accommodation, transportation, meals, etc. all at once based on the travel plan selected by the user.

[1434] The "means for transmitting reservation confirmation information to the terminal" refers to a communication means for transmitting the confirmation information to the user's terminal when the reservation is completed.

[1435] This invention is a "travel concierge" system that utilizes generative AI to optimize a user's travel plans. The system is specifically implemented using the following hardware and software components:

[1436] Hardware and Software Components

[1437] 1. Input method:

[1438] Users input travel requirements using a device (e.g., a smartphone or PC). The input interface is implemented as a web form or a mobile application.

[1439] Specifically, User A enters the following conditions for a family trip into the device: "Budget: 200,000 yen," "Travel companions: husband and two children," "Purpose of trip: relaxation," and "Preference for transportation: car."

[1440] 2. Temporary storage means:

[1441] The terminal temporarily stores the entered travel conditions in local storage.

[1442] 3. Send to server:

[1443] The device sends the temporarily saved conditions to the server using an HTTP request. The communication protocol is HTTPS.

[1444] 4. How to save to database:

[1445] The server persistently stores the received condition data in a database, which can be an SQL-based relational database or a NoSQL database.

[1446] 5. Means for invoking generative AI models:

[1447] The server calls a generative AI model (e.g., a model based on natural language processing) based on the stored condition information and generates an appropriate travel plan.

[1448] The generation AI analyzes the input conditions, lists possible travel destinations, accommodations, means of transportation, tourist spots, restaurants, etc., and creates multiple travel plans.

[1449] 6. Proposal method:

[1450] The server sends the generated travel plans to the user's terminal, allowing the user to view and evaluate the plans on the terminal.

[1451] Example: The generated plans, "Hot Spring Inn 2 Nights 3 Days Course" and "Resort Hotel 2 Nights 3 Days Course" will be displayed on the terminal.

[1452] 7. How to enter additional requests:

[1453] The user can review the proposed itinerary and enter any additional requests. The input interface allows the user to make a rearrangement request.

[1454] Example: User A requests "dinner at a famous local restaurant."

[1455] 8. How to generate a realignment plan:

[1456] The server then calls the generative AI model again based on additional requests and generates a new plan.

[1457] Example: A new plan "2 nights and 3 days at a hot spring inn + dinner at a famous restaurant" is generated.

[1458] 9. Reservation Method:

[1459] Once the user selects the final plan, the server works in conjunction with a bulk reservation system to make reservations for accommodation, transportation, restaurants, tourist attractions, etc. all at once.

[1460] 10. Means for sending reservation confirmation information to the terminal:

[1461] If the reservation is successful, the server sends a confirmation to the user's device, allowing the user to check the reservation details in real time.

[1462] Prompt Sentence Examples

[1463] For example, the prompt for the generated AI is set as follows:

[1464] text

[1465] Travel conditions: Budget - 200,000 yen, Travel companions - husband and two children, Purpose - Relaxation, Transportation - Car

[1466] Suggestion: Generate multiple itineraries for a family trip based on the following criteria: Include hot springs, resorts, pensions, tourist spots, restaurants, etc.

[1467] This system allows users to easily and quickly obtain optimal travel plans, significantly reducing the effort required for travel planning.

[1468] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1469] Step 1:

[1470] The user enters the travel requirements.

[1471] The user enters travel conditions (such as budget, companions, purpose of travel, means of transportation, etc.) through the device's input interface. The entered data is temporarily stored in the device.

[1472] Input: User's travel conditions (budget, companions, purpose, means of transportation, etc.)

[1473] Output: Temporarily saved travel condition data

[1474] Specific operation: User A enters information such as "budget: 200,000 yen," "traveling companions: husband and two children," "purpose of trip: relaxation," and "means of transportation: car" into the form on the device.

[1475] Step 2:

[1476] The terminal sends the conditions to the server.

[1477] The device sends the temporarily saved travel condition data to the server. The data is securely transferred using an HTTP POST request.

[1478] Input: Temporarily saved travel condition data

[1479] Output: Travel condition data sent to the server

[1480] Specific operation: The device sends JSON format data including "budget 200,000 yen," "traveling companions: husband and two children," "purpose of trip: relaxation," and "means of transportation: car" to the server.

[1481] Step 3:

[1482] The server stores the conditions in a database.

[1483] The server receives the travel condition data and stores it permanently in a database, allowing efficient management of user conditions.

[1484] Input: Travel condition data sent to the server

[1485] Output: Travel condition data stored in the database

[1486] Specific operation: The server parses the received JSON data and saves it in the database as records of "User ID," "Budget: 200,000 yen," "Traveling companions: husband and two children," "Purpose of trip: relaxation," and "Method of transportation: car."

[1487] Step 4:

[1488] The server calls the generative AI model to generate a travel plan.

[1489] The server calls up a generative AI model based on the condition information stored in the database to generate a travel plan. The generative AI model analyzes the conditions, lists suitable travel destinations, accommodations, transportation options, tourist spots, and restaurant options, and creates multiple travel plans.

[1490] Input: Travel condition data stored in the database

[1491] Output: Multiple generated itineraries

[1492] Specific operation: The generation AI generates plans such as "2 nights and 3 days at a hot spring inn," "2 nights and 3 days at a resort hotel," and "3 nights and 4 days at a pension."

[1493] Step 5:

[1494] The server sends the generated travel plan to the user's terminal.

[1495] The server sends the generated travel plans to the user's device as an HTTP response, allowing the user to view the plans on their device.

[1496] Input: Multiple itineraries generated by a generative AI model

[1497] Output: A list of itineraries sent to the user's device

[1498] Specific operation: The server responds with the generated travel plan in JSON format, and the device receives and displays it.

[1499] Step 6:

[1500] The user reviews the plan and enters any additional requests.

[1501] The user reviews the proposed travel plan and, if necessary, enters additional requests into the terminal's input interface.

[1502] Input: Travel plan displayed on the device, additional requests from the user

[1503] Output: A new dataset containing additional requests.

[1504] Specific operation: User A selects the "2 nights, 3 days at a hot spring inn" plan and enters "dinner at a famous local restaurant" as an additional request.

[1505] Step 7:

[1506] The terminal sends an additional request to the server.

[1507] The terminal sends the user's request for addition to the server using an HTTP request.

[1508] Input: User's request for addition

[1509] Output: Additional requested data sent to the server

[1510] Specific behavior: The device sends JSON data containing a request for "dinner at a famous local restaurant" to the server.

[1511] Step 8:

[1512] The server again uses the generated AI model to generate an adjustment plan.

[1513] The server analyzes the additional requests and generates a new travel plan using the generative AI model again, which reflects the additional requests.

[1514] Input: Additional requested data sent to the server

[1515] Output: Reworked itinerary

[1516] Specific operation: Based on additional requests, the generation AI generates new plans such as "2 nights, 3 days at a hot spring inn + dinner at a famous restaurant."

[1517] Step 9:

[1518] The server sends the recalibration plan to the user's device.

[1519] The server then sends the adjusted travel plan back to the user's terminal.

[1520] Input: rearranged travel plans

[1521] Output: The adjusted itinerary sent to the user's device

[1522] Specific operation: The server responds in JSON format with a plan for "2 nights and 3 days at a hot spring inn + dinner at a famous restaurant," which the device receives and displays.

[1523] Step 10:

[1524] The user selects the final plan and confirms the reservation.

[1525] The user finally selects the desired travel plan and confirms the reservation through the terminal.

[1526] Input: User's final plan selection

[1527] Output: Reservation confirmation data

[1528] Specific operation: User A selects the "2 nights, 3 days at a hot spring inn + dinner at a famous restaurant" plan and presses the button to confirm the reservation.

[1529] Step 11:

[1530] The server works in conjunction with the reservation system to make bulk reservations.

[1531] The server works in conjunction with the reservation system to make bulk reservations for accommodation, transportation, restaurants, tourist attractions, etc. based on the selected plan.

[1532] Input: User's final plan selection data

[1533] Output: Bulk booking completed

[1534] Specific operation: The server calls reservation APIs for hotels, car rentals, restaurants, etc. to make bulk reservations.

[1535] Step 12:

[1536] The server sends the reservation confirmation information to the terminal and notifies the user.

[1537] If the reservation is successful, the server will send a confirmation to the user's terminal and notify the user.

[1538] Input: Completed bulk reservation data

[1539] Output: Booking confirmation sent to the user's device

[1540] Specific operation: The server responds with reservation confirmation information in JSON format, and the device displays a notification.

[1541] This allows users to smoothly navigate through a series of steps to find the best travel plan.

[1542] (Application example 1)

[1543] 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."

[1544] Conventional travel planning systems have the problem that it takes a lot of time and effort for users to select a travel plan. In addition, the travel plans generated based on the user's input criteria do not always perfectly match the user's needs, and there is a lack of means to compare multiple plans or visually confirm them.

[1545] 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.

[1546] In this invention, the server includes an input means for a user to input travel conditions, a storage means for storing the conditions received from the input means, a generation means for generating a travel plan based on the stored conditions, a 3D visualization means for proposing and visualizing the generated travel plan to the user, and a reservation means for making reservations in bulk based on the travel plan selected by the user. This allows the user to visually check the travel plans, quickly select the most suitable plan, and make a bulk reservation.

[1547] "Input means" refers to a device or interface that allows a user to input travel-related conditions.

[1548] The "storage means" is a database or other storage device for storing the conditions received from the input means.

[1549] The "generation means" refers to an algorithm or program for generating a travel plan based on the stored conditions.

[1550] "3D visualization means" refers to a three-dimensional display device or software that visually displays and allows users to check the generated travel plan.

[1551] A "reservation method" is a system or service that allows users to make reservations in bulk based on a travel plan selected by the user.

[1552] This invention is a system for enabling users to efficiently and effectively create, confirm, modify and book travel plans, and includes as its main components an input means, a storage means, a generation means, a 3D visualization means and a reservation means.

[1553] Users use input methods such as smartphones or computers to enter travel conditions such as budget, travel companions, purpose of trip, and preferred means of transportation. For example, they might enter "budget 200,000 yen, husband and two children, purpose is relaxation, and preferred car for transportation." These conditions are temporarily saved on the device and then sent to the server.

[1554] The server saves the received conditions in a database (storage means). Based on the saved conditions, the server generates a travel plan using a generative AI model (such as the OpenAI API) (generation means). Here, the generative AI analyzes the user's input conditions and assembles multiple travel plans by listing suitable travel destinations, accommodations, means of transportation, tourist spots, restaurants, etc. This plan generation process is carried out using prompt sentences. Examples of prompt sentences are as follows:

[1555] "Optimize your users' travel plans, based on the following criteria:

[1556] Budget: 200,000 yen

[1557] Travelling with: Husband and two children

[1558] Purpose of trip: Relaxation

[1559] Transportation: Car

[1560] Hope: Especially including dinner at a restaurant.”

[1561] The generated travel plans are provided to the user using 3D visualization. Using an HMD (head-mounted display) or smartphone, the user can visually check and evaluate the multiple generated travel plans. For example, a 3-day, 2-night plan at a hot spring inn or a 3-day, 2-night plan at a resort hotel can be displayed in 3D, allowing users to check the scenery and details of the facilities.

[1562] If the user selects the desired plan and makes any additional requests (e.g., "dinner at a local famous restaurant"), this information is sent to the server via the storage means. The server then calls the generation AI again to generate a new plan and propose it to the user again.

[1563] Once the user has finally selected the optimal travel plan, the server will make all reservations for accommodation, transportation, restaurants, etc. via the reservation tool. If the reservation is successful, confirmation information will be sent from the server to the terminal and the user will be notified.

[1564] This configuration allows users to create, check, change and book travel plans smoothly and efficiently, and as the entire process is automated, it significantly reduces the amount of work required.

[1565] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1566] Step 1:

[1567] The device receives travel-related conditions (e.g., budget, travel companions, travel purpose, preferred means of transportation, etc.) from the user through input means. The input conditions are temporarily stored in the device and then sent to the server. The input here is done by the user through the interface, specifically using text boxes and options to enter conditions.

[1568] Step 2:

[1569] The server stores the travel conditions sent from the device in a database. The received data is organized so that it can be managed for each user. The database serves to efficiently store the information needed to generate travel plans and make reservations.

[1570] Step 3:

[1571] Based on the saved conditions, the server calls the generation means (generative AI model) to generate a travel plan. At this time, the generative AI model is given a prompt sentence that includes the conditions. Specifically, the following prompt sentence is used:

[1572] "Optimize your users' travel plans, based on the following criteria:

[1573] Budget: 200,000 yen

[1574] Travelling with: Husband and two children

[1575] Purpose of trip: Relaxation

[1576] Transportation: Car

[1577] Hope: Especially including dinner at a restaurant.”

[1578] The generative AI model analyzes this prompt and generates a travel plan, which is then returned to the server as text data.

[1579] Step 4:

[1580] The generated itinerary is sent from the server to the terminal. The terminal then visualizes the itinerary using 3D visualization tools and displays it to the user. The input here is the text data of the generated itinerary, and the output is a visualized 3D model. Specifically, information such as the itinerary outline, accommodations, tourist spots, and transportation options is modeled in 3D.

[1581] Step 5:

[1582] The user can use the device to view and evaluate the visualized travel plans, select which plan is most appealing, and enter additional requests (e.g., "dinner at a famous local restaurant"). This additional information is sent to the server via the device.

[1583] Step 6:

[1584] The server receives the additional request and generates a new itinerary using the generation method (generative AI model), again using the same prompt as the first time, to further optimize the new itinerary for the user's requirements.

[1585] Step 7:

[1586] Once the user has finally selected the optimal travel plan, the server will make all reservations for accommodation, transportation, restaurants, etc. through the reservation tool. The input here is the final plan based on the user's selection, and the output is reservation confirmation information. If the reservation is successful, this confirmation information is sent from the server to the terminal and the user is notified.

[1587] These processing steps allow users to create, review, modify, and book travel plans efficiently and effectively.

[1588] 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.

[1589] This invention is a "travel concierge" system that utilizes generative AI to optimize users' travel plans, and in particular, incorporates an emotion engine that recognizes users' emotions and reflects them in the travel plan. Each component and program process will be explained in detail below.

[1590] 1. Input method:

[1591] Users input travel-related information using a device (such as a smartphone or PC), including budget, travel companion information, travel purpose, preferred means of transportation, and specific requests. Furthermore, the device is equipped with an emotion engine that recognizes the user's emotions.

[1592] 2. Emotion Engine:

[1593] The emotion engine has the ability to recognize emotions by analyzing the user's voice, facial expressions, or text. This allows it to analyze in real time what emotions the user is feeling while typing. For example, if a user types "I want to relax," it will recognize from their voice and facial expressions that they are feeling stressed.

[1594] 3. Preservation means:

[1595] Once the conditions are entered, the device temporarily stores the conditions and emotion data and sends them to the server, which then stores the received data in a database to efficiently manage the conditions and emotions for each user.

[1596] 4. Generation means:

[1597] The server uses a generative AI to generate a travel plan based on the saved conditions and emotion data. The generative AI comprehensively analyzes the input conditions and the user's emotions, and lists suitable travel destinations, accommodations, transportation options, tourist spots, restaurants, etc.

[1598] 5. Proposal method:

[1599] The server then sends the generated itineraries to the user's device. The suggestion method considers the emotional data and makes suggestions tailored to the user's mood and emotions. For example, if the user is feeling stressed, it will prioritize suggestions for relaxing hot springs and places rich in nature.

[1600] 6. Reservation Method:

[1601] Once the user has selected their final travel plan, the server will link with the reservation system to make all reservations for accommodation, transportation, restaurants, tourist attractions, etc. If the reservation is successful, confirmation information will be sent from the server to the terminal and the user will be notified.

[1602] Explanation of the program processing flow

[1603] This travel concierge system operates as follows.

[1604] Flow of specific example

[1605] Let's use the example of User B, who is planning a solo trip to refresh himself.

[1606] 1. Enter the conditions:

[1607] User B enters the following conditions into the terminal:

[1608] Budget: 100,000 yen

[1609] Accompanying person: None

[1610] Purpose of the trip: Refreshment

[1611] Preferred mode of transportation: Train

[1612] While this input is being made, the device's emotion engine recognizes from Mr. B's tone of voice and facial expression that he is feeling stressed.

[1613] 2. Save condition:

[1614] The device sends these conditions and emotion data to the server, which stores them in a database.

[1615] 3. Plan Generation:

[1616] The server then calls up the AI ​​generator based on the stored conditions and emotion data, and performs analysis. The AI ​​generator then creates a list of relaxing hot spring resorts and nature-rich resorts, and generates multiple travel plans (for example, a 2-night, 3-day plan at a hot spring inn, a 2-night, 3-day plan at a highland resort hotel, etc.).

[1617] 4. Plan proposal:

[1618] The generated travel plans are sent to User B's device. User B checks these plans on his / her device. Using data from the emotion engine, hot spring inns and resort hotels with particularly relaxing effects are prioritized in suggestions.

[1619] 5. Plan Adjustment:

[1620] Person B selects a "hot spring inn" and requests an additional "local spa experience." This information is sent to the server via the device, and the server uses the generation AI to generate a new plan and propose it to Person B again.

[1621] 6. Final Planning and Booking:

[1622] Once B has finalized the plan, the server will make reservations for the hot spring inn and spa experience all at once through the reservation system. If the reservation is successful, the server will send confirmation information to the terminal and notify B.

[1623] This series of processes allows users to easily find the best travel plan for them, and the travel planning process proceeds smoothly. In addition, the emotion engine enables customization based on the user's emotions, resulting in a highly satisfying travel experience.

[1624] The processing flow will be explained below.

[1625] Step 1:

[1626] Users enter their travel requirements into a form on the device, including budget, travel companion information, travel purpose, transportation preferences, and any special requests.

[1627] Step 2:

[1628] The device sends the input conditions to the emotion engine to analyze the user's emotions. For example, in the case of voice input, it analyzes the tone of the voice, and in the case of text input, it infers emotions from the words used.

[1629] Step 3:

[1630] The emotion engine recognizes the user's emotion, temporarily stores it along with the condition data, and sends it to the server.

[1631] Step 4:

[1632] The server stores the received conditions and emotion data in a database designed to efficiently manage each user's conditions and emotion data.

[1633] Step 5:

[1634] The server generates a travel plan based on the stored conditions and emotion data by calling a generation AI. The generation AI comprehensively analyzes the conditions and emotions and lists suitable travel destinations, accommodations, transportation options, tourist spots, restaurants, etc.

[1635] Step 6:

[1636] The generative AI evaluates the list of options and creates multiple itineraries, based on criteria such as whether they fit within the budget, suitability for the travel companions, and consideration for the user's emotions.

[1637] Step 7:

[1638] The server transmits the generated travel plans to the terminal.

[1639] Step 8:

[1640] The device displays the received travel plans to the user, who can then review and rate them. The plan that reflects the emotion data is presented as the one that best suits the user's emotions.

[1641] Step 9:

[1642] Users can select a proposed plan and then enter any additional requests or changes into the device, such as requesting a change of accommodation or adding a specific tourist attraction.

[1643] Step 10:

[1644] The device sends the user's additional request to the server, which then calls the generation AI again and adjusts the plan to accommodate the additional request.

[1645] Step 11:

[1646] The adjusted plans are sent back to the device and presented to the user, who then selects the final plan.

[1647] Step 12:

[1648] Once the user has selected their final travel plan, the terminal sends this to the server.

[1649] Step 13:

[1650] Based on the selected plan, the server makes all reservations for accommodation, transportation, restaurants, tourist attractions, etc. The reservation process is carried out using the APIs of each service provider.

[1651] Step 14:

[1652] If a reservation is successful, the server collects the reservation confirmation information, which is then sent to the terminal and notified to the user.

[1653] Example 2

[1654] 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."

[1655] Conventional travel planning systems generate travel plans based solely on user input, making it impossible to consider the user's emotions or psychological state. This means that they are inadequate in proposing relaxing travel plans to stressed users. Furthermore, if the generated plan does not match the user's emotions, there is a high possibility that the travel plan will leave the user unsatisfied.

[1656] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1657] In this invention, the server includes input means for a user to input travel-related conditions, storage means for storing the conditions received from the input means, means including an emotion engine for analyzing the user's emotions, generation means for generating a travel plan based on the stored conditions and the user's emotion data, proposal means for proposing the generated travel plan to the user, and reservation means for making reservations all at once based on the travel plan selected by the user. This makes it possible to propose a travel plan that suits the user's emotions, thereby increasing user satisfaction.

[1658] "Input means" refers to a hardware or software interface that allows a user to input travel-related conditions into a terminal.

[1659] "Storage Means" means a data storage means for temporarily or permanently storing the conditions entered by the User.

[1660] An "emotion engine" is software or algorithms that analyze a user's voice, facial expressions, or text to recognize their emotions.

[1661] "Generative means" means software means, such as a generative AI model, for generating a travel plan based on the stored conditions and user emotional data.

[1662] The "proposal means" is an interface means for presenting the travel plan generated by the generation means to the user.

[1663] A "booking method" is a method by which a user makes a single reservation for accommodation, transportation, and other services based on the travel plan selected by the user.

[1664] A "database" is a storage system for managing and saving information such as conditions and emotional data entered by users.

[1665] This invention is a "travel concierge" system that utilizes generative AI models to optimize users' travel plans. In particular, it incorporates an emotion engine that recognizes users' emotions and reflects them in the travel plan. Each component and program process is explained in detail below.

[1666] Input methods:

[1667] A user inputs travel-related information using their device (such as a smartphone or PC). These information includes budget, travel companion information, travel purpose, preferred means of transportation, and specific requests. Furthermore, the device is equipped with an emotion engine that analyzes the user's voice, facial expression, or text to recognize emotions. For example, if a user inputs, "I'm planning a solo trip to refresh myself," the emotion engine recognizes that the user is stressed from his tone of voice and facial expression.

[1668] Storage method:

[1669] Once the conditions are entered, the device temporarily stores these conditions and emotion data and sends them to the server. The server then stores the received data in a database, efficiently managing each user's conditions and emotions. This database stores the various conditions entered by the user and the results of emotion analysis.

[1670] Generation means:

[1671] The server generates a travel plan using a generative AI model based on the stored conditions and emotion data. At this time, the server inputs a prompt statement into the generative AI model, which analyzes and generates it. For example, the prompt statement could be "budget under 100,000 yen, solo trip, purpose of relaxation, means of transportation by train, stressful." Based on this prompt, the generative AI model generates multiple travel plans, such as relaxing hot springs and resorts rich in nature.

[1672] Suggested means:

[1673] The generated travel plan is sent from the server to the user's device. When presenting it to the user, the device takes into account the emotional data and prioritizes the plan that best suits the user's emotions. For example, it may prioritize suggestions for relaxing hot spring inns or resort hotels surrounded by nature.

[1674] Reservation method:

[1675] Once the user has selected their final travel plan, the server works with the reservation system to complete the reservation in one go. Specifically, the server makes reservations for hot spring inns and spa experiences. Once the reservation is successful, the server sends confirmation information to the user's terminal and notifies them. In this way, users can skip the complicated reservation process and easily find the travel plan that best suits their feelings and desires.

[1676] Example flow:

[1677] For example, consider the case where User B plans a solo trip to refresh himself and enters the following conditions:

[1678] Budget: 100,000 yen

[1679] Accompanying person: None

[1680] Purpose of the trip: Refreshment

[1681] Preferred mode of transportation: Train

[1682] While inputting, the emotion engine recognizes that B is feeling stressed from his tone of voice and facial expression. The device sends this information to the server, which then inputs the following prompt sentence into the generation AI:

[1683] "Budget: 100,000 yen or less, solo trip, purpose: to refresh, transportation: train, stressful"

[1684] Based on this prompt, the generative AI model lists relaxing hot spring resorts and nature-rich resorts, and generates multiple travel plans (for example, a 2-night, 3-day plan at a hot spring inn, or a 2-night, 3-day plan at a highland resort hotel). The generated plans are then sent to the user's device and displayed preferentially, taking into account emotional data. If User B selects a "hot spring inn" and requests a "local spa experience," the plan is adjusted again.

[1685] In this way, a customized travel plan is proposed that takes the user's emotions into consideration, allowing the user to make a travel plan that is more satisfying.

[1686] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1687] Step 1:

[1688] The user inputs travel-related conditions using a device (smartphone or PC). Specifically, they input budget, travel companion information, travel purpose, preferred means of transportation, and specific requests. At this time, an emotion engine built into the device analyzes the user's voice and facial expressions and collects emotional data. The input data, consisting of conditions and emotional data, becomes the input for the next step.

[1689] Step 2:

[1690] The device temporarily stores the conditions and emotion data entered by the user and sends it to the server. Specifically, the input data is sent to the server in a data format such as JSON. The server then receives the user's conditions and emotion data and stores them in a database. Once the data has been saved to data storage, the server starts the next step based on this data.

[1691] Step 3:

[1692] The server converts the stored conditions and emotional data into a prompt sentence to be input into the generative AI model. Specifically, it generates a prompt sentence that combines the conditions and emotional data. For example, it creates a prompt sentence such as "budget within 100,000 yen, solo trip, purpose of relaxation, means of transportation by train, stressful." This prompt sentence becomes the input to the generative AI model.

[1693] Step 4:

[1694] The server calls the generative AI model, inputs a prompt, and generates a travel plan. Based on the prompt, the generative AI model generates multiple travel plans (for example, a 2-night, 3-day plan at a hot spring inn, a 2-night, 3-day plan at a highland resort hotel, etc.). The generated plan is output to the server, and the process proceeds to the next step.

[1695] Step 5:

[1696] The server sends the generated travel plan to the user's device. Specifically, the generated plan is sent to the device in JSON or XML format. The device receives it and presents it to the user. Based on the emotional data, the plan that is most suitable for the user (for example, a hot spring inn with a high relaxing effect) is displayed in a list with priority.

[1697] Step 6:

[1698] The user reviews the travel plans on the device and selects the one they are most interested in. The user then inputs additional requests (e.g., "local spa experience"). This input data becomes the input for the next step.

[1699] Step 7:

[1700] The device sends the user's selection and additional requests to the server. The server receives this and reflects it in the prompt text again. For example, the prompt text is updated to something like "budget under 100,000 yen, solo travel, purpose of relaxation, means of transportation by train, stressful, local spa experience." This updated prompt text becomes the input to the generative AI model.

[1701] Step 8:

[1702] The server calls the generative AI model again, inputs the updated prompt text, and generates a new travel plan. The generated plan is output to the server again and proposed to the user. The user reviews this new plan and makes a final decision.

[1703] Step 9:

[1704] Once the user has selected their final travel plan, the device sends it to the server. The server then calls the reservation system based on the final plan received. The server then makes all reservations for accommodations and various services. If the reservation is successful, confirmation information is sent from the server to the device and the user is notified.

[1705] As described above, this system generates optimal travel plans from user input, and then proposes and adjusts the plans taking the user's emotions into consideration, seamlessly handling the final booking process, thereby optimizing the user's travel plans.

[1706] (Application example 2)

[1707] 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."

[1708] Traditional travel planning systems only propose travel plans based on user requirements, making it difficult to customize them to take into account user emotions and real-time feedback. This lacks the ability to provide plans tailored to users' specific needs, making it difficult to achieve a satisfying travel experience. Furthermore, the ability of travel agency staff in brick-and-mortar stores to quickly propose appropriate plans to customers is also limited.

[1709] 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.

[1710] In this invention, the server includes an input means for a user to input travel requirements, a storage means for storing the requirements received from the input means, a generation means for generating a travel plan based on the stored requirements, a proposal means for proposing the generated travel plan to the user, a sentiment analysis means for analyzing the user's sentiment, a means for adjusting the generated travel plan based on the user's sentiment, and a reservation means for making reservations in bulk based on the travel plan selected by the user. This enables customization that takes into account the user's sentiment and real-time feedback, providing a highly satisfying travel experience. It also enables brick-and-mortar travel agencies to quickly propose appropriate plans to customers.

[1711] "Input means" refers to devices or software that allow users to input travel-related requirements into the system.

[1712] The "storage means" refers to a device or software that temporarily or permanently stores the travel-related conditions and emotion data received from the input means.

[1713] The "generation means" refers to a device or software for generating an optimal travel plan based on the stored conditions and emotion data.

[1714] The "suggestion means" is a device or software that presents the generated travel plan to the user and provides appropriate feedback.

[1715] "Emotion analysis means" refers to a device or software for analyzing emotions from a user's voice, facial expression, or text.

[1716] The "adjustment means" is a device or software for reconstructing or correcting the generated travel plan based on the user's emotional data obtained by the emotion analysis means.

[1717] "Reservation Facility" means a device or software that allows a user to make a single booking of the accommodation, transportation and other services required based on the final travel plan selected by the user.

[1718] This invention is a system for optimizing a user's travel plan, specifically a "travel concierge" system equipped with an emotion engine that analyzes the user's emotions and reflects them in the travel plan. This system can also be effectively used in brick-and-mortar travel agencies.

[1719] System configuration

[1720] 1. Input Method

[1721] Using smart glasses or a head-mounted display, users input travel-related information by voice, including budget, destination, travel companions, and purpose of trip. The device also reads the user's facial expressions and voice in real time to collect data for sentiment analysis.

[1722] 2. Preservation means

[1723] The travel conditions and emotion data collected by the device are sent to a cloud server and temporarily stored in a database, making it possible to efficiently manage the data of multiple users.

[1724] 3. Generation means

[1725] The server generates a travel plan based on the stored conditions and emotion data using a generative AI model (e.g., OpenAI GPT-4). The generative AI model analyzes past data and current conditions to propose the optimal travel plan.

[1726] 4. Proposal method

[1727] The generated itinerary is sent to the user's device in real time, and based on the results of sentiment analysis, it prioritizes the itinerary that best matches the user's sentiment.

[1728] 5. Emotion analysis method

[1729] Emotion analysis uses IBM Watson Visual Recognition and other tools to analyze collected voice and facial expression data, and adjusts plans based on the results.

[1730] 6. Adjustment means

[1731] If the user makes additional requests regarding the proposed plan, the system will again use the sentiment analysis method and call the generative AI model to regenerate and adjust the plan.

[1732] 7. Reservation Methods

[1733] Once the user selects the final plan, the server will make all the reservations for accommodation and transportation through the reservation system, and the results will be sent to the user's device.

[1734] Specific examples

[1735] Let's say User A visits a brick-and-mortar travel agency to plan a solo trip for relaxation. User A is given a head-mounted display by the agency staff and asked to enter the following information by voice:

[1736] Budget: 100,000 yen

[1737] Accompanying person: None

[1738] Purpose of the trip: Refreshment

[1739] Preferred mode of transportation: Train

[1740] The microphone and camera in the head-mounted display collect user A's tone of voice and facial expressions, which are then analyzed by the emotion analysis means. The emotion analysis means, recognizing that user A is feeling stressed, sends this data along with the condition to the server.

[1741] The server generates multiple travel plans using a generative AI model (OpenAI GPT-4) based on the conditions and emotion data. The generated plans are then proposed to User A via a head-mounted display. User A is presented with a three-day, two-night plan to refresh at a hot spring inn, and if he or she wishes to add a spa experience, the plan is instantly adjusted.

[1742] The plan selected by User A is automatically reserved through the reservation system. Reservation confirmation information is sent to the head-mounted display and User A is informed.

[1743] Prompt Sentence Examples

[1744] User Emotional Data: Feeling Stressed

[1745] Travel conditions: Budget: 100,000 yen, Travel purpose: Refreshment, Transportation: Train

[1746] Desired output from the generative AI: Provide recommended itineraries for relaxation, taking into account hot springs and nature resorts.

[1747] In this way, it becomes possible to generate, suggest, adjust and book optimal travel plans in real time based on the user's emotions.

[1748] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1749] Step 1:

[1750] Collecting user input conditions

[1751] Users input travel information (budget, destination, travel companions, purpose of trip, etc.) by voice through smart glasses or a head-mounted display, while the device's built-in microphone and camera also collect the user's facial expressions and voice tone.

[1752] Input: Travel conditions (voice data), user emotional data (facial expressions, voice tone)

[1753] Data processing: speech-to-text conversion, facial expression and tone analysis

[1754] Output: Textualized travel conditions, analyzed emotion data

[1755] Step 2:

[1756] Data storage

[1757] The collected travel conditions and emotion data are sent from the device to a cloud server and stored in a database, allowing for effective management of the conditions and emotion data.

[1758] Input: Texted travel conditions, analyzed emotion data

[1759] Data processing: sending data and storing it in a database

[1760] Output: Saved travel conditions and emotion data

[1761] Step 3:

[1762] Travel plan generation

[1763] The server generates an optimal travel plan using a generative AI model (e.g., OpenAI GPT-4) based on the stored conditions and emotion data. The generative AI generates multiple travel plans based on the provided prompt sentences.

[1764] Input: saved travel conditions, emotion data, prompt sentence

[1765] Data processing: Generating travel plans using generative AI models

[1766] Output: Generated itinerary

[1767] Step 4:

[1768] Travel plan suggestions

[1769] The generated travel plan is sent to the user's smart glasses or head-mounted display in real time, and the plan that best suits the user's emotions is displayed first, taking into account the user's emotional data.

[1770] Input: Generated itinerary

[1771] Data processing: sending and displaying travel plans

[1772] Output: A suggested itinerary for the user

[1773] Step 5:

[1774] Adjusting travel plans

[1775] If the user makes additional requests, the device will re-analyze the request using the sentiment analysis method and call the generative AI model to re-generate the plan, incorporating the additional requests into the prompt text.

[1776] Input: User requests for additions, emotional data

[1777] Data processing: Incorporating additional requests into prompt sentences and regenerating them using a generative AI model

[1778] Output: Regenerated itinerary

[1779] Step 6:

[1780] Bulk booking

[1781] Once the user selects the final plan, the server connects to the reservation system and makes all necessary reservations for accommodation, transportation, etc. The reservation results are then sent to the terminal.

[1782] Input: Finalized travel plans

[1783] Data processing: Linking with reservation systems and reservation processing

[1784] Output: Reservation confirmation information

[1785] Through this process, users can easily obtain the optimal travel plan that reflects their emotions and specific requirements.

[1786] 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.

[1787] 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.

[1788] 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.

[1789] 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.

[1790] 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.

[1791] 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.

[1792] 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).

[1793] 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.

[1794] 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."

[1795] 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.

[1796] 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).

[1797] 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.

[1798] 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.

[1799] 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.

[1800] 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.

[1801] 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.

[1802] 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.

[1803] 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.

[1804] 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.

[1805] 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.

[1806] 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.

[1807] The following is further disclosed regarding the above embodiment.

[1808] (Claim 1)

[1809] an input means for a user to input travel requirements;

[1810] a storage means for storing the conditions received from the input means;

[1811] a generation means for generating a travel plan based on the stored conditions;

[1812] a suggestion means for suggesting the generated travel plan to a user;

[1813] a booking means for making bulk bookings based on travel plans selected by users;

[1814] A system including:

[1815] (Claim 2)

[1816] 10. The system of claim 1, wherein the suggesting means further comprises means for adjusting the travel plan according to user requests.

[1817] (Claim 3)

[1818] 2. The system according to claim 1, wherein the storage means is a means for storing travel-related conditions in a database.

[1819] "Example 1"

[1820] (Claim 1)

[1821] an input means for a user to input travel requirements;

[1822] means for temporarily storing the conditions received from the input means;

[1823] means for transmitting the temporarily saved conditions to a server;

[1824] a means for storing the submitted conditions in a database;

[1825] means for invoking a generative AI model to generate a travel plan based on the stored criteria;

[1826] a suggestion means for suggesting the generated travel plan to a user;

[1827] A means to review the proposed itinerary and enter any additional requests;

[1828] means for generating a reconditioning plan using a generative AI model based on the additional request;

[1829] a booking means for making bulk bookings based on travel plans selected by users;

[1830] means for transmitting reservation confirmation information to the user's terminal;

[1831] A system including:

[1832] (Claim 2)

[1833] 10. The system of claim 1, wherein the suggesting means further comprises means for adjusting the travel plan according to user requests.

[1834] (Claim 3)

[1835] 2. The system according to claim 1, wherein the storage means is a means for storing travel-related conditions in a database.

[1836] "Application Example 1"

[1837] (Claim 1)

[1838] an input means for a user to input travel requirements;

[1839] a storage means for storing the conditions received from the input means;

[1840] a generation means for generating a travel plan based on the stored conditions;

[1841] a 3D visualization means for suggesting and visualizing the generated itinerary to the user;

[1842] a booking means for making bulk bookings based on travel plans selected by users;

[1843] A system including:

[1844] (Claim 2)

[1845] 10. The system of claim 1, wherein the suggesting means further comprises means for adjusting and regenerating the travel plan according to user requests.

[1846] (Claim 3)

[1847] 2. The system according to claim 1, wherein the storage means is a means for storing travel-related conditions in a data management device.

[1848] "Example 2: Combining Emotion Engines"

[1849] (Claim 1)

[1850] an input means for a user to input travel requirements;

[1851] a storage means for storing the conditions received from the input means;

[1852] means including an emotion engine for analyzing the emotion of the user;

[1853] a generating means for generating a travel plan based on the stored conditions and the user's emotion data;

[1854] a suggestion means for suggesting the generated travel plan to a user;

[1855] a booking means for making bulk bookings based on travel plans selected by users;

[1856] A system including:

[1857] (Claim 2)

[1858] The system of claim 1 , wherein the suggesting means further comprises means for adjusting the travel plan by taking into account the user's emotional data.

[1859] (Claim 3)

[1860] 2. The system according to claim 1, wherein the storage means is a means for storing travel-related conditions and emotion data in a database.

[1861] "Application example 2 when combining emotion engines"

[1862] (Claim 1)

[1863] an input means for a user to input travel requirements;

[1864] a storage means for storing the conditions received from the input means;

[1865] a generation means for generating a travel plan based on the stored conditions;

[1866] a suggestion means for suggesting the generated travel plan to a user;

[1867] An emotion analysis means for analyzing the emotion of a user;

[1868] a means for adjusting the generated travel plan based on the user's emotions;

[1869] a booking means for making bulk bookings based on travel plans selected by users;

[1870] A system including:

[1871] (Claim 2)

[1872] 10. The system of claim 1, wherein the suggesting means further comprises means for adjusting the travel plan according to user requests.

[1873] (Claim 3)

[1874] 2. The system according to claim 1, wherein the storage means is a means for storing travel-related conditions and emotion data in a database. [Explanation of symbols]

[1875] 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. an input means for a user to input travel requirements; a storage means for storing the conditions received from the input means; a generation means for generating a travel plan based on the stored conditions; a suggestion means for suggesting the generated travel plan to a user; a booking means for making bulk bookings based on travel plans selected by users; A system including:

2. The system of claim 1 , wherein the suggestion means further comprises means for adjusting the travel plan according to the user's requests.

3. 2. The system according to claim 1, wherein said storage means is a means for storing travel-related conditions in a database.

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