System

Abstract

A system is provided.SOLUTION: A system comprising: means for obtaining destination information; means for automatically generating a travel itinerary based on the destination information; means for arranging transportation tickets based on the travel itinerary; means for booking accommodations based on the travel itinerary; means for arranging local activities and facilities; means for providing route guidance to a destination; means for providing real-time interpretation; and means for rewarding a 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] Describe the "problem that the invention aims to solve" and the "means for solving the problem."

[0005] Traditionally, travel arrangements and on-site guides, directions, and interpretation services required the use of multiple different services, which often required time and money. Furthermore, making various arrangements on separate platforms often resulted in information being scattered, leading to inefficiencies in planning and execution. The purpose of this invention is to solve these problems and enable users to easily make travel arrangements and receive on-site support through a single application. [Means for solving the problem]

[0006] The present invention solves the above-mentioned problems by providing a system including a means for acquiring destination information, a generative model means for automatically creating a travel itinerary based on the destination information, a means for arranging transportation tickets based on the travel itinerary, a means for booking accommodation based on the travel itinerary, a means for arranging local activities and facilities, a means for providing route guidance to the destination, a means for providing real-time interpretation, and a means for rewarding the user. Furthermore, by including a means for generating multiple travel itinerary proposals based on the destination information and allowing the user to select from them, and a means for coordinating with an external system to execute various arrangements, it is possible to provide a unified service ranging from travel arrangements to on-site support.

[0007] "Destination information" is information about a place designated by a user as a travel destination.

[0008] "Travel itinerary" is information indicating the user's daily plans for the time they will spend at their destination.

[0009] A "generative model" is an artificial intelligence model that automatically generates travel itineraries and other information based on information obtained from the user.

[0010] "Transportation" refers to the means of transportation, such as airplanes, trains, and buses, that users use to travel to their destinations.

[0011] "Ticket" means a reserved or purchased ticket required for a User to use a means of transportation.

[0012] "Accommodation" refers to a place where a user stays during their trip, such as a hotel, inn, or guesthouse.

[0013] "Local activities" refers to sightseeing and experiential activities that users undertake at their travel destinations.

[0014] "Route guidance" refers to information about the route a user must take to travel from their current location to their destination.

[0015] "Interpretation" refers to the translation of spoken content into another language to facilitate communication between people who speak different languages.

[0016] "Rewards" are incentives such as points or cryptocurrency that are awarded to users when they use a specific service.

[0017] "External systems" are other online services or platforms that are integrated to complement the application's functionality. [Brief explanation of the drawings]

[0018] [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

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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."

[0039] The present invention relates to a system that acquires destination information and provides travel arrangements and local support in an integrated manner based on that information. A specific embodiment of this system will be described below.

[0040] The system consists of a smartphone or tablet device, a cloud-based server, and a generative AI model. Users can input destination information using a dedicated application and receive travel arrangements and guide services.

[0041] User Interface

[0042] Terminal: The user launches the application and specifies the destination through the interface where destination information is input. For example, the user inputs the destination "Tokyo."

[0043] Destination information processing

[0044] Server: Receives destination information, inputs it into the generative AI model, and requests automatic itinerary generation. The generative AI model creates an optimal itinerary based on the user's preferences and number of travel days. For example, for a trip to Tokyo, it might consider itineraries such as sightseeing in Asakusa, shopping in Akihabara, the night view of Roppongi, and Disneyland.

[0045] Presentation of proposed schedule

[0046] Terminal: Displays the proposed travel itinerary sent from the server to the user, who can then review the proposed itinerary and modify or approve it as necessary.

[0047] Ticket booking and hotel reservations

[0048] Server: Once the itinerary is approved, transportation tickets are arranged via an external system (for example, an airline or travel agency API). Accommodation reservations are also made using external APIs. For example, a hotel is booked using the Rakuten Travel API.

[0049] Local activity arrangements

[0050] Server: Arranges local activities and facilities based on the itinerary. This is also done using an external API (for example, the API of a tour booking site). For example, when arranging tickets to Disneyland, the reservation information is obtained and confirmed.

[0051] Route guidance

[0052] Device: When a user travels to a destination, they send their current location and destination information to the server. The server calculates the optimal route based on this information and sends it to the device. The user can then receive real-time route guidance via the app.

[0053] Interpretation function

[0054] On-device: When users speak different languages, they activate the in-app translation feature. The device sends voice input to the server, and the generative AI model translates in real time. The translation result is displayed on the device or played aloud.

[0055] Reward System

[0056] Server: When a user purchases or uses a service, the server checks the information and calculates rewards such as PayPay points or cryptocurrency. The calculated rewards are added to the user's account and stored in the database.

[0057] Specific examples

[0058] As an actual usage example, consider a user planning a five-day trip to "Tokyo." When the user enters "Tokyo" into the app, the following steps are executed: The generative AI model creates a five-day itinerary and presents plans such as "Sightseeing Asakusa" on the first day, "Shopping in Akihabara" on the second day, "Night View of Roppongi" on the third day, and "Disneyland" on the fourth day. Once the user approves the itinerary, the server arranges flight and hotel reservations through an external API, as well as local activities. On the day of the trip, real-time route guidance and interpretation functions are provided through the app. In addition, after the trip is completed, PayPay points and cryptocurrency are awarded.

[0059] As described above, the present invention is a system that allows users to make all travel arrangements and receive local support through a single application, significantly reducing the effort and cost of travel.

[0060] The processing flow will be explained below.

[0061] Step 1:

[0062] User: Launches the app and enters "Tokyo" as the destination.

[0063] Step 2:

[0064] Terminal: Checks the entered destination information and sends it to the server.

[0065] Step 3:

[0066] Server: Receives destination information "Tokyo", inputs it into the generative AI model, and requests itinerary creation.

[0067] Step 4:

[0068] Generative AI model: Generates an optimal travel itinerary based on data such as destination information, number of travel days, and user preferences, and sends it back to the server.

[0069] Step 5:

[0070] Server: Stores the generated travel itinerary proposals in a database and sends them to the terminal.

[0071] Step 6:

[0072] Terminal: Displays the proposed travel itinerary received from the server to the user.

[0073] Step 7:

[0074] User: Check the proposed schedule and click the approve button.

[0075] Step 8:

[0076] Terminal: Sends the user's authorization data to the server.

[0077] Step 9:

[0078] Server: Upon approval, arranges transportation tickets via an external API (e.g., an airline or travel agency API).

[0079] Step 10:

[0080] External API: Executes transportation ticket reservations and returns reservation information to the server.

[0081] Step 11:

[0082] Server: Stores the received ticket reservation information in a database.

[0083] Step 12:

[0084] Server: Sends a request to check the availability of accommodation using an external API (for example, the API of a hotel booking site).

[0085] Step 13:

[0086] External API: Returns the accommodation availability information to the server.

[0087] Step 14:

[0088] Server: Enters room availability information into the generative AI model and requests it to select the most suitable accommodation.

[0089] Step 15:

[0090] Generative AI model: Selects the most suitable accommodation based on availability information and returns it to the server.

[0091] Step 16:

[0092] Server: Sends a reservation request for the selected accommodation to an external API.

[0093] Step 17:

[0094] External API: Performs reservation confirmation and returns reservation information to the server.

[0095] Step 18:

[0096] Server: Stores the received accommodation reservation information in a database.

[0097] Step 19:

[0098] Server: Sends a request to arrange a reservation for a local activity or facility via an external API (e.g., the API of a tour booking site).

[0099] Step 20:

[0100] External API: Sends activity and facility reservation information back to the server.

[0101] Step 21:

[0102] Server: Saves the received reservation information in a database.

[0103] Step 22:

[0104] User: Requests route guidance to the destination "Sensoji Temple" from the app while on location.

[0105] Step 23:

[0106] Terminal: Sends current location information and destination information to the server.

[0107] Step 24:

[0108] Server: Calculates the optimal route based on current location and destination information.

[0109] Step 25:

[0110] Server: Sends calculated route information to the device.

[0111] Step 26:

[0112] Terminal: Display route directions to the user.

[0113] Step 27:

[0114] Users: Activate the in-app translation feature if they need to speak a different language locally.

[0115] Step 28:

[0116] Device: Sends voice input to the server.

[0117] Step 29:

[0118] Server: Analyzes the voice data and requests translation from the generative AI model.

[0119] Step 30:

[0120] Generative AI model: Performs real-time translation from Japanese to English (or other languages) and sends the translation results back to the server.

[0121] Step 31:

[0122] Server: Sends the translation results to the device.

[0123] Step 32:

[0124] Terminal: The translation result is displayed to the user or output as speech.

[0125] Step 33:

[0126] Server: Checks information about the user's use of the service and calculates rewards such as PayPay points and cryptocurrency.

[0127] Step 34:

[0128] Server: Calculated rewards are credited to the user's account and stored in a database.

[0129] Example 1

[0130] 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."

[0131] Conventional travel arrangement systems require users to perform various arrangement tasks individually, which is a time-consuming and labor-intensive process. Even when systems exist that can automatically generate travel itineraries, it is difficult to provide customized itinerary proposals based on the user's preferences and specific conditions. Another problem is the lack of real-time support, such as interpreters and route guidance, that is needed on-site.

[0132] 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.

[0133] In this invention, the server includes means for acquiring destination information, a generative model means for automatically creating a travel itinerary based on the destination information, means for arranging transportation tickets based on the travel itinerary, means for booking accommodation based on the travel itinerary, means for arranging local activities and facilities, means for providing route guidance to the destination, means for interpreting in real time, means for rewarding the user, means for using the generative AI model to generate itinerary suggestions, and means for generating prompt sentences and using them as input for the generative AI model. This allows the user to receive all travel arrangement procedures and local support in a centralized manner.

[0134] "Destination information" refers to information such as the travel destination and duration that the user inputs through the application.

[0135] "Generative modeling tools" refer to machine learning models and algorithms that automatically generate travel itineraries based on destination information.

[0136] "A means for arranging transportation tickets" is a system that automatically reserves airline and train tickets based on the user's travel itinerary.

[0137] A "means for booking accommodation" is a system for booking hotels and other accommodations based on a user's travel itinerary.

[0138] "A means of arranging local activities and facilities" is a system that makes reservations for tourist attractions and events based on the user's travel itinerary.

[0139] The "means for providing route guidance to a destination" is a system that calculates and provides guidance on the optimal route based on the user's current location and destination.

[0140] A "real-time interpretation solution" is a system that instantly translates speech and text when users speak different languages.

[0141] The "means for providing rewards to users" is a system that calculates and provides rewards such as points or cryptocurrency when users use the service.

[0142] A "means of using a generative AI model" is a method of using a generative AI model to generate travel itineraries or other information.

[0143] "Means for generating prompt sentences and using them as input for a generative AI model" refers to a method for automatically creating sentences to be input into a generative AI model.

[0144] This invention relates to a system that acquires destination information and provides travel arrangements and local support in an integrated manner based on that information. The system consists of devices such as smartphones and tablets, a cloud-based server, and a generative AI model.

[0145] User Interface

[0146] Terminal: The user inputs destination information using a dedicated application. The user specifies the destination and travel period through the interface, for example, "Destination: Tokyo, Duration: 5 days."

[0147] Destination information processing

[0148] Server: The server receives the destination information sent from the device. Based on the received destination information, it inputs a prompt sentence into the generative AI model. For example, the prompt sentence might be in the format "Please create a five-day itinerary for a trip to Tokyo. Day 1: Sightseeing in Asakusa, Day 2: Shopping in Akihabara, Day 3: Night view of Roppongi, Day 4: Disneyland."

[0149] Generate travel itineraries

[0150] Generative AI model: The generative AI model automatically generates an optimal travel itinerary based on a prompt, including details of the places and times to visit.

[0151] Presentation of proposed schedule

[0152] Server: Receives the travel itinerary proposals created by the generative AI model and sends them to the user's device.

[0153] Terminal: The user checks the proposed itinerary received on the terminal. If necessary, they make corrections and finally approve it. For example, they change the time for "Sightseeing in Asakusa" to "10:00."

[0154] Ticket and accommodation reservations

[0155] Server: Once the user approves the itinerary, the server uses an external system API to arrange transportation tickets and book accommodations. For example, it uses the Rakuten Travel API to book a hotel in Shinjuku.

[0156] Local activity arrangements

[0157] Server: Based on the generated itinerary, make reservations for local activities and facilities. For example, use the API of a tour booking site to book tickets to Disneyland.

[0158] Route guidance

[0159] Device: When a user travels to a destination, they use the application to send their current location information to the server. The server then calculates the optimal route based on this information and sends it to the device. The user can then receive real-time route guidance through the app.

[0160] Interpretation function

[0161] On the device: When users speak different languages, they activate the translation feature within the app. The device sends voice input to the server, and the generative AI model translates in real time. The translation result is displayed on the device or played aloud.

[0162] Reward System

[0163] Server: When a user uses or purchases a service, the server calculates rewards such as points or cryptocurrency based on that information. The calculated rewards are added to the user's account and stored in the database.

[0164] Specific examples

[0165] If a user plans a five-day trip to "Tokyo," the specific steps are as follows: When the user enters "Destination: Tokyo, Duration: 5 days" into the app, the generative AI model creates a five-day itinerary. For example, the itinerary might include "Sightseeing in Asakusa" on the first day, "Shopping in Akihabara" on the second day, "Night View of Roppongi" on the third day, and "Disneyland" on the fourth day. Once the user approves the itinerary, the server uses an external API to book flights and hotels, as well as local activities. On the day of the trip, the app provides real-time route guidance and interpretation functions. After the trip is over, rewards are awarded based on the service usage.

[0166] The above is a specific embodiment of the present invention, which is a system that allows users to make all travel arrangements and receive on-site support through a single application, thereby significantly reducing the hassle and cost of travel.

[0167] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0168] Step 1:

[0169] Input: The user launches a dedicated application and enters destination information such as "Destination: Tokyo, Duration: 5 days."

[0170] Operation: Information entered by the user is sent to the server through the terminal interface.

[0171] Output: The destination information is saved on the server.

[0172] Step 2:

[0173] Input: The destination information the server receives from the user.

[0174] How it works: The server generates prompts for the generative AI model based on the destination information it receives. For example, it generates a prompt like, "Please create a five-day itinerary for a trip to Tokyo. Day 1: Sightseeing in Asakusa, Day 2: Shopping in Akihabara, Day 3: Night view of Roppongi, Day 4: Disneyland."

[0175] Output: The generated prompt sentence is input to the generative AI model.

[0176] Step 3:

[0177] Input: A prompt sentence that the generative AI model will process.

[0178] How it works: Based on the prompt, the generative AI model automatically generates a travel itinerary that matches the user's preferences and travel duration. The generated itinerary includes details of locations and visit times.

[0179] Output: The generated itinerary proposals are returned to the server.

[0180] Step 4:

[0181] Input: Travel itinerary suggestions returned to the server from the generative AI model.

[0182] Operation: The server sends the generated itinerary proposal to the user's terminal.

[0183] Output: A proposed itinerary is displayed on the user's device.

[0184] Step 5:

[0185] Input: A proposed itinerary that the user reviews and modifies or approves.

[0186] Operation: The user checks the proposed travel itinerary through the terminal, makes specific modifications as necessary, and then approves the proposed itinerary.

[0187] Output: The modified or approved itinerary is sent to the server.

[0188] Step 6:

[0189] Input: Revised or approved itinerary.

[0190] Operation: The server uses the API of an external system (for example, a transportation reservation system or a lodging reservation system) to arrange transportation tickets and reserve accommodation based on the schedule.

[0191] Output: Reservation confirmation information is sent to the user's terminal.

[0192] Step 7:

[0193] Input: Booking information for local activities and facilities based on your travel dates.

[0194] What happens: The server uses an external API (e.g., a tour booking site) to complete a booking, such as reserving tickets to Disneyland.

[0195] Output: Local activity and facility reservation confirmation information is stored on the server and sent to the user's device.

[0196] Step 8:

[0197] Input: Location information that the user enters on their device.

[0198] How it works: When a user moves around the area, they input their current location information and send it to the server. The server then calculates the optimal route and sends it to the device.

[0199] Output: Real-time route guidance is displayed on the user's device.

[0200] Step 9:

[0201] Input: Voice input for different languages ​​that the user has set up on their device.

[0202] How it works: The device sends voice input to the server, where the generative AI model translates it in real time. The translation is then displayed on the device or played back aloud.

[0203] Output: The translated text and audio are available on the user's device.

[0204] Step 10:

[0205] Input: Information about the user's use of the service.

[0206] How it works: The server calculates rewards such as points or cryptocurrency based on usage information and credits them to the user's account.

[0207] Output: Reward information is saved in the user's database and reflected in their account.

[0208] The above are the specific steps in the program processing of this system.

[0209] (Application example 1)

[0210] 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."

[0211] Conventional travel arrangement systems do not provide sufficient support for users on how to arrange meals during their trip. Furthermore, there is a lack of systems that allow users to easily make reservations at local restaurants or use food delivery services. This often forces travelers to take the trouble of arranging meals themselves, diminishing the convenience and comfort of their trip.

[0212] 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.

[0213] In this invention, the server includes a means for acquiring destination information, a generative model means for automatically creating a travel itinerary, a means for arranging transportation tickets, a means for reserving accommodations, a means for arranging local activities and facilities, a means for providing route guidance, a means for real-time interpretation, a means for recommending restaurants and making reservations and orders, and a means for rewarding users. This allows travelers to centrally manage and execute meal arrangements and reservations along with their entire travel schedule simply by entering destination information, thereby significantly improving convenience and comfort during travel.

[0214] "Destination information" is information that allows a user to specify a travel destination and itinerary.

[0215] A "generative model means" is a means that uses a generative AI model to automatically generate optimal travel itineraries based on destination information.

[0216] "Means for arranging transportation tickets" refers to means for reserving necessary transportation such as airline tickets, trains, and buses according to the user's travel itinerary.

[0217] A "means for reserving accommodation" is a means for reserving a hotel or other accommodation based on the user's travel itinerary.

[0218] "Means of arranging local activities and facilities" refers to means of arranging sightseeing, experiential activities, use of facilities, etc. at travel destinations.

[0219] The "means for providing route guidance" is a means for providing real-time guidance on the optimal route to a destination while the user is traveling.

[0220] "Means for real-time interpretation" refers to means for providing an interpretation function for translating communication between users who speak different languages ​​in real time.

[0221] "Means for recommending restaurants and making reservations / orders" refers to means for recommending the most suitable restaurant based on destination information, and for making reservations at the restaurant and ordering from the menu.

[0222] The "means for providing rewards to users" refers to a means for providing rewards such as points or coupons to users based on their use of the travel arrangement system.

[0223] The present invention is a system that provides travel arrangements and local support in an integrated manner based on destination information. Specific embodiments of the present invention will be described below.

[0224] User Interface

[0225] Device:

[0226] Users launch a dedicated application on their smartphone, tablet, or other device and specify their destination and travel dates through an interface for entering destination information. For example, a user might enter "Tokyo" and "2023-10-01 to 2023-10-05."

[0227] Destination information processing

[0228] server:

[0229] Destination information is received and input into the generative AI model to request automatic itinerary generation. The generative AI model creates an optimal itinerary based on the user's preferences and the number of days of travel. For example, for a trip to "Tokyo," it will consider itineraries such as sightseeing in Asakusa, shopping in Akihabara, the night view of Roppongi, and Disneyland.

[0230] Presentation of proposed schedule

[0231] Device:

[0232] The proposed travel itinerary sent from the server is displayed to the user, who can then review it and modify or approve it as necessary.

[0233] Ticket booking and hotel reservations

[0234] server:

[0235] Once the itinerary is approved, transportation tickets are arranged via an external system (e.g., an airline or travel agency API), and accommodation reservations are also made using external APIs.

[0236] Local activity arrangements

[0237] server:

[0238] Based on the itinerary, local activities and facilities are arranged, also using external APIs.

[0239] Route guidance

[0240] Device:

[0241] When a user moves to a destination, their current location and destination information are sent to the server. The server then calculates the optimal route based on this information and sends it to the device. The user can then receive real-time route guidance via the app.

[0242] Interpretation function

[0243] Device:

[0244] When users speak different languages, they activate the in-app translation feature. The device sends voice input to the server, and the generative AI model translates in real time. The translation result is displayed on the device or played aloud.

[0245] Restaurant recommendations and reservations / orders

[0246] server:

[0247] Based on the destination information, the system recommends the most suitable restaurant and arranges reservations and orders based on the user's preferences and dates. This also uses an external restaurant API. For example, if a user is looking for an okonomiyaki restaurant while traveling in Tokyo, the AI ​​will recommend the most suitable candidate and allow for on-the-spot reservations.

[0248] Reward System

[0249] server:

[0250] When a user purchases or uses a service, the information is verified and rewards such as points and coupons are calculated and awarded.

[0251] Specific examples

[0252] As a practical example, consider a user planning a five-day trip to Tokyo. When the user enters "Tokyo" and the dates "2023-10-01 to 2023-10-05" into the app, the generative AI model creates a five-day itinerary, suggesting itineraries such as "Sightseeing in Asakusa" on Day 1, "Shopping in Akihabara" on Day 2, "Viewing the Roppongi Nightscape" on Day 3, and "Disneyland" on Day 4. Furthermore, the model recommends and makes reservations for restaurants, making it easy to make reservations at famous sushi restaurants and yakiniku restaurants, for example. Once the user approves the itinerary, the server arranges flight and hotel reservations through an external API, as well as local activities. On the day of the trip, the app provides real-time route guidance and interpretation services. Points and coupons are also awarded after the trip is completed.

[0253] Prompt Sentence Examples

[0254] "Enter your travel destination and dates. We'll then suggest restaurant recommendations and food delivery options."

[0255] "Create an optimal food schedule based on this destination information and dates."

[0256] The present invention aims to provide these functions in a unified manner, allowing travelers to easily arrange and reserve meals along with their overall travel schedule, thereby significantly improving the convenience and comfort of travel.

[0257] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0258] Step 1:

[0259] Enter destination information

[0260] Users start a dedicated application on their smartphone, tablet, or other device and enter their destination information (destination and itinerary), which is then sent to the server.

[0261] Input: Destination information (e.g. Tokyo, 2023-10-01 to 2023-10-05)

[0262] Output: Destination information sent to the server

[0263] Step 2:

[0264] Automatic itinerary generation

[0265] The server inputs the received destination information into the generative AI model and automatically generates a travel itinerary using prompts. The generative AI model creates an optimal travel itinerary based on the user's preferences and the number of travel days.

[0266] Input: Destination information

[0267] Output: Generated itinerary (e.g., Day 1: sightseeing in Asakusa, Day 2: shopping in Akihabara)

[0268] Step 3:

[0269] Proposal of travel itinerary

[0270] The server sends the generated proposed travel itinerary to the terminal, which displays it to the user, who can review the proposed itinerary and modify or approve it as necessary.

[0271] Input: Generated itinerary

[0272] Output: Proposed itinerary displayed to the user

[0273] Step 4:

[0274] Ticket and accommodation booking

[0275] Based on the schedule approved by the user, the server arranges transportation tickets via an external system (e.g., API of an airline or travel agency) and makes reservations for accommodation using the external API.

[0276] Input: Approved travel itinerary

[0277] Output: Booked transportation tickets, accommodation reservation information

[0278] Step 5:

[0279] Local activity arrangements

[0280] The server arranges local activities and facilities based on the travel itinerary using an external API.

[0281] Input: Approved travel itinerary

[0282] Output: Reservation information for arranged activities and facilities

[0283] Step 6:

[0284] Providing route guidance

[0285] During a trip, the user sends information about their current location and destination from their device to the server, which then calculates the optimal route and provides real-time route guidance.

[0286] Input: Current location information, destination information

[0287] Output: Optimal route directions

[0288] Step 7:

[0289] Real-time interpretation

[0290] When users speak different languages, they activate the interpretation function. The device sends voice input to the server, and the generative AI model translates in real time. The translation result is displayed on the device or played aloud.

[0291] Input: Voice input

[0292] Output: Translation result

[0293] Step 8:

[0294] Restaurant recommendations and reservations / orders

[0295] The server recommends the most suitable restaurant based on the destination information, and also arranges reservations and orders based on the user's preferences and dates, again using an external restaurant API.

[0296] Input: Destination information, user preferences, travel itinerary

[0297] Output: Recommended restaurant information, reservation and order confirmation

[0298] Step 9:

[0299] Reward System

[0300] Information about the user's use of the service is sent to the server, and rewards such as points and coupons are added to the user's account.

[0301] Input: Service usage information

[0302] Output: Points and coupons awarded

[0303] The above are the specific processing steps for implementing this invention. By showing the specific hardware and software operations and data flow in each step in detail, the technical scope of the invention will become clearer.

[0304] 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.

[0305] The present invention relates to a system that acquires destination information and provides travel arrangements and local support in an integrated manner based on that information, and aims to increase user satisfaction by further combining it with an emotion engine that recognizes the user's emotions. Specific embodiments of this system will be described below.

[0306] The system is comprised of a smartphone, tablet, or other device, a cloud-based server, a generative AI model, and an emotion engine. Users input their destination information using a dedicated application, and then receive travel arrangements and guide services. The emotion engine analyzes the user's emotions to personalize the travel experience.

[0307] User Interface

[0308] Device: The user launches the application and enters "Tokyo" as their destination. As they enter their destination, the application recognizes their emotions from their facial expressions and tone of voice via a camera and microphone.

[0309] Destination information processing and emotion recognition

[0310] Server: Upon receiving the destination information "Tokyo," the emotion engine analyzes the emotion data and evaluates the user's emotional state during the travel planning stage.

[0311] Generative AI model: Generates optimal travel itineraries based on destination information, travel days, and user emotional state and preferences. For example, if a user is feeling stressed, the model suggests itineraries that emphasize relaxation and refreshment.

[0312] Presentation of proposed schedule

[0313] Terminal: Displays the travel itinerary proposal sent from the server to the user. The itinerary proposal is customized according to the user's emotional state.

[0314] User: Review the proposed schedule and modify or approve it as necessary.

[0315] Ticket arrangements, hotel reservations and local activity arrangements

[0316] Server: Once the itinerary is approved, transportation tickets are arranged via an external API (for example, an airline or travel agency API). Accommodation reservations are also made using an external API. Based on the information from the emotion engine, a more comfortable accommodation environment is selected.

[0317] Server: Arranges local activities and facilities based on the schedule. Using an emotion engine, prioritizes and selects activities that will reduce the user's stress and pique their interest.

[0318] Route guidance and interpretation functions

[0319] Terminal: When a user requests route guidance to the destination "Sensoji Temple" while on-site, the terminal sends current location information and destination information to the server.

[0320] Server: Calculates the optimal route based on the current location and destination information and sends it to the device. The emotion engine takes into account the user's fatigue and stress during the trip and provides the optimal route.

[0321] Device: When users speak different languages, they activate the translation function within the app and send the audio data to the server.

[0322] Generative AI model: Translates voice data in real time and sends the results back to the server. The emotion engine generates translation results that alleviate the user's anxiety and tension.

[0323] Reward System

[0324] Server: Checks the user's usage information and calculates rewards such as PayPay points or cryptocurrency. The emotion engine evaluates the user's gratitude and satisfaction and adjusts the reward accordingly.

[0325] Server: Calculated rewards are credited to the user's account and stored in a database.

[0326] Specific examples

[0327] As a practical example, consider a user planning a five-day trip to Tokyo. When the user enters "Tokyo" into the app, the generative AI model creates a five-day itinerary and suggests activities such as sightseeing in Asakusa on day one, shopping in Akihabara on day two, the night view of Roppongi on day three, and Disneyland on day four. If the user is feeling stressed, a relaxation spa or nature walk can be added to the itinerary. Furthermore, if the user's emotional state changes during the trip, the emotion engine recognizes this and dynamically adjusts the itinerary and activities. For example, if the user feels tired, the plan can be changed to include a massage or relaxation time at the hotel. On the day of the trip, real-time route guidance and interpretation functions are provided through the app. Furthermore, after the trip, bonus points and cryptocurrency based on the emotion engine are awarded.

[0328] As described above, the present invention is a system that allows users to receive all travel arrangements and on-site support through a single application, and by combining it with an emotion engine, it is possible to provide a personalized travel experience that corresponds to the user's emotional state.

[0329] The processing flow will be explained below.

[0330] Step 1:

[0331] User: Launches the app and enters "Tokyo" as the destination.

[0332] Step 2:

[0333] Terminal: Checks the entered destination information and sends it to the server. At the same time, it captures the user's facial expressions and tone of voice via a camera and microphone to obtain emotional data.

[0334] Step 3:

[0335] Server: Receives the destination information "Tokyo" and inputs the destination information and emotion data into the generative AI model and emotion engine, respectively.

[0336] Step 4:

[0337] Emotion engine: Analyzes the acquired emotional data and evaluates the user's emotional state. For example, it generates a result such as "high stress."

[0338] Step 5:

[0339] Generative AI model: Creates travel itinerary suggestions based on destination information and emotional state (e.g., "high stress") derived from the emotion engine, including itineraries focused on relaxation and refreshment to reduce stress.

[0340] Step 6:

[0341] Server: Stores the generated travel itinerary proposals in a database and sends them to the terminal.

[0342] Step 7:

[0343] Terminal: Displays the travel itinerary proposals received from the server to the user, customized to reflect the user's emotional state.

[0344] Step 8:

[0345] User: Checks the proposed schedule and clicks the approve button. User can also request revisions if necessary.

[0346] Step 9:

[0347] Terminal: Sends the user's authorization data to the server.

[0348] Step 10:

[0349] Server: After receiving approval, sends a request to arrange transportation tickets via an external API (e.g., an airline or travel agency API).

[0350] Step 11:

[0351] External API: Executes transportation ticket reservations and returns reservation information to the server.

[0352] Step 12:

[0353] Server: Stores the received ticket reservation information in a database.

[0354] Step 13:

[0355] Server: Sends a request to check the availability of accommodation using an external API (for example, the API of a hotel booking site).

[0356] Step 14:

[0357] External API: Returns the accommodation availability information to the server.

[0358] Step 15:

[0359] Server: Enters vacant room information into the generative AI model and requests it to select the most suitable accommodation, taking into account the evaluation of the emotion engine.

[0360] Step 16:

[0361] Generative AI model: Selects the most suitable accommodation based on availability information and the emotion engine's evaluation, and sends it back to the server.

[0362] Step 17:

[0363] Server: Sends a reservation request for the selected accommodation to an external API.

[0364] Step 18:

[0365] External API: Performs reservation confirmation and returns reservation information to the server.

[0366] Step 19:

[0367] Server: Stores the received accommodation reservation information in a database.

[0368] Step 20:

[0369] Server: Based on the itinerary, it sends a request to arrange reservations for local activities and facilities via an external API (e.g., the API of a tour booking site).

[0370] Step 21:

[0371] External API: Sends activity and facility reservation information back to the server.

[0372] Step 22:

[0373] Server: Stores the received reservation information in a database. The emotion engine monitors the user's emotional state and suggests changes to the activity if necessary.

[0374] Step 23:

[0375] User: Requests route guidance to the destination "Sensoji Temple" from the app while on location.

[0376] Step 24:

[0377] Terminal: Sends current location information and destination information to the server.

[0378] Step 25:

[0379] Server: Calculates the optimal route based on current location and destination information. The emotion engine takes into account fatigue and stress levels during travel and provides the optimal route.

[0380] Step 26:

[0381] Server: Sends calculated route information to the device.

[0382] Step 27:

[0383] Terminal: Display route directions to the user.

[0384] Step 28:

[0385] Users: Activate the in-app translation feature if they need to speak a different language locally.

[0386] Step 29:

[0387] Device: Sends voice input to the server.

[0388] Step 30:

[0389] Server: Analyzes the voice data and requests translation from the generative AI model. The emotion engine generates translation results that alleviate the user's anxiety and tension.

[0390] Step 31:

[0391] Generative AI model: performs real-time translation from Japanese to English (or other languages) and sends it back to the server.

[0392] Step 32:

[0393] Server: Sends the translation results to the device.

[0394] Step 33:

[0395] Terminal: The translation result is displayed to the user or output as speech.

[0396] Step 34:

[0397] Server: Checks the user's usage information and calculates rewards in PayPay points or cryptocurrency. The emotion engine also evaluates the user's emotional state and adjusts rewards accordingly.

[0398] Step 35:

[0399] Server: Calculated rewards are credited to the user's account and stored in a database.

[0400] Example 2

[0401] 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."

[0402] The present invention relates to a system that allows users to centrally plan and arrange trips and personalize the travel experience based on the user's emotional state. Conventional travel support systems can automatically generate and arrange trip plans, but do not personalize the experience based on the user's emotional state. This makes it difficult to maximize user satisfaction. Furthermore, they do not adequately provide real-time route guidance, interpretation functions, or rewards based on the user's evaluated emotions.

[0403] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for acquiring destination information, a generative model means for automatically creating a travel itinerary, a means for arranging transportation tickets, a means for booking accommodations, a means for arranging local activities and facilities, a means for providing route guidance to the destination, a means for real-time interpretation, a means for rewarding the user, a sentiment analysis means for analyzing the user's emotional state, and a means for personalizing the travel itinerary based on the sentiment analysis means. This enables a personalized travel experience according to the user's emotional state, thereby improving user satisfaction. Specifically, by analyzing the user's emotional state, if the user is feeling stressed, the system can suggest relaxation-oriented trips and flexibly respond to emotional changes during the trip, thereby providing an optimal travel experience.

[0404] "Destination information" is information about a geographical location that a user specifies as a travel destination.

[0405] The "generative model means" is an algorithm or program that has the function of automatically generating an optimal travel itinerary based on destination information and other related information.

[0406] The "means for arranging transportation tickets" is a system that has the function of reserving and purchasing transportation necessary for travel, such as airline tickets and train tickets, based on travel itineraries.

[0407] A "means for reserving accommodation" is a system that has the functionality to reserve hotels and other accommodations based on travel dates.

[0408] The "means for arranging local activities and facilities" is a system that has the function of making reservations for participation in local tourist attractions and activities based on travel itineraries.

[0409] The "means for providing route guidance to a destination" is a system that has the function of calculating and providing guidance on the optimal route to a destination specified by a user.

[0410] A "means for performing real-time interpretation" is a system that has the function of translating voice data in real time to support communication between users who speak different languages.

[0411] The "means for providing rewards to users" is a system that has the function of providing rewards such as points or cryptocurrency based on the user's satisfaction and usage information after the trip.

[0412] The "emotion analysis means" is a system that has the function of analyzing the emotional state of a user using data such as facial expressions and tone of voice.

[0413] The "means for personalizing travel itineraries based on emotion analysis means" is a system that has the function of optimizing and personalizing travel itineraries according to the user's emotional state analyzed by the emotion analysis means.

[0414] The present invention relates to a system that acquires destination information and provides travel arrangements and local support in an integrated manner based on that information. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to personalize the user's travel experience and increase satisfaction. A specific embodiment of this system will now be described.

[0415] User Interface

[0416] Device: The user launches a dedicated application on a device such as a smartphone or tablet and inputs destination information. For example, when "Tokyo" is entered as destination information, the information is acquired by the device. When inputting information, data is also acquired through a camera and microphone to recognize emotions from the user's facial expressions and tone of voice. This allows data to be collected based on the user's emotional state.

[0417] Destination information processing and emotion recognition

[0418] Server: Receives the destination information "Tokyo" and emotion data sent from the device. The server is cloud-based and analyzes the emotion data using an emotion engine. This allows the server to evaluate the user's emotional state during the trip planning stage.

[0419] Generative AI model: The generative AI model installed on the server generates an optimal travel itinerary based on destination information, the number of days of travel, the user's emotional state, and past travel preference data. For example, if the user is feeling stressed, it will suggest an itinerary that emphasizes relaxation and refreshment.

[0420] Presentation and confirmation of proposed schedule

[0421] Terminal: Displays the travel itinerary proposals sent from the server to the user. The generated itinerary proposals are customized according to the user's emotional state.

[0422] Users can review the proposed schedule and make any necessary changes or approvals. Changes can be easily made through the application interface.

[0423] Travel arrangements and local support

[0424] Server: After the user approves the itinerary, the server uses external APIs (such as airlines and travel agencies) to arrange transportation tickets and book accommodations. The emotion engine selects a more comfortable accommodation environment. It also arranges local activities and facilities, taking measures to reduce the user's stress and increase their interest.

[0425] Route guidance and interpretation functions

[0426] Terminal: When a user requests route guidance to the destination "Sensoji Temple" while on the spot, the terminal sends current location information and destination information to the server.

[0427] Server: Calculates the optimal route based on the received information and sends it to the device. Using the emotion engine, route guidance is provided that takes into account the user's fatigue and stress during travel.

[0428] On the device: When users speak different languages, they activate the translation function within the app and send the audio data to the server.

[0429] Generative AI model: Translates speech data in real time and sends the results back to the server, generating translation results that alleviate the user's anxiety and tension.

[0430] Post-trip rewards

[0431] Server: Calculates rewards such as PayPay points or cryptocurrency based on the user's usage information of the provided service. The emotion engine evaluates the user's gratitude and satisfaction and adjusts the amount of reward accordingly.

[0432] Server: The calculated reward is credited to the user's account and the data is stored in a database.

[0433] Specific examples

[0434] For example, if a user plans a five-day trip to "Tokyo," they enter "Tokyo" into the app. The generative AI model creates an itinerary based on the following prompt: "Create a five-day itinerary for Tokyo. The user wants to relax." Based on this, it suggests itineraries such as "Sightseeing in Asakusa" on the first day, "Shopping in Akihabara" on the second day, "Viewing the night view of Roppongi" on the third day, and "Disneyland" on the fourth day. If the user is feeling stressed, it adds relaxation-focused spa and nature walks.

[0435] If the user feels tired during the trip, the emotion engine will recognize this and the server will dynamically adjust the itinerary and activities in real time to provide the user with a comfortable travel experience. After the trip is over, the user will be awarded bonus points and cryptocurrency based on the evaluation made by the emotion engine.

[0436] In this way, the system comprehensively supports the entire process from obtaining destination information to providing travel experiences and rewards, and can also provide personalized services according to the user's emotional state.

[0437] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0438] Step 1: Enter your destination information

[0439] Terminal: The user launches the application and enters destination information. For example, if the user enters "Tokyo" as the destination, the information is acquired by the terminal.

[0440] Input: Destination information ("Tokyo")

[0441] Output: Destination information data

[0442] Specific operation: The user enters the destination using the keyboard or voice input, and the device stores the information in a database within the application.

[0443] Step 2: Obtaining emotion data

[0444] Terminal: At the same time as entering destination information, the device captures the user's facial expressions and tone of voice via a camera and microphone.

[0445] Input: User's facial expressions, tone of voice

[0446] Output: Emotion data

[0447] Specific operations: The device activates the camera to capture facial expressions, uses the microphone to collect voice data, and sends this data to the emotion analysis module.

[0448] Step 3: Send destination information and emotion data to the server

[0449] Terminal: Sends the acquired destination information and emotion data to the server.

[0450] Input: Destination information data, emotion data

[0451] Output: Data sent to the server via the HTTP request

[0452] Specific operation: The device packages destination information and emotion data and sends it to the server using an HTTP request.

[0453] Step 4: Generate itinerary

[0454] Server: Receives destination information and emotion data and generates an optimal travel itinerary using a generative AI model.

[0455] Input: Destination information data, emotion data, number of travel days, user preference data

[0456] Output: Travel itinerary proposal

[0457] Specific operation: The generative AI model generates a travel itinerary based on the prompt, "Please create a 5-day travel itinerary for Tokyo. The user wants to relax." The sentiment analysis engine analyzes the emotional data and generates a personalized itinerary that reflects the user's emotional state.

[0458] Step 5: Present and confirm the proposed schedule

[0459] Terminal: Displays the proposed travel itinerary sent from the server to the user.

[0460] Input: Travel itinerary proposal

[0461] Output: Display of proposed schedule

[0462] Specific operation: The device displays the proposed schedule received from the server on the application's UI. The user checks the schedule and makes any necessary corrections or approvals.

[0463] Step 6: Revise the proposed itinerary

[0464] User: Review the proposed schedule and make any necessary changes or approve it through the interface.

[0465] Input: Schedule proposal, correction instructions

[0466] Output: Revised schedule or approval

[0467] Specific operation: The user adjusts the schedule using touch operations or drag-and-drop operations, and presses the "Approve" button to confirm the final schedule.

[0468] Step 7: Making travel arrangements

[0469] Server: Based on the itinerary approved by the user, the server uses external services to arrange transportation tickets and make hotel reservations.

[0470] Input: Approved schedule, emotion data

[0471] Output: Ticket and booking confirmation information

[0472] Specific operation: The server sends a request to an external API (travel agency, airline, hotel, etc.) to confirm the ticket or accommodation reservation, receives the API response, and sends the confirmation information to the terminal.

[0473] Step 8: Local route guidance

[0474] Terminal: The user requests route guidance locally and sends current location information and destination information to the server.

[0475] Input: current location information, destination information

[0476] Output: Optimal route guidance

[0477] Specific operation: The device acquires GPS data and sends it to the server along with destination information.

[0478] Server: Calculates the optimal route based on the received information and sends it to the terminal.

[0479] Input: current location information, destination information

[0480] Output: Route guidance information

[0481] Specific operation: The server uses a route calculation algorithm to calculate the optimal route and sends the result to the terminal.

[0482] Step 9: Run the interpretation function

[0483] Terminal: When a user uses the interpretation function, the terminal sends voice data to the server.

[0484] Input: Audio data

[0485] Output: The translated text

[0486] Specific operation: The device uses a microphone to capture voice data and sends it to the server.

[0487] Server: Receives the voice data and performs real-time translation using the generative AI model.

[0488] Input: Audio data

[0489] Output: The translated text

[0490] How it works: The server passes the voice data to the generative AI model and sends the translated text back to the device.

[0491] Step 10: Post-trip rewards

[0492] Server: Evaluates the user's gratitude and satisfaction based on service usage information and calculates rewards.

[0493] Input: service usage information, emotion data

[0494] Output: Reward points

[0495] Specific operation: The sentiment analysis engine evaluates satisfaction based on the emotional data, calculates rewards, and adds the calculated rewards to the user's account and stores them in the database.

[0496] The above are the specific processing steps of the program of this system.

[0497] (Application example 2)

[0498] 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."

[0499] Conventional travel booking and food delivery systems lack the ability to provide personalized suggestions that take into account the user's emotional state. As a result, they often do not provide the optimal service for the user's current emotional state, preventing improvement in satisfaction. In addition, when the user is tired or stressed, the system does not suggest the optimal menu according to the situation, resulting in a poor user experience.

[0500] 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.

[0501] In this invention, the server includes a means for acquiring destination information, a generative model means, and an emotion engine means. This allows the server to analyze the user's emotional state in real time and propose optimal travel itineraries and menus based on that analysis. Furthermore, by linking with external systems, the server can arrange transportation, accommodations, and local activities, further enhancing user convenience.

[0502] "Destination information" is information about the user's desired travel destination or delivery destination.

[0503] The "generative model means" is a system for automatically creating travel itineraries and menus based on input destination information.

[0504] The "emotion engine means" is a system for analyzing the user's facial expressions and tone of voice to recognize the user's current emotional state.

[0505] The "generative AI model means" is an artificial intelligence model that generates optimal food menus and travel plans based on the user's destination information, emotional state, and preferences.

[0506] "Means for arranging transportation tickets" is a system for automatically reserving transportation according to the user's travel schedule.

[0507] The "means for reserving accommodation" is a system for reserving accommodation based on the user's travel itinerary.

[0508] "A means for arranging local activities and facilities" is a system for making reservations for tourist attractions and entertainment according to the user's travel itinerary.

[0509] The "means for providing route guidance to a destination" is a system for navigating the optimal route based on destination information input by the user.

[0510] A "means for real-time interpretation" is a system for translating speech in real time when users speak different languages.

[0511] The "means for providing rewards to users" is a system for providing points or rewards to users according to their usage of the service.

[0512] System Overview

[0513] The present invention relates to a system that acquires destination information, automatically creates travel itineraries and menus based on that information, and provides them while taking into account the emotional state of the user. Specific embodiments for carrying out the invention are described below.

[0514] Hardware and Software Configuration

[0515] Hardware used

[0516] Device: Smartphone or tablet

[0517] Server: Cloud-based server

[0518] Software used

[0519] Emotion engine: A sentiment analysis library such as the Affectiva SDK

[0520] Generative AI models: Generative models such as OpenAI's GPT-4

[0521] Processing flow

[0522] User Interface

[0523] The user launches the application on their device and enters destination information (travel destination or delivery address). At that time, the device's camera and microphone are used to capture the user's emotional state. The captured emotional data is then analyzed using an emotion engine (such as the Affectiva SDK).

[0524] Destination information processing and emotion recognition

[0525] The server receives the destination information and evaluates the user's emotional data analyzed by the emotion engine, then uses a generative AI model (such as OpenAI's GPT-4) to generate optimal travel itineraries and food menus based on the destination information, the user's emotional state, and past preference data.

[0526] Proposal of schedule and menu

[0527] The terminal displays the customized itinerary and menu proposals sent from the server to the user, who can then review the proposals and modify or approve them as necessary.

[0528] Ticket arrangements, accommodation reservations, activity arrangements

[0529] Based on the approved travel itinerary, the server connects with external systems (such as the APIs of airlines and hotel booking sites) to arrange transportation tickets and book accommodations. It also automates the arrangement of local activities and facilities using external APIs.

[0530] Route guidance and interpretation functions

[0531] When a user requests guidance to a destination, the server provides the device with the optimal route based on the user's current location. Furthermore, if the user speaks a different language, the server collects voice data through the device's microphone and performs real-time interpretation. The server translates the voice data using a generative AI model and sends the results back to the device.

[0532] Reward System

[0533] When a user uses the service, they are given rewards (points or rewards) based on their satisfaction level as assessed by the emotion engine. This information is managed on the server and reflected in the user's account.

[0534] Specific examples

[0535] As an actual usage example, consider the case where a user inputs "I'm tired" and requests delivery to their home in Tokyo. When the user inputs their destination information into the app, the emotion engine retrieves emotion data that evaluates "tired." Based on this information, the generative AI model generates and suggests food menus with a relaxing effect (e.g., "herbal tea" or "comfort food") to the user.

[0536] Prompt Sentence Examples

[0537] Input sentence: "I'm tired from work today. Please deliver to my home in Tokyo."

[0538] Prompt: "Suggest some food options for when the user is feeling tired. Ideally, these would include relaxing foods and drinks."

[0539] The above is a specific embodiment for carrying out the invention. By combining an emotion engine and a generative AI model, it becomes possible to provide personalized services according to the user's emotional state.

[0540] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0541] Step 1:

[0542] Enter destination information

[0543] A user launches the application and enters destination information (e.g., a travel destination or delivery address). The entered data is temporarily stored in the device's memory. At the same time, the device's camera and microphone are used to capture the user's facial expressions and tone of voice.

[0544] Input: Destination information, facial expression data, tone of voice data

[0545] Output: Destination information, emotion data

[0546] Step 2:

[0547] Emotional Data Analysis

[0548] The device sends the acquired facial expression and tone of voice data to an emotion engine (such as the Affectiva SDK) to analyze the user's emotional state. The analysis results in the user's emotional state (e.g., relaxed, stressed, tired).

[0549] Input: Facial expression data, tone of voice data

[0550] Output: Emotional state (e.g., relaxed, stressed, tired)

[0551] Step 3:

[0552] Sending destination information and emotion data

[0553] The device transmits destination information and emotional state to the server, which stores the information in an integrated database.

[0554] Input: Destination information, emotional state

[0555] Output: Destination information and emotional state stored on the server

[0556] Step 4:

[0557] Generating the optimal plan

[0558] The server uses a generative AI model (such as OpenAI's GPT-4) to generate optimal travel itineraries and food menus based on the saved destination information and emotional state, while also referencing past user preference data.

[0559] Input: Destination information, emotional state, past user preference data

[0560] Output: Suggested travel itinerary or food menu

[0561] Step 5:

[0562] Plan presentation and approval

[0563] The server sends the generated proposal to the terminal and displays the information to the user, who can review the proposed plan and modify or approve it as necessary.

[0564] Input: Proposed plan

[0565] Output: User approves or modifies the plan

[0566] Step 6:

[0567] Ticket and reservation arrangements

[0568] Based on the approved travel itinerary, the server connects with external systems (such as airline and booking site APIs) to arrange transportation tickets and book accommodations, and in the case of food delivery, connects with the suggested menu items to order.

[0569] Input: Approved itinerary or menu proposal

[0570] Output: Booked tickets, accommodation, or food orders

[0571] Step 7:

[0572] Route guidance and interpretation features

[0573] When a user requests directions to a destination, the server provides the optimal route based on the user's current location. Furthermore, if a different language is used, the server collects voice data and translates it in real time.

[0574] Input: Current location, voice data

[0575] Output: Optimal route guidance, translated audio data

[0576] Step 8:

[0577] Rewarding

[0578] When a user uses the service, the server grants rewards (points or rewards) to the user's account based on the satisfaction level assessed by the emotion engine.

[0579] Input: Service usage data, emotion evaluation data

[0580] Output: Points or rewards awarded

[0581] 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.

[0582] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0583] 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.

[0584] [Second embodiment]

[0585] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0586] 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.

[0587] 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).

[0588] 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.

[0589] 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.

[0590] 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).

[0591] 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.

[0592] 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.

[0593] 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.

[0594] 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.

[0595] 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.

[0596] 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."

[0597] The present invention relates to a system that acquires destination information and provides travel arrangements and local support in an integrated manner based on that information. A specific embodiment of this system will be described below.

[0598] The system consists of a smartphone or tablet device, a cloud-based server, and a generative AI model. Users can input destination information using a dedicated application and receive travel arrangements and guide services.

[0599] User Interface

[0600] Terminal: The user launches the application and specifies the destination through the interface where destination information is input. For example, the user inputs the destination "Tokyo."

[0601] Destination information processing

[0602] Server: Receives destination information, inputs it into the generative AI model, and requests automatic itinerary generation. The generative AI model creates an optimal itinerary based on the user's preferences and number of travel days. For example, for a trip to Tokyo, it might consider itineraries such as sightseeing in Asakusa, shopping in Akihabara, the night view of Roppongi, and Disneyland.

[0603] Presentation of proposed schedule

[0604] Terminal: Displays the proposed travel itinerary sent from the server to the user, who can then review the proposed itinerary and modify or approve it as necessary.

[0605] Ticket booking and hotel reservations

[0606] Server: Once the itinerary is approved, transportation tickets are arranged via an external system (for example, an airline or travel agency API). Accommodation reservations are also made using external APIs. For example, a hotel is booked using the Rakuten Travel API.

[0607] Local activity arrangements

[0608] Server: Arranges local activities and facilities based on the itinerary. This is also done using an external API (for example, the API of a tour booking site). For example, when arranging tickets to Disneyland, the reservation information is obtained and confirmed.

[0609] Route guidance

[0610] Device: When a user travels to a destination, they send their current location and destination information to the server. The server calculates the optimal route based on this information and sends it to the device. The user can then receive real-time route guidance via the app.

[0611] Interpretation function

[0612] On-device: When users speak different languages, they activate the in-app translation feature. The device sends voice input to the server, and the generative AI model translates in real time. The translation result is displayed on the device or played aloud.

[0613] Reward System

[0614] Server: When a user purchases or uses a service, the server checks the information and calculates rewards such as PayPay points or cryptocurrency. The calculated rewards are added to the user's account and stored in the database.

[0615] Specific examples

[0616] As an actual usage example, consider a user planning a five-day trip to "Tokyo." When the user enters "Tokyo" into the app, the following steps are executed: The generative AI model creates a five-day itinerary and presents plans such as "Sightseeing Asakusa" on the first day, "Shopping in Akihabara" on the second day, "Night View of Roppongi" on the third day, and "Disneyland" on the fourth day. Once the user approves the itinerary, the server arranges flight and hotel reservations through an external API, as well as local activities. On the day of the trip, real-time route guidance and interpretation functions are provided through the app. In addition, after the trip is completed, PayPay points and cryptocurrency are awarded.

[0617] As described above, the present invention is a system that allows users to make all travel arrangements and receive local support through a single application, significantly reducing the effort and cost of travel.

[0618] The processing flow will be explained below.

[0619] Step 1:

[0620] User: Launches the app and enters "Tokyo" as the destination.

[0621] Step 2:

[0622] Terminal: Checks the entered destination information and sends it to the server.

[0623] Step 3:

[0624] Server: Receives destination information "Tokyo", inputs it into the generative AI model, and requests itinerary creation.

[0625] Step 4:

[0626] Generative AI model: Generates an optimal travel itinerary based on data such as destination information, number of travel days, and user preferences, and sends it back to the server.

[0627] Step 5:

[0628] Server: Stores the generated travel itinerary proposals in a database and sends them to the terminal.

[0629] Step 6:

[0630] Terminal: Displays the proposed travel itinerary received from the server to the user.

[0631] Step 7:

[0632] User: Check the proposed schedule and click the approve button.

[0633] Step 8:

[0634] Terminal: Sends the user's authorization data to the server.

[0635] Step 9:

[0636] Server: Upon approval, arranges transportation tickets via an external API (e.g., an airline or travel agency API).

[0637] Step 10:

[0638] External API: Executes transportation ticket reservations and returns reservation information to the server.

[0639] Step 11:

[0640] Server: Stores the received ticket reservation information in a database.

[0641] Step 12:

[0642] Server: Sends a request to check the availability of accommodation using an external API (for example, the API of a hotel booking site).

[0643] Step 13:

[0644] External API: Returns the accommodation availability information to the server.

[0645] Step 14:

[0646] Server: Enters room availability information into the generative AI model and requests it to select the most suitable accommodation.

[0647] Step 15:

[0648] Generative AI model: Selects the most suitable accommodation based on availability information and returns it to the server.

[0649] Step 16:

[0650] Server: Sends a reservation request for the selected accommodation to an external API.

[0651] Step 17:

[0652] External API: Performs reservation confirmation and returns reservation information to the server.

[0653] Step 18:

[0654] Server: Stores the received accommodation reservation information in a database.

[0655] Step 19:

[0656] Server: Sends a request to arrange a reservation for a local activity or facility via an external API (e.g., the API of a tour booking site).

[0657] Step 20:

[0658] External API: Sends activity and facility reservation information back to the server.

[0659] Step 21:

[0660] Server: Saves the received reservation information in a database.

[0661] Step 22:

[0662] User: Requests route guidance to the destination "Sensoji Temple" from the app while on location.

[0663] Step 23:

[0664] Terminal: Sends current location information and destination information to the server.

[0665] Step 24:

[0666] Server: Calculates the optimal route based on current location and destination information.

[0667] Step 25:

[0668] Server: Sends calculated route information to the device.

[0669] Step 26:

[0670] Terminal: Display route directions to the user.

[0671] Step 27:

[0672] Users: Activate the in-app translation feature if they need to speak a different language locally.

[0673] Step 28:

[0674] Device: Sends voice input to the server.

[0675] Step 29:

[0676] Server: Analyzes the voice data and requests translation from the generative AI model.

[0677] Step 30:

[0678] Generative AI model: Performs real-time translation from Japanese to English (or other languages) and sends the translation results back to the server.

[0679] Step 31:

[0680] Server: Sends the translation results to the device.

[0681] Step 32:

[0682] Terminal: The translation result is displayed to the user or output as speech.

[0683] Step 33:

[0684] Server: Checks information about the user's use of the service and calculates rewards such as PayPay points and cryptocurrency.

[0685] Step 34:

[0686] Server: Calculated rewards are credited to the user's account and stored in a database.

[0687] Example 1

[0688] 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."

[0689] Conventional travel arrangement systems require users to perform various arrangement tasks individually, which is a time-consuming and labor-intensive process. Even when systems exist that can automatically generate travel itineraries, it is difficult to provide customized itinerary proposals based on the user's preferences and specific conditions. Another problem is the lack of real-time support, such as interpreters and route guidance, that is needed on-site.

[0690] 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.

[0691] In this invention, the server includes means for acquiring destination information, a generative model means for automatically creating a travel itinerary based on the destination information, means for arranging transportation tickets based on the travel itinerary, means for booking accommodation based on the travel itinerary, means for arranging local activities and facilities, means for providing route guidance to the destination, means for interpreting in real time, means for rewarding the user, means for using the generative AI model to generate itinerary suggestions, and means for generating prompt sentences and using them as input for the generative AI model. This allows the user to receive all travel arrangement procedures and local support in a centralized manner.

[0692] "Destination information" refers to information such as the travel destination and duration that the user inputs through the application.

[0693] "Generative modeling tools" refer to machine learning models and algorithms that automatically generate travel itineraries based on destination information.

[0694] "A means for arranging transportation tickets" is a system that automatically reserves airline and train tickets based on the user's travel itinerary.

[0695] A "means for booking accommodation" is a system for booking hotels and other accommodations based on a user's travel itinerary.

[0696] "A means of arranging local activities and facilities" is a system that makes reservations for tourist attractions and events based on the user's travel itinerary.

[0697] The "means for providing route guidance to a destination" is a system that calculates and provides guidance on the optimal route based on the user's current location and destination.

[0698] A "real-time interpretation solution" is a system that instantly translates speech and text when users speak different languages.

[0699] The "means for providing rewards to users" is a system that calculates and provides rewards such as points or cryptocurrency when users use the service.

[0700] A "means of using a generative AI model" is a method of using a generative AI model to generate travel itineraries or other information.

[0701] "Means for generating prompt sentences and using them as input for a generative AI model" refers to a method for automatically creating sentences to be input into a generative AI model.

[0702] This invention relates to a system that acquires destination information and provides travel arrangements and local support in an integrated manner based on that information. The system consists of devices such as smartphones and tablets, a cloud-based server, and a generative AI model.

[0703] User Interface

[0704] Terminal: The user inputs destination information using a dedicated application. The user specifies the destination and travel period through the interface, for example, "Destination: Tokyo, Duration: 5 days."

[0705] Destination information processing

[0706] Server: The server receives the destination information sent from the device. Based on the received destination information, it inputs a prompt sentence into the generative AI model. For example, the prompt sentence might be in the format "Please create a five-day itinerary for a trip to Tokyo. Day 1: Sightseeing in Asakusa, Day 2: Shopping in Akihabara, Day 3: Night view of Roppongi, Day 4: Disneyland."

[0707] Generate travel itineraries

[0708] Generative AI model: The generative AI model automatically generates an optimal travel itinerary based on a prompt, including details of the places and times to visit.

[0709] Presentation of proposed schedule

[0710] Server: Receives the travel itinerary proposals created by the generative AI model and sends them to the user's device.

[0711] Terminal: The user checks the proposed itinerary received on the terminal. If necessary, they make corrections and finally approve it. For example, they change the time for "Sightseeing in Asakusa" to "10:00."

[0712] Ticket and accommodation reservations

[0713] Server: Once the user approves the itinerary, the server uses an external system API to arrange transportation tickets and book accommodations. For example, it uses the Rakuten Travel API to book a hotel in Shinjuku.

[0714] Local activity arrangements

[0715] Server: Based on the generated itinerary, make reservations for local activities and facilities. For example, use the API of a tour booking site to book tickets to Disneyland.

[0716] Route guidance

[0717] Device: When a user travels to a destination, they use the application to send their current location information to the server. The server then calculates the optimal route based on this information and sends it to the device. The user can then receive real-time route guidance through the app.

[0718] Interpretation function

[0719] On the device: When users speak different languages, they activate the translation feature within the app. The device sends voice input to the server, and the generative AI model translates in real time. The translation result is displayed on the device or played aloud.

[0720] Reward System

[0721] Server: When a user uses or purchases a service, the server calculates rewards such as points or cryptocurrency based on that information. The calculated rewards are added to the user's account and stored in the database.

[0722] Specific examples

[0723] If a user plans a five-day trip to "Tokyo," the specific steps are as follows: When the user enters "Destination: Tokyo, Duration: 5 days" into the app, the generative AI model creates a five-day itinerary. For example, the itinerary might include "Sightseeing in Asakusa" on the first day, "Shopping in Akihabara" on the second day, "Night View of Roppongi" on the third day, and "Disneyland" on the fourth day. Once the user approves the itinerary, the server uses an external API to book flights and hotels, as well as local activities. On the day of the trip, the app provides real-time route guidance and interpretation functions. After the trip is over, rewards are awarded based on the service usage.

[0724] The above is a specific embodiment of the present invention, which is a system that allows users to make all travel arrangements and receive on-site support through a single application, thereby significantly reducing the hassle and cost of travel.

[0725] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0726] Step 1:

[0727] Input: The user launches a dedicated application and enters destination information such as "Destination: Tokyo, Duration: 5 days."

[0728] Operation: Information entered by the user is sent to the server through the terminal interface.

[0729] Output: The destination information is saved on the server.

[0730] Step 2:

[0731] Input: The destination information the server receives from the user.

[0732] How it works: The server generates prompts for the generative AI model based on the destination information it receives. For example, it generates a prompt like, "Please create a five-day itinerary for a trip to Tokyo. Day 1: Sightseeing in Asakusa, Day 2: Shopping in Akihabara, Day 3: Night view of Roppongi, Day 4: Disneyland."

[0733] Output: The generated prompt sentence is input to the generative AI model.

[0734] Step 3:

[0735] Input: A prompt sentence that the generative AI model will process.

[0736] How it works: Based on the prompt, the generative AI model automatically generates a travel itinerary that matches the user's preferences and travel duration. The generated itinerary includes details of locations and visit times.

[0737] Output: The generated itinerary proposals are returned to the server.

[0738] Step 4:

[0739] Input: Travel itinerary suggestions returned to the server from the generative AI model.

[0740] Operation: The server sends the generated itinerary proposal to the user's terminal.

[0741] Output: A proposed itinerary is displayed on the user's device.

[0742] Step 5:

[0743] Input: A proposed itinerary that the user reviews and modifies or approves.

[0744] Operation: The user checks the proposed travel itinerary through the terminal, makes specific modifications as necessary, and then approves the proposed itinerary.

[0745] Output: The modified or approved itinerary is sent to the server.

[0746] Step 6:

[0747] Input: Revised or approved itinerary.

[0748] Operation: The server uses the API of an external system (for example, a transportation reservation system or a lodging reservation system) to arrange transportation tickets and reserve accommodation based on the schedule.

[0749] Output: Reservation confirmation information is sent to the user's terminal.

[0750] Step 7:

[0751] Input: Booking information for local activities and facilities based on your travel dates.

[0752] What happens: The server uses an external API (e.g., a tour booking site) to complete a booking, such as reserving tickets to Disneyland.

[0753] Output: Local activity and facility reservation confirmation information is stored on the server and sent to the user's device.

[0754] Step 8:

[0755] Input: Location information that the user enters on their device.

[0756] How it works: When a user moves around the area, they input their current location information and send it to the server. The server then calculates the optimal route and sends it to the device.

[0757] Output: Real-time route guidance is displayed on the user's device.

[0758] Step 9:

[0759] Input: Voice input for different languages ​​that the user has set up on their device.

[0760] How it works: The device sends voice input to the server, where the generative AI model translates it in real time. The translation is then displayed on the device or played back aloud.

[0761] Output: The translated text and audio are available on the user's device.

[0762] Step 10:

[0763] Input: Information about the user's use of the service.

[0764] How it works: The server calculates rewards such as points or cryptocurrency based on usage information and credits them to the user's account.

[0765] Output: Reward information is saved in the user's database and reflected in their account.

[0766] The above are the specific steps in the program processing of this system.

[0767] (Application example 1)

[0768] 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."

[0769] Conventional travel arrangement systems do not provide sufficient support for users on how to arrange meals during their trip. Furthermore, there is a lack of systems that allow users to easily make reservations at local restaurants or use food delivery services. This often forces travelers to take the trouble of arranging meals themselves, diminishing the convenience and comfort of their trip.

[0770] 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.

[0771] In this invention, the server includes a means for acquiring destination information, a generative model means for automatically creating a travel itinerary, a means for arranging transportation tickets, a means for reserving accommodations, a means for arranging local activities and facilities, a means for providing route guidance, a means for real-time interpretation, a means for recommending restaurants and making reservations and orders, and a means for rewarding users. This allows travelers to centrally manage and execute meal arrangements and reservations along with their entire travel schedule simply by entering destination information, thereby significantly improving convenience and comfort during travel.

[0772] "Destination information" is information that allows a user to specify a travel destination and itinerary.

[0773] A "generative model means" is a means that uses a generative AI model to automatically generate optimal travel itineraries based on destination information.

[0774] "Means for arranging transportation tickets" refers to means for reserving necessary transportation such as airline tickets, trains, and buses according to the user's travel itinerary.

[0775] A "means for reserving accommodation" is a means for reserving a hotel or other accommodation based on the user's travel itinerary.

[0776] "Means of arranging local activities and facilities" refers to means of arranging sightseeing, experiential activities, use of facilities, etc. at travel destinations.

[0777] The "means for providing route guidance" is a means for providing real-time guidance on the optimal route to a destination while the user is traveling.

[0778] "Means for real-time interpretation" refers to means for providing an interpretation function for translating communication between users who speak different languages ​​in real time.

[0779] "Means for recommending restaurants and making reservations / orders" refers to means for recommending the most suitable restaurant based on destination information, and for making reservations at the restaurant and ordering from the menu.

[0780] The "means for providing rewards to users" refers to a means for providing rewards such as points or coupons to users based on their use of the travel arrangement system.

[0781] The present invention is a system that provides travel arrangements and local support in an integrated manner based on destination information. Specific embodiments of the present invention will be described below.

[0782] User Interface

[0783] Device:

[0784] Users launch a dedicated application on their smartphone, tablet, or other device and specify their destination and travel dates through an interface for entering destination information. For example, a user might enter "Tokyo" and "2023-10-01 to 2023-10-05."

[0785] Destination information processing

[0786] server:

[0787] Destination information is received and input into the generative AI model to request automatic itinerary generation. The generative AI model creates an optimal itinerary based on the user's preferences and the number of days of travel. For example, for a trip to "Tokyo," it will consider itineraries such as sightseeing in Asakusa, shopping in Akihabara, the night view of Roppongi, and Disneyland.

[0788] Presentation of proposed schedule

[0789] Device:

[0790] The proposed travel itinerary sent from the server is displayed to the user, who can then review it and modify or approve it as necessary.

[0791] Ticket booking and hotel reservations

[0792] server:

[0793] Once the itinerary is approved, transportation tickets are arranged via an external system (e.g., an airline or travel agency API), and accommodation reservations are also made using external APIs.

[0794] Local activity arrangements

[0795] server:

[0796] Based on the itinerary, local activities and facilities are arranged, also using external APIs.

[0797] Route guidance

[0798] Device:

[0799] When a user moves to a destination, their current location and destination information are sent to the server. The server then calculates the optimal route based on this information and sends it to the device. The user can then receive real-time route guidance via the app.

[0800] Interpretation function

[0801] Device:

[0802] When users speak different languages, they activate the in-app translation feature. The device sends voice input to the server, and the generative AI model translates in real time. The translation result is displayed on the device or played aloud.

[0803] Restaurant recommendations and reservations / orders

[0804] server:

[0805] Based on the destination information, the system recommends the most suitable restaurant and arranges reservations and orders based on the user's preferences and dates. This also uses an external restaurant API. For example, if a user is looking for an okonomiyaki restaurant while traveling in Tokyo, the AI ​​will recommend the most suitable candidate and allow for on-the-spot reservations.

[0806] Reward System

[0807] server:

[0808] When a user purchases or uses a service, the information is verified and rewards such as points and coupons are calculated and awarded.

[0809] Specific examples

[0810] As a practical example, consider a user planning a five-day trip to Tokyo. When the user enters "Tokyo" and the dates "2023-10-01 to 2023-10-05" into the app, the generative AI model creates a five-day itinerary, suggesting itineraries such as "Sightseeing in Asakusa" on Day 1, "Shopping in Akihabara" on Day 2, "Viewing the Roppongi Nightscape" on Day 3, and "Disneyland" on Day 4. Furthermore, the model recommends and makes reservations for restaurants, making it easy to make reservations at famous sushi restaurants and yakiniku restaurants, for example. Once the user approves the itinerary, the server arranges flight and hotel reservations through an external API, as well as local activities. On the day of the trip, the app provides real-time route guidance and interpretation services. Points and coupons are also awarded after the trip is completed.

[0811] Prompt Sentence Examples

[0812] "Enter your travel destination and dates. We'll then suggest restaurant recommendations and food delivery options."

[0813] "Create an optimal food schedule based on this destination information and dates."

[0814] The present invention aims to provide these functions in a unified manner, allowing travelers to easily arrange and reserve meals along with their overall travel schedule, thereby significantly improving the convenience and comfort of travel.

[0815] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0816] Step 1:

[0817] Enter destination information

[0818] Users start a dedicated application on their smartphone, tablet, or other device and enter their destination information (destination and itinerary), which is then sent to the server.

[0819] Input: Destination information (e.g. Tokyo, 2023-10-01 to 2023-10-05)

[0820] Output: Destination information sent to the server

[0821] Step 2:

[0822] Automatic itinerary generation

[0823] The server inputs the received destination information into the generative AI model and automatically generates a travel itinerary using prompts. The generative AI model creates an optimal travel itinerary based on the user's preferences and the number of travel days.

[0824] Input: Destination information

[0825] Output: Generated itinerary (e.g., Day 1: sightseeing in Asakusa, Day 2: shopping in Akihabara)

[0826] Step 3:

[0827] Proposal of travel itinerary

[0828] The server sends the generated proposed travel itinerary to the terminal, which displays it to the user, who can review the proposed itinerary and modify or approve it as necessary.

[0829] Input: Generated itinerary

[0830] Output: Proposed itinerary displayed to the user

[0831] Step 4:

[0832] Ticket and accommodation booking

[0833] Based on the schedule approved by the user, the server arranges transportation tickets via an external system (e.g., API of an airline or travel agency) and makes reservations for accommodation using the external API.

[0834] Input: Approved travel itinerary

[0835] Output: Booked transportation tickets, accommodation reservation information

[0836] Step 5:

[0837] Local activity arrangements

[0838] The server arranges local activities and facilities based on the travel itinerary using an external API.

[0839] Input: Approved travel itinerary

[0840] Output: Reservation information for arranged activities and facilities

[0841] Step 6:

[0842] Providing route guidance

[0843] During a trip, the user sends information about their current location and destination from their device to the server, which then calculates the optimal route and provides real-time route guidance.

[0844] Input: Current location information, destination information

[0845] Output: Optimal route directions

[0846] Step 7:

[0847] Real-time interpretation

[0848] When users speak different languages, they activate the interpretation function. The device sends voice input to the server, and the generative AI model translates in real time. The translation result is displayed on the device or played aloud.

[0849] Input: Voice input

[0850] Output: Translation result

[0851] Step 8:

[0852] Restaurant recommendations and reservations / orders

[0853] The server recommends the most suitable restaurant based on the destination information, and also arranges reservations and orders based on the user's preferences and dates, again using an external restaurant API.

[0854] Input: Destination information, user preferences, travel itinerary

[0855] Output: Recommended restaurant information, reservation and order confirmation

[0856] Step 9:

[0857] Reward System

[0858] Information about the user's use of the service is sent to the server, and rewards such as points and coupons are added to the user's account.

[0859] Input: Service usage information

[0860] Output: Points and coupons awarded

[0861] The above are the specific processing steps for implementing this invention. By showing the specific hardware and software operations and data flow in each step in detail, the technical scope of the invention will become clearer.

[0862] 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.

[0863] The present invention relates to a system that acquires destination information and provides travel arrangements and local support in an integrated manner based on that information, and aims to increase user satisfaction by further combining it with an emotion engine that recognizes the user's emotions. Specific embodiments of this system will be described below.

[0864] The system is comprised of a smartphone, tablet, or other device, a cloud-based server, a generative AI model, and an emotion engine. Users input their destination information using a dedicated application, and then receive travel arrangements and guide services. The emotion engine analyzes the user's emotions to personalize the travel experience.

[0865] User Interface

[0866] Device: The user launches the application and enters "Tokyo" as their destination. As they enter their destination, the application recognizes their emotions from their facial expressions and tone of voice via a camera and microphone.

[0867] Destination information processing and emotion recognition

[0868] Server: Upon receiving the destination information "Tokyo," the emotion engine analyzes the emotion data and evaluates the user's emotional state during the travel planning stage.

[0869] Generative AI model: Generates optimal travel itineraries based on destination information, travel days, and user emotional state and preferences. For example, if a user is feeling stressed, the model suggests itineraries that emphasize relaxation and refreshment.

[0870] Presentation of proposed schedule

[0871] Terminal: Displays the travel itinerary proposal sent from the server to the user. The itinerary proposal is customized according to the user's emotional state.

[0872] User: Review the proposed schedule and modify or approve it as necessary.

[0873] Ticket arrangements, hotel reservations and local activity arrangements

[0874] Server: Once the itinerary is approved, transportation tickets are arranged via an external API (for example, an airline or travel agency API). Accommodation reservations are also made using an external API. Based on the information from the emotion engine, a more comfortable accommodation environment is selected.

[0875] Server: Arranges local activities and facilities based on the schedule. Using an emotion engine, prioritizes and selects activities that will reduce the user's stress and pique their interest.

[0876] Route guidance and interpretation functions

[0877] Terminal: When a user requests route guidance to the destination "Sensoji Temple" while on-site, the terminal sends current location information and destination information to the server.

[0878] Server: Calculates the optimal route based on the current location and destination information and sends it to the device. The emotion engine takes into account the user's fatigue and stress during the trip and provides the optimal route.

[0879] Device: When users speak different languages, they activate the translation function within the app and send the audio data to the server.

[0880] Generative AI model: Translates voice data in real time and sends the results back to the server. The emotion engine generates translation results that alleviate the user's anxiety and tension.

[0881] Reward System

[0882] Server: Checks the user's usage information and calculates rewards such as PayPay points or cryptocurrency. The emotion engine evaluates the user's gratitude and satisfaction and adjusts the reward accordingly.

[0883] Server: Calculated rewards are credited to the user's account and stored in a database.

[0884] Specific examples

[0885] As a practical example, consider a user planning a five-day trip to Tokyo. When the user enters "Tokyo" into the app, the generative AI model creates a five-day itinerary and suggests activities such as sightseeing in Asakusa on day one, shopping in Akihabara on day two, the night view of Roppongi on day three, and Disneyland on day four. If the user is feeling stressed, a relaxation spa or nature walk can be added to the itinerary. Furthermore, if the user's emotional state changes during the trip, the emotion engine recognizes this and dynamically adjusts the itinerary and activities. For example, if the user feels tired, the plan can be changed to include a massage or relaxation time at the hotel. On the day of the trip, real-time route guidance and interpretation functions are provided through the app. Furthermore, after the trip, bonus points and cryptocurrency based on the emotion engine are awarded.

[0886] As described above, the present invention is a system that allows users to receive all travel arrangements and on-site support through a single application, and by combining it with an emotion engine, it is possible to provide a personalized travel experience that corresponds to the user's emotional state.

[0887] The processing flow will be explained below.

[0888] Step 1:

[0889] User: Launches the app and enters "Tokyo" as the destination.

[0890] Step 2:

[0891] Terminal: Checks the entered destination information and sends it to the server. At the same time, it captures the user's facial expressions and tone of voice via a camera and microphone to obtain emotional data.

[0892] Step 3:

[0893] Server: Receives the destination information "Tokyo" and inputs the destination information and emotion data into the generative AI model and emotion engine, respectively.

[0894] Step 4:

[0895] Emotion engine: Analyzes the acquired emotional data and evaluates the user's emotional state. For example, it generates a result such as "high stress."

[0896] Step 5:

[0897] Generative AI model: Creates travel itinerary suggestions based on destination information and emotional state (e.g., "high stress") derived from the emotion engine, including itineraries focused on relaxation and refreshment to reduce stress.

[0898] Step 6:

[0899] Server: Stores the generated travel itinerary proposals in a database and sends them to the terminal.

[0900] Step 7:

[0901] Terminal: Displays the travel itinerary proposals received from the server to the user, customized to reflect the user's emotional state.

[0902] Step 8:

[0903] User: Checks the proposed schedule and clicks the approve button. User can also request revisions if necessary.

[0904] Step 9:

[0905] Terminal: Sends the user's authorization data to the server.

[0906] Step 10:

[0907] Server: After receiving approval, sends a request to arrange transportation tickets via an external API (e.g., an airline or travel agency API).

[0908] Step 11:

[0909] External API: Executes transportation ticket reservations and returns reservation information to the server.

[0910] Step 12:

[0911] Server: Stores the received ticket reservation information in a database.

[0912] Step 13:

[0913] Server: Sends a request to check the availability of accommodation using an external API (for example, the API of a hotel booking site).

[0914] Step 14:

[0915] External API: Returns the accommodation availability information to the server.

[0916] Step 15:

[0917] Server: Enters vacant room information into the generative AI model and requests it to select the most suitable accommodation, taking into account the evaluation of the emotion engine.

[0918] Step 16:

[0919] Generative AI model: Selects the most suitable accommodation based on availability information and the emotion engine's evaluation, and sends it back to the server.

[0920] Step 17:

[0921] Server: Sends a reservation request for the selected accommodation to an external API.

[0922] Step 18:

[0923] External API: Performs reservation confirmation and returns reservation information to the server.

[0924] Step 19:

[0925] Server: Stores the received accommodation reservation information in a database.

[0926] Step 20:

[0927] Server: Based on the itinerary, it sends a request to arrange reservations for local activities and facilities via an external API (e.g., the API of a tour booking site).

[0928] Step 21:

[0929] External API: Sends activity and facility reservation information back to the server.

[0930] Step 22:

[0931] Server: Stores the received reservation information in a database. The emotion engine monitors the user's emotional state and suggests changes to the activity if necessary.

[0932] Step 23:

[0933] User: Requests route guidance to the destination "Sensoji Temple" from the app while on location.

[0934] Step 24:

[0935] Terminal: Sends current location information and destination information to the server.

[0936] Step 25:

[0937] Server: Calculates the optimal route based on current location and destination information. The emotion engine takes into account fatigue and stress levels during travel and provides the optimal route.

[0938] Step 26:

[0939] Server: Sends calculated route information to the device.

[0940] Step 27:

[0941] Terminal: Display route directions to the user.

[0942] Step 28:

[0943] Users: Activate the in-app translation feature if they need to speak a different language locally.

[0944] Step 29:

[0945] Device: Sends voice input to the server.

[0946] Step 30:

[0947] Server: Analyzes the voice data and requests translation from the generative AI model. The emotion engine generates translation results that alleviate the user's anxiety and tension.

[0948] Step 31:

[0949] Generative AI model: performs real-time translation from Japanese to English (or other languages) and sends it back to the server.

[0950] Step 32:

[0951] Server: Sends the translation results to the device.

[0952] Step 33:

[0953] Terminal: The translation result is displayed to the user or output as speech.

[0954] Step 34:

[0955] Server: Checks the user's usage information and calculates rewards in PayPay points or cryptocurrency. The emotion engine also evaluates the user's emotional state and adjusts rewards accordingly.

[0956] Step 35:

[0957] Server: Calculated rewards are credited to the user's account and stored in a database.

[0958] Example 2

[0959] 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."

[0960] The present invention relates to a system that allows users to centrally plan and arrange trips and personalize the travel experience based on the user's emotional state. Conventional travel support systems can automatically generate and arrange trip plans, but do not personalize the experience based on the user's emotional state. This makes it difficult to maximize user satisfaction. Furthermore, they do not adequately provide real-time route guidance, interpretation functions, or rewards based on the user's evaluated emotions.

[0961] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for acquiring destination information, a generative model means for automatically creating a travel itinerary, a means for arranging transportation tickets, a means for booking accommodations, a means for arranging local activities and facilities, a means for providing route guidance to the destination, a means for real-time interpretation, a means for rewarding the user, a sentiment analysis means for analyzing the user's emotional state, and a means for personalizing the travel itinerary based on the sentiment analysis means. This enables a personalized travel experience according to the user's emotional state, thereby improving user satisfaction. Specifically, by analyzing the user's emotional state, if the user is feeling stressed, the system can suggest relaxation-oriented trips and flexibly respond to emotional changes during the trip, thereby providing an optimal travel experience.

[0962] "Destination information" is information about a geographical location that a user specifies as a travel destination.

[0963] The "generative model means" is an algorithm or program that has the function of automatically generating an optimal travel itinerary based on destination information and other related information.

[0964] The "means for arranging transportation tickets" is a system that has the function of reserving and purchasing transportation necessary for travel, such as airline tickets and train tickets, based on travel itineraries.

[0965] A "means for reserving accommodation" is a system that has the functionality to reserve hotels and other accommodations based on travel dates.

[0966] The "means for arranging local activities and facilities" is a system that has the function of making reservations for participation in local tourist attractions and activities based on travel itineraries.

[0967] The "means for providing route guidance to a destination" is a system that has the function of calculating and providing guidance on the optimal route to a destination specified by a user.

[0968] A "means for performing real-time interpretation" is a system that has the function of translating voice data in real time to support communication between users who speak different languages.

[0969] The "means for providing rewards to users" is a system that has the function of providing rewards such as points or cryptocurrency based on the user's satisfaction and usage information after the trip.

[0970] The "emotion analysis means" is a system that has the function of analyzing the emotional state of a user using data such as facial expressions and tone of voice.

[0971] The "means for personalizing travel itineraries based on emotion analysis means" is a system that has the function of optimizing and personalizing travel itineraries according to the user's emotional state analyzed by the emotion analysis means.

[0972] The present invention relates to a system that acquires destination information and provides travel arrangements and local support in an integrated manner based on that information. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to personalize the user's travel experience and increase satisfaction. A specific embodiment of this system will now be described.

[0973] User Interface

[0974] Device: The user launches a dedicated application on a device such as a smartphone or tablet and inputs destination information. For example, when "Tokyo" is entered as destination information, the information is acquired by the device. When inputting information, data is also acquired through a camera and microphone to recognize emotions from the user's facial expressions and tone of voice. This allows data to be collected based on the user's emotional state.

[0975] Destination information processing and emotion recognition

[0976] Server: Receives the destination information "Tokyo" and emotion data sent from the device. The server is cloud-based and analyzes the emotion data using an emotion engine. This allows the server to evaluate the user's emotional state during the trip planning stage.

[0977] Generative AI model: The generative AI model installed on the server generates an optimal travel itinerary based on destination information, the number of days of travel, the user's emotional state, and past travel preference data. For example, if the user is feeling stressed, it will suggest an itinerary that emphasizes relaxation and refreshment.

[0978] Presentation and confirmation of proposed schedule

[0979] Terminal: Displays the travel itinerary proposals sent from the server to the user. The generated itinerary proposals are customized according to the user's emotional state.

[0980] Users can review the proposed schedule and make any necessary changes or approvals. Changes can be easily made through the application interface.

[0981] Travel arrangements and local support

[0982] Server: After the user approves the itinerary, the server uses external APIs (such as airlines and travel agencies) to arrange transportation tickets and book accommodations. The emotion engine selects a more comfortable accommodation environment. It also arranges local activities and facilities, taking measures to reduce the user's stress and increase their interest.

[0983] Route guidance and interpretation functions

[0984] Terminal: When a user requests route guidance to the destination "Sensoji Temple" while on the spot, the terminal sends current location information and destination information to the server.

[0985] Server: Calculates the optimal route based on the received information and sends it to the device. Using the emotion engine, route guidance is provided that takes into account the user's fatigue and stress during travel.

[0986] On the device: When users speak different languages, they activate the translation function within the app and send the audio data to the server.

[0987] Generative AI model: Translates speech data in real time and sends the results back to the server, generating translation results that alleviate the user's anxiety and tension.

[0988] Post-trip rewards

[0989] Server: Calculates rewards such as PayPay points or cryptocurrency based on the user's usage information of the provided service. The emotion engine evaluates the user's gratitude and satisfaction and adjusts the amount of reward accordingly.

[0990] Server: The calculated reward is credited to the user's account and the data is stored in a database.

[0991] Specific examples

[0992] For example, if a user plans a five-day trip to "Tokyo," they enter "Tokyo" into the app. The generative AI model creates an itinerary based on the following prompt: "Create a five-day itinerary for Tokyo. The user wants to relax." Based on this, it suggests itineraries such as "Sightseeing in Asakusa" on the first day, "Shopping in Akihabara" on the second day, "Viewing the night view of Roppongi" on the third day, and "Disneyland" on the fourth day. If the user is feeling stressed, it adds relaxation-focused spa and nature walks.

[0993] If the user feels tired during the trip, the emotion engine will recognize this and the server will dynamically adjust the itinerary and activities in real time to provide the user with a comfortable travel experience. After the trip is over, the user will be awarded bonus points and cryptocurrency based on the evaluation made by the emotion engine.

[0994] In this way, the system comprehensively supports the entire process from obtaining destination information to providing travel experiences and rewards, and can also provide personalized services according to the user's emotional state.

[0995] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0996] Step 1: Enter your destination information

[0997] Terminal: The user launches the application and enters destination information. For example, if the user enters "Tokyo" as the destination, the information is acquired by the terminal.

[0998] Input: Destination information ("Tokyo")

[0999] Output: Destination information data

[1000] Specific operation: The user enters the destination using the keyboard or voice input, and the device stores the information in a database within the application.

[1001] Step 2: Obtaining emotion data

[1002] Terminal: At the same time as entering destination information, the device captures the user's facial expressions and tone of voice via a camera and microphone.

[1003] Input: User's facial expressions, tone of voice

[1004] Output: Emotion data

[1005] Specific operations: The device activates the camera to capture facial expressions, uses the microphone to collect voice data, and sends this data to the emotion analysis module.

[1006] Step 3: Send destination information and emotion data to the server

[1007] Terminal: Sends the acquired destination information and emotion data to the server.

[1008] Input: Destination information data, emotion data

[1009] Output: Data sent to the server via the HTTP request

[1010] Specific operation: The device packages destination information and emotion data and sends it to the server using an HTTP request.

[1011] Step 4: Generate itinerary

[1012] Server: Receives destination information and emotion data and generates an optimal travel itinerary using a generative AI model.

[1013] Input: Destination information data, emotion data, number of travel days, user preference data

[1014] Output: Travel itinerary proposal

[1015] Specific operation: The generative AI model generates a travel itinerary based on the prompt, "Please create a 5-day travel itinerary for Tokyo. The user wants to relax." The sentiment analysis engine analyzes the emotional data and generates a personalized itinerary that reflects the user's emotional state.

[1016] Step 5: Present and confirm the proposed schedule

[1017] Terminal: Displays the proposed travel itinerary sent from the server to the user.

[1018] Input: Travel itinerary proposal

[1019] Output: Display of proposed schedule

[1020] Specific operation: The device displays the proposed schedule received from the server on the application's UI. The user checks the schedule and makes any necessary corrections or approvals.

[1021] Step 6: Revise the proposed itinerary

[1022] User: Review the proposed schedule and make any necessary changes or approve it through the interface.

[1023] Input: Schedule proposal, correction instructions

[1024] Output: Revised schedule or approval

[1025] Specific operation: The user adjusts the schedule using touch operations or drag-and-drop operations, and presses the "Approve" button to confirm the final schedule.

[1026] Step 7: Making travel arrangements

[1027] Server: Based on the itinerary approved by the user, the server uses external services to arrange transportation tickets and make hotel reservations.

[1028] Input: Approved schedule, emotion data

[1029] Output: Ticket and booking confirmation information

[1030] Specific operation: The server sends a request to an external API (travel agency, airline, hotel, etc.) to confirm the ticket or accommodation reservation, receives the API response, and sends the confirmation information to the terminal.

[1031] Step 8: Local route guidance

[1032] Terminal: The user requests route guidance locally and sends current location information and destination information to the server.

[1033] Input: current location information, destination information

[1034] Output: Optimal route guidance

[1035] Specific operation: The device acquires GPS data and sends it to the server along with destination information.

[1036] Server: Calculates the optimal route based on the received information and sends it to the terminal.

[1037] Input: current location information, destination information

[1038] Output: Route guidance information

[1039] Specific operation: The server uses a route calculation algorithm to calculate the optimal route and sends the result to the terminal.

[1040] Step 9: Run the interpretation function

[1041] Terminal: When a user uses the interpretation function, the terminal sends voice data to the server.

[1042] Input: Audio data

[1043] Output: The translated text

[1044] Specific operation: The device uses a microphone to capture voice data and sends it to the server.

[1045] Server: Receives the voice data and performs real-time translation using the generative AI model.

[1046] Input: Audio data

[1047] Output: The translated text

[1048] How it works: The server passes the voice data to the generative AI model and sends the translated text back to the device.

[1049] Step 10: Post-trip rewards

[1050] Server: Evaluates the user's gratitude and satisfaction based on service usage information and calculates rewards.

[1051] Input: service usage information, emotion data

[1052] Output: Reward points

[1053] Specific operation: The sentiment analysis engine evaluates satisfaction based on the emotional data, calculates rewards, and adds the calculated rewards to the user's account and stores them in the database.

[1054] The above are the specific processing steps of the program of this system.

[1055] (Application example 2)

[1056] 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."

[1057] Conventional travel booking and food delivery systems lack the ability to provide personalized suggestions that take into account the user's emotional state. As a result, they often do not provide the optimal service for the user's current emotional state, preventing improvement in satisfaction. In addition, when the user is tired or stressed, the system does not suggest the optimal menu according to the situation, resulting in a poor user experience.

[1058] 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.

[1059] In this invention, the server includes a means for acquiring destination information, a generative model means, and an emotion engine means. This allows the server to analyze the user's emotional state in real time and propose optimal travel itineraries and menus based on that analysis. Furthermore, by linking with external systems, the server can arrange transportation, accommodations, and local activities, further enhancing user convenience.

[1060] "Destination information" is information about the user's desired travel destination or delivery destination.

[1061] The "generative model means" is a system for automatically creating travel itineraries and menus based on input destination information.

[1062] The "emotion engine means" is a system for analyzing the user's facial expressions and tone of voice to recognize the user's current emotional state.

[1063] The "generative AI model means" is an artificial intelligence model that generates optimal food menus and travel plans based on the user's destination information, emotional state, and preferences.

[1064] "Means for arranging transportation tickets" is a system for automatically reserving transportation according to the user's travel schedule.

[1065] The "means for reserving accommodation" is a system for reserving accommodation based on the user's travel itinerary.

[1066] "A means for arranging local activities and facilities" is a system for making reservations for tourist attractions and entertainment according to the user's travel itinerary.

[1067] The "means for providing route guidance to a destination" is a system for navigating the optimal route based on destination information input by the user.

[1068] A "means for real-time interpretation" is a system for translating speech in real time when users speak different languages.

[1069] The "means for providing rewards to users" is a system for providing points or rewards to users according to their usage of the service.

[1070] System Overview

[1071] The present invention relates to a system that acquires destination information, automatically creates travel itineraries and menus based on that information, and provides them while taking into account the emotional state of the user. Specific embodiments for carrying out the invention are described below.

[1072] Hardware and Software Configuration

[1073] Hardware used

[1074] Device: Smartphone or tablet

[1075] Server: Cloud-based server

[1076] Software used

[1077] Emotion engine: A sentiment analysis library such as the Affectiva SDK

[1078] Generative AI models: Generative models such as OpenAI's GPT-4

[1079] Processing flow

[1080] User Interface

[1081] The user launches the application on their device and enters destination information (travel destination or delivery address). At that time, the device's camera and microphone are used to capture the user's emotional state. The captured emotional data is then analyzed using an emotion engine (such as the Affectiva SDK).

[1082] Destination information processing and emotion recognition

[1083] The server receives the destination information and evaluates the user's emotional data analyzed by the emotion engine, then uses a generative AI model (such as OpenAI's GPT-4) to generate optimal travel itineraries and food menus based on the destination information, the user's emotional state, and past preference data.

[1084] Proposal of schedule and menu

[1085] The terminal displays the customized itinerary and menu proposals sent from the server to the user, who can then review the proposals and modify or approve them as necessary.

[1086] Ticket arrangements, accommodation reservations, activity arrangements

[1087] Based on the approved travel itinerary, the server connects with external systems (such as the APIs of airlines and hotel booking sites) to arrange transportation tickets and book accommodations. It also automates the arrangement of local activities and facilities using external APIs.

[1088] Route guidance and interpretation functions

[1089] When a user requests guidance to a destination, the server provides the device with the optimal route based on the user's current location. Furthermore, if the user speaks a different language, the server collects voice data through the device's microphone and performs real-time interpretation. The server translates the voice data using a generative AI model and sends the results back to the device.

[1090] Reward System

[1091] When a user uses the service, they are given rewards (points or rewards) based on their satisfaction level as assessed by the emotion engine. This information is managed on the server and reflected in the user's account.

[1092] Specific examples

[1093] As an actual usage example, consider the case where a user inputs "I'm tired" and requests delivery to their home in Tokyo. When the user inputs their destination information into the app, the emotion engine retrieves emotion data that evaluates "tired." Based on this information, the generative AI model generates and suggests food menus with a relaxing effect (e.g., "herbal tea" or "comfort food") to the user.

[1094] Prompt Sentence Examples

[1095] Input sentence: "I'm tired from work today. Please deliver to my home in Tokyo."

[1096] Prompt: "Suggest some food options for when the user is feeling tired. Ideally, these would include relaxing foods and drinks."

[1097] The above is a specific embodiment for carrying out the invention. By combining an emotion engine and a generative AI model, it becomes possible to provide personalized services according to the user's emotional state.

[1098] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1099] Step 1:

[1100] Enter destination information

[1101] A user launches the application and enters destination information (e.g., a travel destination or delivery address). The entered data is temporarily stored in the device's memory. At the same time, the device's camera and microphone are used to capture the user's facial expressions and tone of voice.

[1102] Input: Destination information, facial expression data, tone of voice data

[1103] Output: Destination information, emotion data

[1104] Step 2:

[1105] Emotional Data Analysis

[1106] The device sends the acquired facial expression and tone of voice data to an emotion engine (such as the Affectiva SDK) to analyze the user's emotional state. The analysis results in the user's emotional state (e.g., relaxed, stressed, tired).

[1107] Input: Facial expression data, tone of voice data

[1108] Output: Emotional state (e.g., relaxed, stressed, tired)

[1109] Step 3:

[1110] Sending destination information and emotion data

[1111] The device transmits destination information and emotional state to the server, which stores the information in an integrated database.

[1112] Input: Destination information, emotional state

[1113] Output: Destination information and emotional state stored on the server

[1114] Step 4:

[1115] Generating the optimal plan

[1116] The server uses a generative AI model (such as OpenAI's GPT-4) to generate optimal travel itineraries and food menus based on the saved destination information and emotional state, while also referencing past user preference data.

[1117] Input: Destination information, emotional state, past user preference data

[1118] Output: Suggested travel itinerary or food menu

[1119] Step 5:

[1120] Plan presentation and approval

[1121] The server sends the generated proposal to the terminal and displays the information to the user, who can review the proposed plan and modify or approve it as necessary.

[1122] Input: Proposed plan

[1123] Output: User approves or modifies the plan

[1124] Step 6:

[1125] Ticket and reservation arrangements

[1126] Based on the approved travel itinerary, the server connects with external systems (such as airline and booking site APIs) to arrange transportation tickets and book accommodations, and in the case of food delivery, connects with the suggested menu items to order.

[1127] Input: Approved itinerary or menu proposal

[1128] Output: Booked tickets, accommodation, or food orders

[1129] Step 7:

[1130] Route guidance and interpretation features

[1131] When a user requests directions to a destination, the server provides the optimal route based on the user's current location. Furthermore, if a different language is used, the server collects voice data and translates it in real time.

[1132] Input: Current location, voice data

[1133] Output: Optimal route guidance, translated audio data

[1134] Step 8:

[1135] Rewarding

[1136] When a user uses the service, the server grants rewards (points or rewards) to the user's account based on the satisfaction level assessed by the emotion engine.

[1137] Input: Service usage data, emotion evaluation data

[1138] Output: Points or rewards awarded

[1139] 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.

[1140] 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.

[1141] 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.

[1142] [Third embodiment]

[1143] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1144] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1145] 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).

[1146] 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.

[1147] 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.

[1148] 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).

[1149] 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.

[1150] 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.

[1151] 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.

[1152] 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.

[1153] 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.

[1154] 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."

[1155] The present invention relates to a system that acquires destination information and provides travel arrangements and local support in an integrated manner based on that information. A specific embodiment of this system will be described below.

[1156] The system consists of a smartphone or tablet device, a cloud-based server, and a generative AI model. Users can input destination information using a dedicated application and receive travel arrangements and guide services.

[1157] User Interface

[1158] Terminal: The user launches the application and specifies the destination through the interface where destination information is input. For example, the user inputs the destination "Tokyo."

[1159] Destination information processing

[1160] Server: Receives destination information, inputs it into the generative AI model, and requests automatic itinerary generation. The generative AI model creates an optimal itinerary based on the user's preferences and number of travel days. For example, for a trip to Tokyo, it might consider itineraries such as sightseeing in Asakusa, shopping in Akihabara, the night view of Roppongi, and Disneyland.

[1161] Presentation of proposed schedule

[1162] Terminal: Displays the proposed travel itinerary sent from the server to the user, who can then review the proposed itinerary and modify or approve it as necessary.

[1163] Ticket booking and hotel reservations

[1164] Server: Once the itinerary is approved, transportation tickets are arranged via an external system (for example, an airline or travel agency API). Accommodation reservations are also made using external APIs. For example, a hotel is booked using the Rakuten Travel API.

[1165] Local activity arrangements

[1166] Server: Arranges local activities and facilities based on the itinerary. This is also done using an external API (for example, the API of a tour booking site). For example, when arranging tickets to Disneyland, the reservation information is obtained and confirmed.

[1167] Route guidance

[1168] Device: When a user travels to a destination, they send their current location and destination information to the server. The server calculates the optimal route based on this information and sends it to the device. The user can then receive real-time route guidance via the app.

[1169] Interpretation function

[1170] On-device: When users speak different languages, they activate the in-app translation feature. The device sends voice input to the server, and the generative AI model translates in real time. The translation result is displayed on the device or played aloud.

[1171] Reward System

[1172] Server: When a user purchases or uses a service, the server checks the information and calculates rewards such as PayPay points or cryptocurrency. The calculated rewards are added to the user's account and stored in the database.

[1173] Specific examples

[1174] As an actual usage example, consider a user planning a five-day trip to "Tokyo." When the user enters "Tokyo" into the app, the following steps are executed: The generative AI model creates a five-day itinerary and presents plans such as "Sightseeing Asakusa" on the first day, "Shopping in Akihabara" on the second day, "Night View of Roppongi" on the third day, and "Disneyland" on the fourth day. Once the user approves the itinerary, the server arranges flight and hotel reservations through an external API, as well as local activities. On the day of the trip, real-time route guidance and interpretation functions are provided through the app. In addition, after the trip is completed, PayPay points and cryptocurrency are awarded.

[1175] As described above, the present invention is a system that allows users to make all travel arrangements and receive local support through a single application, significantly reducing the effort and cost of travel.

[1176] The processing flow will be explained below.

[1177] Step 1:

[1178] User: Launches the app and enters "Tokyo" as the destination.

[1179] Step 2:

[1180] Terminal: Checks the entered destination information and sends it to the server.

[1181] Step 3:

[1182] Server: Receives destination information "Tokyo", inputs it into the generative AI model, and requests itinerary creation.

[1183] Step 4:

[1184] Generative AI model: Generates an optimal travel itinerary based on data such as destination information, number of travel days, and user preferences, and sends it back to the server.

[1185] Step 5:

[1186] Server: Stores the generated travel itinerary proposals in a database and sends them to the terminal.

[1187] Step 6:

[1188] Terminal: Displays the proposed travel itinerary received from the server to the user.

[1189] Step 7:

[1190] User: Check the proposed schedule and click the approve button.

[1191] Step 8:

[1192] Terminal: Sends the user's authorization data to the server.

[1193] Step 9:

[1194] Server: Upon approval, arranges transportation tickets via an external API (e.g., an airline or travel agency API).

[1195] Step 10:

[1196] External API: Executes transportation ticket reservations and returns reservation information to the server.

[1197] Step 11:

[1198] Server: Stores the received ticket reservation information in a database.

[1199] Step 12:

[1200] Server: Sends a request to check the availability of accommodation using an external API (for example, the API of a hotel booking site).

[1201] Step 13:

[1202] External API: Returns the accommodation availability information to the server.

[1203] Step 14:

[1204] Server: Enters room availability information into the generative AI model and requests it to select the most suitable accommodation.

[1205] Step 15:

[1206] Generative AI model: Selects the most suitable accommodation based on availability information and returns it to the server.

[1207] Step 16:

[1208] Server: Sends a reservation request for the selected accommodation to an external API.

[1209] Step 17:

[1210] External API: Performs reservation confirmation and returns reservation information to the server.

[1211] Step 18:

[1212] Server: Stores the received accommodation reservation information in a database.

[1213] Step 19:

[1214] Server: Sends a request to arrange a reservation for a local activity or facility via an external API (e.g., the API of a tour booking site).

[1215] Step 20:

[1216] External API: Sends activity and facility reservation information back to the server.

[1217] Step 21:

[1218] Server: Saves the received reservation information in a database.

[1219] Step 22:

[1220] User: Requests route guidance to the destination "Sensoji Temple" from the app while on location.

[1221] Step 23:

[1222] Terminal: Sends current location information and destination information to the server.

[1223] Step 24:

[1224] Server: Calculates the optimal route based on current location and destination information.

[1225] Step 25:

[1226] Server: Sends calculated route information to the device.

[1227] Step 26:

[1228] Terminal: Display route directions to the user.

[1229] Step 27:

[1230] Users: Activate the in-app translation feature if they need to speak a different language locally.

[1231] Step 28:

[1232] Device: Sends voice input to the server.

[1233] Step 29:

[1234] Server: Analyzes the voice data and requests translation from the generative AI model.

[1235] Step 30:

[1236] Generative AI model: Performs real-time translation from Japanese to English (or other languages) and sends the translation results back to the server.

[1237] Step 31:

[1238] Server: Sends the translation results to the device.

[1239] Step 32:

[1240] Terminal: The translation result is displayed to the user or output as speech.

[1241] Step 33:

[1242] Server: Checks information about the user's use of the service and calculates rewards such as PayPay points and cryptocurrency.

[1243] Step 34:

[1244] Server: Calculated rewards are credited to the user's account and stored in a database.

[1245] Example 1

[1246] 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."

[1247] Conventional travel arrangement systems require users to perform various arrangement tasks individually, which is a time-consuming and labor-intensive process. Even when systems exist that can automatically generate travel itineraries, it is difficult to provide customized itinerary proposals based on the user's preferences and specific conditions. Another problem is the lack of real-time support, such as interpreters and route guidance, that is needed on-site.

[1248] 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.

[1249] In this invention, the server includes means for acquiring destination information, a generative model means for automatically creating a travel itinerary based on the destination information, means for arranging transportation tickets based on the travel itinerary, means for booking accommodation based on the travel itinerary, means for arranging local activities and facilities, means for providing route guidance to the destination, means for interpreting in real time, means for rewarding the user, means for using the generative AI model to generate itinerary suggestions, and means for generating prompt sentences and using them as input for the generative AI model. This allows the user to receive all travel arrangement procedures and local support in a centralized manner.

[1250] "Destination information" refers to information such as the travel destination and duration that the user inputs through the application.

[1251] "Generative modeling tools" refer to machine learning models and algorithms that automatically generate travel itineraries based on destination information.

[1252] "A means for arranging transportation tickets" is a system that automatically reserves airline and train tickets based on the user's travel itinerary.

[1253] A "means for booking accommodation" is a system for booking hotels and other accommodations based on a user's travel itinerary.

[1254] "A means of arranging local activities and facilities" is a system that makes reservations for tourist attractions and events based on the user's travel itinerary.

[1255] The "means for providing route guidance to a destination" is a system that calculates and provides guidance on the optimal route based on the user's current location and destination.

[1256] A "real-time interpretation solution" is a system that instantly translates speech and text when users speak different languages.

[1257] The "means for providing rewards to users" is a system that calculates and provides rewards such as points or cryptocurrency when users use the service.

[1258] A "means of using a generative AI model" is a method of using a generative AI model to generate travel itineraries or other information.

[1259] "Means for generating prompt sentences and using them as input for a generative AI model" refers to a method for automatically creating sentences to be input into a generative AI model.

[1260] This invention relates to a system that acquires destination information and provides travel arrangements and local support in an integrated manner based on that information. The system consists of devices such as smartphones and tablets, a cloud-based server, and a generative AI model.

[1261] User Interface

[1262] Terminal: The user inputs destination information using a dedicated application. The user specifies the destination and travel period through the interface, for example, "Destination: Tokyo, Duration: 5 days."

[1263] Destination information processing

[1264] Server: The server receives the destination information sent from the device. Based on the received destination information, it inputs a prompt sentence into the generative AI model. For example, the prompt sentence might be in the format "Please create a five-day itinerary for a trip to Tokyo. Day 1: Sightseeing in Asakusa, Day 2: Shopping in Akihabara, Day 3: Night view of Roppongi, Day 4: Disneyland."

[1265] Generate travel itineraries

[1266] Generative AI model: The generative AI model automatically generates an optimal travel itinerary based on a prompt, including details of the places and times to visit.

[1267] Presentation of proposed schedule

[1268] Server: Receives the travel itinerary proposals created by the generative AI model and sends them to the user's device.

[1269] Terminal: The user checks the proposed itinerary received on the terminal. If necessary, they make corrections and finally approve it. For example, they change the time for "Sightseeing in Asakusa" to "10:00."

[1270] Ticket and accommodation reservations

[1271] Server: Once the user approves the itinerary, the server uses an external system API to arrange transportation tickets and book accommodations. For example, it uses the Rakuten Travel API to book a hotel in Shinjuku.

[1272] Local activity arrangements

[1273] Server: Based on the generated itinerary, make reservations for local activities and facilities. For example, use the API of a tour booking site to book tickets to Disneyland.

[1274] Route guidance

[1275] Device: When a user travels to a destination, they use the application to send their current location information to the server. The server then calculates the optimal route based on this information and sends it to the device. The user can then receive real-time route guidance through the app.

[1276] Interpretation function

[1277] On the device: When users speak different languages, they activate the translation feature within the app. The device sends voice input to the server, and the generative AI model translates in real time. The translation result is displayed on the device or played aloud.

[1278] Reward System

[1279] Server: When a user uses or purchases a service, the server calculates rewards such as points or cryptocurrency based on that information. The calculated rewards are added to the user's account and stored in the database.

[1280] Specific examples

[1281] If a user plans a five-day trip to "Tokyo," the specific steps are as follows: When the user enters "Destination: Tokyo, Duration: 5 days" into the app, the generative AI model creates a five-day itinerary. For example, the itinerary might include "Sightseeing in Asakusa" on the first day, "Shopping in Akihabara" on the second day, "Night View of Roppongi" on the third day, and "Disneyland" on the fourth day. Once the user approves the itinerary, the server uses an external API to book flights and hotels, as well as local activities. On the day of the trip, the app provides real-time route guidance and interpretation functions. After the trip is over, rewards are awarded based on the service usage.

[1282] The above is a specific embodiment of the present invention, which is a system that allows users to make all travel arrangements and receive on-site support through a single application, thereby significantly reducing the hassle and cost of travel.

[1283] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1284] Step 1:

[1285] Input: The user launches a dedicated application and enters destination information such as "Destination: Tokyo, Duration: 5 days."

[1286] Operation: Information entered by the user is sent to the server through the terminal interface.

[1287] Output: The destination information is saved on the server.

[1288] Step 2:

[1289] Input: The destination information the server receives from the user.

[1290] How it works: The server generates prompts for the generative AI model based on the destination information it receives. For example, it generates a prompt like, "Please create a five-day itinerary for a trip to Tokyo. Day 1: Sightseeing in Asakusa, Day 2: Shopping in Akihabara, Day 3: Night view of Roppongi, Day 4: Disneyland."

[1291] Output: The generated prompt sentence is input to the generative AI model.

[1292] Step 3:

[1293] Input: A prompt sentence that the generative AI model will process.

[1294] How it works: Based on the prompt, the generative AI model automatically generates a travel itinerary that matches the user's preferences and travel duration. The generated itinerary includes details of locations and visit times.

[1295] Output: The generated itinerary proposals are returned to the server.

[1296] Step 4:

[1297] Input: Travel itinerary suggestions returned to the server from the generative AI model.

[1298] Operation: The server sends the generated itinerary proposal to the user's terminal.

[1299] Output: A proposed itinerary is displayed on the user's device.

[1300] Step 5:

[1301] Input: A proposed itinerary that the user reviews and modifies or approves.

[1302] Operation: The user checks the proposed travel itinerary through the terminal, makes specific modifications as necessary, and then approves the proposed itinerary.

[1303] Output: The modified or approved itinerary is sent to the server.

[1304] Step 6:

[1305] Input: Revised or approved itinerary.

[1306] Operation: The server uses the API of an external system (for example, a transportation reservation system or a lodging reservation system) to arrange transportation tickets and reserve accommodation based on the schedule.

[1307] Output: Reservation confirmation information is sent to the user's terminal.

[1308] Step 7:

[1309] Input: Booking information for local activities and facilities based on your travel dates.

[1310] What happens: The server uses an external API (e.g., a tour booking site) to complete a booking, such as reserving tickets to Disneyland.

[1311] Output: Local activity and facility reservation confirmation information is stored on the server and sent to the user's device.

[1312] Step 8:

[1313] Input: Location information that the user enters on their device.

[1314] How it works: When a user moves around the area, they input their current location information and send it to the server. The server then calculates the optimal route and sends it to the device.

[1315] Output: Real-time route guidance is displayed on the user's device.

[1316] Step 9:

[1317] Input: Voice input for different languages ​​that the user has set up on their device.

[1318] How it works: The device sends voice input to the server, where the generative AI model translates it in real time. The translation is then displayed on the device or played back aloud.

[1319] Output: The translated text and audio are available on the user's device.

[1320] Step 10:

[1321] Input: Information about the user's use of the service.

[1322] How it works: The server calculates rewards such as points or cryptocurrency based on usage information and credits them to the user's account.

[1323] Output: Reward information is saved in the user's database and reflected in their account.

[1324] The above are the specific steps in the program processing of this system.

[1325] (Application example 1)

[1326] 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."

[1327] Conventional travel arrangement systems do not provide sufficient support for users on how to arrange meals during their trip. Furthermore, there is a lack of systems that allow users to easily make reservations at local restaurants or use food delivery services. This often forces travelers to take the trouble of arranging meals themselves, diminishing the convenience and comfort of their trip.

[1328] 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.

[1329] In this invention, the server includes a means for acquiring destination information, a generative model means for automatically creating a travel itinerary, a means for arranging transportation tickets, a means for reserving accommodations, a means for arranging local activities and facilities, a means for providing route guidance, a means for real-time interpretation, a means for recommending restaurants and making reservations and orders, and a means for rewarding users. This allows travelers to centrally manage and execute meal arrangements and reservations along with their entire travel schedule simply by entering destination information, thereby significantly improving convenience and comfort during travel.

[1330] "Destination information" is information that allows a user to specify a travel destination and itinerary.

[1331] A "generative model means" is a means that uses a generative AI model to automatically generate optimal travel itineraries based on destination information.

[1332] "Means for arranging transportation tickets" refers to means for reserving necessary transportation such as airline tickets, trains, and buses according to the user's travel itinerary.

[1333] A "means for reserving accommodation" is a means for reserving a hotel or other accommodation based on the user's travel itinerary.

[1334] "Means of arranging local activities and facilities" refers to means of arranging sightseeing, experiential activities, use of facilities, etc. at travel destinations.

[1335] The "means for providing route guidance" is a means for providing real-time guidance on the optimal route to a destination while the user is traveling.

[1336] "Means for real-time interpretation" refers to means for providing an interpretation function for translating communication between users who speak different languages ​​in real time.

[1337] "Means for recommending restaurants and making reservations / orders" refers to means for recommending the most suitable restaurant based on destination information, and for making reservations at the restaurant and ordering from the menu.

[1338] The "means for providing rewards to users" refers to a means for providing rewards such as points or coupons to users based on their use of the travel arrangement system.

[1339] The present invention is a system that provides travel arrangements and local support in an integrated manner based on destination information. Specific embodiments of the present invention will be described below.

[1340] User Interface

[1341] Device:

[1342] Users launch a dedicated application on their smartphone, tablet, or other device and specify their destination and travel dates through an interface for entering destination information. For example, a user might enter "Tokyo" and "2023-10-01 to 2023-10-05."

[1343] Destination information processing

[1344] server:

[1345] Destination information is received and input into the generative AI model to request automatic itinerary generation. The generative AI model creates an optimal itinerary based on the user's preferences and the number of days of travel. For example, for a trip to "Tokyo," it will consider itineraries such as sightseeing in Asakusa, shopping in Akihabara, the night view of Roppongi, and Disneyland.

[1346] Presentation of proposed schedule

[1347] Device:

[1348] The proposed travel itinerary sent from the server is displayed to the user, who can then review it and modify or approve it as necessary.

[1349] Ticket booking and hotel reservations

[1350] server:

[1351] Once the itinerary is approved, transportation tickets are arranged via an external system (e.g., an airline or travel agency API), and accommodation reservations are also made using external APIs.

[1352] Local activity arrangements

[1353] server:

[1354] Based on the itinerary, local activities and facilities are arranged, also using external APIs.

[1355] Route guidance

[1356] Device:

[1357] When a user moves to a destination, their current location and destination information are sent to the server. The server then calculates the optimal route based on this information and sends it to the device. The user can then receive real-time route guidance via the app.

[1358] Interpretation function

[1359] Device:

[1360] When users speak different languages, they activate the in-app translation feature. The device sends voice input to the server, and the generative AI model translates in real time. The translation result is displayed on the device or played aloud.

[1361] Restaurant recommendations and reservations / orders

[1362] server:

[1363] Based on the destination information, the system recommends the most suitable restaurant and arranges reservations and orders based on the user's preferences and dates. This also uses an external restaurant API. For example, if a user is looking for an okonomiyaki restaurant while traveling in Tokyo, the AI ​​will recommend the most suitable candidate and allow for on-the-spot reservations.

[1364] Reward System

[1365] server:

[1366] When a user purchases or uses a service, the information is verified and rewards such as points and coupons are calculated and awarded.

[1367] Specific examples

[1368] As a practical example, consider a user planning a five-day trip to Tokyo. When the user enters "Tokyo" and the dates "2023-10-01 to 2023-10-05" into the app, the generative AI model creates a five-day itinerary, suggesting itineraries such as "Sightseeing in Asakusa" on Day 1, "Shopping in Akihabara" on Day 2, "Viewing the Roppongi Nightscape" on Day 3, and "Disneyland" on Day 4. Furthermore, the model recommends and makes reservations for restaurants, making it easy to make reservations at famous sushi restaurants and yakiniku restaurants, for example. Once the user approves the itinerary, the server arranges flight and hotel reservations through an external API, as well as local activities. On the day of the trip, the app provides real-time route guidance and interpretation services. Points and coupons are also awarded after the trip is completed.

[1369] Prompt Sentence Examples

[1370] "Enter your travel destination and dates. We'll then suggest restaurant recommendations and food delivery options."

[1371] "Create an optimal food schedule based on this destination information and dates."

[1372] The present invention aims to provide these functions in a unified manner, allowing travelers to easily arrange and reserve meals along with their overall travel schedule, thereby significantly improving the convenience and comfort of travel.

[1373] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1374] Step 1:

[1375] Enter destination information

[1376] Users start a dedicated application on their smartphone, tablet, or other device and enter their destination information (destination and itinerary), which is then sent to the server.

[1377] Input: Destination information (e.g. Tokyo, 2023-10-01 to 2023-10-05)

[1378] Output: Destination information sent to the server

[1379] Step 2:

[1380] Automatic itinerary generation

[1381] The server inputs the received destination information into the generative AI model and automatically generates a travel itinerary using prompts. The generative AI model creates an optimal travel itinerary based on the user's preferences and the number of travel days.

[1382] Input: Destination information

[1383] Output: Generated itinerary (e.g., Day 1: sightseeing in Asakusa, Day 2: shopping in Akihabara)

[1384] Step 3:

[1385] Proposal of travel itinerary

[1386] The server sends the generated proposed travel itinerary to the terminal, which displays it to the user, who can review the proposed itinerary and modify or approve it as necessary.

[1387] Input: Generated itinerary

[1388] Output: Proposed itinerary displayed to the user

[1389] Step 4:

[1390] Ticket and accommodation booking

[1391] Based on the schedule approved by the user, the server arranges transportation tickets via an external system (e.g., API of an airline or travel agency) and makes reservations for accommodation using the external API.

[1392] Input: Approved travel itinerary

[1393] Output: Booked transportation tickets, accommodation reservation information

[1394] Step 5:

[1395] Local activity arrangements

[1396] The server arranges local activities and facilities based on the travel itinerary using an external API.

[1397] Input: Approved travel itinerary

[1398] Output: Reservation information for arranged activities and facilities

[1399] Step 6:

[1400] Providing route guidance

[1401] During a trip, the user sends information about their current location and destination from their device to the server, which then calculates the optimal route and provides real-time route guidance.

[1402] Input: Current location information, destination information

[1403] Output: Optimal route directions

[1404] Step 7:

[1405] Real-time interpretation

[1406] When users speak different languages, they activate the interpretation function. The device sends voice input to the server, and the generative AI model translates in real time. The translation result is displayed on the device or played aloud.

[1407] Input: Voice input

[1408] Output: Translation result

[1409] Step 8:

[1410] Restaurant recommendations and reservations / orders

[1411] The server recommends the most suitable restaurant based on the destination information, and also arranges reservations and orders based on the user's preferences and dates, again using an external restaurant API.

[1412] Input: Destination information, user preferences, travel itinerary

[1413] Output: Recommended restaurant information, reservation and order confirmation

[1414] Step 9:

[1415] Reward System

[1416] Information about the user's use of the service is sent to the server, and rewards such as points and coupons are added to the user's account.

[1417] Input: Service usage information

[1418] Output: Points and coupons awarded

[1419] The above are the specific processing steps for implementing this invention. By showing the specific hardware and software operations and data flow in each step in detail, the technical scope of the invention will become clearer.

[1420] 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.

[1421] The present invention relates to a system that acquires destination information and provides travel arrangements and local support in an integrated manner based on that information, and aims to increase user satisfaction by further combining it with an emotion engine that recognizes the user's emotions. Specific embodiments of this system will be described below.

[1422] The system is comprised of a smartphone, tablet, or other device, a cloud-based server, a generative AI model, and an emotion engine. Users input their destination information using a dedicated application, and then receive travel arrangements and guide services. The emotion engine analyzes the user's emotions to personalize the travel experience.

[1423] User Interface

[1424] Device: The user launches the application and enters "Tokyo" as their destination. As they enter their destination, the application recognizes their emotions from their facial expressions and tone of voice via a camera and microphone.

[1425] Destination information processing and emotion recognition

[1426] Server: Upon receiving the destination information "Tokyo," the emotion engine analyzes the emotion data and evaluates the user's emotional state during the travel planning stage.

[1427] Generative AI model: Generates optimal travel itineraries based on destination information, travel days, and user emotional state and preferences. For example, if a user is feeling stressed, the model suggests itineraries that emphasize relaxation and refreshment.

[1428] Presentation of proposed schedule

[1429] Terminal: Displays the travel itinerary proposal sent from the server to the user. The itinerary proposal is customized according to the user's emotional state.

[1430] User: Review the proposed schedule and modify or approve it as necessary.

[1431] Ticket arrangements, hotel reservations and local activity arrangements

[1432] Server: Once the itinerary is approved, transportation tickets are arranged via an external API (for example, an airline or travel agency API). Accommodation reservations are also made using an external API. Based on the information from the emotion engine, a more comfortable accommodation environment is selected.

[1433] Server: Arranges local activities and facilities based on the schedule. Using an emotion engine, prioritizes and selects activities that will reduce the user's stress and pique their interest.

[1434] Route guidance and interpretation functions

[1435] Terminal: When a user requests route guidance to the destination "Sensoji Temple" while on-site, the terminal sends current location information and destination information to the server.

[1436] Server: Calculates the optimal route based on the current location and destination information and sends it to the device. The emotion engine takes into account the user's fatigue and stress during the trip and provides the optimal route.

[1437] Device: When users speak different languages, they activate the translation function within the app and send the audio data to the server.

[1438] Generative AI model: Translates voice data in real time and sends the results back to the server. The emotion engine generates translation results that alleviate the user's anxiety and tension.

[1439] Reward System

[1440] Server: Checks the user's usage information and calculates rewards such as PayPay points or cryptocurrency. The emotion engine evaluates the user's gratitude and satisfaction and adjusts the reward accordingly.

[1441] Server: Calculated rewards are credited to the user's account and stored in a database.

[1442] Specific examples

[1443] As a practical example, consider a user planning a five-day trip to Tokyo. When the user enters "Tokyo" into the app, the generative AI model creates a five-day itinerary and suggests activities such as sightseeing in Asakusa on day one, shopping in Akihabara on day two, the night view of Roppongi on day three, and Disneyland on day four. If the user is feeling stressed, a relaxation spa or nature walk can be added to the itinerary. Furthermore, if the user's emotional state changes during the trip, the emotion engine recognizes this and dynamically adjusts the itinerary and activities. For example, if the user feels tired, the plan can be changed to include a massage or relaxation time at the hotel. On the day of the trip, real-time route guidance and interpretation functions are provided through the app. Furthermore, after the trip, bonus points and cryptocurrency based on the emotion engine are awarded.

[1444] As described above, the present invention is a system that allows users to receive all travel arrangements and on-site support through a single application, and by combining it with an emotion engine, it is possible to provide a personalized travel experience that corresponds to the user's emotional state.

[1445] The processing flow will be explained below.

[1446] Step 1:

[1447] User: Launches the app and enters "Tokyo" as the destination.

[1448] Step 2:

[1449] Terminal: Checks the entered destination information and sends it to the server. At the same time, it captures the user's facial expressions and tone of voice via a camera and microphone to obtain emotional data.

[1450] Step 3:

[1451] Server: Receives the destination information "Tokyo" and inputs the destination information and emotion data into the generative AI model and emotion engine, respectively.

[1452] Step 4:

[1453] Emotion engine: Analyzes the acquired emotional data and evaluates the user's emotional state. For example, it generates a result such as "high stress."

[1454] Step 5:

[1455] Generative AI model: Creates travel itinerary suggestions based on destination information and emotional state (e.g., "high stress") derived from the emotion engine, including itineraries focused on relaxation and refreshment to reduce stress.

[1456] Step 6:

[1457] Server: Stores the generated travel itinerary proposals in a database and sends them to the terminal.

[1458] Step 7:

[1459] Terminal: Displays the travel itinerary proposals received from the server to the user, customized to reflect the user's emotional state.

[1460] Step 8:

[1461] User: Checks the proposed schedule and clicks the approve button. User can also request revisions if necessary.

[1462] Step 9:

[1463] Terminal: Sends the user's authorization data to the server.

[1464] Step 10:

[1465] Server: After receiving approval, sends a request to arrange transportation tickets via an external API (e.g., an airline or travel agency API).

[1466] Step 11:

[1467] External API: Executes transportation ticket reservations and returns reservation information to the server.

[1468] Step 12:

[1469] Server: Stores the received ticket reservation information in a database.

[1470] Step 13:

[1471] Server: Sends a request to check the availability of accommodation using an external API (for example, the API of a hotel booking site).

[1472] Step 14:

[1473] External API: Returns the accommodation availability information to the server.

[1474] Step 15:

[1475] Server: Enters vacant room information into the generative AI model and requests it to select the most suitable accommodation, taking into account the evaluation of the emotion engine.

[1476] Step 16:

[1477] Generative AI model: Selects the most suitable accommodation based on availability information and the emotion engine's evaluation, and sends it back to the server.

[1478] Step 17:

[1479] Server: Sends a reservation request for the selected accommodation to an external API.

[1480] Step 18:

[1481] External API: Performs reservation confirmation and returns reservation information to the server.

[1482] Step 19:

[1483] Server: Stores the received accommodation reservation information in a database.

[1484] Step 20:

[1485] Server: Based on the itinerary, it sends a request to arrange reservations for local activities and facilities via an external API (e.g., the API of a tour booking site).

[1486] Step 21:

[1487] External API: Sends activity and facility reservation information back to the server.

[1488] Step 22:

[1489] Server: Stores the received reservation information in a database. The emotion engine monitors the user's emotional state and suggests changes to the activity if necessary.

[1490] Step 23:

[1491] User: Requests route guidance to the destination "Sensoji Temple" from the app while on location.

[1492] Step 24:

[1493] Terminal: Sends current location information and destination information to the server.

[1494] Step 25:

[1495] Server: Calculates the optimal route based on current location and destination information. The emotion engine takes into account fatigue and stress levels during travel and provides the optimal route.

[1496] Step 26:

[1497] Server: Sends calculated route information to the device.

[1498] Step 27:

[1499] Terminal: Display route directions to the user.

[1500] Step 28:

[1501] Users: Activate the in-app translation feature if they need to speak a different language locally.

[1502] Step 29:

[1503] Device: Sends voice input to the server.

[1504] Step 30:

[1505] Server: Analyzes the voice data and requests translation from the generative AI model. The emotion engine generates translation results that alleviate the user's anxiety and tension.

[1506] Step 31:

[1507] Generative AI model: performs real-time translation from Japanese to English (or other languages) and sends it back to the server.

[1508] Step 32:

[1509] Server: Sends the translation results to the device.

[1510] Step 33:

[1511] Terminal: The translation result is displayed to the user or output as speech.

[1512] Step 34:

[1513] Server: Checks the user's usage information and calculates rewards in PayPay points or cryptocurrency. The emotion engine also evaluates the user's emotional state and adjusts rewards accordingly.

[1514] Step 35:

[1515] Server: Calculated rewards are credited to the user's account and stored in a database.

[1516] Example 2

[1517] 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."

[1518] The present invention relates to a system that allows users to centrally plan and arrange trips and personalize the travel experience based on the user's emotional state. Conventional travel support systems can automatically generate and arrange trip plans, but do not personalize the experience based on the user's emotional state. This makes it difficult to maximize user satisfaction. Furthermore, they do not adequately provide real-time route guidance, interpretation functions, or rewards based on the user's evaluated emotions.

[1519] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for acquiring destination information, a generative model means for automatically creating a travel itinerary, a means for arranging transportation tickets, a means for booking accommodations, a means for arranging local activities and facilities, a means for providing route guidance to the destination, a means for real-time interpretation, a means for rewarding the user, a sentiment analysis means for analyzing the user's emotional state, and a means for personalizing the travel itinerary based on the sentiment analysis means. This enables a personalized travel experience according to the user's emotional state, thereby improving user satisfaction. Specifically, by analyzing the user's emotional state, if the user is feeling stressed, the system can suggest relaxation-oriented trips and flexibly respond to emotional changes during the trip, thereby providing an optimal travel experience.

[1520] "Destination information" is information about a geographical location that a user specifies as a travel destination.

[1521] The "generative model means" is an algorithm or program that has the function of automatically generating an optimal travel itinerary based on destination information and other related information.

[1522] The "means for arranging transportation tickets" is a system that has the function of reserving and purchasing transportation necessary for travel, such as airline tickets and train tickets, based on travel itineraries.

[1523] A "means for reserving accommodation" is a system that has the functionality to reserve hotels and other accommodations based on travel dates.

[1524] The "means for arranging local activities and facilities" is a system that has the function of making reservations for participation in local tourist attractions and activities based on travel itineraries.

[1525] The "means for providing route guidance to a destination" is a system that has the function of calculating and providing guidance on the optimal route to a destination specified by a user.

[1526] A "means for performing real-time interpretation" is a system that has the function of translating voice data in real time to support communication between users who speak different languages.

[1527] The "means for providing rewards to users" is a system that has the function of providing rewards such as points or cryptocurrency based on the user's satisfaction and usage information after the trip.

[1528] The "emotion analysis means" is a system that has the function of analyzing the emotional state of a user using data such as facial expressions and tone of voice.

[1529] The "means for personalizing travel itineraries based on emotion analysis means" is a system that has the function of optimizing and personalizing travel itineraries according to the user's emotional state analyzed by the emotion analysis means.

[1530] The present invention relates to a system that acquires destination information and provides travel arrangements and local support in an integrated manner based on that information. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to personalize the user's travel experience and increase satisfaction. A specific embodiment of this system will now be described.

[1531] User Interface

[1532] Device: The user launches a dedicated application on a device such as a smartphone or tablet and inputs destination information. For example, when "Tokyo" is entered as destination information, the information is acquired by the device. When inputting information, data is also acquired through a camera and microphone to recognize emotions from the user's facial expressions and tone of voice. This allows data to be collected based on the user's emotional state.

[1533] Destination information processing and emotion recognition

[1534] Server: Receives the destination information "Tokyo" and emotion data sent from the device. The server is cloud-based and analyzes the emotion data using an emotion engine. This allows the server to evaluate the user's emotional state during the trip planning stage.

[1535] Generative AI model: The generative AI model installed on the server generates an optimal travel itinerary based on destination information, the number of days of travel, the user's emotional state, and past travel preference data. For example, if the user is feeling stressed, it will suggest an itinerary that emphasizes relaxation and refreshment.

[1536] Presentation and confirmation of proposed schedule

[1537] Terminal: Displays the travel itinerary proposals sent from the server to the user. The generated itinerary proposals are customized according to the user's emotional state.

[1538] Users can review the proposed schedule and make any necessary changes or approvals. Changes can be easily made through the application interface.

[1539] Travel arrangements and local support

[1540] Server: After the user approves the itinerary, the server uses external APIs (such as airlines and travel agencies) to arrange transportation tickets and book accommodations. The emotion engine selects a more comfortable accommodation environment. It also arranges local activities and facilities, taking measures to reduce the user's stress and increase their interest.

[1541] Route guidance and interpretation functions

[1542] Terminal: When a user requests route guidance to the destination "Sensoji Temple" while on the spot, the terminal sends current location information and destination information to the server.

[1543] Server: Calculates the optimal route based on the received information and sends it to the device. Using the emotion engine, route guidance is provided that takes into account the user's fatigue and stress during travel.

[1544] On the device: When users speak different languages, they activate the translation function within the app and send the audio data to the server.

[1545] Generative AI model: Translates speech data in real time and sends the results back to the server, generating translation results that alleviate the user's anxiety and tension.

[1546] Post-trip rewards

[1547] Server: Calculates rewards such as PayPay points or cryptocurrency based on the user's usage information of the provided service. The emotion engine evaluates the user's gratitude and satisfaction and adjusts the amount of reward accordingly.

[1548] Server: The calculated reward is credited to the user's account and the data is stored in a database.

[1549] Specific examples

[1550] For example, if a user plans a five-day trip to "Tokyo," they enter "Tokyo" into the app. The generative AI model creates an itinerary based on the following prompt: "Create a five-day itinerary for Tokyo. The user wants to relax." Based on this, it suggests itineraries such as "Sightseeing in Asakusa" on the first day, "Shopping in Akihabara" on the second day, "Viewing the night view of Roppongi" on the third day, and "Disneyland" on the fourth day. If the user is feeling stressed, it adds relaxation-focused spa and nature walks.

[1551] If the user feels tired during the trip, the emotion engine will recognize this and the server will dynamically adjust the itinerary and activities in real time to provide the user with a comfortable travel experience. After the trip is over, the user will be awarded bonus points and cryptocurrency based on the evaluation made by the emotion engine.

[1552] In this way, the system comprehensively supports the entire process from obtaining destination information to providing travel experiences and rewards, and can also provide personalized services according to the user's emotional state.

[1553] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1554] Step 1: Enter your destination information

[1555] Terminal: The user launches the application and enters destination information. For example, if the user enters "Tokyo" as the destination, the information is acquired by the terminal.

[1556] Input: Destination information ("Tokyo")

[1557] Output: Destination information data

[1558] Specific operation: The user enters the destination using the keyboard or voice input, and the device stores the information in a database within the application.

[1559] Step 2: Obtaining emotion data

[1560] Terminal: At the same time as entering destination information, the device captures the user's facial expressions and tone of voice via a camera and microphone.

[1561] Input: User's facial expressions, tone of voice

[1562] Output: Emotion data

[1563] Specific operations: The device activates the camera to capture facial expressions, uses the microphone to collect voice data, and sends this data to the emotion analysis module.

[1564] Step 3: Send destination information and emotion data to the server

[1565] Terminal: Sends the acquired destination information and emotion data to the server.

[1566] Input: Destination information data, emotion data

[1567] Output: Data sent to the server via the HTTP request

[1568] Specific operation: The device packages destination information and emotion data and sends it to the server using an HTTP request.

[1569] Step 4: Generate itinerary

[1570] Server: Receives destination information and emotion data and generates an optimal travel itinerary using a generative AI model.

[1571] Input: Destination information data, emotion data, number of travel days, user preference data

[1572] Output: Travel itinerary proposal

[1573] Specific operation: The generative AI model generates a travel itinerary based on the prompt, "Please create a 5-day travel itinerary for Tokyo. The user wants to relax." The sentiment analysis engine analyzes the emotional data and generates a personalized itinerary that reflects the user's emotional state.

[1574] Step 5: Present and confirm the proposed schedule

[1575] Terminal: Displays the proposed travel itinerary sent from the server to the user.

[1576] Input: Travel itinerary proposal

[1577] Output: Display of proposed schedule

[1578] Specific operation: The device displays the proposed schedule received from the server on the application's UI. The user checks the schedule and makes any necessary corrections or approvals.

[1579] Step 6: Revise the proposed itinerary

[1580] User: Review the proposed schedule and make any necessary changes or approve it through the interface.

[1581] Input: Schedule proposal, correction instructions

[1582] Output: Revised schedule or approval

[1583] Specific operation: The user adjusts the schedule using touch operations or drag-and-drop operations, and presses the "Approve" button to confirm the final schedule.

[1584] Step 7: Making travel arrangements

[1585] Server: Based on the itinerary approved by the user, the server uses external services to arrange transportation tickets and make hotel reservations.

[1586] Input: Approved schedule, emotion data

[1587] Output: Ticket and booking confirmation information

[1588] Specific operation: The server sends a request to an external API (travel agency, airline, hotel, etc.) to confirm the ticket or accommodation reservation, receives the API response, and sends the confirmation information to the terminal.

[1589] Step 8: Local route guidance

[1590] Terminal: The user requests route guidance locally and sends current location information and destination information to the server.

[1591] Input: current location information, destination information

[1592] Output: Optimal route guidance

[1593] Specific operation: The device acquires GPS data and sends it to the server along with destination information.

[1594] Server: Calculates the optimal route based on the received information and sends it to the terminal.

[1595] Input: current location information, destination information

[1596] Output: Route guidance information

[1597] Specific operation: The server uses a route calculation algorithm to calculate the optimal route and sends the result to the terminal.

[1598] Step 9: Run the interpretation function

[1599] Terminal: When a user uses the interpretation function, the terminal sends voice data to the server.

[1600] Input: Audio data

[1601] Output: The translated text

[1602] Specific operation: The device uses a microphone to capture voice data and sends it to the server.

[1603] Server: Receives the voice data and performs real-time translation using the generative AI model.

[1604] Input: Audio data

[1605] Output: The translated text

[1606] How it works: The server passes the voice data to the generative AI model and sends the translated text back to the device.

[1607] Step 10: Post-trip rewards

[1608] Server: Evaluates the user's gratitude and satisfaction based on service usage information and calculates rewards.

[1609] Input: service usage information, emotion data

[1610] Output: Reward points

[1611] Specific operation: The sentiment analysis engine evaluates satisfaction based on the emotional data, calculates rewards, and adds the calculated rewards to the user's account and stores them in the database.

[1612] The above are the specific processing steps of the program of this system.

[1613] (Application example 2)

[1614] 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."

[1615] Conventional travel booking and food delivery systems lack the ability to provide personalized suggestions that take into account the user's emotional state. As a result, they often do not provide the optimal service for the user's current emotional state, preventing improvement in satisfaction. In addition, when the user is tired or stressed, the system does not suggest the optimal menu according to the situation, resulting in a poor user experience.

[1616] 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.

[1617] In this invention, the server includes a means for acquiring destination information, a generative model means, and an emotion engine means. This allows the server to analyze the user's emotional state in real time and propose optimal travel itineraries and menus based on that analysis. Furthermore, by linking with external systems, the server can arrange transportation, accommodations, and local activities, further enhancing user convenience.

[1618] "Destination information" is information about the user's desired travel destination or delivery destination.

[1619] The "generative model means" is a system for automatically creating travel itineraries and menus based on input destination information.

[1620] The "emotion engine means" is a system for analyzing the user's facial expressions and tone of voice to recognize the user's current emotional state.

[1621] The "generative AI model means" is an artificial intelligence model that generates optimal food menus and travel plans based on the user's destination information, emotional state, and preferences.

[1622] "Means for arranging transportation tickets" is a system for automatically reserving transportation according to the user's travel schedule.

[1623] The "means for reserving accommodation" is a system for reserving accommodation based on the user's travel itinerary.

[1624] "A means for arranging local activities and facilities" is a system for making reservations for tourist attractions and entertainment according to the user's travel itinerary.

[1625] The "means for providing route guidance to a destination" is a system for navigating the optimal route based on destination information input by the user.

[1626] A "means for real-time interpretation" is a system for translating speech in real time when users speak different languages.

[1627] The "means for providing rewards to users" is a system for providing points or rewards to users according to their usage of the service.

[1628] System Overview

[1629] The present invention relates to a system that acquires destination information, automatically creates travel itineraries and menus based on that information, and provides them while taking into account the emotional state of the user. Specific embodiments for carrying out the invention are described below.

[1630] Hardware and Software Configuration

[1631] Hardware used

[1632] Device: Smartphone or tablet

[1633] Server: Cloud-based server

[1634] Software used

[1635] Emotion engine: A sentiment analysis library such as the Affectiva SDK

[1636] Generative AI models: Generative models such as OpenAI's GPT-4

[1637] Processing flow

[1638] User Interface

[1639] The user launches the application on their device and enters destination information (travel destination or delivery address). At that time, the device's camera and microphone are used to capture the user's emotional state. The captured emotional data is then analyzed using an emotion engine (such as the Affectiva SDK).

[1640] Destination information processing and emotion recognition

[1641] The server receives the destination information and evaluates the user's emotional data analyzed by the emotion engine, then uses a generative AI model (such as OpenAI's GPT-4) to generate optimal travel itineraries and food menus based on the destination information, the user's emotional state, and past preference data.

[1642] Proposal of schedule and menu

[1643] The terminal displays the customized itinerary and menu proposals sent from the server to the user, who can then review the proposals and modify or approve them as necessary.

[1644] Ticket arrangements, accommodation reservations, activity arrangements

[1645] Based on the approved travel itinerary, the server connects with external systems (such as the APIs of airlines and hotel booking sites) to arrange transportation tickets and book accommodations. It also automates the arrangement of local activities and facilities using external APIs.

[1646] Route guidance and interpretation functions

[1647] When a user requests guidance to a destination, the server provides the device with the optimal route based on the user's current location. Furthermore, if the user speaks a different language, the server collects voice data through the device's microphone and performs real-time interpretation. The server translates the voice data using a generative AI model and sends the results back to the device.

[1648] Reward System

[1649] When a user uses the service, they are given rewards (points or rewards) based on their satisfaction level as assessed by the emotion engine. This information is managed on the server and reflected in the user's account.

[1650] Specific examples

[1651] As an actual usage example, consider the case where a user inputs "I'm tired" and requests delivery to their home in Tokyo. When the user inputs their destination information into the app, the emotion engine retrieves emotion data that evaluates "tired." Based on this information, the generative AI model generates and suggests food menus with a relaxing effect (e.g., "herbal tea" or "comfort food") to the user.

[1652] Prompt Sentence Examples

[1653] Input sentence: "I'm tired from work today. Please deliver to my home in Tokyo."

[1654] Prompt: "Suggest some food options for when the user is feeling tired. Ideally, these would include relaxing foods and drinks."

[1655] The above is a specific embodiment for carrying out the invention. By combining an emotion engine and a generative AI model, it becomes possible to provide personalized services according to the user's emotional state.

[1656] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1657] Step 1:

[1658] Enter destination information

[1659] A user launches the application and enters destination information (e.g., a travel destination or delivery address). The entered data is temporarily stored in the device's memory. At the same time, the device's camera and microphone are used to capture the user's facial expressions and tone of voice.

[1660] Input: Destination information, facial expression data, tone of voice data

[1661] Output: Destination information, emotion data

[1662] Step 2:

[1663] Emotional Data Analysis

[1664] The device sends the acquired facial expression and tone of voice data to an emotion engine (such as the Affectiva SDK) to analyze the user's emotional state. The analysis results in the user's emotional state (e.g., relaxed, stressed, tired).

[1665] Input: Facial expression data, tone of voice data

[1666] Output: Emotional state (e.g., relaxed, stressed, tired)

[1667] Step 3:

[1668] Sending destination information and emotion data

[1669] The device transmits destination information and emotional state to the server, which stores the information in an integrated database.

[1670] Input: Destination information, emotional state

[1671] Output: Destination information and emotional state stored on the server

[1672] Step 4:

[1673] Generating the optimal plan

[1674] The server uses a generative AI model (such as OpenAI's GPT-4) to generate optimal travel itineraries and food menus based on the saved destination information and emotional state, while also referencing past user preference data.

[1675] Input: Destination information, emotional state, past user preference data

[1676] Output: Suggested travel itinerary or food menu

[1677] Step 5:

[1678] Plan presentation and approval

[1679] The server sends the generated proposal to the terminal and displays the information to the user, who can review the proposed plan and modify or approve it as necessary.

[1680] Input: Proposed plan

[1681] Output: User approves or modifies the plan

[1682] Step 6:

[1683] Ticket and reservation arrangements

[1684] Based on the approved travel itinerary, the server connects with external systems (such as airline and booking site APIs) to arrange transportation tickets and book accommodations, and in the case of food delivery, connects with the suggested menu items to order.

[1685] Input: Approved itinerary or menu proposal

[1686] Output: Booked tickets, accommodation, or food orders

[1687] Step 7:

[1688] Route guidance and interpretation features

[1689] When a user requests directions to a destination, the server provides the optimal route based on the user's current location. Furthermore, if a different language is used, the server collects voice data and translates it in real time.

[1690] Input: Current location, voice data

[1691] Output: Optimal route guidance, translated audio data

[1692] Step 8:

[1693] Rewarding

[1694] When a user uses the service, the server grants rewards (points or rewards) to the user's account based on the satisfaction level assessed by the emotion engine.

[1695] Input: Service usage data, emotion evaluation data

[1696] Output: Points or rewards awarded

[1697] 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.

[1698] 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.

[1699] 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.

[1700] [Fourth embodiment]

[1701] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1702] 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.

[1703] 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).

[1704] 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.

[1705] 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.

[1706] 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).

[1707] 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.

[1708] 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.

[1709] 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.

[1710] 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.

[1711] 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.

[1712] 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.

[1713] 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."

[1714] The present invention relates to a system that acquires destination information and provides travel arrangements and local support in an integrated manner based on that information. A specific embodiment of this system will be described below.

[1715] The system consists of a smartphone or tablet device, a cloud-based server, and a generative AI model. Users can input destination information using a dedicated application and receive travel arrangements and guide services.

[1716] User Interface

[1717] Terminal: The user launches the application and specifies the destination through the interface where destination information is input. For example, the user inputs the destination "Tokyo."

[1718] Destination information processing

[1719] Server: Receives destination information, inputs it into the generative AI model, and requests automatic itinerary generation. The generative AI model creates an optimal itinerary based on the user's preferences and number of travel days. For example, for a trip to Tokyo, it might consider itineraries such as sightseeing in Asakusa, shopping in Akihabara, the night view of Roppongi, and Disneyland.

[1720] Presentation of proposed schedule

[1721] Terminal: Displays the proposed travel itinerary sent from the server to the user, who can then review the proposed itinerary and modify or approve it as necessary.

[1722] Ticket booking and hotel reservations

[1723] Server: Once the itinerary is approved, transportation tickets are arranged via an external system (for example, an airline or travel agency API). Accommodation reservations are also made using external APIs. For example, a hotel is booked using the Rakuten Travel API.

[1724] Local activity arrangements

[1725] Server: Arranges local activities and facilities based on the itinerary. This is also done using an external API (for example, the API of a tour booking site). For example, when arranging tickets to Disneyland, the reservation information is obtained and confirmed.

[1726] Route guidance

[1727] Device: When a user travels to a destination, they send their current location and destination information to the server. The server calculates the optimal route based on this information and sends it to the device. The user can then receive real-time route guidance via the app.

[1728] Interpretation function

[1729] On-device: When users speak different languages, they activate the in-app translation feature. The device sends voice input to the server, and the generative AI model translates in real time. The translation result is displayed on the device or played aloud.

[1730] Reward System

[1731] Server: When a user purchases or uses a service, the server checks the information and calculates rewards such as PayPay points or cryptocurrency. The calculated rewards are added to the user's account and stored in the database.

[1732] Specific examples

[1733] As an actual usage example, consider a user planning a five-day trip to "Tokyo." When the user enters "Tokyo" into the app, the following steps are executed: The generative AI model creates a five-day itinerary and presents plans such as "Sightseeing Asakusa" on the first day, "Shopping in Akihabara" on the second day, "Night View of Roppongi" on the third day, and "Disneyland" on the fourth day. Once the user approves the itinerary, the server arranges flight and hotel reservations through an external API, as well as local activities. On the day of the trip, real-time route guidance and interpretation functions are provided through the app. In addition, after the trip is completed, PayPay points and cryptocurrency are awarded.

[1734] As described above, the present invention is a system that allows users to make all travel arrangements and receive local support through a single application, significantly reducing the effort and cost of travel.

[1735] The processing flow will be explained below.

[1736] Step 1:

[1737] User: Launches the app and enters "Tokyo" as the destination.

[1738] Step 2:

[1739] Terminal: Checks the entered destination information and sends it to the server.

[1740] Step 3:

[1741] Server: Receives destination information "Tokyo", inputs it into the generative AI model, and requests itinerary creation.

[1742] Step 4:

[1743] Generative AI model: Generates an optimal travel itinerary based on data such as destination information, number of travel days, and user preferences, and sends it back to the server.

[1744] Step 5:

[1745] Server: Stores the generated travel itinerary proposals in a database and sends them to the terminal.

[1746] Step 6:

[1747] Terminal: Displays the proposed travel itinerary received from the server to the user.

[1748] Step 7:

[1749] User: Check the proposed schedule and click the approve button.

[1750] Step 8:

[1751] Terminal: Sends the user's authorization data to the server.

[1752] Step 9:

[1753] Server: Upon approval, arranges transportation tickets via an external API (e.g., an airline or travel agency API).

[1754] Step 10:

[1755] External API: Executes transportation ticket reservations and returns reservation information to the server.

[1756] Step 11:

[1757] Server: Stores the received ticket reservation information in a database.

[1758] Step 12:

[1759] Server: Sends a request to check the availability of accommodation using an external API (for example, the API of a hotel booking site).

[1760] Step 13:

[1761] External API: Returns the accommodation availability information to the server.

[1762] Step 14:

[1763] Server: Enters room availability information into the generative AI model and requests it to select the most suitable accommodation.

[1764] Step 15:

[1765] Generative AI model: Selects the most suitable accommodation based on availability information and returns it to the server.

[1766] Step 16:

[1767] Server: Sends a reservation request for the selected accommodation to an external API.

[1768] Step 17:

[1769] External API: Performs reservation confirmation and returns reservation information to the server.

[1770] Step 18:

[1771] Server: Stores the received accommodation reservation information in a database.

[1772] Step 19:

[1773] Server: Sends a request to arrange a reservation for a local activity or facility via an external API (e.g., the API of a tour booking site).

[1774] Step 20:

[1775] External API: Sends activity and facility reservation information back to the server.

[1776] Step 21:

[1777] Server: Saves the received reservation information in a database.

[1778] Step 22:

[1779] User: Requests route guidance to the destination "Sensoji Temple" from the app while on location.

[1780] Step 23:

[1781] Terminal: Sends current location information and destination information to the server.

[1782] Step 24:

[1783] Server: Calculates the optimal route based on current location and destination information.

[1784] Step 25:

[1785] Server: Sends calculated route information to the device.

[1786] Step 26:

[1787] Terminal: Display route directions to the user.

[1788] Step 27:

[1789] Users: Activate the in-app translation feature if they need to speak a different language locally.

[1790] Step 28:

[1791] Device: Sends voice input to the server.

[1792] Step 29:

[1793] Server: Analyzes the voice data and requests translation from the generative AI model.

[1794] Step 30:

[1795] Generative AI model: Performs real-time translation from Japanese to English (or other languages) and sends the translation results back to the server.

[1796] Step 31:

[1797] Server: Sends the translation results to the device.

[1798] Step 32:

[1799] Terminal: The translation result is displayed to the user or output as speech.

[1800] Step 33:

[1801] Server: Checks information about the user's use of the service and calculates rewards such as PayPay points and cryptocurrency.

[1802] Step 34:

[1803] Server: Calculated rewards are credited to the user's account and stored in a database.

[1804] Example 1

[1805] 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."

[1806] Conventional travel arrangement systems require users to perform various arrangement tasks individually, which is a time-consuming and labor-intensive process. Even when systems exist that can automatically generate travel itineraries, it is difficult to provide customized itinerary proposals based on the user's preferences and specific conditions. Another problem is the lack of real-time support, such as interpreters and route guidance, that is needed on-site.

[1807] 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.

[1808] In this invention, the server includes means for acquiring destination information, a generative model means for automatically creating a travel itinerary based on the destination information, means for arranging transportation tickets based on the travel itinerary, means for booking accommodation based on the travel itinerary, means for arranging local activities and facilities, means for providing route guidance to the destination, means for interpreting in real time, means for rewarding the user, means for using the generative AI model to generate itinerary suggestions, and means for generating prompt sentences and using them as input for the generative AI model. This allows the user to receive all travel arrangement procedures and local support in a centralized manner.

[1809] "Destination information" refers to information such as the travel destination and duration that the user inputs through the application.

[1810] "Generative modeling tools" refer to machine learning models and algorithms that automatically generate travel itineraries based on destination information.

[1811] "A means for arranging transportation tickets" is a system that automatically reserves airline and train tickets based on the user's travel itinerary.

[1812] A "means for booking accommodation" is a system for booking hotels and other accommodations based on a user's travel itinerary.

[1813] "A means of arranging local activities and facilities" is a system that makes reservations for tourist attractions and events based on the user's travel itinerary.

[1814] The "means for providing route guidance to a destination" is a system that calculates and provides guidance on the optimal route based on the user's current location and destination.

[1815] A "real-time interpretation solution" is a system that instantly translates speech and text when users speak different languages.

[1816] The "means for providing rewards to users" is a system that calculates and provides rewards such as points or cryptocurrency when users use the service.

[1817] A "means of using a generative AI model" is a method of using a generative AI model to generate travel itineraries or other information.

[1818] "Means for generating prompt sentences and using them as input for a generative AI model" refers to a method for automatically creating sentences to be input into a generative AI model.

[1819] This invention relates to a system that acquires destination information and provides travel arrangements and local support in an integrated manner based on that information. The system consists of devices such as smartphones and tablets, a cloud-based server, and a generative AI model.

[1820] User Interface

[1821] Terminal: The user inputs destination information using a dedicated application. The user specifies the destination and travel period through the interface, for example, "Destination: Tokyo, Duration: 5 days."

[1822] Destination information processing

[1823] Server: The server receives the destination information sent from the device. Based on the received destination information, it inputs a prompt sentence into the generative AI model. For example, the prompt sentence might be in the format "Please create a five-day itinerary for a trip to Tokyo. Day 1: Sightseeing in Asakusa, Day 2: Shopping in Akihabara, Day 3: Night view of Roppongi, Day 4: Disneyland."

[1824] Generate travel itineraries

[1825] Generative AI model: The generative AI model automatically generates an optimal travel itinerary based on a prompt, including details of the places and times to visit.

[1826] Presentation of proposed schedule

[1827] Server: Receives the travel itinerary proposals created by the generative AI model and sends them to the user's device.

[1828] Terminal: The user checks the proposed itinerary received on the terminal. If necessary, they make corrections and finally approve it. For example, they change the time for "Sightseeing in Asakusa" to "10:00."

[1829] Ticket and accommodation reservations

[1830] Server: Once the user approves the itinerary, the server uses an external system API to arrange transportation tickets and book accommodations. For example, it uses the Rakuten Travel API to book a hotel in Shinjuku.

[1831] Local activity arrangements

[1832] Server: Based on the generated itinerary, make reservations for local activities and facilities. For example, use the API of a tour booking site to book tickets to Disneyland.

[1833] Route guidance

[1834] Device: When a user travels to a destination, they use the application to send their current location information to the server. The server then calculates the optimal route based on this information and sends it to the device. The user can then receive real-time route guidance through the app.

[1835] Interpretation function

[1836] On the device: When users speak different languages, they activate the translation feature within the app. The device sends voice input to the server, and the generative AI model translates in real time. The translation result is displayed on the device or played aloud.

[1837] Reward System

[1838] Server: When a user uses or purchases a service, the server calculates rewards such as points or cryptocurrency based on that information. The calculated rewards are added to the user's account and stored in the database.

[1839] Specific examples

[1840] If a user plans a five-day trip to "Tokyo," the specific steps are as follows: When the user enters "Destination: Tokyo, Duration: 5 days" into the app, the generative AI model creates a five-day itinerary. For example, the itinerary might include "Sightseeing in Asakusa" on the first day, "Shopping in Akihabara" on the second day, "Night View of Roppongi" on the third day, and "Disneyland" on the fourth day. Once the user approves the itinerary, the server uses an external API to book flights and hotels, as well as local activities. On the day of the trip, the app provides real-time route guidance and interpretation functions. After the trip is over, rewards are awarded based on the service usage.

[1841] The above is a specific embodiment of the present invention, which is a system that allows users to make all travel arrangements and receive on-site support through a single application, thereby significantly reducing the hassle and cost of travel.

[1842] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1843] Step 1:

[1844] Input: The user launches a dedicated application and enters destination information such as "Destination: Tokyo, Duration: 5 days."

[1845] Operation: Information entered by the user is sent to the server through the terminal interface.

[1846] Output: The destination information is saved on the server.

[1847] Step 2:

[1848] Input: The destination information the server receives from the user.

[1849] How it works: The server generates prompts for the generative AI model based on the destination information it receives. For example, it generates a prompt like, "Please create a five-day itinerary for a trip to Tokyo. Day 1: Sightseeing in Asakusa, Day 2: Shopping in Akihabara, Day 3: Night view of Roppongi, Day 4: Disneyland."

[1850] Output: The generated prompt sentence is input to the generative AI model.

[1851] Step 3:

[1852] Input: A prompt sentence that the generative AI model will process.

[1853] How it works: Based on the prompt, the generative AI model automatically generates a travel itinerary that matches the user's preferences and travel duration. The generated itinerary includes details of locations and visit times.

[1854] Output: The generated itinerary proposals are returned to the server.

[1855] Step 4:

[1856] Input: Travel itinerary suggestions returned to the server from the generative AI model.

[1857] Operation: The server sends the generated itinerary proposal to the user's terminal.

[1858] Output: A proposed itinerary is displayed on the user's device.

[1859] Step 5:

[1860] Input: A proposed itinerary that the user reviews and modifies or approves.

[1861] Operation: The user checks the proposed travel itinerary through the terminal, makes specific modifications as necessary, and then approves the proposed itinerary.

[1862] Output: The modified or approved itinerary is sent to the server.

[1863] Step 6:

[1864] Input: Revised or approved itinerary.

[1865] Operation: The server uses the API of an external system (for example, a transportation reservation system or a lodging reservation system) to arrange transportation tickets and reserve accommodation based on the schedule.

[1866] Output: Reservation confirmation information is sent to the user's terminal.

[1867] Step 7:

[1868] Input: Booking information for local activities and facilities based on your travel dates.

[1869] What happens: The server uses an external API (e.g., a tour booking site) to complete a booking, such as reserving tickets to Disneyland.

[1870] Output: Local activity and facility reservation confirmation information is stored on the server and sent to the user's device.

[1871] Step 8:

[1872] Input: Location information that the user enters on their device.

[1873] How it works: When a user moves around the area, they input their current location information and send it to the server. The server then calculates the optimal route and sends it to the device.

[1874] Output: Real-time route guidance is displayed on the user's device.

[1875] Step 9:

[1876] Input: Voice input for different languages ​​that the user has set up on their device.

[1877] How it works: The device sends voice input to the server, where the generative AI model translates it in real time. The translation is then displayed on the device or played back aloud.

[1878] Output: The translated text and audio are available on the user's device.

[1879] Step 10:

[1880] Input: Information about the user's use of the service.

[1881] How it works: The server calculates rewards such as points or cryptocurrency based on usage information and credits them to the user's account.

[1882] Output: Reward information is saved in the user's database and reflected in their account.

[1883] The above are the specific steps in the program processing of this system.

[1884] (Application example 1)

[1885] 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."

[1886] Conventional travel arrangement systems do not provide sufficient support for users on how to arrange meals during their trip. Furthermore, there is a lack of systems that allow users to easily make reservations at local restaurants or use food delivery services. This often forces travelers to take the trouble of arranging meals themselves, diminishing the convenience and comfort of their trip.

[1887] 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.

[1888] In this invention, the server includes a means for acquiring destination information, a generative model means for automatically creating a travel itinerary, a means for arranging transportation tickets, a means for reserving accommodations, a means for arranging local activities and facilities, a means for providing route guidance, a means for real-time interpretation, a means for recommending restaurants and making reservations and orders, and a means for rewarding users. This allows travelers to centrally manage and execute meal arrangements and reservations along with their entire travel schedule simply by entering destination information, thereby significantly improving convenience and comfort during travel.

[1889] "Destination information" is information that allows a user to specify a travel destination and itinerary.

[1890] A "generative model means" is a means that uses a generative AI model to automatically generate optimal travel itineraries based on destination information.

[1891] "Means for arranging transportation tickets" refers to means for reserving necessary transportation such as airline tickets, trains, and buses according to the user's travel itinerary.

[1892] A "means for reserving accommodation" is a means for reserving a hotel or other accommodation based on the user's travel itinerary.

[1893] "Means of arranging local activities and facilities" refers to means of arranging sightseeing, experiential activities, use of facilities, etc. at travel destinations.

[1894] The "means for providing route guidance" is a means for providing real-time guidance on the optimal route to a destination while the user is traveling.

[1895] "Means for real-time interpretation" refers to means for providing an interpretation function for translating communication between users who speak different languages ​​in real time.

[1896] "Means for recommending restaurants and making reservations / orders" refers to means for recommending the most suitable restaurant based on destination information, and for making reservations at the restaurant and ordering from the menu.

[1897] The "means for providing rewards to users" refers to a means for providing rewards such as points or coupons to users based on their use of the travel arrangement system.

[1898] The present invention is a system that provides travel arrangements and local support in an integrated manner based on destination information. Specific embodiments of the present invention will be described below.

[1899] User Interface

[1900] Device:

[1901] Users launch a dedicated application on their smartphone, tablet, or other device and specify their destination and travel dates through an interface for entering destination information. For example, a user might enter "Tokyo" and "2023-10-01 to 2023-10-05."

[1902] Destination information processing

[1903] server:

[1904] Destination information is received and input into the generative AI model to request automatic itinerary generation. The generative AI model creates an optimal itinerary based on the user's preferences and the number of days of travel. For example, for a trip to "Tokyo," it will consider itineraries such as sightseeing in Asakusa, shopping in Akihabara, the night view of Roppongi, and Disneyland.

[1905] Presentation of proposed schedule

[1906] Device:

[1907] The proposed travel itinerary sent from the server is displayed to the user, who can then review it and modify or approve it as necessary.

[1908] Ticket booking and hotel reservations

[1909] server:

[1910] Once the itinerary is approved, transportation tickets are arranged via an external system (e.g., an airline or travel agency API), and accommodation reservations are also made using external APIs.

[1911] Local activity arrangements

[1912] server:

[1913] Based on the itinerary, local activities and facilities are arranged, also using external APIs.

[1914] Route guidance

[1915] Device:

[1916] When a user moves to a destination, their current location and destination information are sent to the server. The server then calculates the optimal route based on this information and sends it to the device. The user can then receive real-time route guidance via the app.

[1917] Interpretation function

[1918] Device:

[1919] When users speak different languages, they activate the in-app translation feature. The device sends voice input to the server, and the generative AI model translates in real time. The translation result is displayed on the device or played aloud.

[1920] Restaurant recommendations and reservations / orders

[1921] server:

[1922] Based on the destination information, the system recommends the most suitable restaurant and arranges reservations and orders based on the user's preferences and dates. This also uses an external restaurant API. For example, if a user is looking for an okonomiyaki restaurant while traveling in Tokyo, the AI ​​will recommend the most suitable candidate and allow for on-the-spot reservations.

[1923] Reward System

[1924] server:

[1925] When a user purchases or uses a service, the information is verified and rewards such as points and coupons are calculated and awarded.

[1926] Specific examples

[1927] As a practical example, consider a user planning a five-day trip to Tokyo. When the user enters "Tokyo" and the dates "2023-10-01 to 2023-10-05" into the app, the generative AI model creates a five-day itinerary, suggesting itineraries such as "Sightseeing in Asakusa" on Day 1, "Shopping in Akihabara" on Day 2, "Viewing the Roppongi Nightscape" on Day 3, and "Disneyland" on Day 4. Furthermore, the model recommends and makes reservations for restaurants, making it easy to make reservations at famous sushi restaurants and yakiniku restaurants, for example. Once the user approves the itinerary, the server arranges flight and hotel reservations through an external API, as well as local activities. On the day of the trip, the app provides real-time route guidance and interpretation services. Points and coupons are also awarded after the trip is completed.

[1928] Prompt Sentence Examples

[1929] "Enter your travel destination and dates. We'll then suggest restaurant recommendations and food delivery options."

[1930] "Create an optimal food schedule based on this destination information and dates."

[1931] The present invention aims to provide these functions in a unified manner, allowing travelers to easily arrange and reserve meals along with their overall travel schedule, thereby significantly improving the convenience and comfort of travel.

[1932] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1933] Step 1:

[1934] Enter destination information

[1935] Users start a dedicated application on their smartphone, tablet, or other device and enter their destination information (destination and itinerary), which is then sent to the server.

[1936] Input: Destination information (e.g. Tokyo, 2023-10-01 to 2023-10-05)

[1937] Output: Destination information sent to the server

[1938] Step 2:

[1939] Automatic itinerary generation

[1940] The server inputs the received destination information into the generative AI model and automatically generates a travel itinerary using prompts. The generative AI model creates an optimal travel itinerary based on the user's preferences and the number of travel days.

[1941] Input: Destination information

[1942] Output: Generated itinerary (e.g., Day 1: sightseeing in Asakusa, Day 2: shopping in Akihabara)

[1943] Step 3:

[1944] Proposal of travel itinerary

[1945] The server sends the generated proposed travel itinerary to the terminal, which displays it to the user, who can review the proposed itinerary and modify or approve it as necessary.

[1946] Input: Generated itinerary

[1947] Output: Proposed itinerary displayed to the user

[1948] Step 4:

[1949] Ticket and accommodation booking

[1950] Based on the schedule approved by the user, the server arranges transportation tickets via an external system (e.g., API of an airline or travel agency) and makes reservations for accommodation using the external API.

[1951] Input: Approved travel itinerary

[1952] Output: Booked transportation tickets, accommodation reservation information

[1953] Step 5:

[1954] Local activity arrangements

[1955] The server arranges local activities and facilities based on the travel itinerary using an external API.

[1956] Input: Approved travel itinerary

[1957] Output: Reservation information for arranged activities and facilities

[1958] Step 6:

[1959] Providing route guidance

[1960] During a trip, the user sends information about their current location and destination from their device to the server, which then calculates the optimal route and provides real-time route guidance.

[1961] Input: Current location information, destination information

[1962] Output: Optimal route directions

[1963] Step 7:

[1964] Real-time interpretation

[1965] When users speak different languages, they activate the interpretation function. The device sends voice input to the server, and the generative AI model translates in real time. The translation result is displayed on the device or played aloud.

[1966] Input: Voice input

[1967] Output: Translation result

[1968] Step 8:

[1969] Restaurant recommendations and reservations / orders

[1970] The server recommends the most suitable restaurant based on the destination information, and also arranges reservations and orders based on the user's preferences and dates, again using an external restaurant API.

[1971] Input: Destination information, user preferences, travel itinerary

[1972] Output: Recommended restaurant information, reservation and order confirmation

[1973] Step 9:

[1974] Reward System

[1975] Information about the user's use of the service is sent to the server, and rewards such as points and coupons are added to the user's account.

[1976] Input: Service usage information

[1977] Output: Points and coupons awarded

[1978] The above are the specific processing steps for implementing this invention. By showing the specific hardware and software operations and data flow in each step in detail, the technical scope of the invention will become clearer.

[1979] 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.

[1980] The present invention relates to a system that acquires destination information and provides travel arrangements and local support in an integrated manner based on that information, and aims to increase user satisfaction by further combining it with an emotion engine that recognizes the user's emotions. Specific embodiments of this system will be described below.

[1981] The system is comprised of a smartphone, tablet, or other device, a cloud-based server, a generative AI model, and an emotion engine. Users input their destination information using a dedicated application, and then receive travel arrangements and guide services. The emotion engine analyzes the user's emotions to personalize the travel experience.

[1982] User Interface

[1983] Device: The user launches the application and enters "Tokyo" as their destination. As they enter their destination, the application recognizes their emotions from their facial expressions and tone of voice via a camera and microphone.

[1984] Destination information processing and emotion recognition

[1985] Server: Upon receiving the destination information "Tokyo," the emotion engine analyzes the emotion data and evaluates the user's emotional state during the travel planning stage.

[1986] Generative AI model: Generates optimal travel itineraries based on destination information, travel days, and user emotional state and preferences. For example, if a user is feeling stressed, the model suggests itineraries that emphasize relaxation and refreshment.

[1987] Presentation of proposed schedule

[1988] Terminal: Displays the travel itinerary proposal sent from the server to the user. The itinerary proposal is customized according to the user's emotional state.

[1989] User: Review the proposed schedule and modify or approve it as necessary.

[1990] Ticket arrangements, hotel reservations and local activity arrangements

[1991] Server: Once the itinerary is approved, transportation tickets are arranged via an external API (for example, an airline or travel agency API). Accommodation reservations are also made using an external API. Based on the information from the emotion engine, a more comfortable accommodation environment is selected.

[1992] Server: Arranges local activities and facilities based on the schedule. Using an emotion engine, prioritizes and selects activities that will reduce the user's stress and pique their interest.

[1993] Route guidance and interpretation functions

[1994] Terminal: When a user requests route guidance to the destination "Sensoji Temple" while on-site, the terminal sends current location information and destination information to the server.

[1995] Server: Calculates the optimal route based on the current location and destination information and sends it to the device. The emotion engine takes into account the user's fatigue and stress during the trip and provides the optimal route.

[1996] Device: When users speak different languages, they activate the translation function within the app and send the audio data to the server.

[1997] Generative AI model: Translates voice data in real time and sends the results back to the server. The emotion engine generates translation results that alleviate the user's anxiety and tension.

[1998] Reward System

[1999] Server: Checks the user's usage information and calculates rewards such as PayPay points or cryptocurrency. The emotion engine evaluates the user's gratitude and satisfaction and adjusts the reward accordingly.

[2000] Server: Calculated rewards are credited to the user's account and stored in a database.

[2001] Specific examples

[2002] As a practical example, consider a user planning a five-day trip to Tokyo. When the user enters "Tokyo" into the app, the generative AI model creates a five-day itinerary and suggests activities such as sightseeing in Asakusa on day one, shopping in Akihabara on day two, the night view of Roppongi on day three, and Disneyland on day four. If the user is feeling stressed, a relaxation spa or nature walk can be added to the itinerary. Furthermore, if the user's emotional state changes during the trip, the emotion engine recognizes this and dynamically adjusts the itinerary and activities. For example, if the user feels tired, the plan can be changed to include a massage or relaxation time at the hotel. On the day of the trip, real-time route guidance and interpretation functions are provided through the app. Furthermore, after the trip, bonus points and cryptocurrency based on the emotion engine are awarded.

[2003] As described above, the present invention is a system that allows users to receive all travel arrangements and on-site support through a single application, and by combining it with an emotion engine, it is possible to provide a personalized travel experience that corresponds to the user's emotional state.

[2004] The processing flow will be explained below.

[2005] Step 1:

[2006] User: Launches the app and enters "Tokyo" as the destination.

[2007] Step 2:

[2008] Terminal: Checks the entered destination information and sends it to the server. At the same time, it captures the user's facial expressions and tone of voice via a camera and microphone to obtain emotional data.

[2009] Step 3:

[2010] Server: Receives the destination information "Tokyo" and inputs the destination information and emotion data into the generative AI model and emotion engine, respectively.

[2011] Step 4:

[2012] Emotion engine: Analyzes the acquired emotional data and evaluates the user's emotional state. For example, it generates a result such as "high stress."

[2013] Step 5:

[2014] Generative AI model: Creates travel itinerary suggestions based on destination information and emotional state (e.g., "high stress") derived from the emotion engine, including itineraries focused on relaxation and refreshment to reduce stress.

[2015] Step 6:

[2016] Server: Stores the generated travel itinerary proposals in a database and sends them to the terminal.

[2017] Step 7:

[2018] Terminal: Displays the travel itinerary proposals received from the server to the user, customized to reflect the user's emotional state.

[2019] Step 8:

[2020] User: Checks the proposed schedule and clicks the approve button. User can also request revisions if necessary.

[2021] Step 9:

[2022] Terminal: Sends the user's authorization data to the server.

[2023] Step 10:

[2024] Server: After receiving approval, sends a request to arrange transportation tickets via an external API (e.g., an airline or travel agency API).

[2025] Step 11:

[2026] External API: Executes transportation ticket reservations and returns reservation information to the server.

[2027] Step 12:

[2028] Server: Stores the received ticket reservation information in a database.

[2029] Step 13:

[2030] Server: Sends a request to check the availability of accommodation using an external API (for example, the API of a hotel booking site).

[2031] Step 14:

[2032] External API: Returns the accommodation availability information to the server.

[2033] Step 15:

[2034] Server: Enters vacant room information into the generative AI model and requests it to select the most suitable accommodation, taking into account the evaluation of the emotion engine.

[2035] Step 16:

[2036] Generative AI model: Selects the most suitable accommodation based on availability information and the emotion engine's evaluation, and sends it back to the server.

[2037] Step 17:

[2038] Server: Sends a reservation request for the selected accommodation to an external API.

[2039] Step 18:

[2040] External API: Performs reservation confirmation and returns reservation information to the server.

[2041] Step 19:

[2042] Server: Stores the received accommodation reservation information in a database.

[2043] Step 20:

[2044] Server: Based on the itinerary, it sends a request to arrange reservations for local activities and facilities via an external API (e.g., the API of a tour booking site).

[2045] Step 21:

[2046] External API: Sends activity and facility reservation information back to the server.

[2047] Step 22:

[2048] Server: Stores the received reservation information in a database. The emotion engine monitors the user's emotional state and suggests changes to the activity if necessary.

[2049] Step 23:

[2050] User: Requests route guidance to the destination "Sensoji Temple" from the app while on location.

[2051] Step 24:

[2052] Terminal: Sends current location information and destination information to the server.

[2053] Step 25:

[2054] Server: Calculates the optimal route based on current location and destination information. The emotion engine takes into account fatigue and stress levels during travel and provides the optimal route.

[2055] Step 26:

[2056] Server: Sends calculated route information to the device.

[2057] Step 27:

[2058] Terminal: Display route directions to the user.

[2059] Step 28:

[2060] Users: Activate the in-app translation feature if they need to speak a different language locally.

[2061] Step 29:

[2062] Device: Sends voice input to the server.

[2063] Step 30:

[2064] Server: Analyzes the voice data and requests translation from the generative AI model. The emotion engine generates translation results that alleviate the user's anxiety and tension.

[2065] Step 31:

[2066] Generative AI model: performs real-time translation from Japanese to English (or other languages) and sends it back to the server.

[2067] Step 32:

[2068] Server: Sends the translation results to the device.

[2069] Step 33:

[2070] Terminal: The translation result is displayed to the user or output as speech.

[2071] Step 34:

[2072] Server: Checks the user's usage information and calculates rewards in PayPay points or cryptocurrency. The emotion engine also evaluates the user's emotional state and adjusts rewards accordingly.

[2073] Step 35:

[2074] Server: Calculated rewards are credited to the user's account and stored in a database.

[2075] Example 2

[2076] 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."

[2077] The present invention relates to a system that allows users to centrally plan and arrange trips and personalize the travel experience based on the user's emotional state. Conventional travel support systems can automatically generate and arrange trip plans, but do not personalize the experience based on the user's emotional state. This makes it difficult to maximize user satisfaction. Furthermore, they do not adequately provide real-time route guidance, interpretation functions, or rewards based on the user's evaluated emotions.

[2078] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for acquiring destination information, a generative model means for automatically creating a travel itinerary, a means for arranging transportation tickets, a means for booking accommodations, a means for arranging local activities and facilities, a means for providing route guidance to the destination, a means for real-time interpretation, a means for rewarding the user, a sentiment analysis means for analyzing the user's emotional state, and a means for personalizing the travel itinerary based on the sentiment analysis means. This enables a personalized travel experience according to the user's emotional state, thereby improving user satisfaction. Specifically, by analyzing the user's emotional state, if the user is feeling stressed, the system can suggest relaxation-oriented trips and flexibly respond to emotional changes during the trip, thereby providing an optimal travel experience.

[2079] "Destination information" is information about a geographical location that a user specifies as a travel destination.

[2080] The "generative model means" is an algorithm or program that has the function of automatically generating an optimal travel itinerary based on destination information and other related information.

[2081] The "means for arranging transportation tickets" is a system that has the function of reserving and purchasing transportation necessary for travel, such as airline tickets and train tickets, based on travel itineraries.

[2082] A "means for reserving accommodation" is a system that has the functionality to reserve hotels and other accommodations based on travel dates.

[2083] The "means for arranging local activities and facilities" is a system that has the function of making reservations for participation in local tourist attractions and activities based on travel itineraries.

[2084] The "means for providing route guidance to a destination" is a system that has the function of calculating and providing guidance on the optimal route to a destination specified by a user.

[2085] A "means for performing real-time interpretation" is a system that has the function of translating voice data in real time to support communication between users who speak different languages.

[2086] The "means for providing rewards to users" is a system that has the function of providing rewards such as points or cryptocurrency based on the user's satisfaction and usage information after the trip.

[2087] The "emotion analysis means" is a system that has the function of analyzing the emotional state of a user using data such as facial expressions and tone of voice.

[2088] The "means for personalizing travel itineraries based on emotion analysis means" is a system that has the function of optimizing and personalizing travel itineraries according to the user's emotional state analyzed by the emotion analysis means.

[2089] The present invention relates to a system that acquires destination information and provides travel arrangements and local support in an integrated manner based on that information. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to personalize the user's travel experience and increase satisfaction. A specific embodiment of this system will now be described.

[2090] User Interface

[2091] Device: The user launches a dedicated application on a device such as a smartphone or tablet and inputs destination information. For example, when "Tokyo" is entered as destination information, the information is acquired by the device. When inputting information, data is also acquired through a camera and microphone to recognize emotions from the user's facial expressions and tone of voice. This allows data to be collected based on the user's emotional state.

[2092] Destination information processing and emotion recognition

[2093] Server: Receives the destination information "Tokyo" and emotion data sent from the device. The server is cloud-based and analyzes the emotion data using an emotion engine. This allows the server to evaluate the user's emotional state during the trip planning stage.

[2094] Generative AI model: The generative AI model installed on the server generates an optimal travel itinerary based on destination information, the number of days of travel, the user's emotional state, and past travel preference data. For example, if the user is feeling stressed, it will suggest an itinerary that emphasizes relaxation and refreshment.

[2095] Presentation and confirmation of proposed schedule

[2096] Terminal: Displays the travel itinerary proposals sent from the server to the user. The generated itinerary proposals are customized according to the user's emotional state.

[2097] Users can review the proposed schedule and make any necessary changes or approvals. Changes can be easily made through the application interface.

[2098] Travel arrangements and local support

[2099] Server: After the user approves the itinerary, the server uses external APIs (such as airlines and travel agencies) to arrange transportation tickets and book accommodations. The emotion engine selects a more comfortable accommodation environment. It also arranges local activities and facilities, taking measures to reduce the user's stress and increase their interest.

[2100] Route guidance and interpretation functions

[2101] Terminal: When a user requests route guidance to the destination "Sensoji Temple" while on the spot, the terminal sends current location information and destination information to the server.

[2102] Server: Calculates the optimal route based on the received information and sends it to the device. Using the emotion engine, route guidance is provided that takes into account the user's fatigue and stress during travel.

[2103] On the device: When users speak different languages, they activate the translation function within the app and send the audio data to the server.

[2104] Generative AI model: Translates speech data in real time and sends the results back to the server, generating translation results that alleviate the user's anxiety and tension.

[2105] Post-trip rewards

[2106] Server: Calculates rewards such as PayPay points or cryptocurrency based on the user's usage information of the provided service. The emotion engine evaluates the user's gratitude and satisfaction and adjusts the amount of reward accordingly.

[2107] Server: The calculated reward is credited to the user's account and the data is stored in a database.

[2108] Specific examples

[2109] For example, if a user plans a five-day trip to "Tokyo," they enter "Tokyo" into the app. The generative AI model creates an itinerary based on the following prompt: "Create a five-day itinerary for Tokyo. The user wants to relax." Based on this, it suggests itineraries such as "Sightseeing in Asakusa" on the first day, "Shopping in Akihabara" on the second day, "Viewing the night view of Roppongi" on the third day, and "Disneyland" on the fourth day. If the user is feeling stressed, it adds relaxation-focused spa and nature walks.

[2110] If the user feels tired during the trip, the emotion engine will recognize this and the server will dynamically adjust the itinerary and activities in real time to provide the user with a comfortable travel experience. After the trip is over, the user will be awarded bonus points and cryptocurrency based on the evaluation made by the emotion engine.

[2111] In this way, the system comprehensively supports the entire process from obtaining destination information to providing travel experiences and rewards, and can also provide personalized services according to the user's emotional state.

[2112] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2113] Step 1: Enter your destination information

[2114] Terminal: The user launches the application and enters destination information. For example, if the user enters "Tokyo" as the destination, the information is acquired by the terminal.

[2115] Input: Destination information ("Tokyo")

[2116] Output: Destination information data

[2117] Specific operation: The user enters the destination using the keyboard or voice input, and the device stores the information in a database within the application.

[2118] Step 2: Obtaining emotion data

[2119] Terminal: At the same time as entering destination information, the device captures the user's facial expressions and tone of voice via a camera and microphone.

[2120] Input: User's facial expressions, tone of voice

[2121] Output: Emotion data

[2122] Specific operations: The device activates the camera to capture facial expressions, uses the microphone to collect voice data, and sends this data to the emotion analysis module.

[2123] Step 3: Send destination information and emotion data to the server

[2124] Terminal: Sends the acquired destination information and emotion data to the server.

[2125] Input: Destination information data, emotion data

[2126] Output: Data sent to the server via the HTTP request

[2127] Specific operation: The device packages destination information and emotion data and sends it to the server using an HTTP request.

[2128] Step 4: Generate itinerary

[2129] Server: Receives destination information and emotion data and generates an optimal travel itinerary using a generative AI model.

[2130] Input: Destination information data, emotion data, number of travel days, user preference data

[2131] Output: Travel itinerary proposal

[2132] Specific operation: The generative AI model generates a travel itinerary based on the prompt, "Please create a 5-day travel itinerary for Tokyo. The user wants to relax." The sentiment analysis engine analyzes the emotional data and generates a personalized itinerary that reflects the user's emotional state.

[2133] Step 5: Present and confirm the proposed schedule

[2134] Terminal: Displays the proposed travel itinerary sent from the server to the user.

[2135] Input: Travel itinerary proposal

[2136] Output: Display of proposed schedule

[2137] Specific operation: The device displays the proposed schedule received from the server on the application's UI. The user checks the schedule and makes any necessary corrections or approvals.

[2138] Step 6: Revise the proposed itinerary

[2139] User: Review the proposed schedule and make any necessary changes or approve it through the interface.

[2140] Input: Schedule proposal, correction instructions

[2141] Output: Revised schedule or approval

[2142] Specific operation: The user adjusts the schedule using touch operations or drag-and-drop operations, and presses the "Approve" button to confirm the final schedule.

[2143] Step 7: Making travel arrangements

[2144] Server: Based on the itinerary approved by the user, the server uses external services to arrange transportation tickets and make hotel reservations.

[2145] Input: Approved schedule, emotion data

[2146] Output: Ticket and booking confirmation information

[2147] Specific operation: The server sends a request to an external API (travel agency, airline, hotel, etc.) to confirm the ticket or accommodation reservation, receives the API response, and sends the confirmation information to the terminal.

[2148] Step 8: Local route guidance

[2149] Terminal: The user requests route guidance locally and sends current location information and destination information to the server.

[2150] Input: current location information, destination information

[2151] Output: Optimal route guidance

[2152] Specific operation: The device acquires GPS data and sends it to the server along with destination information.

[2153] Server: Calculates the optimal route based on the received information and sends it to the terminal.

[2154] Input: current location information, destination information

[2155] Output: Route guidance information

[2156] Specific operation: The server uses a route calculation algorithm to calculate the optimal route and sends the result to the terminal.

[2157] Step 9: Run the interpretation function

[2158] Terminal: When a user uses the interpretation function, the terminal sends voice data to the server.

[2159] Input: Audio data

[2160] Output: The translated text

[2161] Specific operation: The device uses a microphone to capture voice data and sends it to the server.

[2162] Server: Receives the voice data and performs real-time translation using the generative AI model.

[2163] Input: Audio data

[2164] Output: The translated text

[2165] How it works: The server passes the voice data to the generative AI model and sends the translated text back to the device.

[2166] Step 10: Post-trip rewards

[2167] Server: Evaluates the user's gratitude and satisfaction based on service usage information and calculates rewards.

[2168] Input: service usage information, emotion data

[2169] Output: Reward points

[2170] Specific operation: The sentiment analysis engine evaluates satisfaction based on the emotional data, calculates rewards, and adds the calculated rewards to the user's account and stores them in the database.

[2171] The above are the specific processing steps of the program of this system.

[2172] (Application example 2)

[2173] 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."

[2174] Conventional travel booking and food delivery systems lack the ability to provide personalized suggestions that take into account the user's emotional state. As a result, they often do not provide the optimal service for the user's current emotional state, preventing improvement in satisfaction. In addition, when the user is tired or stressed, the system does not suggest the optimal menu according to the situation, resulting in a poor user experience.

[2175] 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.

[2176] In this invention, the server includes a means for acquiring destination information, a generative model means, and an emotion engine means. This allows the server to analyze the user's emotional state in real time and propose optimal travel itineraries and menus based on that analysis. Furthermore, by linking with external systems, the server can arrange transportation, accommodations, and local activities, further enhancing user convenience.

[2177] "Destination information" is information about the user's desired travel destination or delivery destination.

[2178] The "generative model means" is a system for automatically creating travel itineraries and menus based on input destination information.

[2179] The "emotion engine means" is a system for analyzing the user's facial expressions and tone of voice to recognize the user's current emotional state.

[2180] The "generative AI model means" is an artificial intelligence model that generates optimal food menus and travel plans based on the user's destination information, emotional state, and preferences.

[2181] "Means for arranging transportation tickets" is a system for automatically reserving transportation according to the user's travel schedule.

[2182] The "means for reserving accommodation" is a system for reserving accommodation based on the user's travel itinerary.

[2183] "A means for arranging local activities and facilities" is a system for making reservations for tourist attractions and entertainment according to the user's travel itinerary.

[2184] The "means for providing route guidance to a destination" is a system for navigating the optimal route based on destination information input by the user.

[2185] A "means for real-time interpretation" is a system for translating speech in real time when users speak different languages.

[2186] The "means for providing rewards to users" is a system for providing points or rewards to users according to their usage of the service.

[2187] System Overview

[2188] The present invention relates to a system that acquires destination information, automatically creates travel itineraries and menus based on that information, and provides them while taking into account the emotional state of the user. Specific embodiments for carrying out the invention are described below.

[2189] Hardware and Software Configuration

[2190] Hardware used

[2191] Device: Smartphone or tablet

[2192] Server: Cloud-based server

[2193] Software used

[2194] Emotion engine: A sentiment analysis library such as the Affectiva SDK

[2195] Generative AI models: Generative models such as OpenAI's GPT-4

[2196] Processing flow

[2197] User Interface

[2198] The user launches the application on their device and enters destination information (travel destination or delivery address). At that time, the device's camera and microphone are used to capture the user's emotional state. The captured emotional data is then analyzed using an emotion engine (such as the Affectiva SDK).

[2199] Destination information processing and emotion recognition

[2200] The server receives the destination information and evaluates the user's emotional data analyzed by the emotion engine, then uses a generative AI model (such as OpenAI's GPT-4) to generate optimal travel itineraries and food menus based on the destination information, the user's emotional state, and past preference data.

[2201] Proposal of schedule and menu

[2202] The terminal displays the customized itinerary and menu proposals sent from the server to the user, who can then review the proposals and modify or approve them as necessary.

[2203] Ticket arrangements, accommodation reservations, activity arrangements

[2204] Based on the approved travel itinerary, the server connects with external systems (such as the APIs of airlines and hotel booking sites) to arrange transportation tickets and book accommodations. It also automates the arrangement of local activities and facilities using external APIs.

[2205] Route guidance and interpretation functions

[2206] When a user requests guidance to a destination, the server provides the device with the optimal route based on the user's current location. Furthermore, if the user speaks a different language, the server collects voice data through the device's microphone and performs real-time interpretation. The server translates the voice data using a generative AI model and sends the results back to the device.

[2207] Reward System

[2208] When a user uses the service, they are given rewards (points or rewards) based on their satisfaction level as assessed by the emotion engine. This information is managed on the server and reflected in the user's account.

[2209] Specific examples

[2210] As an actual usage example, consider the case where a user inputs "I'm tired" and requests delivery to their home in Tokyo. When the user inputs their destination information into the app, the emotion engine retrieves emotion data that evaluates "tired." Based on this information, the generative AI model generates and suggests food menus with a relaxing effect (e.g., "herbal tea" or "comfort food") to the user.

[2211] Prompt Sentence Examples

[2212] Input sentence: "I'm tired from work today. Please deliver to my home in Tokyo."

[2213] Prompt: "Suggest some food options for when the user is feeling tired. Ideally, these would include relaxing foods and drinks."

[2214] The above is a specific embodiment for carrying out the invention. By combining an emotion engine and a generative AI model, it becomes possible to provide personalized services according to the user's emotional state.

[2215] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2216] Step 1:

[2217] Enter destination information

[2218] A user launches the application and enters destination information (e.g., a travel destination or delivery address). The entered data is temporarily stored in the device's memory. At the same time, the device's camera and mic...

Claims

1. a means for obtaining destination information; a generative model means for automatically generating a travel itinerary based on the destination information; A means for arranging tickets for transportation based on the travel itinerary; a means for reserving accommodations based on said travel itinerary; A means of arranging local activities and facilities; a means for providing route guidance to a destination; A means of real-time interpretation; a means for awarding a reward to a user; A system including:

2. 10. The system of claim 1, further comprising means for generating a plurality of proposed itineraries based on said destination information for selection by a user.

3. The system according to claim 1 , further comprising means for coordinating with an external system to arrange tickets for the means of transportation, reservations for accommodation, and arrangements for local activities and facilities. That's all.

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