system

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving a request for generating a travel plan from a user; means for transmitting current location information of the user to a server along with the request; means for acquiring weather forecast information, map information, and route information using an external API; and means including a generative AI model for generating an optimal travel plan based on the generated weather forecast information, map information, and route information; and means for updating the generative AI model based on the feedback.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] The project aims to solve the problem of travelers having difficulty gathering information and making plans when their plans suddenly change or when they have to make plans while they are in an unfamiliar place, which reduces the quality of their trip. It also aims to solve the problem of the difficulty of creating flexible travel plans that can be affected by changes in weather and traffic conditions. [Means for solving the problem]

[0005] This system includes a means for receiving a travel plan generation request from a user, a means for sending the user's current location information along with the request to a server, a means for acquiring weather information, map information, and route information using an external API, a means including a generative AI model that generates an optimal travel plan based on the generated weather information, map information, and route information, a means for sending and displaying the generated travel plan to the user's device, a means for receiving feedback from the user after the trip and sending it to the server, and a means for updating the generative AI model based on the feedback and reflecting it in the next plan generation. This system efficiently creates optimal travel plans even for travelers in unfamiliar locations. Furthermore, the generative AI model automatically selects indoor and outdoor activities based on weather conditions, enabling the creation of travel plans that can flexibly adapt to changing weather conditions. Furthermore, the server has a database containing users' past travel history and reviews, making it possible to provide highly personalized travel plans tailored to each traveler's individual preferences.

[0006] "User" refers to a traveler using this system or a user who searches for and creates travel plans.

[0007] A "travel plan generation request" refers to a request that includes information such as a user's desired travel destination, itinerary, places to visit, and activities.

[0008] "Current location information" refers to location information (such as GPS data) of the user's current location.

[0009] "Server" refers to the central processing unit that receives data from users, obtains necessary information using external APIs, and generates travel plans using generative AI models.

[0010] "External API" refers to an external application program interface that the server uses to obtain weather information, map information, and route information.

[0011] "Weather Information" means current and future weather data for a particular geographic area.

[0012] "Map information" refers to geographical data for a specific area and location information for tourist attractions and facilities.

[0013] "Route information" refers to data such as public transportation schedules, fares, and travel times.

[0014] "Generative AI model" refers to an artificial intelligence model that runs on a server and generates optimal travel plans based on weather information, map information, route information, and the user's past travel history and ratings.

[0015] "Travel plan" refers to a plan that includes detailed information such as a travel itinerary, tourist spots, transportation, and places to eat, generated based on a user's request.

[0016] "Terminal" refers to electronic devices used by users, such as smartphones, tablets, and personal computers.

[0017] "Feedback" refers to the impressions and evaluation information provided by users after completing their trip.

[0018] "Updating" refers to adjusting the parameters of the generative AI model based on feedback and reflecting this in the next plan generation.

[0019] "Database" refers to a collection of data held by the server that includes users' past travel history and rating information. [Brief explanation of the drawings]

[0020] [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

[0021] 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.

[0022] First, the terms used in the following description will be explained.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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."

[0041] The present invention is a system that enables travelers to easily and efficiently create optimal travel plans. Specific embodiments for carrying out the present invention will be described below.

[0042] The system of the present invention allows users to make travel plan generation requests, and automatically generates and provides optimal travel plans based on those requests. The system is broadly composed of a user terminal, a server, an external API, and a generation AI model.

[0043] Explanation of program processing

[0044] 1. User requests to generate a travel plan

[0045] A user requests the creation of a travel plan using their device (smartphone, tablet, PC, etc.) This request includes information such as the departure point, destination, planned dates, desired activities and tourist spots, etc.

[0046] 2. The device sends its location and user input to the server

[0047] The user's device sends the user's current location information (GPS data) and input request data to the server, allowing the server to understand the user's current situation and make appropriate suggestions in real time.

[0048] 3. The server obtains weather, map, and route information.

[0049] The server uses external APIs to obtain weather, map, and route information for the current location and destination. This data is updated in real time, so the latest information can always be provided.

[0050] 4. Generative AI models generate optimal travel plans

[0051] The server is equipped with a generative AI model that automatically generates an optimal travel plan based on the acquired data. This plan includes transportation options, travel times, recommended tourist spots, places to eat, etc. It also takes weather conditions into account, suggesting indoor activities in case of rain.

[0052] 5. Send and display your travel plans on your device

[0053] The server sends the generated travel plan to the user's device, which receives it and displays it in an easy-to-understand format for the user. The user can then proceed with their trip according to this plan.

[0054] 6. Gather user feedback

[0055] After completing the trip, the user provides their impressions and feedback. The feedback is entered through the user's device, which then sends it to the server. The server stores this data and uses it to train the generative AI model. This process allows the generative AI model to be continuously optimized and reflected in the generation of the next travel plan.

[0056] Specific examples

[0057] Example 1: Generating a Travel Plan

[0058] 1. User: "I'd like to travel from Tokyo to Kyoto. I'll be leaving next Saturday. I'd like to visit historical sites and museums."

[0059] 2. The device sends a request to the server:

[0060] The terminal transmits the user's current location information and the input request data to the server.

[0061] 3. The server retrieves the relevant information:

[0062] The server obtains weather information (weather forecasts for Tokyo and Kyoto), map information (information on tourist spots in Kyoto), and route information (transportation methods and travel times from Tokyo to Kyoto).

[0063] 4. The generative AI model in the server generates a plan:

[0064] The generative AI model generates a trip plan like this:

[0065] Departs Tokyo Station at 8:00, arrives Kyoto Station at 9:45 (by Shinkansen)

[0066] 10:00 Walk from Kyoto Station to Kiyomizu-dera Temple

[0067] 12:00 Lunch near Kiyomizu-dera Temple (with restaurant recommendations)

[0068] 14:00 Move to Kyoto National Museum (by bus)

[0069] 17:00 Hotel check-in

[0070] 5. View your plan on your device:

[0071] The generated plan is sent to the terminal and displayed to the user, who can then enjoy their trip according to the plan.

[0072] 6. Users provide feedback:

[0073] After the trip, the user inputs their evaluation and impressions into the device. Specific feedback, such as "The restaurants around Kiyomizu-dera Temple were great," is sent to the server.

[0074] By using this system, users can efficiently create optimal travel plans even in unfamiliar places, and can flexibly respond to local information gathering and changes in the weather.

[0075] The processing flow will be explained below.

[0076] Step 1:

[0077] A user uses a terminal to input a request to generate a travel plan, which includes information such as the origin, destination, planned dates, desired activities and attractions, etc.

[0078] Step 2:

[0079] The device obtains the user's current location information by using the GPS function to obtain current location data.

[0080] Step 3:

[0081] The device sends the user's input data and current location information together to the server, allowing the server to understand the user's request and current location.

[0082] Step 4:

[0083] The server retrieves weather information using an external API. The server retrieves the weather forecast for the specified departure date and travel period.

[0084] Step 5:

[0085] The server obtains map information. The server collects the location information of the tourist spots and activities the user wishes to visit, as well as the geographical data of the surrounding areas.

[0086] Step 6:

[0087] The server obtains route information. The server collects data on the user's transportation method (e.g., train, bus, walking) and calculates the optimal route and travel time.

[0088] Step 7:

[0089] The AI ​​model on the server generates an optimal travel plan based on weather, map, and route information, taking into account the user's requests and weather conditions, and adjusting indoor and outdoor activities.

[0090] Step 8:

[0091] The server sends the generated travel plan to the user's terminal, which receives it and displays it in an easy-to-understand manner for the user.

[0092] Step 9:

[0093] The user acts according to the travel plan, and if changes are needed during the trip, the system can generate a new plan in real time.

[0094] Step 10:

[0095] After completing the trip, users can enter feedback through the device, specifically describing their impressions and evaluations.

[0096] Step 11:

[0097] The device sends user feedback data to a server, which uses the feedback to train a generative AI model.

[0098] Step 12:

[0099] The server updates the generative AI model based on the feedback data and reflects it in the next itinerary generation. This continuous feedback loop improves the accuracy of the generative AI model, enabling more personalized travel plans to be provided.

[0100] Example 1

[0101] 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."

[0102] When planning a trip, users must collect a huge amount of information and consider each piece of information to create the optimal plan. This process takes time and effort, and is difficult to perform efficiently, especially when traveling to unfamiliar places. It is also difficult to flexibly change plans in response to changes in the weather or local conditions. To solve this problem, a system is needed that allows users to easily and efficiently automatically generate optimal travel plans and respond in real time.

[0103] 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.

[0104] In this invention, the server includes: means for receiving a travel plan generation request from a user; means for transmitting the user's current location information to the server along with the request; means for acquiring weather, location, and traffic information using an external service; means including a generative AI model for generating an optimal travel plan based on the generated weather, location, and traffic information; means for transmitting and displaying the generated travel plan to the user's terminal; means for receiving feedback from the user after the trip and transmitting it to the server; and means for updating the generative AI model based on the feedback and reflecting it in the next plan generation. This allows the user to efficiently obtain an optimal travel plan and flexibly respond to weather and local conditions.

[0105] "User" means an end user who requests the generation of a travel plan and uses the plan provided.

[0106] "Terminal" refers to the electronic device used by a user to make a travel plan request, including a smartphone, tablet, or PC.

[0107] The "server" is a central processing unit that receives user requests, retrieves relevant information, and generates a travel plan using a generative AI model.

[0108] "External services" refers to external databases and APIs that provide weather information, location information, traffic information, etc.

[0109] "Weather Information" means weather forecasts and related meteorological data for a particular location and time.

[0110] "Location information" refers to geographical data necessary for travel planning, such as map information and the locations of tourist attractions.

[0111] "Transportation information" refers to data relating to the means of transportation from a departure point to a destination and the travel time required.

[0112] A "generative AI model" refers to an artificial intelligence system that automatically creates optimal travel plans based on collected data.

[0113] "Sentiments" refers to the feedback and ratings users provide after completing a trip, and this information is used to improve the generative AI model.

[0114] The present invention is a system that enables travelers to easily and efficiently create optimal travel plans. Specific embodiments for carrying out the present invention will be described below.

[0115] This system consists of a user terminal, a server, and an external service. The role and specific operation of each component are explained below.

[0116] User terminal

[0117] A user terminal is an electronic device such as a smartphone, tablet, or PC that a user uses to request the creation of a travel plan. A dedicated application is installed on the terminal, and the user uses this application to input information about the departure point, destination, planned date, desired activities, and tourist spots. The terminal also uses its GPS function to obtain the user's current location information and sends it to the server along with the request data.

[0118] server

[0119] The server is the central processing unit of this system. When it receives a user request, it uses external services to collect the necessary information. Specifically, it obtains the following three pieces of information through external services:

[0120] Weather information: Uses external weather information services to obtain real-time weather information for the user's current location and destination.

[0121] Location information: Use a map information service to obtain map information for your destination and location information for tourist attractions.

[0122] Traffic information: Use a traffic information service to obtain the best means of transportation and travel time from your departure point to your destination.

[0123] Generative AI Models

[0124] The server is equipped with a generative AI model that automatically generates optimal travel plans based on collected weather, location, and traffic information. The generated plans include information on transportation methods, travel times, recommended tourist spots, and places to eat. Weather conditions are also taken into consideration, and in the event of rain, for example, indoor activities are suggested.

[0125] Providing and displaying travel plans

[0126] The server sends the generated travel plan to the user's device. The device receives it and displays it in an easy-to-understand format for the user through the application. The user can then enjoy their trip according to the plan.

[0127] Gathering and implementing feedback

[0128] After the trip is over, the user provides their feedback through their device. For example, they can enter a comment or rating within the application, such as "The restaurants around Kiyomizu-dera Temple were great." The device then sends this feedback to the server, which then stores the feedback data. This data is used to train the generative AI model and is reflected in the generation of the next travel plan.

[0129] Specific examples

[0130] Prompt Sentence Examples

[0131] Here is an example of what a user might input when requesting a travel plan:

[0132] "I'd like to travel from Tokyo to Kyoto. I'll be leaving next Saturday. I'd like to visit historical sites and art museums."

[0133] By using this system, users can efficiently create optimal travel plans even in unfamiliar places, and can flexibly respond to local information gathering and changes in the weather.

[0134] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0135] Step 1:

[0136] The user requests the creation of a travel plan. The user opens the application on their device (smartphone, tablet, PC, etc.) and inputs the departure point, destination, itinerary, and desired sightseeing spots and activities. All input data is temporarily stored on the device. Specifically, the device's GPS function is used to automatically obtain current location information.

[0137] Input: departure point, destination, itinerary, desired sightseeing spots and activities, current location information (GPS data)

[0138] Output: Request data saved on the device

[0139] Step 2:

[0140] The device sends the current location and user input to the server. The device then sends the request data to the server as an HTTP request. The server then receives the user's input and current location information and proceeds to the next step. The device then displays a confirmation message that the request has been sent.

[0141] Input: Request data stored on the device

[0142] Output: Request data sent to the server

[0143] Step 3:

[0144] The server obtains weather, map, and route information. The server sends an API request to an external service (e.g., weather information service, map information service, traffic information service) to collect weather, map, and traffic information for the current location and destination. The obtained data is temporarily stored on the server. The server checks the API response and records a log indicating that data acquisition has been completed.

[0145] Input: The request data sent to the server

[0146] Output: Weather information, map information, and traffic information temporarily stored on the server

[0147] Step 4:

[0148] The generative AI model generates the optimal travel plan. The generative AI model inside the server generates the optimal travel plan based on collected weather, map, and traffic information. The model takes into account the user's desired tourist spots and activities, and creates a plan by optimizing transportation methods and travel times. The resulting plan is saved on the server. Specific operations include calculating weather conditions and transportation methods, and creating an optimal visiting schedule.

[0149] Input: Weather information, map information, traffic information, and request data stored on the server

[0150] Output: Generated itinerary (schedule, transportation, attractions, etc.)

[0151] Step 5:

[0152] The travel plan is sent to the device and displayed. The server sends the generated travel plan to the device. The device receives it and displays it in an easy-to-understand manner for the user through the application. The display format can be a timeline or on a map. The device displays a confirmation message that the plan has been displayed.

[0153] Input: Travel plans stored on the server

[0154] Output: Travel plan displayed on the device

[0155] Step 6:

[0156] Collect user feedback. After completing the trip, the user enters their impressions and evaluations into the device. The device sends this to the server as an HTTP request. The server receives the feedback data and stores it in the learning database of the generative AI model. This allows the feedback to be reflected when generating the next travel plan.

[0157] Input: User's impressions and ratings after the trip

[0158] Output: Feedback data stored in the training database of the generative AI model

[0159] (Application example 1)

[0160] 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."

[0161] Conventional travel plan generation systems generate travel plans based only on weather, map, and route information, without taking into account local shopping districts, special offers, and event information. This makes it difficult to make the most of the local attractions at a travel destination, preventing travelers from enjoying their trips efficiently. Another issue is that the system does not adequately reflect information in real time, making it impossible to respond to immediate plan changes.

[0162] 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.

[0163] In this invention, the server includes: means for receiving a travel plan generation request from a user; means for transmitting the user's current location information along with the request to the server; means for acquiring weather information, map information, route information, and shopping district and event information using an external API; means including a generative AI model for generating an optimal travel plan based on the generated weather information, map information, route information, shopping district and event information; means for transmitting and displaying the generated travel plan to the user's terminal; means for receiving feedback from the user after the trip and transmitting it to the server; and means for updating the generative AI model based on the feedback and reflecting it in the next plan generation. This not only makes it possible to generate plans that incorporate the local attractions of the travel destination, but also makes it possible to respond to immediate plan changes by reflecting information in real time.

[0164] "User device" means a device used to send a travel plan request and receive and display the generated travel plan, including a smartphone, tablet, or PC.

[0165] A "server" is a computer system whose role is to receive requests from users, obtain various information using external APIs, generate travel plans using generative AI models, and send them to the user's terminal.

[0166] An "external API" is an external service that provides various data in real time, such as weather information, map information, route information, and information on shopping districts and events.

[0167] A "generative AI model" is an artificial intelligence model that has the ability to automatically generate optimal travel plans based on various data received.

[0168] "Shopping street information" is data about shopping streets that exist at the travel destination, and includes store locations, special offers, sale information, and the like.

[0169] "Event information" is data about events held in the travel destination area, including the date, time, location, and content of the event.

[0170] "Feedback" refers to opinions such as ratings and impressions provided by users after completing their trip, and is data used by the generative AI model to optimize the generation of the next plan.

[0171] The present invention is a system for enabling travelers to efficiently plan and enjoy trips. Specific embodiments for carrying out the present invention will be described below.

[0172] The system configuration consists of a user terminal, a server, an external API, and a generative AI model. We will now describe the specific components and their operation.

[0173] User terminal

[0174] The user terminal may be a smartphone, tablet, or PC. This terminal is used by the user to input a request to generate a travel plan and to display the generated travel plan. The user terminal is equipped with a GPS function and transmits the user's current location information to the server.

[0175] server

[0176] The server is where the core functions of the travel plan generation system are concentrated. The server performs the following processes:

[0177] 1. Receiving a travel plan request:

[0178] The server receives a request to generate a travel plan from a user terminal, which includes information such as the origin, destination, travel dates, desired activities and tourist attractions, etc.

[0179] 2. Retrieving data from external APIs:

[0180] The server uses external APIs to obtain weather information, map information, route information, shopping mall information, and event information, and generates an optimal travel plan based on the latest information.

[0181] 3. Plan generation using generative AI models:

[0182] The server is equipped with a generative AI model that uses the acquired data to generate an optimal itinerary, taking into account weather conditions, real-time special offers and sales, local event information, and more.

[0183] 4. Sending the travel plan to the user's device:

[0184] The generated travel plan is sent to the user's terminal, and the user can use it to proceed with the trip.

[0185] 5. Collect user feedback and update the AI ​​model:

[0186] After completing the trip, the user provides feedback, which is sent to the server and used to train the generative AI model, which then optimizes the generation of the next itinerary.

[0187] Specific examples

[0188] Example of a user request

[0189] The user inputs a request such as "I would like to travel from Tokyo to Kyoto. The departure date is next Saturday. As tourist spots, I would like to visit historical places and art museums."

[0190] Examples of prompt statements

[0191] {

[0192] "start_location": "Tokyo",

[0193] "destination": "Kyoto",

[0194] "date": "2023-10-20",

[0195] "weather": {

[0196] "forecast": [

[0197] {"date": "2023-10-20", "condition": "Sunny", "max_temp": 25, "min_temp": 18},

[0198] ...

[0199] ]

[0200] },

[0201] "stores": [

[0202] {"name": "Shopping District", "type": "Shopping District", ...},

[0203] ...

[0204] ],

[0205] "events": [

[0206] {"name": "Museum Exhibition", "location": "Museum", ...},

[0207] ...

[0208] ]

[0209] }

[0210] Based on the prompts above, the generative AI model generates an optimal travel plan to support the user's trip, allowing travelers to efficiently experience local attractions and flexibly respond to real-time information changes.

[0211] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0212] Step 1:

[0213] The user terminal receives a request to create a travel plan from the user as input. The user inputs information such as the departure point, destination, travel dates, desired activities and tourist spots, etc. This input data is transmitted to the server via communication.

[0214] Input: origin, destination, travel dates, desired activities and attractions

[0215] Output: User request data sent to the server

[0216] Step 2:

[0217] The server receives the user request data as input, and then acquires the current location information (GPS data) from the user terminal and stores it together with the request data.

[0218] Input: User request data, current location information (GPS data)

[0219] Output: Consolidated user request data (including location information)

[0220] Step 3:

[0221] The server calls external APIs to obtain weather information, map information, route information, and information on shopping malls and events. This information is obtained in real time and used to generate travel plans.

[0222] Input: Consolidated user request data

[0223] Output: Weather information, map information, route information, shopping street information, event information

[0224] Step 4:

[0225] The server inputs prompts into the generative AI model based on the acquired external data. The generative AI model then generates an optimal travel plan based on this information. The plan includes information on transportation, recommended sightseeing spots, special offers at shopping malls, and event information.

[0226] Input: Weather information, map information, route information, shopping street information, event information

[0227] Output: Travel plan

[0228] Step 5:

[0229] The server transmits the generated travel plan to the user terminal, which displays the received travel plan and enables the user to travel according to the plan.

[0230] Input: Travel Plan

[0231] Output: Travel plan displayed on the user's device

[0232] Step 6:

[0233] After completing the trip, the user inputs feedback. The user terminal transmits this feedback to the server. The feedback includes an evaluation and impressions of the trip.

[0234] Input: User feedback

[0235] Output: Feedback data sent to the server

[0236] Step 7:

[0237] The server updates the generative AI model based on the received feedback data, and this update is used to provide improved suggestions the next time a travel plan is generated.

[0238] Input: Feedback data

[0239] Output: Updated generative AI model

[0240] Through these steps, the system can provide users with the most optimal travel plans and constantly optimize the generative AI model based on the latest information and user feedback.

[0241] 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.

[0242] The present invention is a system that enables travelers to easily and efficiently create optimal travel plans, and in particular incorporates an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention will be described below.

[0243] The system of the present invention receives a request from a user to generate a travel plan, automatically generates an optimal travel plan, and provides it to the user. The system is broadly composed of a user terminal, a server, an external API, a generation AI model, and an emotion engine.

[0244] Explanation of program processing

[0245] 1. User requests to generate a travel plan

[0246] A user requests the creation of a travel plan using their device (smartphone, tablet, PC, etc.) This request includes information such as the departure point, destination, planned dates, desired activities and tourist spots, etc.

[0247] 2. The device sends its location and user input to the server

[0248] The user's terminal transmits the user's current location information (GPS data) and the input request data to the server.

[0249] 3. The server obtains weather, map, and route information.

[0250] The server uses external APIs to obtain weather, map, and route information for the current location and destination. This data is updated in real time, so the latest information can always be provided.

[0251] 4. Generative AI models generate optimal travel plans

[0252] The server is equipped with a generative AI model that automatically generates an optimal travel plan based on acquired weather, map, and route information. This plan includes transportation options, travel times, recommended tourist spots, and places to eat. Weather conditions are also taken into account, and indoor activities are suggested in case of rain.

[0253] 5. Emotion engine recognizes user emotions

[0254] When a user checks the travel plan created through the device, the emotion engine analyzes the user's facial expressions and voice to recognize the user's emotions, and this information is sent to the server in real time.

[0255] 6. Modifying your plan based on emotions

[0256] The generative AI model on the server uses the data sent from the emotion engine to adjust the plan if the user is dissatisfied. For example, if it determines that the user is not interested in a particular tourist spot, it will suggest another tourist spot.

[0257] 7. Send and display your travel plans on your device

[0258] The server sends the revised travel plan to the user's device, which receives it and displays it in an easy-to-understand manner for the user.

[0259] 8. Gather user feedback

[0260] After the trip is over, the user provides their impressions and feedback. The emotion engine recognizes the user's emotions when providing feedback and sends the data including those to the server. This data is used to train the generative AI model.

[0261] Specific examples

[0262] Example 1: Generating a travel plan and using the emotion engine

[0263] 1. User: "I'd like to travel from Tokyo to Kyoto this weekend. I'm leaving next Saturday. I'd like to visit historical sites and museums."

[0264] 2. The device sends a request to the server

[0265] The terminal transmits the user's current location information and request data to the server.

[0266] 3. The server retrieves the relevant information

[0267] The server obtains weather information (weather forecasts for Tokyo and Kyoto), map information (information on tourist spots in Kyoto), and route information (transportation methods and travel times from Tokyo to Kyoto).

[0268] 4. The generative AI model on the server generates a plan

[0269] The generative AI model generates a trip plan like this:

[0270] Departs Tokyo Station at 8:00, arrives Kyoto Station at 9:45 (by Shinkansen)

[0271] 10:00 Walk from Kyoto Station to Kiyomizu-dera Temple

[0272] 12:00 Lunch near Kiyomizu-dera Temple (with restaurant recommendations)

[0273] 14:00 Move to Kyoto National Museum (by bus)

[0274] 17:00 Hotel check-in

[0275] 5. Emotion engine recognizes user emotions

[0276] The emotion engine analyzes the user's facial expressions and voice while checking the travel plan, and recognizes the user's emotions. For example, if the user looks dissatisfied, it sends this to the server.

[0277] 6. The server modifies the plan

[0278] The generative AI model detects user frustrations and adjusts travel plans, for example, if a user isn't interested in historical sites, it suggests shopping spots instead.

[0279] 7. Display the revised plan on your device

[0280] The modified plan is sent to the terminal and displayed to the user.

[0281] 8. Users provide feedback

[0282] After the trip, the user enters feedback, and the emotion engine recognizes the emotions expressed and sends them to the server. The server uses this data to update the generative AI model and reflect it in the generation of the next travel plan.

[0283] By using this system, users can enjoy more personalized travel plans that reflect their individual preferences and feelings.

[0284] The processing flow will be explained below.

[0285] Step 1:

[0286] A user uses a terminal to input a request to generate a travel plan, which includes information such as the origin, destination, planned dates, desired activities and attractions, etc.

[0287] Step 2:

[0288] The device obtains the user's current location information by using the GPS function to obtain current location data.

[0289] Step 3:

[0290] The device sends the user's input data and current location information together to the server, allowing the server to understand the user's request and current location.

[0291] Step 4:

[0292] The server retrieves weather information using an external API. The server retrieves the weather forecast for the specified departure date and travel period.

[0293] Step 5:

[0294] The server obtains map information. The server collects the location information of the tourist spots and activities the user wishes to visit, as well as the geographical data of the surrounding areas.

[0295] Step 6:

[0296] The server obtains route information. The server collects data on the user's transportation method (e.g., train, bus, walking) and calculates the optimal route and travel time.

[0297] Step 7:

[0298] The AI ​​model on the server generates an optimal travel plan based on weather, map, and route information, taking into account the user's requests and weather conditions, and adjusting indoor and outdoor activities.

[0299] Step 8:

[0300] The server sends the generated travel plan to the user's terminal, which receives it and displays it in an easy-to-understand manner for the user.

[0301] Step 9:

[0302] When a user checks the travel plan created through their device, the emotion engine analyzes the user's facial expressions and voice to recognize their emotions, and this information is sent to the server in real time.

[0303] Step 10:

[0304] The generative AI model on the server uses the data sent from the emotion engine to adjust the plan if the user is dissatisfied. For example, if it determines that the user is not interested in a particular tourist spot, it will suggest another tourist spot.

[0305] Step 11:

[0306] The server sends the revised travel plan to the user's terminal, which receives it and displays the revised plan to the user.

[0307] Step 12:

[0308] The user acts according to the travel plan, and if changes are needed during the trip, the system can generate a new plan in real time.

[0309] Step 13:

[0310] After the trip is over, the user inputs feedback through the terminal. The emotion engine recognizes the user's emotions when giving feedback and sends the data including the emotions to the server.

[0311] Step 14:

[0312] The server updates the generative AI model based on the feedback data and reflects it in the next itinerary generation. This continuous feedback loop improves the accuracy of the generative AI model, enabling more personalized travel plans to be provided.

[0313] Example 2

[0314] 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."

[0315] Conventional travel plan generation systems have difficulty generating optimal travel plans that reflect individual user preferences and emotions. Furthermore, they provide uniform plans without considering user emotions, resulting in low user satisfaction. Therefore, there is a demand for a system that allows users to easily and efficiently create optimal travel plans based on their individual needs and emotions.

[0316] 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: means for receiving a travel plan generation request from a user; means for transmitting the user's current location information to the server along with the request; means for acquiring weather information, map information, and route information using an external API; means including a generative AI model that generates an optimal travel plan based on the generated weather information, map information, and route information; means for transmitting and displaying the generated travel plan to the user's terminal; means for recognizing the user's emotions when checking the travel plan using an emotion engine and adjusting the travel plan based on the emotions; means for receiving feedback from the user after the trip ends and transmitting it to the server; and means for updating the generative AI model based on the feedback and reflecting it in the next plan generation. This enables automatic generation and real-time adjustment of an optimal travel plan according to the user's individual requests and emotions.

[0317] A "request to create a travel plan" is data that includes a request or information that a user makes to the system to create a travel plan.

[0318] "User's current location information" refers to geographical data used to identify the user's location, and typically refers to GPS data.

[0319] The "server" is a computer system that receives user requests, communicates with external APIs, analyzes data, runs generative AI models, and generates travel plans and provides feedback to the user.

[0320] An "external API" is a program interface that allows a server to obtain data such as weather information, map information, and route information from external sources.

[0321] "Weather information" is data about local and destination weather forecasts and weather conditions.

[0322] "Map information" is data that includes geographical information about the destination and its surroundings, location information about tourist spots, and the like.

[0323] "Route information" is data relating to the means of transportation from the departure point to the destination, its operation schedule, and the required travel time.

[0324] A "generative AI model" is an artificial intelligence model that automatically generates optimal travel plans based on user requests and data obtained from external APIs.

[0325] An "emotion engine" is a technology that analyzes a user's facial expressions and tone of voice to recognize their emotions.

[0326] "Feedback" is data including evaluations and impressions of the travel plan provided by the user after the trip is completed.

[0327] The present invention is a system that allows a user to easily and efficiently create an optimal travel plan, and in particular incorporates an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention will be described below.

[0328] The system of the present invention receives a request from a user to create a travel plan, automatically generates an optimal travel plan, and provides it to the user. The system is broadly composed of a user terminal, a server, an external API, a generation AI model, and an emotion engine.

[0329] About user devices

[0330] User terminals include a variety of devices, such as smartphones, tablets, and PCs. These terminals have hardware such as GPS sensors, cameras, and microphones, and dedicated applications are installed to utilize these devices. Users input travel plan generation requests through the application and send them to the server.

[0331] About the Server

[0332] The server is a computer system that receives user requests, communicates with external APIs, analyzes data, runs generative AI models, and generates travel plans and provides feedback to users. The server has the following functions:

[0333] 1. Request reception and analysis: Receives the request data and current location information sent from the user terminal and analyzes them.

[0334] 2. Integration with external APIs: Obtain necessary information using external weather information APIs, map information APIs, and route information APIs. Examples include the OpenWeatherMap API, Google Maps API, and transport route APIs.

[0335] 3. Execution of the generative AI model: Based on the acquired weather, map, and route information, the generative AI model generates the optimal travel plan. An example of a generative AI model is OpenAI's GPT-4.

[0336] About the Emotion Engine

[0337] The emotion engine is a system that uses a camera and microphone to capture the user's facial expressions and voice when checking a travel plan on a device, and analyzes their emotions. This emotion data is sent to a server in real time and fed back to a generative AI model, which then automatically modifies the travel plan.

[0338] Specific examples

[0339] Travel plan generation and emotion engine utilization

[0340] 1. User: "I'd like to travel from Tokyo to Kyoto this weekend. I'm leaving next Saturday. I'd like to visit historical sites and museums."

[0341] 2. The device sends a request to the server:

[0342] The device sends the user's current location information and request data to the server using the HTTPS protocol.

[0343] 3. The server retrieves the relevant information:

[0344] The server calls the OpenWeatherMap API to get the weather forecast for Tokyo and Kyoto.

[0345] The server uses the Google Maps API to obtain map information and tourist information for Kyoto.

[0346] The server calls the transport route API to obtain information and travel time for the Shinkansen from Tokyo to Kyoto.

[0347] 4. The generative AI model in the server generates a plan:

[0348] The generative AI model generates a trip plan like this:

[0349] Departs Tokyo Station at 8:00, arrives Kyoto Station at 9:45 (by Shinkansen)

[0350] 10:00 Walk from Kyoto Station to Kiyomizu-dera Temple

[0351] 12:00 Lunch near Kiyomizu-dera Temple (with restaurant recommendations)

[0352] 14:00 Move to Kyoto National Museum (by bus)

[0353] 17:00 Hotel check-in

[0354] 5. Emotion engine recognizes user emotions:

[0355] While the user is checking their travel plans, the device's camera and microphone capture the user's facial expressions and voice, which are then analyzed by the emotion engine.

[0356] The analysis results are sent to the server in real time.

[0357] 6. The server modifies the plan:

[0358] If the generative AI model senses the user's frustration and determines that they are not particularly interested in "historical places," it will create a new plan that includes shopping spots instead.

[0359] 7. View the revised plan on your device:

[0360] The revised plan is sent to the terminal and displayed in an easy-to-understand user interface.

[0361] 8. Users provide feedback:

[0362] After the trip, the user enters feedback, and the emotion engine recognizes the emotions expressed at the time and sends them to the server. The generative AI model learns from this and reflects it in future plans.

[0363] In this way, the system of the present invention can generate personalized travel plans based on the user's needs and emotions, improving the overall travel experience.

[0364] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0365] Step 1: User requests to generate a travel plan

[0366] Input: A user launches a trip planning application and enters details such as origin, destination, planned dates, and desired activities and attractions.

[0367] Specific operation: When the user presses the "send" button, the terminal compiles this request information.

[0368] Step 2: The device sends its location and user input to the server

[0369] Input: User's current location (GPS data) and the request information entered in step 1.

[0370] Data processing: Create a single request packet containing the current location information and request data.

[0371] Specific operation: The terminal sends this request packet to the server using a secure communication protocol (e.g., HTTPS).

[0372] Output: A request packet is sent to the server.

[0373] Step 3: The server retrieves weather, map, and route information.

[0374] Input: The server parses the request packet and obtains the destination and origin information.

[0375] Data calculation: The server calls the weather API (e.g., general weather information API), map API (e.g., general map information API), and route information API (e.g., general traffic information API) to obtain each piece of information.

[0376] Specific operation: The weather information, map information, and route information obtained by the server are saved.

[0377] Output: Obtained weather information, map information, and route information.

[0378] Step 4: The generative AI model generates an optimal travel plan

[0379] Input: Weather information, map information, and route information stored by the server.

[0380] Data calculation: A generative AI model (e.g., a general generative AI model) installed on the server creates a prompt based on this information, and then generates a travel plan based on that.

[0381] How it works: The generative AI model automatically generates a travel plan including transportation options, travel time, recommended tourist spots, places to eat, etc.

[0382] Output: The generated itinerary.

[0383] Step 5: The emotion engine recognizes the user's emotion

[0384] Input: Facial and voice data captured while the user is reviewing the itinerary generated through the device.

[0385] Data calculation: The emotion engine analyzes facial expressions and tone of voice to detect the user's emotions.

[0386] Specific operation: Detects user dissatisfaction or satisfaction in real time and sends emotional data to the server.

[0387] Output: User emotion data.

[0388] Step 6: Modify your plan based on your emotions

[0389] Input: User emotion data, generated travel plan.

[0390] Data calculation: Based on emotion data, a generative AI model adjusts the contents of the travel plan.

[0391] Specific operation: For example, if the user is not interested in a particular tourist spot, a new tourist spot will be suggested.

[0392] Output: The revised itinerary.

[0393] Step 7: Send and display the revised plan on your device

[0394] Input: The modified itinerary in the server.

[0395] Specific operation: The server sends the modified plan to the user's terminal.

[0396] Output: The terminal displays the revised itinerary.

[0397] Step 8: Gather user feedback

[0398] Input: User feedback after the trip, captured facial and voice data.

[0399] Data computation: Combining feedback data and emotion data.

[0400] Specific operation: User feedback and emotional data are sent to the server, and the generative AI model uses this as training data.

[0401] Output: An updated generative AI model for future plan generation.

[0402] (Application example 2)

[0403] 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."

[0404] Conventional travel plan generation systems have had difficulty providing individual travel plans that fully reflect the user's preferences and feelings. This has resulted in the problem that it takes a lot of time and effort for users to plan a trip that satisfies them. In addition, travel plan revisions are not performed automatically, so users have to adjust multiple pieces of information themselves. It is necessary to provide a system that solves these problems and provides more personalized travel plans.

[0405] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for receiving a travel plan generation request from a user; means for transmitting the user's current location information to the server along with the request; means for acquiring weather information, map information, and route information using an external API; means including a generative AI model that generates an optimal travel plan based on the generated weather information, map information, and route information; means for transmitting and displaying the generated travel plan to the user's terminal; means including an emotion engine that analyzes the user's facial expressions and voice and recognizes the user's emotions; means for modifying the travel plan based on data obtained from the emotion engine; means for receiving feedback from the user after the trip and transmitting it to the server; and means for updating the generative AI model based on the feedback and reflecting it in the next plan generation. This makes it possible to automatically provide a highly satisfying customized travel plan that takes user emotions and feedback into consideration in real time.

[0406] Definitions of important words

[0407] The "means for receiving a request to create a travel plan" refers to a mechanism by which a user inputs requests and information necessary to create a travel plan.

[0408] The "means for transmitting current location information to a server" refers to a method for transmitting the user's current location information to a server via the Internet.

[0409] "Means of obtaining weather information, map information, and route information using external APIs" refers to methods of obtaining the necessary weather information, map information, and transportation route information via external data sources.

[0410] "Means including generative AI models" refers to a system that has an artificial intelligence algorithm that automatically generates optimal travel plans based on information received from users or data obtained through external APIs.

[0411] "Means for sending and displaying the generated itinerary on the user's device" refers to an interface for sending the itinerary created by the AI ​​model to the user's device and displaying it on that device.

[0412] "Means including an emotion engine" refers to a system used to analyze a user's facial expression and voice data and recognize their current emotional state.

[0413] "Means for modifying travel plans based on data obtained from the emotion engine" refers to a mechanism that uses the user's emotion data obtained by the emotion engine to modify the generated travel plan according to the user's requests.

[0414] "Means for receiving feedback from users after the trip has ended and transmitting it to a server" refers to a method for receiving impressions and evaluations from users after the trip has ended and transmitting that information to a server.

[0415] "Means for updating the generative AI model based on feedback and reflecting it in the next plan generation" refers to the learning process for improving the generative AI model based on feedback received from users and reflecting it in the next plan generation.

[0416] Description: Form for carrying out the invention

[0417] The present invention is a system that allows travelers to easily and efficiently create optimal travel plans, and in particular incorporates an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention will be described below.

[0418] System configuration

[0419] The system consists of the following components:

[0420] 1. User Device

[0421] The device on which the user requests the itinerary to be generated. This can be a smartphone, tablet, or computer.

[0422] 2. Server

[0423] The central processing unit processes data and runs AI models. It retrieves weather, map, and route information, generates travel plans, and modifies plans based on emotion data.

[0424] 3. External API

[0425] Interface to external services that provide weather, map, and route information.

[0426] 4. Generative AI Models

[0427] An algorithm that generates optimal travel plans based on user input and information from external APIs. OpenAI's GPT-3 is an example.

[0428] 5. Emotion Engine

[0429] A system that recognizes emotions by analyzing a user's facial expressions and voice. Emotion analysis frameworks such as DeepFace fall into this category.

[0430] Data Processing Flow

[0431] User terminal

[0432] When a user requests the creation of a travel plan, they input information such as the departure point, destination, desired activities, etc. The terminal then transmits this information along with the user's current location to the server.

[0433] server

[0434] The server uses external APIs to obtain weather, map, and route information. This data is updated in real time and always keeps the latest information. The generative AI model generates the optimal travel plan based on this data. The user's past travel history and ratings are also taken into consideration.

[0435] Generative AI Models

[0436] The generative AI model primarily uses natural language generation algorithms to generate optimal travel plans based on user requests, for example, using the following prompt:

[0437] Please suggest the best itinerary for a trip from Tokyo to Kyoto. The traveler wants to visit historical sites and museums.

[0438] Based on this prompt, a specific travel schedule is generated.

[0439] Emotion Engine

[0440] When the user checks the generated travel plan, the device's camera and microphone are used to analyze the user's facial expressions and voice, and the emotion engine recognizes the user's emotions. For example, if the user makes a dissatisfied expression, that data is sent to the server. The model in the server reflects this data and modifies the plan according to the user's emotions.

[0441] Specific examples

[0442] For example, if a user enters "I want to travel from Tokyo to Kyoto this weekend. I'll leave on Saturday. I want to visit historical sites and museums," the system will do the following:

[0443] 1. The user's request and current location information are sent to the server.

[0444] 2. The server obtains weather information, map information, and route information from an external API.

[0445] 3. The generative AI model generates a trip plan like this:

[0446] Departs Tokyo Station at 8:00, arrives Kyoto Station at 9:45 (by Shinkansen)

[0447] 10:00 Walk from Kyoto Station to Kiyomizu-dera Temple

[0448] 12:00 Lunch near Kiyomizu-dera Temple (with restaurant recommendations)

[0449] 14:00 Move to Kyoto National Museum (by bus)

[0450] 17:00 Hotel check-in

[0451] When the user reviews this plan, emotion recognition is performed, and if the emotion "unhappy" is detected, the plan may be modified as follows:

[0452] Suggestions for shopping spots and museum visits have been shortened, and cafe breaks have been added.

[0453] In this way, the present invention can provide a highly satisfying customized travel plan that is in line with the user's feelings and preferences.

[0454] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0455] Program processing flow

[0456] Step 1:

[0457] The user inputs a request to generate a travel plan using a terminal. The input includes information such as the departure point, destination, planned dates, desired activities and sightseeing spots, etc. This input data is necessary to generate a travel plan based on the user's preferences and wishes.

[0458] Step 2:

[0459] The device sends the user's current location information (GPS data) and the requested data to the server. Specifically, the device sends the user's current location information (GPS data) along with the departure point, destination, and desired activity entered by the user. This data is used by the server to obtain subsequent information and generate plans.

[0460] Step 3:

[0461] The server uses external APIs to obtain weather, map, and route information for the current location and destination. In this step, the server communicates with external services to obtain real-time weather forecasts, maps, and transportation options and travel times. By calling external APIs, the latest information is gathered.

[0462] Step 4:

[0463] The generative AI model automatically generates an optimal travel plan based on the weather, map, and route information it has acquired. In this step, the generative AI model installed on the server combines the user's request data with information collected from external APIs and creates a travel plan based on the prompt text.

[0464] Example: Suggest the best itinerary for a trip from Tokyo to Kyoto. The traveller wants to visit historical sites and museums.

[0465] Based on this, the generative AI model suggests specific schedules and places to visit.

[0466] Step 5:

[0467] The generated travel plan is sent to the user's terminal and displayed. The server sends the generated travel plan to the user's terminal and displays it on the terminal screen for the user to check. At this stage, the user can check the proposed plan in detail.

[0468] Step 6:

[0469] When a user checks their travel plans, the device captures their facial expressions and voice and analyzes them using an emotion engine. The device uses its built-in camera and microphone to collect the user's facial expressions and tone of voice in real time, and sends the data to a server for emotion analysis.

[0470] Step 7:

[0471] The emotion engine analyzes the user's emotions and sends them to the server. In this step, the emotion analysis results (e.g., "dissatisfaction" or "joy") are sent to the server, and the user's emotional state is stored as numerical data on the server.

[0472] Step 8:

[0473] The generative AI model modifies the travel plan based on the data sent from the emotion engine. If the user expresses dissatisfaction, the generative AI model adjusts the plan, for example, adding new tourist spots or changing existing ones.

[0474] Step 9:

[0475] The server retransmits the revised travel plan to the user's terminal and displays it. The server generates a new travel plan, transmits it to the user's terminal and redisplays it, so the user can check the revised plan.

[0476] Step 10:

[0477] After the user has completed the trip, they input their feedback via the device and send it to the server. The user inputs feedback such as their satisfaction with the trip, what they liked, and what needs to be improved through the device.

[0478] Step 11:

[0479] The server updates the generative AI model based on the user's feedback data and reflects it in the next plan generation.The server trains the generative AI model based on the received feedback and sentiment analysis results, thereby improving the accuracy of future travel plan generation.

[0480] 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.

[0481] 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.

[0482] 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.

[0483] [Second embodiment]

[0484] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0485] 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.

[0486] 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).

[0487] 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.

[0488] 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.

[0489] 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).

[0490] 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.

[0491] 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.

[0492] 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.

[0493] 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.

[0494] 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.

[0495] 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."

[0496] The present invention is a system that enables travelers to easily and efficiently create optimal travel plans. Specific embodiments for carrying out the present invention will be described below.

[0497] The system of the present invention allows users to make travel plan generation requests, and automatically generates and provides optimal travel plans based on those requests. The system is broadly composed of a user terminal, a server, an external API, and a generation AI model.

[0498] Explanation of program processing

[0499] 1. User requests to generate a travel plan

[0500] A user requests the creation of a travel plan using their device (smartphone, tablet, PC, etc.) This request includes information such as the departure point, destination, planned dates, desired activities and tourist spots, etc.

[0501] 2. The device sends its location and user input to the server

[0502] The user's device sends the user's current location information (GPS data) and input request data to the server, allowing the server to understand the user's current situation and make appropriate suggestions in real time.

[0503] 3. The server obtains weather, map, and route information.

[0504] The server uses external APIs to obtain weather, map, and route information for the current location and destination. This data is updated in real time, so the latest information can always be provided.

[0505] 4. Generative AI models generate optimal travel plans

[0506] The server is equipped with a generative AI model that automatically generates an optimal travel plan based on the acquired data. This plan includes transportation options, travel times, recommended tourist spots, places to eat, etc. It also takes weather conditions into account, suggesting indoor activities in case of rain.

[0507] 5. Send and display your travel plans on your device

[0508] The server sends the generated travel plan to the user's device, which receives it and displays it in an easy-to-understand format for the user. The user can then proceed with their trip according to this plan.

[0509] 6. Gather user feedback

[0510] After completing the trip, the user provides their impressions and feedback. The feedback is entered through the user's device, which then sends it to the server. The server stores this data and uses it to train the generative AI model. This process allows the generative AI model to be continuously optimized and reflected in the generation of the next travel plan.

[0511] Specific examples

[0512] Example 1: Generating a Travel Plan

[0513] 1. User: "I'd like to travel from Tokyo to Kyoto. I'll be leaving next Saturday. I'd like to visit historical sites and museums."

[0514] 2. The device sends a request to the server:

[0515] The terminal transmits the user's current location information and the input request data to the server.

[0516] 3. The server retrieves the relevant information:

[0517] The server obtains weather information (weather forecasts for Tokyo and Kyoto), map information (information on tourist spots in Kyoto), and route information (transportation methods and travel times from Tokyo to Kyoto).

[0518] 4. The generative AI model in the server generates a plan:

[0519] The generative AI model generates a trip plan like this:

[0520] Departs Tokyo Station at 8:00, arrives Kyoto Station at 9:45 (by Shinkansen)

[0521] 10:00 Walk from Kyoto Station to Kiyomizu-dera Temple

[0522] 12:00 Lunch near Kiyomizu-dera Temple (with restaurant recommendations)

[0523] 14:00 Move to Kyoto National Museum (by bus)

[0524] 17:00 Hotel check-in

[0525] 5. View your plan on your device:

[0526] The generated plan is sent to the terminal and displayed to the user, who can then enjoy their trip according to the plan.

[0527] 6. Users provide feedback:

[0528] After the trip, the user inputs their evaluation and impressions into the device. Specific feedback, such as "The restaurants around Kiyomizu-dera Temple were great," is sent to the server.

[0529] By using this system, users can efficiently create optimal travel plans even in unfamiliar places, and can flexibly respond to local information gathering and changes in the weather.

[0530] The processing flow will be explained below.

[0531] Step 1:

[0532] A user uses a terminal to input a request to generate a travel plan, which includes information such as the origin, destination, planned dates, desired activities and attractions, etc.

[0533] Step 2:

[0534] The device obtains the user's current location information by using the GPS function to obtain current location data.

[0535] Step 3:

[0536] The device sends the user's input data and current location information together to the server, allowing the server to understand the user's request and current location.

[0537] Step 4:

[0538] The server retrieves weather information using an external API. The server retrieves the weather forecast for the specified departure date and travel period.

[0539] Step 5:

[0540] The server obtains map information. The server collects the location information of the tourist spots and activities the user wishes to visit, as well as the geographical data of the surrounding areas.

[0541] Step 6:

[0542] The server obtains route information. The server collects data on the user's transportation method (e.g., train, bus, walking) and calculates the optimal route and travel time.

[0543] Step 7:

[0544] The AI ​​model on the server generates an optimal travel plan based on weather, map, and route information, taking into account the user's requests and weather conditions, and adjusting indoor and outdoor activities.

[0545] Step 8:

[0546] The server sends the generated travel plan to the user's terminal, which receives it and displays it in an easy-to-understand manner for the user.

[0547] Step 9:

[0548] The user acts according to the travel plan, and if changes are needed during the trip, the system can generate a new plan in real time.

[0549] Step 10:

[0550] After completing the trip, users can enter feedback through the device, specifically describing their impressions and evaluations.

[0551] Step 11:

[0552] The device sends user feedback data to a server, which uses the feedback to train a generative AI model.

[0553] Step 12:

[0554] The server updates the generative AI model based on the feedback data and reflects it in the next itinerary generation. This continuous feedback loop improves the accuracy of the generative AI model, enabling more personalized travel plans to be provided.

[0555] Example 1

[0556] 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."

[0557] When planning a trip, users must collect a huge amount of information and consider each piece of information to create the optimal plan. This process takes time and effort, and is difficult to perform efficiently, especially when traveling to unfamiliar places. It is also difficult to flexibly change plans in response to changes in the weather or local conditions. To solve this problem, a system is needed that allows users to easily and efficiently automatically generate optimal travel plans and respond in real time.

[0558] 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.

[0559] In this invention, the server includes: means for receiving a travel plan generation request from a user; means for transmitting the user's current location information to the server along with the request; means for acquiring weather, location, and traffic information using an external service; means including a generative AI model for generating an optimal travel plan based on the generated weather, location, and traffic information; means for transmitting and displaying the generated travel plan to the user's terminal; means for receiving feedback from the user after the trip and transmitting it to the server; and means for updating the generative AI model based on the feedback and reflecting it in the next plan generation. This allows the user to efficiently obtain an optimal travel plan and flexibly respond to weather and local conditions.

[0560] "User" means an end user who requests the generation of a travel plan and uses the plan provided.

[0561] "Terminal" refers to the electronic device used by a user to make a travel plan request, including a smartphone, tablet, or PC.

[0562] The "server" is a central processing unit that receives user requests, retrieves relevant information, and generates a travel plan using a generative AI model.

[0563] "External services" refers to external databases and APIs that provide weather information, location information, traffic information, etc.

[0564] "Weather Information" means weather forecasts and related meteorological data for a particular location and time.

[0565] "Location information" refers to geographical data necessary for travel planning, such as map information and the locations of tourist attractions.

[0566] "Transportation information" refers to data relating to the means of transportation from a departure point to a destination and the travel time required.

[0567] A "generative AI model" refers to an artificial intelligence system that automatically creates optimal travel plans based on collected data.

[0568] "Sentiments" refers to the feedback and ratings users provide after completing a trip, and this information is used to improve the generative AI model.

[0569] The present invention is a system that enables travelers to easily and efficiently create optimal travel plans. Specific embodiments for carrying out the present invention will be described below.

[0570] This system consists of a user terminal, a server, and an external service. The role and specific operation of each component are explained below.

[0571] User terminal

[0572] A user terminal is an electronic device such as a smartphone, tablet, or PC that a user uses to request the creation of a travel plan. A dedicated application is installed on the terminal, and the user uses this application to input information about the departure point, destination, planned date, desired activities, and tourist spots. The terminal also uses its GPS function to obtain the user's current location information and sends it to the server along with the request data.

[0573] server

[0574] The server is the central processing unit of this system. When it receives a user request, it uses external services to collect the necessary information. Specifically, it obtains the following three pieces of information through external services:

[0575] Weather information: Uses external weather information services to obtain real-time weather information for the user's current location and destination.

[0576] Location information: Use a map information service to obtain map information for your destination and location information for tourist attractions.

[0577] Traffic information: Use a traffic information service to obtain the best means of transportation and travel time from your departure point to your destination.

[0578] Generative AI Models

[0579] The server is equipped with a generative AI model that automatically generates optimal travel plans based on collected weather, location, and traffic information. The generated plans include information on transportation methods, travel times, recommended tourist spots, and places to eat. Weather conditions are also taken into consideration, and in the event of rain, for example, indoor activities are suggested.

[0580] Providing and displaying travel plans

[0581] The server sends the generated travel plan to the user's device. The device receives it and displays it in an easy-to-understand format for the user through the application. The user can then enjoy their trip according to the plan.

[0582] Gathering and implementing feedback

[0583] After the trip is over, the user provides their feedback through their device. For example, they can enter a comment or rating within the application, such as "The restaurants around Kiyomizu-dera Temple were great." The device then sends this feedback to the server, which then stores the feedback data. This data is used to train the generative AI model and is reflected in the generation of the next travel plan.

[0584] Specific examples

[0585] Prompt Sentence Examples

[0586] Here is an example of what a user might input when requesting a travel plan:

[0587] "I'd like to travel from Tokyo to Kyoto. I'll be leaving next Saturday. I'd like to visit historical sites and art museums."

[0588] By using this system, users can efficiently create optimal travel plans even in unfamiliar places, and can flexibly respond to local information gathering and changes in the weather.

[0589] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0590] Step 1:

[0591] The user requests the creation of a travel plan. The user opens the application on their device (smartphone, tablet, PC, etc.) and inputs the departure point, destination, itinerary, and desired sightseeing spots and activities. All input data is temporarily stored on the device. Specifically, the device's GPS function is used to automatically obtain current location information.

[0592] Input: departure point, destination, itinerary, desired sightseeing spots and activities, current location information (GPS data)

[0593] Output: Request data saved on the device

[0594] Step 2:

[0595] The device sends the current location and user input to the server. The device then sends the request data to the server as an HTTP request. The server then receives the user's input and current location information and proceeds to the next step. The device then displays a confirmation message that the request has been sent.

[0596] Input: Request data stored on the device

[0597] Output: Request data sent to the server

[0598] Step 3:

[0599] The server obtains weather, map, and route information. The server sends an API request to an external service (e.g., weather information service, map information service, traffic information service) to collect weather, map, and traffic information for the current location and destination. The obtained data is temporarily stored on the server. The server checks the API response and records a log indicating that data acquisition has been completed.

[0600] Input: The request data sent to the server

[0601] Output: Weather information, map information, and traffic information temporarily stored on the server

[0602] Step 4:

[0603] The generative AI model generates the optimal travel plan. The generative AI model inside the server generates the optimal travel plan based on collected weather, map, and traffic information. The model takes into account the user's desired tourist spots and activities, and creates a plan by optimizing transportation methods and travel times. The resulting plan is saved on the server. Specific operations include calculating weather conditions and transportation methods, and creating an optimal visiting schedule.

[0604] Input: Weather information, map information, traffic information, and request data stored on the server

[0605] Output: Generated itinerary (schedule, transportation, attractions, etc.)

[0606] Step 5:

[0607] The travel plan is sent to the device and displayed. The server sends the generated travel plan to the device. The device receives it and displays it in an easy-to-understand manner for the user through the application. The display format can be a timeline or on a map. The device displays a confirmation message that the plan has been displayed.

[0608] Input: Travel plans stored on the server

[0609] Output: Travel plan displayed on the device

[0610] Step 6:

[0611] Collect user feedback. After completing the trip, the user enters their impressions and evaluations into the device. The device sends this to the server as an HTTP request. The server receives the feedback data and stores it in the learning database of the generative AI model. This allows the feedback to be reflected when generating the next travel plan.

[0612] Input: User's impressions and ratings after the trip

[0613] Output: Feedback data stored in the training database of the generative AI model

[0614] (Application example 1)

[0615] 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."

[0616] Conventional travel plan generation systems generate travel plans based only on weather, map, and route information, without taking into account local shopping districts, special offers, and event information. This makes it difficult to make the most of the local attractions at a travel destination, preventing travelers from enjoying their trips efficiently. Another issue is that the system does not adequately reflect information in real time, making it impossible to respond to immediate plan changes.

[0617] 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.

[0618] In this invention, the server includes: means for receiving a travel plan generation request from a user; means for transmitting the user's current location information along with the request to the server; means for acquiring weather information, map information, route information, and shopping district and event information using an external API; means including a generative AI model for generating an optimal travel plan based on the generated weather information, map information, route information, shopping district and event information; means for transmitting and displaying the generated travel plan to the user's terminal; means for receiving feedback from the user after the trip and transmitting it to the server; and means for updating the generative AI model based on the feedback and reflecting it in the next plan generation. This not only makes it possible to generate plans that incorporate the local attractions of the travel destination, but also makes it possible to respond to immediate plan changes by reflecting information in real time.

[0619] "User device" means a device used to send a travel plan request and receive and display the generated travel plan, including a smartphone, tablet, or PC.

[0620] A "server" is a computer system whose role is to receive requests from users, obtain various information using external APIs, generate travel plans using generative AI models, and send them to the user's terminal.

[0621] An "external API" is an external service that provides various data in real time, such as weather information, map information, route information, and information on shopping districts and events.

[0622] A "generative AI model" is an artificial intelligence model that has the ability to automatically generate optimal travel plans based on various data received.

[0623] "Shopping street information" is data about shopping streets that exist at the travel destination, and includes store locations, special offers, sale information, and the like.

[0624] "Event information" is data about events held in the travel destination area, including the date, time, location, and content of the event.

[0625] "Feedback" refers to opinions such as ratings and impressions provided by users after completing their trip, and is data used by the generative AI model to optimize the generation of the next plan.

[0626] The present invention is a system for enabling travelers to efficiently plan and enjoy trips. Specific embodiments for carrying out the present invention will be described below.

[0627] The system configuration consists of a user terminal, a server, an external API, and a generative AI model. We will now describe the specific components and their operation.

[0628] User terminal

[0629] The user terminal may be a smartphone, tablet, or PC. This terminal is used by the user to input a request to generate a travel plan and to display the generated travel plan. The user terminal is equipped with a GPS function and transmits the user's current location information to the server.

[0630] server

[0631] The server is where the core functions of the travel plan generation system are concentrated. The server performs the following processes:

[0632] 1. Receiving a travel plan request:

[0633] The server receives a request to generate a travel plan from a user terminal, which includes information such as the origin, destination, travel dates, desired activities and tourist attractions, etc.

[0634] 2. Retrieving data from external APIs:

[0635] The server uses external APIs to obtain weather information, map information, route information, shopping mall information, and event information, and generates an optimal travel plan based on the latest information.

[0636] 3. Plan generation using generative AI models:

[0637] The server is equipped with a generative AI model that uses the acquired data to generate an optimal itinerary, taking into account weather conditions, real-time special offers and sales, local event information, and more.

[0638] 4. Sending the travel plan to the user's device:

[0639] The generated travel plan is sent to the user's terminal, and the user can use it to proceed with the trip.

[0640] 5. Collect user feedback and update the AI ​​model:

[0641] After completing the trip, the user provides feedback, which is sent to the server and used to train the generative AI model, which then optimizes the generation of the next itinerary.

[0642] Specific examples

[0643] Example of a user request

[0644] The user inputs a request such as "I would like to travel from Tokyo to Kyoto. The departure date is next Saturday. As tourist spots, I would like to visit historical places and art museums."

[0645] Examples of prompt statements

[0646] {

[0647] "start_location": "Tokyo",

[0648] "destination": "Kyoto",

[0649] "date": "2023-10-20",

[0650] "weather": {

[0651] "forecast": [

[0652] {"date": "2023-10-20", "condition": "Sunny", "max_temp": 25, "min_temp": 18},

[0653] ...

[0654] ]

[0655] },

[0656] "stores": [

[0657] {"name": "Shopping District", "type": "Shopping District", ...},

[0658] ...

[0659] ],

[0660] "events": [

[0661] {"name": "Museum Exhibition", "location": "Museum", ...},

[0662] ...

[0663] ]

[0664] }

[0665] Based on the prompts above, the generative AI model generates an optimal travel plan to support the user's trip, allowing travelers to efficiently experience local attractions and flexibly respond to real-time information changes.

[0666] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0667] Step 1:

[0668] The user terminal receives a request to create a travel plan from the user as input. The user inputs information such as the departure point, destination, travel dates, desired activities and tourist spots, etc. This input data is transmitted to the server via communication.

[0669] Input: origin, destination, travel dates, desired activities and attractions

[0670] Output: User request data sent to the server

[0671] Step 2:

[0672] The server receives the user request data as input, and then acquires the current location information (GPS data) from the user terminal and stores it together with the request data.

[0673] Input: User request data, current location information (GPS data)

[0674] Output: Consolidated user request data (including location information)

[0675] Step 3:

[0676] The server calls external APIs to obtain weather information, map information, route information, and information on shopping malls and events. This information is obtained in real time and used to generate travel plans.

[0677] Input: Consolidated user request data

[0678] Output: Weather information, map information, route information, shopping street information, event information

[0679] Step 4:

[0680] The server inputs prompts into the generative AI model based on the acquired external data. The generative AI model then generates an optimal travel plan based on this information. The plan includes information on transportation, recommended sightseeing spots, special offers at shopping malls, and event information.

[0681] Input: Weather information, map information, route information, shopping street information, event information

[0682] Output: Travel plan

[0683] Step 5:

[0684] The server transmits the generated travel plan to the user terminal, which displays the received travel plan and enables the user to travel according to the plan.

[0685] Input: Travel Plan

[0686] Output: Travel plan displayed on the user's device

[0687] Step 6:

[0688] After completing the trip, the user inputs feedback. The user terminal transmits this feedback to the server. The feedback includes an evaluation and impressions of the trip.

[0689] Input: User feedback

[0690] Output: Feedback data sent to the server

[0691] Step 7:

[0692] The server updates the generative AI model based on the received feedback data, and this update is used to provide improved suggestions the next time a travel plan is generated.

[0693] Input: Feedback data

[0694] Output: Updated generative AI model

[0695] Through these steps, the system can provide users with the most optimal travel plans and constantly optimize the generative AI model based on the latest information and user feedback.

[0696] 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.

[0697] The present invention is a system that enables travelers to easily and efficiently create optimal travel plans, and in particular incorporates an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention will be described below.

[0698] The system of the present invention receives a request from a user to generate a travel plan, automatically generates an optimal travel plan, and provides it to the user. The system is broadly composed of a user terminal, a server, an external API, a generation AI model, and an emotion engine.

[0699] Explanation of program processing

[0700] 1. User requests to generate a travel plan

[0701] A user requests the creation of a travel plan using their device (smartphone, tablet, PC, etc.) This request includes information such as the departure point, destination, planned dates, desired activities and tourist spots, etc.

[0702] 2. The device sends its location and user input to the server

[0703] The user's terminal transmits the user's current location information (GPS data) and the input request data to the server.

[0704] 3. The server obtains weather, map, and route information.

[0705] The server uses external APIs to obtain weather, map, and route information for the current location and destination. This data is updated in real time, so the latest information can always be provided.

[0706] 4. Generative AI models generate optimal travel plans

[0707] The server is equipped with a generative AI model that automatically generates an optimal travel plan based on acquired weather, map, and route information. This plan includes transportation options, travel times, recommended tourist spots, and places to eat. Weather conditions are also taken into account, and indoor activities are suggested in case of rain.

[0708] 5. Emotion engine recognizes user emotions

[0709] When a user checks the travel plan created through the device, the emotion engine analyzes the user's facial expressions and voice to recognize the user's emotions, and this information is sent to the server in real time.

[0710] 6. Modifying your plan based on emotions

[0711] The generative AI model on the server uses the data sent from the emotion engine to adjust the plan if the user is dissatisfied. For example, if it determines that the user is not interested in a particular tourist spot, it will suggest another tourist spot.

[0712] 7. Send and display your travel plans on your device

[0713] The server sends the revised travel plan to the user's device, which receives it and displays it in an easy-to-understand manner for the user.

[0714] 8. Gather user feedback

[0715] After the trip is over, the user provides their impressions and feedback. The emotion engine recognizes the user's emotions when providing feedback and sends the data including those to the server. This data is used to train the generative AI model.

[0716] Specific examples

[0717] Example 1: Generating a travel plan and using the emotion engine

[0718] 1. User: "I'd like to travel from Tokyo to Kyoto this weekend. I'm leaving next Saturday. I'd like to visit historical sites and museums."

[0719] 2. The device sends a request to the server

[0720] The terminal transmits the user's current location information and request data to the server.

[0721] 3. The server retrieves the relevant information

[0722] The server obtains weather information (weather forecasts for Tokyo and Kyoto), map information (information on tourist spots in Kyoto), and route information (transportation methods and travel times from Tokyo to Kyoto).

[0723] 4. The generative AI model on the server generates a plan

[0724] The generative AI model generates a trip plan like this:

[0725] Departs Tokyo Station at 8:00, arrives Kyoto Station at 9:45 (by Shinkansen)

[0726] 10:00 Walk from Kyoto Station to Kiyomizu-dera Temple

[0727] 12:00 Lunch near Kiyomizu-dera Temple (with restaurant recommendations)

[0728] 14:00 Move to Kyoto National Museum (by bus)

[0729] 17:00 Hotel check-in

[0730] 5. Emotion engine recognizes user emotions

[0731] The emotion engine analyzes the user's facial expressions and voice while checking the travel plan, and recognizes the user's emotions. For example, if the user looks dissatisfied, it sends this to the server.

[0732] 6. The server modifies the plan

[0733] The generative AI model detects user frustrations and adjusts travel plans, for example, if a user isn't interested in historical sites, it suggests shopping spots instead.

[0734] 7. Display the revised plan on your device

[0735] The modified plan is sent to the terminal and displayed to the user.

[0736] 8. Users provide feedback

[0737] After the trip, the user enters feedback, and the emotion engine recognizes the emotions expressed and sends them to the server. The server uses this data to update the generative AI model and reflect it in the generation of the next travel plan.

[0738] By using this system, users can enjoy more personalized travel plans that reflect their individual preferences and feelings.

[0739] The processing flow will be explained below.

[0740] Step 1:

[0741] A user uses a terminal to input a request to generate a travel plan, which includes information such as the origin, destination, planned dates, desired activities and attractions, etc.

[0742] Step 2:

[0743] The device obtains the user's current location information by using the GPS function to obtain current location data.

[0744] Step 3:

[0745] The device sends the user's input data and current location information together to the server, allowing the server to understand the user's request and current location.

[0746] Step 4:

[0747] The server retrieves weather information using an external API. The server retrieves the weather forecast for the specified departure date and travel period.

[0748] Step 5:

[0749] The server obtains map information. The server collects the location information of the tourist spots and activities the user wishes to visit, as well as the geographical data of the surrounding areas.

[0750] Step 6:

[0751] The server obtains route information. The server collects data on the user's transportation method (e.g., train, bus, walking) and calculates the optimal route and travel time.

[0752] Step 7:

[0753] The AI ​​model on the server generates an optimal travel plan based on weather, map, and route information, taking into account the user's requests and weather conditions, and adjusting indoor and outdoor activities.

[0754] Step 8:

[0755] The server sends the generated travel plan to the user's terminal, which receives it and displays it in an easy-to-understand manner for the user.

[0756] Step 9:

[0757] When a user checks the travel plan created through their device, the emotion engine analyzes the user's facial expressions and voice to recognize their emotions, and this information is sent to the server in real time.

[0758] Step 10:

[0759] The generative AI model on the server uses the data sent from the emotion engine to adjust the plan if the user is dissatisfied. For example, if it determines that the user is not interested in a particular tourist spot, it will suggest another tourist spot.

[0760] Step 11:

[0761] The server sends the revised travel plan to the user's terminal, which receives it and displays the revised plan to the user.

[0762] Step 12:

[0763] The user acts according to the travel plan, and if changes are needed during the trip, the system can generate a new plan in real time.

[0764] Step 13:

[0765] After the trip is over, the user inputs feedback through the terminal. The emotion engine recognizes the user's emotions when giving feedback and sends the data including the emotions to the server.

[0766] Step 14:

[0767] The server updates the generative AI model based on the feedback data and reflects it in the next itinerary generation. This continuous feedback loop improves the accuracy of the generative AI model, enabling more personalized travel plans to be provided.

[0768] Example 2

[0769] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0770] Conventional travel plan generation systems have difficulty generating optimal travel plans that reflect individual user preferences and emotions. Furthermore, they provide uniform plans without considering user emotions, resulting in low user satisfaction. Therefore, there is a demand for a system that allows users to easily and efficiently create optimal travel plans based on their individual needs and emotions.

[0771] 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: means for receiving a travel plan generation request from a user; means for transmitting the user's current location information to the server along with the request; means for acquiring weather information, map information, and route information using an external API; means including a generative AI model that generates an optimal travel plan based on the generated weather information, map information, and route information; means for transmitting and displaying the generated travel plan to the user's terminal; means for recognizing the user's emotions when checking the travel plan using an emotion engine and adjusting the travel plan based on the emotions; means for receiving feedback from the user after the trip ends and transmitting it to the server; and means for updating the generative AI model based on the feedback and reflecting it in the next plan generation. This enables automatic generation and real-time adjustment of an optimal travel plan according to the user's individual requests and emotions.

[0772] A "request to create a travel plan" is data that includes a request or information that a user makes to the system to create a travel plan.

[0773] "User's current location information" refers to geographical data used to identify the user's location, and typically refers to GPS data.

[0774] The "server" is a computer system that receives user requests, communicates with external APIs, analyzes data, runs generative AI models, and generates travel plans and provides feedback to the user.

[0775] An "external API" is a program interface that allows a server to obtain data such as weather information, map information, and route information from external sources.

[0776] "Weather information" is data about local and destination weather forecasts and weather conditions.

[0777] "Map information" is data that includes geographical information about the destination and its surroundings, location information about tourist spots, and the like.

[0778] "Route information" is data relating to the means of transportation from the departure point to the destination, its operation schedule, and the required travel time.

[0779] A "generative AI model" is an artificial intelligence model that automatically generates optimal travel plans based on user requests and data obtained from external APIs.

[0780] An "emotion engine" is a technology that analyzes a user's facial expressions and tone of voice to recognize their emotions.

[0781] "Feedback" is data including evaluations and impressions of the travel plan provided by the user after the trip is completed.

[0782] The present invention is a system that allows a user to easily and efficiently create an optimal travel plan, and in particular incorporates an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention will be described below.

[0783] The system of the present invention receives a request from a user to create a travel plan, automatically generates an optimal travel plan, and provides it to the user. The system is broadly composed of a user terminal, a server, an external API, a generation AI model, and an emotion engine.

[0784] About user devices

[0785] User terminals include a variety of devices, such as smartphones, tablets, and PCs. These terminals have hardware such as GPS sensors, cameras, and microphones, and dedicated applications are installed to utilize these devices. Users input travel plan generation requests through the application and send them to the server.

[0786] About the Server

[0787] The server is a computer system that receives user requests, communicates with external APIs, analyzes data, runs generative AI models, and generates travel plans and provides feedback to users. The server has the following functions:

[0788] 1. Request reception and analysis: Receives the request data and current location information sent from the user terminal and analyzes them.

[0789] 2. Integration with external APIs: Obtain necessary information using external weather information APIs, map information APIs, and route information APIs. Examples include the OpenWeatherMap API, Google Maps API, and transport route APIs.

[0790] 3. Execution of the generative AI model: Based on the acquired weather, map, and route information, the generative AI model generates the optimal travel plan. An example of a generative AI model is OpenAI's GPT-4.

[0791] About the Emotion Engine

[0792] The emotion engine is a system that uses a camera and microphone to capture the user's facial expressions and voice when checking a travel plan on a device, and analyzes their emotions. This emotion data is sent to a server in real time and fed back to a generative AI model, which then automatically modifies the travel plan.

[0793] Specific examples

[0794] Travel plan generation and emotion engine utilization

[0795] 1. User: "I'd like to travel from Tokyo to Kyoto this weekend. I'm leaving next Saturday. I'd like to visit historical sites and museums."

[0796] 2. The device sends a request to the server:

[0797] The device sends the user's current location information and request data to the server using the HTTPS protocol.

[0798] 3. The server retrieves the relevant information:

[0799] The server calls the OpenWeatherMap API to get the weather forecast for Tokyo and Kyoto.

[0800] The server uses the Google Maps API to obtain map information and tourist information for Kyoto.

[0801] The server calls the transport route API to obtain information and travel time for the Shinkansen from Tokyo to Kyoto.

[0802] 4. The generative AI model in the server generates a plan:

[0803] The generative AI model generates a trip plan like this:

[0804] Departs Tokyo Station at 8:00, arrives Kyoto Station at 9:45 (by Shinkansen)

[0805] 10:00 Walk from Kyoto Station to Kiyomizu-dera Temple

[0806] 12:00 Lunch near Kiyomizu-dera Temple (with restaurant recommendations)

[0807] 14:00 Move to Kyoto National Museum (by bus)

[0808] 17:00 Hotel check-in

[0809] 5. Emotion engine recognizes user emotions:

[0810] While the user is checking their travel plans, the device's camera and microphone capture the user's facial expressions and voice, which are then analyzed by the emotion engine.

[0811] The analysis results are sent to the server in real time.

[0812] 6. The server modifies the plan:

[0813] If the generative AI model senses the user's frustration and determines that they are not particularly interested in "historical places," it will create a new plan that includes shopping spots instead.

[0814] 7. View the revised plan on your device:

[0815] The revised plan is sent to the terminal and displayed in an easy-to-understand user interface.

[0816] 8. Users provide feedback:

[0817] After the trip, the user enters feedback, and the emotion engine recognizes the emotions expressed at the time and sends them to the server. The generative AI model learns from this and reflects it in future plans.

[0818] In this way, the system of the present invention can generate personalized travel plans based on the user's needs and emotions, improving the overall travel experience.

[0819] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0820] Step 1: User requests to generate a travel plan

[0821] Input: A user launches a trip planning application and enters details such as origin, destination, planned dates, and desired activities and attractions.

[0822] Specific operation: When the user presses the "send" button, the terminal compiles this request information.

[0823] Step 2: The device sends its location and user input to the server

[0824] Input: User's current location (GPS data) and the request information entered in step 1.

[0825] Data processing: Create a single request packet containing the current location information and request data.

[0826] Specific operation: The terminal sends this request packet to the server using a secure communication protocol (e.g., HTTPS).

[0827] Output: A request packet is sent to the server.

[0828] Step 3: The server retrieves weather, map, and route information.

[0829] Input: The server parses the request packet and obtains the destination and origin information.

[0830] Data calculation: The server calls the weather API (e.g., general weather information API), map API (e.g., general map information API), and route information API (e.g., general traffic information API) to obtain each piece of information.

[0831] Specific operation: The weather information, map information, and route information obtained by the server are saved.

[0832] Output: Obtained weather information, map information, and route information.

[0833] Step 4: The generative AI model generates an optimal travel plan

[0834] Input: Weather information, map information, and route information stored by the server.

[0835] Data calculation: A generative AI model (e.g., a general generative AI model) installed on the server creates a prompt based on this information, and then generates a travel plan based on that.

[0836] How it works: The generative AI model automatically generates a travel plan including transportation options, travel time, recommended tourist spots, places to eat, etc.

[0837] Output: The generated itinerary.

[0838] Step 5: The emotion engine recognizes the user's emotion

[0839] Input: Facial and voice data captured while the user is reviewing the itinerary generated through the device.

[0840] Data calculation: The emotion engine analyzes facial expressions and tone of voice to detect the user's emotions.

[0841] Specific operation: Detects user dissatisfaction or satisfaction in real time and sends emotional data to the server.

[0842] Output: User emotion data.

[0843] Step 6: Modify your plan based on your emotions

[0844] Input: User emotion data, generated travel plan.

[0845] Data calculation: Based on emotion data, a generative AI model adjusts the contents of the travel plan.

[0846] Specific operation: For example, if the user is not interested in a particular tourist spot, a new tourist spot will be suggested.

[0847] Output: The revised itinerary.

[0848] Step 7: Send and display the revised plan on your device

[0849] Input: The modified itinerary in the server.

[0850] Specific operation: The server sends the modified plan to the user's terminal.

[0851] Output: The terminal displays the revised itinerary.

[0852] Step 8: Gather user feedback

[0853] Input: User feedback after the trip, captured facial and voice data.

[0854] Data computation: Combining feedback data and emotion data.

[0855] Specific operation: User feedback and emotional data are sent to the server, and the generative AI model uses this as training data.

[0856] Output: An updated generative AI model for future plan generation.

[0857] (Application example 2)

[0858] 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."

[0859] Conventional travel plan generation systems have had difficulty providing individual travel plans that fully reflect the user's preferences and feelings. This has resulted in the problem that it takes a lot of time and effort for users to plan a trip that satisfies them. In addition, travel plan revisions are not performed automatically, so users have to adjust multiple pieces of information themselves. It is necessary to provide a system that solves these problems and provides more personalized travel plans.

[0860] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for receiving a travel plan generation request from a user; means for transmitting the user's current location information to the server along with the request; means for acquiring weather information, map information, and route information using an external API; means including a generative AI model that generates an optimal travel plan based on the generated weather information, map information, and route information; means for transmitting and displaying the generated travel plan to the user's terminal; means including an emotion engine that analyzes the user's facial expressions and voice and recognizes the user's emotions; means for modifying the travel plan based on data obtained from the emotion engine; means for receiving feedback from the user after the trip and transmitting it to the server; and means for updating the generative AI model based on the feedback and reflecting it in the next plan generation. This makes it possible to automatically provide a highly satisfying customized travel plan that takes user emotions and feedback into consideration in real time.

[0861] Definitions of important words

[0862] The "means for receiving a request to create a travel plan" refers to a mechanism by which a user inputs requests and information necessary to create a travel plan.

[0863] The "means for transmitting current location information to a server" refers to a method for transmitting the user's current location information to a server via the Internet.

[0864] "Means of obtaining weather information, map information, and route information using external APIs" refers to methods of obtaining the necessary weather information, map information, and transportation route information via external data sources.

[0865] "Means including generative AI models" refers to a system that has an artificial intelligence algorithm that automatically generates optimal travel plans based on information received from users or data obtained through external APIs.

[0866] "Means for sending and displaying the generated itinerary on the user's device" refers to an interface for sending the itinerary created by the AI ​​model to the user's device and displaying it on that device.

[0867] "Means including an emotion engine" refers to a system used to analyze a user's facial expression and voice data and recognize their current emotional state.

[0868] "Means for modifying travel plans based on data obtained from the emotion engine" refers to a mechanism that uses the user's emotion data obtained by the emotion engine to modify the generated travel plan according to the user's requests.

[0869] "Means for receiving feedback from users after the trip has ended and transmitting it to a server" refers to a method for receiving impressions and evaluations from users after the trip has ended and transmitting that information to a server.

[0870] "Means for updating the generative AI model based on feedback and reflecting it in the next plan generation" refers to the learning process for improving the generative AI model based on feedback received from users and reflecting it in the next plan generation.

[0871] Description: Form for carrying out the invention

[0872] The present invention is a system that allows travelers to easily and efficiently create optimal travel plans, and in particular incorporates an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention will be described below.

[0873] System configuration

[0874] The system consists of the following components:

[0875] 1. User Device

[0876] The device on which the user requests the itinerary to be generated. This can be a smartphone, tablet, or computer.

[0877] 2. Server

[0878] The central processing unit processes data and runs AI models. It retrieves weather, map, and route information, generates travel plans, and modifies plans based on emotion data.

[0879] 3. External API

[0880] Interface to external services that provide weather, map, and route information.

[0881] 4. Generative AI Models

[0882] An algorithm that generates optimal travel plans based on user input and information from external APIs. OpenAI's GPT-3 is an example.

[0883] 5. Emotion Engine

[0884] A system that recognizes emotions by analyzing a user's facial expressions and voice. Emotion analysis frameworks such as DeepFace fall into this category.

[0885] Data Processing Flow

[0886] User terminal

[0887] When a user requests the creation of a travel plan, they input information such as the departure point, destination, desired activities, etc. The terminal then transmits this information along with the user's current location to the server.

[0888] server

[0889] The server uses external APIs to obtain weather, map, and route information. This data is updated in real time and always keeps the latest information. The generative AI model generates the optimal travel plan based on this data. The user's past travel history and ratings are also taken into consideration.

[0890] Generative AI Models

[0891] The generative AI model primarily uses natural language generation algorithms to generate optimal travel plans based on user requests, for example, using the following prompt:

[0892] Please suggest the best itinerary for a trip from Tokyo to Kyoto. The traveler wants to visit historical sites and museums.

[0893] Based on this prompt, a specific travel schedule is generated.

[0894] Emotion Engine

[0895] When the user checks the generated travel plan, the device's camera and microphone are used to analyze the user's facial expressions and voice, and the emotion engine recognizes the user's emotions. For example, if the user makes a dissatisfied expression, that data is sent to the server. The model in the server reflects this data and modifies the plan according to the user's emotions.

[0896] Specific examples

[0897] For example, if a user enters "I want to travel from Tokyo to Kyoto this weekend. I'll leave on Saturday. I want to visit historical sites and museums," the system will do the following:

[0898] 1. The user's request and current location information are sent to the server.

[0899] 2. The server obtains weather information, map information, and route information from an external API.

[0900] 3. The generative AI model generates a trip plan like this:

[0901] Departs Tokyo Station at 8:00, arrives Kyoto Station at 9:45 (by Shinkansen)

[0902] 10:00 Walk from Kyoto Station to Kiyomizu-dera Temple

[0903] 12:00 Lunch near Kiyomizu-dera Temple (with restaurant recommendations)

[0904] 14:00 Move to Kyoto National Museum (by bus)

[0905] 17:00 Hotel check-in

[0906] When the user reviews this plan, emotion recognition is performed, and if the emotion "unhappy" is detected, the plan may be modified as follows:

[0907] Suggestions for shopping spots and museum visits have been shortened, and cafe breaks have been added.

[0908] In this way, the present invention can provide a highly satisfying customized travel plan that is in line with the user's feelings and preferences.

[0909] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0910] Program processing flow

[0911] Step 1:

[0912] The user inputs a request to generate a travel plan using a terminal. The input includes information such as the departure point, destination, planned dates, desired activities and sightseeing spots, etc. This input data is necessary to generate a travel plan based on the user's preferences and wishes.

[0913] Step 2:

[0914] The device sends the user's current location information (GPS data) and the requested data to the server. Specifically, the device sends the user's current location information (GPS data) along with the departure point, destination, and desired activity entered by the user. This data is used by the server to obtain subsequent information and generate plans.

[0915] Step 3:

[0916] The server uses external APIs to obtain weather, map, and route information for the current location and destination. In this step, the server communicates with external services to obtain real-time weather forecasts, maps, and transportation options and travel times. By calling external APIs, the latest information is gathered.

[0917] Step 4:

[0918] The generative AI model automatically generates an optimal travel plan based on the weather, map, and route information it has acquired. In this step, the generative AI model installed on the server combines the user's request data with information collected from external APIs and creates a travel plan based on the prompt text.

[0919] Example: Suggest the best itinerary for a trip from Tokyo to Kyoto. The traveller wants to visit historical sites and museums.

[0920] Based on this, the generative AI model suggests specific schedules and places to visit.

[0921] Step 5:

[0922] The generated travel plan is sent to the user's terminal and displayed. The server sends the generated travel plan to the user's terminal and displays it on the terminal screen for the user to check. At this stage, the user can check the proposed plan in detail.

[0923] Step 6:

[0924] When a user checks their travel plans, the device captures their facial expressions and voice and analyzes them using an emotion engine. The device uses its built-in camera and microphone to collect the user's facial expressions and tone of voice in real time, and sends the data to a server for emotion analysis.

[0925] Step 7:

[0926] The emotion engine analyzes the user's emotions and sends them to the server. In this step, the emotion analysis results (e.g., "dissatisfaction" or "joy") are sent to the server, and the user's emotional state is stored as numerical data on the server.

[0927] Step 8:

[0928] The generative AI model modifies the travel plan based on the data sent from the emotion engine. If the user expresses dissatisfaction, the generative AI model adjusts the plan, for example, adding new tourist spots or changing existing ones.

[0929] Step 9:

[0930] The server retransmits the revised travel plan to the user's terminal and displays it. The server generates a new travel plan, transmits it to the user's terminal and redisplays it, so the user can check the revised plan.

[0931] Step 10:

[0932] After the user has completed the trip, they input their feedback via the device and send it to the server. The user inputs feedback such as their satisfaction with the trip, what they liked, and what needs to be improved through the device.

[0933] Step 11:

[0934] The server updates the generative AI model based on the user's feedback data and reflects it in the next plan generation.The server trains the generative AI model based on the received feedback and sentiment analysis results, thereby improving the accuracy of future travel plan generation.

[0935] 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.

[0936] 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.

[0937] 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.

[0938] [Third embodiment]

[0939] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0940] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0941] 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).

[0942] 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.

[0943] 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.

[0944] 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).

[0945] 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.

[0946] 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.

[0947] 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.

[0948] 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.

[0949] 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.

[0950] 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."

[0951] The present invention is a system that enables travelers to easily and efficiently create optimal travel plans. Specific embodiments for carrying out the present invention will be described below.

[0952] The system of the present invention allows users to make travel plan generation requests, and automatically generates and provides optimal travel plans based on those requests. The system is broadly composed of a user terminal, a server, an external API, and a generation AI model.

[0953] Explanation of program processing

[0954] 1. User requests to generate a travel plan

[0955] A user requests the creation of a travel plan using their device (smartphone, tablet, PC, etc.) This request includes information such as the departure point, destination, planned dates, desired activities and tourist spots, etc.

[0956] 2. The device sends its location and user input to the server

[0957] The user's device sends the user's current location information (GPS data) and input request data to the server, allowing the server to understand the user's current situation and make appropriate suggestions in real time.

[0958] 3. The server obtains weather, map, and route information.

[0959] The server uses external APIs to obtain weather, map, and route information for the current location and destination. This data is updated in real time, so the latest information can always be provided.

[0960] 4. Generative AI models generate optimal travel plans

[0961] The server is equipped with a generative AI model that automatically generates an optimal travel plan based on the acquired data. This plan includes transportation options, travel times, recommended tourist spots, places to eat, etc. It also takes weather conditions into account, suggesting indoor activities in case of rain.

[0962] 5. Send and display your travel plans on your device

[0963] The server sends the generated travel plan to the user's device, which receives it and displays it in an easy-to-understand format for the user. The user can then proceed with their trip according to this plan.

[0964] 6. Gather user feedback

[0965] After completing the trip, the user provides their impressions and feedback. The feedback is entered through the user's device, which then sends it to the server. The server stores this data and uses it to train the generative AI model. This process allows the generative AI model to be continuously optimized and reflected in the generation of the next travel plan.

[0966] Specific examples

[0967] Example 1: Generating a Travel Plan

[0968] 1. User: "I'd like to travel from Tokyo to Kyoto. I'll be leaving next Saturday. I'd like to visit historical sites and museums."

[0969] 2. The device sends a request to the server:

[0970] The terminal transmits the user's current location information and the input request data to the server.

[0971] 3. The server retrieves the relevant information:

[0972] The server obtains weather information (weather forecasts for Tokyo and Kyoto), map information (information on tourist spots in Kyoto), and route information (transportation methods and travel times from Tokyo to Kyoto).

[0973] 4. The generative AI model in the server generates a plan:

[0974] The generative AI model generates a trip plan like this:

[0975] Departs Tokyo Station at 8:00, arrives Kyoto Station at 9:45 (by Shinkansen)

[0976] 10:00 Walk from Kyoto Station to Kiyomizu-dera Temple

[0977] 12:00 Lunch near Kiyomizu-dera Temple (with restaurant recommendations)

[0978] 14:00 Move to Kyoto National Museum (by bus)

[0979] 17:00 Hotel check-in

[0980] 5. View your plan on your device:

[0981] The generated plan is sent to the terminal and displayed to the user, who can then enjoy their trip according to the plan.

[0982] 6. Users provide feedback:

[0983] After the trip, the user inputs their evaluation and impressions into the device. Specific feedback, such as "The restaurants around Kiyomizu-dera Temple were great," is sent to the server.

[0984] By using this system, users can efficiently create optimal travel plans even in unfamiliar places, and can flexibly respond to local information gathering and changes in the weather.

[0985] The processing flow will be explained below.

[0986] Step 1:

[0987] A user uses a terminal to input a request to generate a travel plan, which includes information such as the origin, destination, planned dates, desired activities and attractions, etc.

[0988] Step 2:

[0989] The device obtains the user's current location information by using the GPS function to obtain current location data.

[0990] Step 3:

[0991] The device sends the user's input data and current location information together to the server, allowing the server to understand the user's request and current location.

[0992] Step 4:

[0993] The server retrieves weather information using an external API. The server retrieves the weather forecast for the specified departure date and travel period.

[0994] Step 5:

[0995] The server obtains map information. The server collects the location information of the tourist spots and activities the user wishes to visit, as well as the geographical data of the surrounding areas.

[0996] Step 6:

[0997] The server obtains route information. The server collects data on the user's transportation method (e.g., train, bus, walking) and calculates the optimal route and travel time.

[0998] Step 7:

[0999] The AI ​​model on the server generates an optimal travel plan based on weather, map, and route information, taking into account the user's requests and weather conditions, and adjusting indoor and outdoor activities.

[1000] Step 8:

[1001] The server sends the generated travel plan to the user's terminal, which receives it and displays it in an easy-to-understand manner for the user.

[1002] Step 9:

[1003] The user acts according to the travel plan, and if changes are needed during the trip, the system can generate a new plan in real time.

[1004] Step 10:

[1005] After completing the trip, users can enter feedback through the device, specifically describing their impressions and evaluations.

[1006] Step 11:

[1007] The device sends user feedback data to a server, which uses the feedback to train a generative AI model.

[1008] Step 12:

[1009] The server updates the generative AI model based on the feedback data and reflects it in the next itinerary generation. This continuous feedback loop improves the accuracy of the generative AI model, enabling more personalized travel plans to be provided.

[1010] Example 1

[1011] 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."

[1012] When planning a trip, users must collect a huge amount of information and consider each piece of information to create the optimal plan. This process takes time and effort, and is difficult to perform efficiently, especially when traveling to unfamiliar places. It is also difficult to flexibly change plans in response to changes in the weather or local conditions. To solve this problem, a system is needed that allows users to easily and efficiently automatically generate optimal travel plans and respond in real time.

[1013] 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.

[1014] In this invention, the server includes: means for receiving a travel plan generation request from a user; means for transmitting the user's current location information to the server along with the request; means for acquiring weather, location, and traffic information using an external service; means including a generative AI model for generating an optimal travel plan based on the generated weather, location, and traffic information; means for transmitting and displaying the generated travel plan to the user's terminal; means for receiving feedback from the user after the trip and transmitting it to the server; and means for updating the generative AI model based on the feedback and reflecting it in the next plan generation. This allows the user to efficiently obtain an optimal travel plan and flexibly respond to weather and local conditions.

[1015] "User" means an end user who requests the generation of a travel plan and uses the plan provided.

[1016] "Terminal" refers to the electronic device used by a user to make a travel plan request, including a smartphone, tablet, or PC.

[1017] The "server" is a central processing unit that receives user requests, retrieves relevant information, and generates a travel plan using a generative AI model.

[1018] "External services" refers to external databases and APIs that provide weather information, location information, traffic information, etc.

[1019] "Weather Information" means weather forecasts and related meteorological data for a particular location and time.

[1020] "Location information" refers to geographical data necessary for travel planning, such as map information and the locations of tourist attractions.

[1021] "Transportation information" refers to data relating to the means of transportation from a departure point to a destination and the travel time required.

[1022] A "generative AI model" refers to an artificial intelligence system that automatically creates optimal travel plans based on collected data.

[1023] "Sentiments" refers to the feedback and ratings users provide after completing a trip, and this information is used to improve the generative AI model.

[1024] The present invention is a system that enables travelers to easily and efficiently create optimal travel plans. Specific embodiments for carrying out the present invention will be described below.

[1025] This system consists of a user terminal, a server, and an external service. The role and specific operation of each component are explained below.

[1026] User terminal

[1027] A user terminal is an electronic device such as a smartphone, tablet, or PC that a user uses to request the creation of a travel plan. A dedicated application is installed on the terminal, and the user uses this application to input information about the departure point, destination, planned date, desired activities, and tourist spots. The terminal also uses its GPS function to obtain the user's current location information and sends it to the server along with the request data.

[1028] server

[1029] The server is the central processing unit of this system. When it receives a user request, it uses external services to collect the necessary information. Specifically, it obtains the following three pieces of information through external services:

[1030] Weather information: Uses external weather information services to obtain real-time weather information for the user's current location and destination.

[1031] Location information: Use a map information service to obtain map information for your destination and location information for tourist attractions.

[1032] Traffic information: Use a traffic information service to obtain the best means of transportation and travel time from your departure point to your destination.

[1033] Generative AI Models

[1034] The server is equipped with a generative AI model that automatically generates optimal travel plans based on collected weather, location, and traffic information. The generated plans include information on transportation methods, travel times, recommended tourist spots, and places to eat. Weather conditions are also taken into consideration, and in the event of rain, for example, indoor activities are suggested.

[1035] Providing and displaying travel plans

[1036] The server sends the generated travel plan to the user's device. The device receives it and displays it in an easy-to-understand format for the user through the application. The user can then enjoy their trip according to the plan.

[1037] Gathering and implementing feedback

[1038] After the trip is over, the user provides their feedback through their device. For example, they can enter a comment or rating within the application, such as "The restaurants around Kiyomizu-dera Temple were great." The device then sends this feedback to the server, which then stores the feedback data. This data is used to train the generative AI model and is reflected in the generation of the next travel plan.

[1039] Specific examples

[1040] Prompt Sentence Examples

[1041] Here is an example of what a user might input when requesting a travel plan:

[1042] "I'd like to travel from Tokyo to Kyoto. I'll be leaving next Saturday. I'd like to visit historical sites and art museums."

[1043] By using this system, users can efficiently create optimal travel plans even in unfamiliar places, and can flexibly respond to local information gathering and changes in the weather.

[1044] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1045] Step 1:

[1046] The user requests the creation of a travel plan. The user opens the application on their device (smartphone, tablet, PC, etc.) and inputs the departure point, destination, itinerary, and desired sightseeing spots and activities. All input data is temporarily stored on the device. Specifically, the device's GPS function is used to automatically obtain current location information.

[1047] Input: departure point, destination, itinerary, desired sightseeing spots and activities, current location information (GPS data)

[1048] Output: Request data saved on the device

[1049] Step 2:

[1050] The device sends the current location and user input to the server. The device then sends the request data to the server as an HTTP request. The server then receives the user's input and current location information and proceeds to the next step. The device then displays a confirmation message that the request has been sent.

[1051] Input: Request data stored on the device

[1052] Output: Request data sent to the server

[1053] Step 3:

[1054] The server obtains weather, map, and route information. The server sends an API request to an external service (e.g., weather information service, map information service, traffic information service) to collect weather, map, and traffic information for the current location and destination. The obtained data is temporarily stored on the server. The server checks the API response and records a log indicating that data acquisition has been completed.

[1055] Input: The request data sent to the server

[1056] Output: Weather information, map information, and traffic information temporarily stored on the server

[1057] Step 4:

[1058] The generative AI model generates the optimal travel plan. The generative AI model inside the server generates the optimal travel plan based on collected weather, map, and traffic information. The model takes into account the user's desired tourist spots and activities, and creates a plan by optimizing transportation methods and travel times. The resulting plan is saved on the server. Specific operations include calculating weather conditions and transportation methods, and creating an optimal visiting schedule.

[1059] Input: Weather information, map information, traffic information, and request data stored on the server

[1060] Output: Generated itinerary (schedule, transportation, attractions, etc.)

[1061] Step 5:

[1062] The travel plan is sent to the device and displayed. The server sends the generated travel plan to the device. The device receives it and displays it in an easy-to-understand manner for the user through the application. The display format can be a timeline or on a map. The device displays a confirmation message that the plan has been displayed.

[1063] Input: Travel plans stored on the server

[1064] Output: Travel plan displayed on the device

[1065] Step 6:

[1066] Collect user feedback. After completing the trip, the user enters their impressions and evaluations into the device. The device sends this to the server as an HTTP request. The server receives the feedback data and stores it in the learning database of the generative AI model. This allows the feedback to be reflected when generating the next travel plan.

[1067] Input: User's impressions and ratings after the trip

[1068] Output: Feedback data stored in the training database of the generative AI model

[1069] (Application example 1)

[1070] 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."

[1071] Conventional travel plan generation systems generate travel plans based only on weather, map, and route information, without taking into account local shopping districts, special offers, and event information. This makes it difficult to make the most of the local attractions at a travel destination, preventing travelers from enjoying their trips efficiently. Another issue is that the system does not adequately reflect information in real time, making it impossible to respond to immediate plan changes.

[1072] 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.

[1073] In this invention, the server includes: means for receiving a travel plan generation request from a user; means for transmitting the user's current location information along with the request to the server; means for acquiring weather information, map information, route information, and shopping district and event information using an external API; means including a generative AI model for generating an optimal travel plan based on the generated weather information, map information, route information, shopping district and event information; means for transmitting and displaying the generated travel plan to the user's terminal; means for receiving feedback from the user after the trip and transmitting it to the server; and means for updating the generative AI model based on the feedback and reflecting it in the next plan generation. This not only makes it possible to generate plans that incorporate the local attractions of the travel destination, but also makes it possible to respond to immediate plan changes by reflecting information in real time.

[1074] "User device" means a device used to send a travel plan request and receive and display the generated travel plan, including a smartphone, tablet, or PC.

[1075] A "server" is a computer system whose role is to receive requests from users, obtain various information using external APIs, generate travel plans using generative AI models, and send them to the user's terminal.

[1076] An "external API" is an external service that provides various data in real time, such as weather information, map information, route information, and information on shopping districts and events.

[1077] A "generative AI model" is an artificial intelligence model that has the ability to automatically generate optimal travel plans based on various data received.

[1078] "Shopping street information" is data about shopping streets that exist at the travel destination, and includes store locations, special offers, sale information, and the like.

[1079] "Event information" is data about events held in the travel destination area, including the date, time, location, and content of the event.

[1080] "Feedback" refers to opinions such as ratings and impressions provided by users after completing their trip, and is data used by the generative AI model to optimize the generation of the next plan.

[1081] The present invention is a system for enabling travelers to efficiently plan and enjoy trips. Specific embodiments for carrying out the present invention will be described below.

[1082] The system configuration consists of a user terminal, a server, an external API, and a generative AI model. We will now describe the specific components and their operation.

[1083] User terminal

[1084] The user terminal may be a smartphone, tablet, or PC. This terminal is used by the user to input a request to generate a travel plan and to display the generated travel plan. The user terminal is equipped with a GPS function and transmits the user's current location information to the server.

[1085] server

[1086] The server is where the core functions of the travel plan generation system are concentrated. The server performs the following processes:

[1087] 1. Receiving a travel plan request:

[1088] The server receives a request to generate a travel plan from a user terminal, which includes information such as the origin, destination, travel dates, desired activities and tourist attractions, etc.

[1089] 2. Retrieving data from external APIs:

[1090] The server uses external APIs to obtain weather information, map information, route information, shopping mall information, and event information, and generates an optimal travel plan based on the latest information.

[1091] 3. Plan generation using generative AI models:

[1092] The server is equipped with a generative AI model that uses the acquired data to generate an optimal itinerary, taking into account weather conditions, real-time special offers and sales, local event information, and more.

[1093] 4. Sending the travel plan to the user's device:

[1094] The generated travel plan is sent to the user's terminal, and the user can use it to proceed with the trip.

[1095] 5. Collect user feedback and update the AI ​​model:

[1096] After completing the trip, the user provides feedback, which is sent to the server and used to train the generative AI model, which then optimizes the generation of the next itinerary.

[1097] Specific examples

[1098] Example of a user request

[1099] The user inputs a request such as "I would like to travel from Tokyo to Kyoto. The departure date is next Saturday. As tourist spots, I would like to visit historical places and art museums."

[1100] Examples of prompt statements

[1101] {

[1102] "start_location": "Tokyo",

[1103] "destination": "Kyoto",

[1104] "date": "2023-10-20",

[1105] "weather": {

[1106] "forecast": [

[1107] {"date": "2023-10-20", "condition": "Sunny", "max_temp": 25, "min_temp": 18},

[1108] ...

[1109] ]

[1110] },

[1111] "stores": [

[1112] {"name": "Shopping District", "type": "Shopping District", ...},

[1113] ...

[1114] ],

[1115] "events": [

[1116] {"name": "Museum Exhibition", "location": "Museum", ...},

[1117] ...

[1118] ]

[1119] }

[1120] Based on the prompts above, the generative AI model generates an optimal travel plan to support the user's trip, allowing travelers to efficiently experience local attractions and flexibly respond to real-time information changes.

[1121] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1122] Step 1:

[1123] The user terminal receives a request to create a travel plan from the user as input. The user inputs information such as the departure point, destination, travel dates, desired activities and tourist spots, etc. This input data is transmitted to the server via communication.

[1124] Input: origin, destination, travel dates, desired activities and attractions

[1125] Output: User request data sent to the server

[1126] Step 2:

[1127] The server receives the user request data as input, and then acquires the current location information (GPS data) from the user terminal and stores it together with the request data.

[1128] Input: User request data, current location information (GPS data)

[1129] Output: Consolidated user request data (including location information)

[1130] Step 3:

[1131] The server calls external APIs to obtain weather information, map information, route information, and information on shopping malls and events. This information is obtained in real time and used to generate travel plans.

[1132] Input: Consolidated user request data

[1133] Output: Weather information, map information, route information, shopping street information, event information

[1134] Step 4:

[1135] The server inputs prompts into the generative AI model based on the acquired external data. The generative AI model then generates an optimal travel plan based on this information. The plan includes information on transportation, recommended sightseeing spots, special offers at shopping malls, and event information.

[1136] Input: Weather information, map information, route information, shopping street information, event information

[1137] Output: Travel plan

[1138] Step 5:

[1139] The server transmits the generated travel plan to the user terminal, which displays the received travel plan and enables the user to travel according to the plan.

[1140] Input: Travel Plan

[1141] Output: Travel plan displayed on the user's device

[1142] Step 6:

[1143] After completing the trip, the user inputs feedback. The user terminal transmits this feedback to the server. The feedback includes an evaluation and impressions of the trip.

[1144] Input: User feedback

[1145] Output: Feedback data sent to the server

[1146] Step 7:

[1147] The server updates the generative AI model based on the received feedback data, and this update is used to provide improved suggestions the next time a travel plan is generated.

[1148] Input: Feedback data

[1149] Output: Updated generative AI model

[1150] Through these steps, the system can provide users with the most optimal travel plans and constantly optimize the generative AI model based on the latest information and user feedback.

[1151] 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.

[1152] The present invention is a system that enables travelers to easily and efficiently create optimal travel plans, and in particular incorporates an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention will be described below.

[1153] The system of the present invention receives a request from a user to generate a travel plan, automatically generates an optimal travel plan, and provides it to the user. The system is broadly composed of a user terminal, a server, an external API, a generation AI model, and an emotion engine.

[1154] Explanation of program processing

[1155] 1. User requests to generate a travel plan

[1156] A user requests the creation of a travel plan using their device (smartphone, tablet, PC, etc.) This request includes information such as the departure point, destination, planned dates, desired activities and tourist spots, etc.

[1157] 2. The device sends its location and user input to the server

[1158] The user's terminal transmits the user's current location information (GPS data) and the input request data to the server.

[1159] 3. The server obtains weather, map, and route information.

[1160] The server uses external APIs to obtain weather, map, and route information for the current location and destination. This data is updated in real time, so the latest information can always be provided.

[1161] 4. Generative AI models generate optimal travel plans

[1162] The server is equipped with a generative AI model that automatically generates an optimal travel plan based on acquired weather, map, and route information. This plan includes transportation options, travel times, recommended tourist spots, and places to eat. Weather conditions are also taken into account, and indoor activities are suggested in case of rain.

[1163] 5. Emotion engine recognizes user emotions

[1164] When a user checks the travel plan created through the device, the emotion engine analyzes the user's facial expressions and voice to recognize the user's emotions, and this information is sent to the server in real time.

[1165] 6. Modifying your plan based on emotions

[1166] The generative AI model on the server uses the data sent from the emotion engine to adjust the plan if the user is dissatisfied. For example, if it determines that the user is not interested in a particular tourist spot, it will suggest another tourist spot.

[1167] 7. Send and display your travel plans on your device

[1168] The server sends the revised travel plan to the user's device, which receives it and displays it in an easy-to-understand manner for the user.

[1169] 8. Gather user feedback

[1170] After the trip is over, the user provides their impressions and feedback. The emotion engine recognizes the user's emotions when providing feedback and sends the data including those to the server. This data is used to train the generative AI model.

[1171] Specific examples

[1172] Example 1: Generating a travel plan and using the emotion engine

[1173] 1. User: "I'd like to travel from Tokyo to Kyoto this weekend. I'm leaving next Saturday. I'd like to visit historical sites and museums."

[1174] 2. The device sends a request to the server

[1175] The terminal transmits the user's current location information and request data to the server.

[1176] 3. The server retrieves the relevant information

[1177] The server obtains weather information (weather forecasts for Tokyo and Kyoto), map information (information on tourist spots in Kyoto), and route information (transportation methods and travel times from Tokyo to Kyoto).

[1178] 4. The generative AI model on the server generates a plan

[1179] The generative AI model generates a trip plan like this:

[1180] Departs Tokyo Station at 8:00, arrives Kyoto Station at 9:45 (by Shinkansen)

[1181] 10:00 Walk from Kyoto Station to Kiyomizu-dera Temple

[1182] 12:00 Lunch near Kiyomizu-dera Temple (with restaurant recommendations)

[1183] 14:00 Move to Kyoto National Museum (by bus)

[1184] 17:00 Hotel check-in

[1185] 5. Emotion engine recognizes user emotions

[1186] The emotion engine analyzes the user's facial expressions and voice while checking the travel plan, and recognizes the user's emotions. For example, if the user looks dissatisfied, it sends this to the server.

[1187] 6. The server modifies the plan

[1188] The generative AI model detects user frustrations and adjusts travel plans, for example, if a user isn't interested in historical sites, it suggests shopping spots instead.

[1189] 7. Display the revised plan on your device

[1190] The modified plan is sent to the terminal and displayed to the user.

[1191] 8. Users provide feedback

[1192] After the trip, the user enters feedback, and the emotion engine recognizes the emotions expressed and sends them to the server. The server uses this data to update the generative AI model and reflect it in the generation of the next travel plan.

[1193] By using this system, users can enjoy more personalized travel plans that reflect their individual preferences and feelings.

[1194] The processing flow will be explained below.

[1195] Step 1:

[1196] A user uses a terminal to input a request to generate a travel plan, which includes information such as the origin, destination, planned dates, desired activities and attractions, etc.

[1197] Step 2:

[1198] The device obtains the user's current location information by using the GPS function to obtain current location data.

[1199] Step 3:

[1200] The device sends the user's input data and current location information together to the server, allowing the server to understand the user's request and current location.

[1201] Step 4:

[1202] The server retrieves weather information using an external API. The server retrieves the weather forecast for the specified departure date and travel period.

[1203] Step 5:

[1204] The server obtains map information. The server collects the location information of the tourist spots and activities the user wishes to visit, as well as the geographical data of the surrounding areas.

[1205] Step 6:

[1206] The server obtains route information. The server collects data on the user's transportation method (e.g., train, bus, walking) and calculates the optimal route and travel time.

[1207] Step 7:

[1208] The AI ​​model on the server generates an optimal travel plan based on weather, map, and route information, taking into account the user's requests and weather conditions, and adjusting indoor and outdoor activities.

[1209] Step 8:

[1210] The server sends the generated travel plan to the user's terminal, which receives it and displays it in an easy-to-understand manner for the user.

[1211] Step 9:

[1212] When a user checks the travel plan created through their device, the emotion engine analyzes the user's facial expressions and voice to recognize their emotions, and this information is sent to the server in real time.

[1213] Step 10:

[1214] The generative AI model on the server uses the data sent from the emotion engine to adjust the plan if the user is dissatisfied. For example, if it determines that the user is not interested in a particular tourist spot, it will suggest another tourist spot.

[1215] Step 11:

[1216] The server sends the revised travel plan to the user's terminal, which receives it and displays the revised plan to the user.

[1217] Step 12:

[1218] The user acts according to the travel plan, and if changes are needed during the trip, the system can generate a new plan in real time.

[1219] Step 13:

[1220] After the trip is over, the user inputs feedback through the terminal. The emotion engine recognizes the user's emotions when giving feedback and sends the data including the emotions to the server.

[1221] Step 14:

[1222] The server updates the generative AI model based on the feedback data and reflects it in the next itinerary generation. This continuous feedback loop improves the accuracy of the generative AI model, enabling more personalized travel plans to be provided.

[1223] Example 2

[1224] 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."

[1225] Conventional travel plan generation systems have difficulty generating optimal travel plans that reflect individual user preferences and emotions. Furthermore, they provide uniform plans without considering user emotions, resulting in low user satisfaction. Therefore, there is a demand for a system that allows users to easily and efficiently create optimal travel plans based on their individual needs and emotions.

[1226] 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: means for receiving a travel plan generation request from a user; means for transmitting the user's current location information to the server along with the request; means for acquiring weather information, map information, and route information using an external API; means including a generative AI model that generates an optimal travel plan based on the generated weather information, map information, and route information; means for transmitting and displaying the generated travel plan to the user's terminal; means for recognizing the user's emotions when checking the travel plan using an emotion engine and adjusting the travel plan based on the emotions; means for receiving feedback from the user after the trip ends and transmitting it to the server; and means for updating the generative AI model based on the feedback and reflecting it in the next plan generation. This enables automatic generation and real-time adjustment of an optimal travel plan according to the user's individual requests and emotions.

[1227] A "request to create a travel plan" is data that includes a request or information that a user makes to the system to create a travel plan.

[1228] "User's current location information" refers to geographical data used to identify the user's location, and typically refers to GPS data.

[1229] The "server" is a computer system that receives user requests, communicates with external APIs, analyzes data, runs generative AI models, and generates travel plans and provides feedback to the user.

[1230] An "external API" is a program interface that allows a server to obtain data such as weather information, map information, and route information from external sources.

[1231] "Weather information" is data about local and destination weather forecasts and weather conditions.

[1232] "Map information" is data that includes geographical information about the destination and its surroundings, location information about tourist spots, and the like.

[1233] "Route information" is data relating to the means of transportation from the departure point to the destination, its operation schedule, and the required travel time.

[1234] A "generative AI model" is an artificial intelligence model that automatically generates optimal travel plans based on user requests and data obtained from external APIs.

[1235] An "emotion engine" is a technology that analyzes a user's facial expressions and tone of voice to recognize their emotions.

[1236] "Feedback" is data including evaluations and impressions of the travel plan provided by the user after the trip is completed.

[1237] The present invention is a system that allows a user to easily and efficiently create an optimal travel plan, and in particular incorporates an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention will be described below.

[1238] The system of the present invention receives a request from a user to create a travel plan, automatically generates an optimal travel plan, and provides it to the user. The system is broadly composed of a user terminal, a server, an external API, a generation AI model, and an emotion engine.

[1239] About user devices

[1240] User terminals include a variety of devices, such as smartphones, tablets, and PCs. These terminals have hardware such as GPS sensors, cameras, and microphones, and dedicated applications are installed to utilize these devices. Users input travel plan generation requests through the application and send them to the server.

[1241] About the Server

[1242] The server is a computer system that receives user requests, communicates with external APIs, analyzes data, runs generative AI models, and generates travel plans and provides feedback to users. The server has the following functions:

[1243] 1. Request reception and analysis: Receives the request data and current location information sent from the user terminal and analyzes them.

[1244] 2. Integration with external APIs: Obtain necessary information using external weather information APIs, map information APIs, and route information APIs. Examples include the OpenWeatherMap API, Google Maps API, and transport route APIs.

[1245] 3. Execution of the generative AI model: Based on the acquired weather, map, and route information, the generative AI model generates the optimal travel plan. An example of a generative AI model is OpenAI's GPT-4.

[1246] About the Emotion Engine

[1247] The emotion engine is a system that uses a camera and microphone to capture the user's facial expressions and voice when checking a travel plan on a device, and analyzes their emotions. This emotion data is sent to a server in real time and fed back to a generative AI model, which then automatically modifies the travel plan.

[1248] Specific examples

[1249] Travel plan generation and emotion engine utilization

[1250] 1. User: "I'd like to travel from Tokyo to Kyoto this weekend. I'm leaving next Saturday. I'd like to visit historical sites and museums."

[1251] 2. The device sends a request to the server:

[1252] The device sends the user's current location information and request data to the server using the HTTPS protocol.

[1253] 3. The server retrieves the relevant information:

[1254] The server calls the OpenWeatherMap API to get the weather forecast for Tokyo and Kyoto.

[1255] The server uses the Google Maps API to obtain map information and tourist information for Kyoto.

[1256] The server calls the transport route API to obtain information and travel time for the Shinkansen from Tokyo to Kyoto.

[1257] 4. The generative AI model in the server generates a plan:

[1258] The generative AI model generates a trip plan like this:

[1259] Departs Tokyo Station at 8:00, arrives Kyoto Station at 9:45 (by Shinkansen)

[1260] 10:00 Walk from Kyoto Station to Kiyomizu-dera Temple

[1261] 12:00 Lunch near Kiyomizu-dera Temple (with restaurant recommendations)

[1262] 14:00 Move to Kyoto National Museum (by bus)

[1263] 17:00 Hotel check-in

[1264] 5. Emotion engine recognizes user emotions:

[1265] While the user is checking their travel plans, the device's camera and microphone capture the user's facial expressions and voice, which are then analyzed by the emotion engine.

[1266] The analysis results are sent to the server in real time.

[1267] 6. The server modifies the plan:

[1268] If the generative AI model senses the user's frustration and determines that they are not particularly interested in "historical places," it will create a new plan that includes shopping spots instead.

[1269] 7. View the revised plan on your device:

[1270] The revised plan is sent to the terminal and displayed in an easy-to-understand user interface.

[1271] 8. Users provide feedback:

[1272] After the trip, the user enters feedback, and the emotion engine recognizes the emotions expressed at the time and sends them to the server. The generative AI model learns from this and reflects it in future plans.

[1273] In this way, the system of the present invention can generate personalized travel plans based on the user's needs and emotions, improving the overall travel experience.

[1274] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1275] Step 1: User requests to generate a travel plan

[1276] Input: A user launches a trip planning application and enters details such as origin, destination, planned dates, and desired activities and attractions.

[1277] Specific operation: When the user presses the "send" button, the terminal compiles this request information.

[1278] Step 2: The device sends its location and user input to the server

[1279] Input: User's current location (GPS data) and the request information entered in step 1.

[1280] Data processing: Create a single request packet containing the current location information and request data.

[1281] Specific operation: The terminal sends this request packet to the server using a secure communication protocol (e.g., HTTPS).

[1282] Output: A request packet is sent to the server.

[1283] Step 3: The server retrieves weather, map, and route information.

[1284] Input: The server parses the request packet and obtains the destination and origin information.

[1285] Data calculation: The server calls the weather API (e.g., general weather information API), map API (e.g., general map information API), and route information API (e.g., general traffic information API) to obtain each piece of information.

[1286] Specific operation: The weather information, map information, and route information obtained by the server are saved.

[1287] Output: Obtained weather information, map information, and route information.

[1288] Step 4: The generative AI model generates an optimal travel plan

[1289] Input: Weather information, map information, and route information stored by the server.

[1290] Data calculation: A generative AI model (e.g., a general generative AI model) installed on the server creates a prompt based on this information, and then generates a travel plan based on that.

[1291] How it works: The generative AI model automatically generates a travel plan including transportation options, travel time, recommended tourist spots, places to eat, etc.

[1292] Output: The generated itinerary.

[1293] Step 5: The emotion engine recognizes the user's emotion

[1294] Input: Facial and voice data captured while the user is reviewing the itinerary generated through the device.

[1295] Data calculation: The emotion engine analyzes facial expressions and tone of voice to detect the user's emotions.

[1296] Specific operation: Detects user dissatisfaction or satisfaction in real time and sends emotional data to the server.

[1297] Output: User emotion data.

[1298] Step 6: Modify your plan based on your emotions

[1299] Input: User emotion data, generated travel plan.

[1300] Data calculation: Based on emotion data, a generative AI model adjusts the contents of the travel plan.

[1301] Specific operation: For example, if the user is not interested in a particular tourist spot, a new tourist spot will be suggested.

[1302] Output: The revised itinerary.

[1303] Step 7: Send and display the revised plan on your device

[1304] Input: The modified itinerary in the server.

[1305] Specific operation: The server sends the modified plan to the user's terminal.

[1306] Output: The terminal displays the revised itinerary.

[1307] Step 8: Gather user feedback

[1308] Input: User feedback after the trip, captured facial and voice data.

[1309] Data computation: Combining feedback data and emotion data.

[1310] Specific operation: User feedback and emotional data are sent to the server, and the generative AI model uses this as training data.

[1311] Output: An updated generative AI model for future plan generation.

[1312] (Application example 2)

[1313] 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."

[1314] Conventional travel plan generation systems have had difficulty providing individual travel plans that fully reflect the user's preferences and feelings. This has resulted in the problem that it takes a lot of time and effort for users to plan a trip that satisfies them. In addition, travel plan revisions are not performed automatically, so users have to adjust multiple pieces of information themselves. It is necessary to provide a system that solves these problems and provides more personalized travel plans.

[1315] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for receiving a travel plan generation request from a user; means for transmitting the user's current location information to the server along with the request; means for acquiring weather information, map information, and route information using an external API; means including a generative AI model that generates an optimal travel plan based on the generated weather information, map information, and route information; means for transmitting and displaying the generated travel plan to the user's terminal; means including an emotion engine that analyzes the user's facial expressions and voice and recognizes the user's emotions; means for modifying the travel plan based on data obtained from the emotion engine; means for receiving feedback from the user after the trip and transmitting it to the server; and means for updating the generative AI model based on the feedback and reflecting it in the next plan generation. This makes it possible to automatically provide a highly satisfying customized travel plan that takes user emotions and feedback into consideration in real time.

[1316] Definitions of important words

[1317] The "means for receiving a request to create a travel plan" refers to a mechanism by which a user inputs requests and information necessary to create a travel plan.

[1318] The "means for transmitting current location information to a server" refers to a method for transmitting the user's current location information to a server via the Internet.

[1319] "Means of obtaining weather information, map information, and route information using external APIs" refers to methods of obtaining the necessary weather information, map information, and transportation route information via external data sources.

[1320] "Means including generative AI models" refers to a system that has an artificial intelligence algorithm that automatically generates optimal travel plans based on information received from users or data obtained through external APIs.

[1321] "Means for sending and displaying the generated itinerary on the user's device" refers to an interface for sending the itinerary created by the AI ​​model to the user's device and displaying it on that device.

[1322] "Means including an emotion engine" refers to a system used to analyze a user's facial expression and voice data and recognize their current emotional state.

[1323] "Means for modifying travel plans based on data obtained from the emotion engine" refers to a mechanism that uses the user's emotion data obtained by the emotion engine to modify the generated travel plan according to the user's requests.

[1324] "Means for receiving feedback from users after the trip has ended and transmitting it to a server" refers to a method for receiving impressions and evaluations from users after the trip has ended and transmitting that information to a server.

[1325] "Means for updating the generative AI model based on feedback and reflecting it in the next plan generation" refers to the learning process for improving the generative AI model based on feedback received from users and reflecting it in the next plan generation.

[1326] Description: Form for carrying out the invention

[1327] The present invention is a system that allows travelers to easily and efficiently create optimal travel plans, and in particular incorporates an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention will be described below.

[1328] System configuration

[1329] The system consists of the following components:

[1330] 1. User Device

[1331] The device on which the user requests the itinerary to be generated. This can be a smartphone, tablet, or computer.

[1332] 2. Server

[1333] The central processing unit processes data and runs AI models. It retrieves weather, map, and route information, generates travel plans, and modifies plans based on emotion data.

[1334] 3. External API

[1335] Interface to external services that provide weather, map, and route information.

[1336] 4. Generative AI Models

[1337] An algorithm that generates optimal travel plans based on user input and information from external APIs. OpenAI's GPT-3 is an example.

[1338] 5. Emotion Engine

[1339] A system that recognizes emotions by analyzing a user's facial expressions and voice. Emotion analysis frameworks such as DeepFace fall into this category.

[1340] Data Processing Flow

[1341] User terminal

[1342] When a user requests the creation of a travel plan, they input information such as the departure point, destination, desired activities, etc. The terminal then transmits this information along with the user's current location to the server.

[1343] server

[1344] The server uses external APIs to obtain weather, map, and route information. This data is updated in real time and always keeps the latest information. The generative AI model generates the optimal travel plan based on this data. The user's past travel history and ratings are also taken into consideration.

[1345] Generative AI Models

[1346] The generative AI model primarily uses natural language generation algorithms to generate optimal travel plans based on user requests, for example, using the following prompt:

[1347] Please suggest the best itinerary for a trip from Tokyo to Kyoto. The traveler wants to visit historical sites and museums.

[1348] Based on this prompt, a specific travel schedule is generated.

[1349] Emotion Engine

[1350] When the user checks the generated travel plan, the device's camera and microphone are used to analyze the user's facial expressions and voice, and the emotion engine recognizes the user's emotions. For example, if the user makes a dissatisfied expression, that data is sent to the server. The model in the server reflects this data and modifies the plan according to the user's emotions.

[1351] Specific examples

[1352] For example, if a user enters "I want to travel from Tokyo to Kyoto this weekend. I'll leave on Saturday. I want to visit historical sites and museums," the system will do the following:

[1353] 1. The user's request and current location information are sent to the server.

[1354] 2. The server obtains weather information, map information, and route information from an external API.

[1355] 3. The generative AI model generates a trip plan like this:

[1356] Departs Tokyo Station at 8:00, arrives Kyoto Station at 9:45 (by Shinkansen)

[1357] 10:00 Walk from Kyoto Station to Kiyomizu-dera Temple

[1358] 12:00 Lunch near Kiyomizu-dera Temple (with restaurant recommendations)

[1359] 14:00 Move to Kyoto National Museum (by bus)

[1360] 17:00 Hotel check-in

[1361] When the user reviews this plan, emotion recognition is performed, and if the emotion "unhappy" is detected, the plan may be modified as follows:

[1362] Suggestions for shopping spots and museum visits have been shortened, and cafe breaks have been added.

[1363] In this way, the present invention can provide a highly satisfying customized travel plan that is in line with the user's feelings and preferences.

[1364] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1365] Program processing flow

[1366] Step 1:

[1367] The user inputs a request to generate a travel plan using a terminal. The input includes information such as the departure point, destination, planned dates, desired activities and sightseeing spots, etc. This input data is necessary to generate a travel plan based on the user's preferences and wishes.

[1368] Step 2:

[1369] The device sends the user's current location information (GPS data) and the requested data to the server. Specifically, the device sends the user's current location information (GPS data) along with the departure point, destination, and desired activity entered by the user. This data is used by the server to obtain subsequent information and generate plans.

[1370] Step 3:

[1371] The server uses external APIs to obtain weather, map, and route information for the current location and destination. In this step, the server communicates with external services to obtain real-time weather forecasts, maps, and transportation options and travel times. By calling external APIs, the latest information is gathered.

[1372] Step 4:

[1373] The generative AI model automatically generates an optimal travel plan based on the weather, map, and route information it has acquired. In this step, the generative AI model installed on the server combines the user's request data with information collected from external APIs and creates a travel plan based on the prompt text.

[1374] Example: Suggest the best itinerary for a trip from Tokyo to Kyoto. The traveller wants to visit historical sites and museums.

[1375] Based on this, the generative AI model suggests specific schedules and places to visit.

[1376] Step 5:

[1377] The generated travel plan is sent to the user's terminal and displayed. The server sends the generated travel plan to the user's terminal and displays it on the terminal screen for the user to check. At this stage, the user can check the proposed plan in detail.

[1378] Step 6:

[1379] When a user checks their travel plans, the device captures their facial expressions and voice and analyzes them using an emotion engine. The device uses its built-in camera and microphone to collect the user's facial expressions and tone of voice in real time, and sends the data to a server for emotion analysis.

[1380] Step 7:

[1381] The emotion engine analyzes the user's emotions and sends them to the server. In this step, the emotion analysis results (e.g., "dissatisfaction" or "joy") are sent to the server, and the user's emotional state is stored as numerical data on the server.

[1382] Step 8:

[1383] The generative AI model modifies the travel plan based on the data sent from the emotion engine. If the user expresses dissatisfaction, the generative AI model adjusts the plan, for example, adding new tourist spots or changing existing ones.

[1384] Step 9:

[1385] The server retransmits the revised travel plan to the user's terminal and displays it. The server generates a new travel plan, transmits it to the user's terminal and redisplays it, so the user can check the revised plan.

[1386] Step 10:

[1387] After the user has completed the trip, they input their feedback via the device and send it to the server. The user inputs feedback such as their satisfaction with the trip, what they liked, and what needs to be improved through the device.

[1388] Step 11:

[1389] The server updates the generative AI model based on the user's feedback data and reflects it in the next plan generation.The server trains the generative AI model based on the received feedback and sentiment analysis results, thereby improving the accuracy of future travel plan generation.

[1390] 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.

[1391] 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.

[1392] 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.

[1393] [Fourth embodiment]

[1394] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1395] 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.

[1396] 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).

[1397] 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.

[1398] 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.

[1399] 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).

[1400] 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.

[1401] 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.

[1402] 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.

[1403] 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.

[1404] 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.

[1405] 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.

[1406] 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."

[1407] The present invention is a system that enables travelers to easily and efficiently create optimal travel plans. Specific embodiments for carrying out the present invention will be described below.

[1408] The system of the present invention allows users to make travel plan generation requests, and automatically generates and provides optimal travel plans based on those requests. The system is broadly composed of a user terminal, a server, an external API, and a generation AI model.

[1409] Explanation of program processing

[1410] 1. User requests to generate a travel plan

[1411] A user requests the creation of a travel plan using their device (smartphone, tablet, PC, etc.) This request includes information such as the departure point, destination, planned dates, desired activities and tourist spots, etc.

[1412] 2. The device sends its location and user input to the server

[1413] The user's device sends the user's current location information (GPS data) and input request data to the server, allowing the server to understand the user's current situation and make appropriate suggestions in real time.

[1414] 3. The server obtains weather, map, and route information.

[1415] The server uses external APIs to obtain weather, map, and route information for the current location and destination. This data is updated in real time, so the latest information can always be provided.

[1416] 4. Generative AI models generate optimal travel plans

[1417] The server is equipped with a generative AI model that automatically generates an optimal travel plan based on the acquired data. This plan includes transportation options, travel times, recommended tourist spots, places to eat, etc. It also takes weather conditions into account, suggesting indoor activities in case of rain.

[1418] 5. Send and display your travel plans on your device

[1419] The server sends the generated travel plan to the user's device, which receives it and displays it in an easy-to-understand format for the user. The user can then proceed with their trip according to this plan.

[1420] 6. Gather user feedback

[1421] After completing the trip, the user provides their impressions and feedback. The feedback is entered through the user's device, which then sends it to the server. The server stores this data and uses it to train the generative AI model. This process allows the generative AI model to be continuously optimized and reflected in the generation of the next travel plan.

[1422] Specific examples

[1423] Example 1: Generating a Travel Plan

[1424] 1. User: "I'd like to travel from Tokyo to Kyoto. I'll be leaving next Saturday. I'd like to visit historical sites and museums."

[1425] 2. The device sends a request to the server:

[1426] The terminal transmits the user's current location information and the input request data to the server.

[1427] 3. The server retrieves the relevant information:

[1428] The server obtains weather information (weather forecasts for Tokyo and Kyoto), map information (information on tourist spots in Kyoto), and route information (transportation methods and travel times from Tokyo to Kyoto).

[1429] 4. The generative AI model in the server generates a plan:

[1430] The generative AI model generates a trip plan like this:

[1431] Departs Tokyo Station at 8:00, arrives Kyoto Station at 9:45 (by Shinkansen)

[1432] 10:00 Walk from Kyoto Station to Kiyomizu-dera Temple

[1433] 12:00 Lunch near Kiyomizu-dera Temple (with restaurant recommendations)

[1434] 14:00 Move to Kyoto National Museum (by bus)

[1435] 17:00 Hotel check-in

[1436] 5. View your plan on your device:

[1437] The generated plan is sent to the terminal and displayed to the user, who can then enjoy their trip according to the plan.

[1438] 6. Users provide feedback:

[1439] After the trip, the user inputs their evaluation and impressions into the device. Specific feedback, such as "The restaurants around Kiyomizu-dera Temple were great," is sent to the server.

[1440] By using this system, users can efficiently create optimal travel plans even in unfamiliar places, and can flexibly respond to local information gathering and changes in the weather.

[1441] The processing flow will be explained below.

[1442] Step 1:

[1443] A user uses a terminal to input a request to generate a travel plan, which includes information such as the origin, destination, planned dates, desired activities and attractions, etc.

[1444] Step 2:

[1445] The device obtains the user's current location information by using the GPS function to obtain current location data.

[1446] Step 3:

[1447] The device sends the user's input data and current location information together to the server, allowing the server to understand the user's request and current location.

[1448] Step 4:

[1449] The server retrieves weather information using an external API. The server retrieves the weather forecast for the specified departure date and travel period.

[1450] Step 5:

[1451] The server obtains map information. The server collects the location information of the tourist spots and activities the user wishes to visit, as well as the geographical data of the surrounding areas.

[1452] Step 6:

[1453] The server obtains route information. The server collects data on the user's transportation method (e.g., train, bus, walking) and calculates the optimal route and travel time.

[1454] Step 7:

[1455] The AI ​​model on the server generates an optimal travel plan based on weather, map, and route information, taking into account the user's requests and weather conditions, and adjusting indoor and outdoor activities.

[1456] Step 8:

[1457] The server sends the generated travel plan to the user's terminal, which receives it and displays it in an easy-to-understand manner for the user.

[1458] Step 9:

[1459] The user acts according to the travel plan, and if changes are needed during the trip, the system can generate a new plan in real time.

[1460] Step 10:

[1461] After completing the trip, users can enter feedback through the device, specifically describing their impressions and evaluations.

[1462] Step 11:

[1463] The device sends user feedback data to a server, which uses the feedback to train a generative AI model.

[1464] Step 12:

[1465] The server updates the generative AI model based on the feedback data and reflects it in the next itinerary generation. This continuous feedback loop improves the accuracy of the generative AI model, enabling more personalized travel plans to be provided.

[1466] Example 1

[1467] 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."

[1468] When planning a trip, users must collect a huge amount of information and consider each piece of information to create the optimal plan. This process takes time and effort, and is difficult to perform efficiently, especially when traveling to unfamiliar places. It is also difficult to flexibly change plans in response to changes in the weather or local conditions. To solve this problem, a system is needed that allows users to easily and efficiently automatically generate optimal travel plans and respond in real time.

[1469] 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.

[1470] In this invention, the server includes: means for receiving a travel plan generation request from a user; means for transmitting the user's current location information to the server along with the request; means for acquiring weather, location, and traffic information using an external service; means including a generative AI model for generating an optimal travel plan based on the generated weather, location, and traffic information; means for transmitting and displaying the generated travel plan to the user's terminal; means for receiving feedback from the user after the trip and transmitting it to the server; and means for updating the generative AI model based on the feedback and reflecting it in the next plan generation. This allows the user to efficiently obtain an optimal travel plan and flexibly respond to weather and local conditions.

[1471] "User" means an end user who requests the generation of a travel plan and uses the plan provided.

[1472] "Terminal" refers to the electronic device used by a user to make a travel plan request, including a smartphone, tablet, or PC.

[1473] The "server" is a central processing unit that receives user requests, retrieves relevant information, and generates a travel plan using a generative AI model.

[1474] "External services" refers to external databases and APIs that provide weather information, location information, traffic information, etc.

[1475] "Weather Information" means weather forecasts and related meteorological data for a particular location and time.

[1476] "Location information" refers to geographical data necessary for travel planning, such as map information and the locations of tourist attractions.

[1477] "Transportation information" refers to data relating to the means of transportation from a departure point to a destination and the travel time required.

[1478] A "generative AI model" refers to an artificial intelligence system that automatically creates optimal travel plans based on collected data.

[1479] "Sentiments" refers to the feedback and ratings users provide after completing a trip, and this information is used to improve the generative AI model.

[1480] The present invention is a system that enables travelers to easily and efficiently create optimal travel plans. Specific embodiments for carrying out the present invention will be described below.

[1481] This system consists of a user terminal, a server, and an external service. The role and specific operation of each component are explained below.

[1482] User terminal

[1483] A user terminal is an electronic device such as a smartphone, tablet, or PC that a user uses to request the creation of a travel plan. A dedicated application is installed on the terminal, and the user uses this application to input information about the departure point, destination, planned date, desired activities, and tourist spots. The terminal also uses its GPS function to obtain the user's current location information and sends it to the server along with the request data.

[1484] server

[1485] The server is the central processing unit of this system. When it receives a user request, it uses external services to collect the necessary information. Specifically, it obtains the following three pieces of information through external services:

[1486] Weather information: Uses external weather information services to obtain real-time weather information for the user's current location and destination.

[1487] Location information: Use a map information service to obtain map information for your destination and location information for tourist attractions.

[1488] Traffic information: Use a traffic information service to obtain the best means of transportation and travel time from your departure point to your destination.

[1489] Generative AI Models

[1490] The server is equipped with a generative AI model that automatically generates optimal travel plans based on collected weather, location, and traffic information. The generated plans include information on transportation methods, travel times, recommended tourist spots, and places to eat. Weather conditions are also taken into consideration, and in the event of rain, for example, indoor activities are suggested.

[1491] Providing and displaying travel plans

[1492] The server sends the generated travel plan to the user's device. The device receives it and displays it in an easy-to-understand format for the user through the application. The user can then enjoy their trip according to the plan.

[1493] Gathering and implementing feedback

[1494] After the trip is over, the user provides their feedback through their device. For example, they can enter a comment or rating within the application, such as "The restaurants around Kiyomizu-dera Temple were great." The device then sends this feedback to the server, which then stores the feedback data. This data is used to train the generative AI model and is reflected in the generation of the next travel plan.

[1495] Specific examples

[1496] Prompt Sentence Examples

[1497] Here is an example of what a user might input when requesting a travel plan:

[1498] "I'd like to travel from Tokyo to Kyoto. I'll be leaving next Saturday. I'd like to visit historical sites and art museums."

[1499] By using this system, users can efficiently create optimal travel plans even in unfamiliar places, and can flexibly respond to local information gathering and changes in the weather.

[1500] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1501] Step 1:

[1502] The user requests the creation of a travel plan. The user opens the application on their device (smartphone, tablet, PC, etc.) and inputs the departure point, destination, itinerary, and desired sightseeing spots and activities. All input data is temporarily stored on the device. Specifically, the device's GPS function is used to automatically obtain current location information.

[1503] Input: departure point, destination, itinerary, desired sightseeing spots and activities, current location information (GPS data)

[1504] Output: Request data saved on the device

[1505] Step 2:

[1506] The device sends the current location and user input to the server. The device then sends the request data to the server as an HTTP request. The server then receives the user's input and current location information and proceeds to the next step. The device then displays a confirmation message that the request has been sent.

[1507] Input: Request data stored on the device

[1508] Output: Request data sent to the server

[1509] Step 3:

[1510] The server obtains weather, map, and route information. The server sends an API request to an external service (e.g., weather information service, map information service, traffic information service) to collect weather, map, and traffic information for the current location and destination. The obtained data is temporarily stored on the server. The server checks the API response and records a log indicating that data acquisition has been completed.

[1511] Input: The request data sent to the server

[1512] Output: Weather information, map information, and traffic information temporarily stored on the server

[1513] Step 4:

[1514] The generative AI model generates the optimal travel plan. The generative AI model inside the server generates the optimal travel plan based on collected weather, map, and traffic information. The model takes into account the user's desired tourist spots and activities, and creates a plan by optimizing transportation methods and travel times. The resulting plan is saved on the server. Specific operations include calculating weather conditions and transportation methods, and creating an optimal visiting schedule.

[1515] Input: Weather information, map information, traffic information, and request data stored on the server

[1516] Output: Generated itinerary (schedule, transportation, attractions, etc.)

[1517] Step 5:

[1518] The travel plan is sent to the device and displayed. The server sends the generated travel plan to the device. The device receives it and displays it in an easy-to-understand manner for the user through the application. The display format can be a timeline or on a map. The device displays a confirmation message that the plan has been displayed.

[1519] Input: Travel plans stored on the server

[1520] Output: Travel plan displayed on the device

[1521] Step 6:

[1522] Collect user feedback. After completing the trip, the user enters their impressions and evaluations into the device. The device sends this to the server as an HTTP request. The server receives the feedback data and stores it in the learning database of the generative AI model. This allows the feedback to be reflected when generating the next travel plan.

[1523] Input: User's impressions and ratings after the trip

[1524] Output: Feedback data stored in the training database of the generative AI model

[1525] (Application example 1)

[1526] 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."

[1527] Conventional travel plan generation systems generate travel plans based only on weather, map, and route information, without taking into account local shopping districts, special offers, and event information. This makes it difficult to make the most of the local attractions at a travel destination, preventing travelers from enjoying their trips efficiently. Another issue is that the system does not adequately reflect information in real time, making it impossible to respond to immediate plan changes.

[1528] 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.

[1529] In this invention, the server includes: means for receiving a travel plan generation request from a user; means for transmitting the user's current location information along with the request to the server; means for acquiring weather information, map information, route information, and shopping district and event information using an external API; means including a generative AI model for generating an optimal travel plan based on the generated weather information, map information, route information, shopping district and event information; means for transmitting and displaying the generated travel plan to the user's terminal; means for receiving feedback from the user after the trip and transmitting it to the server; and means for updating the generative AI model based on the feedback and reflecting it in the next plan generation. This not only makes it possible to generate plans that incorporate the local attractions of the travel destination, but also makes it possible to respond to immediate plan changes by reflecting information in real time.

[1530] "User device" means a device used to send a travel plan request and receive and display the generated travel plan, including a smartphone, tablet, or PC.

[1531] A "server" is a computer system whose role is to receive requests from users, obtain various information using external APIs, generate travel plans using generative AI models, and send them to the user's terminal.

[1532] An "external API" is an external service that provides various data in real time, such as weather information, map information, route information, and information on shopping districts and events.

[1533] A "generative AI model" is an artificial intelligence model that has the ability to automatically generate optimal travel plans based on various data received.

[1534] "Shopping street information" is data about shopping streets that exist at the travel destination, and includes store locations, special offers, sale information, and the like.

[1535] "Event information" is data about events held in the travel destination area, including the date, time, location, and content of the event.

[1536] "Feedback" refers to opinions such as ratings and impressions provided by users after completing their trip, and is data used by the generative AI model to optimize the generation of the next plan.

[1537] The present invention is a system for enabling travelers to efficiently plan and enjoy trips. Specific embodiments for carrying out the present invention will be described below.

[1538] The system configuration consists of a user terminal, a server, an external API, and a generative AI model. We will now describe the specific components and their operation.

[1539] User terminal

[1540] The user terminal may be a smartphone, tablet, or PC. This terminal is used by the user to input a request to generate a travel plan and to display the generated travel plan. The user terminal is equipped with a GPS function and transmits the user's current location information to the server.

[1541] server

[1542] The server is where the core functions of the travel plan generation system are concentrated. The server performs the following processes:

[1543] 1. Receiving a travel plan request:

[1544] The server receives a request to generate a travel plan from a user terminal, which includes information such as the origin, destination, travel dates, desired activities and tourist attractions, etc.

[1545] 2. Retrieving data from external APIs:

[1546] The server uses external APIs to obtain weather information, map information, route information, shopping mall information, and event information, and generates an optimal travel plan based on the latest information.

[1547] 3. Plan generation using generative AI models:

[1548] The server is equipped with a generative AI model that uses the acquired data to generate an optimal itinerary, taking into account weather conditions, real-time special offers and sales, local event information, and more.

[1549] 4. Sending the travel plan to the user's device:

[1550] The generated travel plan is sent to the user's terminal, and the user can use it to proceed with the trip.

[1551] 5. Collect user feedback and update the AI ​​model:

[1552] After completing the trip, the user provides feedback, which is sent to the server and used to train the generative AI model, which then optimizes the generation of the next itinerary.

[1553] Specific examples

[1554] Example of a user request

[1555] The user inputs a request such as "I would like to travel from Tokyo to Kyoto. The departure date is next Saturday. As tourist spots, I would like to visit historical places and art museums."

[1556] Examples of prompt statements

[1557] {

[1558] "start_location": "Tokyo",

[1559] "destination": "Kyoto",

[1560] "date": "2023-10-20",

[1561] "weather": {

[1562] "forecast": [

[1563] {"date": "2023-10-20", "condition": "Sunny", "max_temp": 25, "min_temp": 18},

[1564] ...

[1565] ]

[1566] },

[1567] "stores": [

[1568] {"name": "Shopping District", "type": "Shopping District", ...},

[1569] ...

[1570] ],

[1571] "events": [

[1572] {"name": "Museum Exhibition", "location": "Museum", ...},

[1573] ...

[1574] ]

[1575] }

[1576] Based on the prompts above, the generative AI model generates an optimal travel plan to support the user's trip, allowing travelers to efficiently experience local attractions and flexibly respond to real-time information changes.

[1577] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1578] Step 1:

[1579] The user terminal receives a request to create a travel plan from the user as input. The user inputs information such as the departure point, destination, travel dates, desired activities and tourist spots, etc. This input data is transmitted to the server via communication.

[1580] Input: origin, destination, travel dates, desired activities and attractions

[1581] Output: User request data sent to the server

[1582] Step 2:

[1583] The server receives the user request data as input, and then acquires the current location information (GPS data) from the user terminal and stores it together with the request data.

[1584] Input: User request data, current location information (GPS data)

[1585] Output: Consolidated user request data (including location information)

[1586] Step 3:

[1587] The server calls external APIs to obtain weather information, map information, route information, and information on shopping malls and events. This information is obtained in real time and used to generate travel plans.

[1588] Input: Consolidated user request data

[1589] Output: Weather information, map information, route information, shopping street information, event information

[1590] Step 4:

[1591] The server inputs prompts into the generative AI model based on the acquired external data. The generative AI model then generates an optimal travel plan based on this information. The plan includes information on transportation, recommended sightseeing spots, special offers at shopping malls, and event information.

[1592] Input: Weather information, map information, route information, shopping street information, event information

[1593] Output: Travel plan

[1594] Step 5:

[1595] The server transmits the generated travel plan to the user terminal, which displays the received travel plan and enables the user to travel according to the plan.

[1596] Input: Travel Plan

[1597] Output: Travel plan displayed on the user's device

[1598] Step 6:

[1599] After completing the trip, the user inputs feedback. The user terminal transmits this feedback to the server. The feedback includes an evaluation and impressions of the trip.

[1600] Input: User feedback

[1601] Output: Feedback data sent to the server

[1602] Step 7:

[1603] The server updates the generative AI model based on the received feedback data, and this update is used to provide improved suggestions the next time a travel plan is generated.

[1604] Input: Feedback data

[1605] Output: Updated generative AI model

[1606] Through these steps, the system can provide users with the most optimal travel plans and constantly optimize the generative AI model based on the latest information and user feedback.

[1607] 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.

[1608] The present invention is a system that enables travelers to easily and efficiently create optimal travel plans, and in particular incorporates an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention will be described below.

[1609] The system of the present invention receives a request from a user to generate a travel plan, automatically generates an optimal travel plan, and provides it to the user. The system is broadly composed of a user terminal, a server, an external API, a generation AI model, and an emotion engine.

[1610] Explanation of program processing

[1611] 1. User requests to generate a travel plan

[1612] A user requests the creation of a travel plan using their device (smartphone, tablet, PC, etc.) This request includes information such as the departure point, destination, planned dates, desired activities and tourist spots, etc.

[1613] 2. The device sends its location and user input to the server

[1614] The user's terminal transmits the user's current location information (GPS data) and the input request data to the server.

[1615] 3. The server obtains weather, map, and route information.

[1616] The server uses external APIs to obtain weather, map, and route information for the current location and destination. This data is updated in real time, so the latest information can always be provided.

[1617] 4. Generative AI models generate optimal travel plans

[1618] The server is equipped with a generative AI model that automatically generates an optimal travel plan based on acquired weather, map, and route information. This plan includes transportation options, travel times, recommended tourist spots, and places to eat. Weather conditions are also taken into account, and indoor activities are suggested in case of rain.

[1619] 5. Emotion engine recognizes user emotions

[1620] When a user checks the travel plan created through the device, the emotion engine analyzes the user's facial expressions and voice to recognize the user's emotions, and this information is sent to the server in real time.

[1621] 6. Modifying your plan based on emotions

[1622] The generative AI model on the server uses the data sent from the emotion engine to adjust the plan if the user is dissatisfied. For example, if it determines that the user is not interested in a particular tourist spot, it will suggest another tourist spot.

[1623] 7. Send and display your travel plans on your device

[1624] The server sends the revised travel plan to the user's device, which receives it and displays it in an easy-to-understand manner for the user.

[1625] 8. Gather user feedback

[1626] After the trip is over, the user provides their impressions and feedback. The emotion engine recognizes the user's emotions when providing feedback and sends the data including those to the server. This data is used to train the generative AI model.

[1627] Specific examples

[1628] Example 1: Generating a travel plan and using the emotion engine

[1629] 1. User: "I'd like to travel from Tokyo to Kyoto this weekend. I'm leaving next Saturday. I'd like to visit historical sites and museums."

[1630] 2. The device sends a request to the server

[1631] The terminal transmits the user's current location information and request data to the server.

[1632] 3. The server retrieves the relevant information

[1633] The server obtains weather information (weather forecasts for Tokyo and Kyoto), map information (information on tourist spots in Kyoto), and route information (transportation methods and travel times from Tokyo to Kyoto).

[1634] 4. The generative AI model on the server generates a plan

[1635] The generative AI model generates a trip plan like this:

[1636] Departs Tokyo Station at 8:00, arrives Kyoto Station at 9:45 (by Shinkansen)

[1637] 10:00 Walk from Kyoto Station to Kiyomizu-dera Temple

[1638] 12:00 Lunch near Kiyomizu-dera Temple (with restaurant recommendations)

[1639] 14:00 Move to Kyoto National Museum (by bus)

[1640] 17:00 Hotel check-in

[1641] 5. Emotion engine recognizes user emotions

[1642] The emotion engine analyzes the user's facial expressions and voice while checking the travel plan, and recognizes the user's emotions. For example, if the user looks dissatisfied, it sends this to the server.

[1643] 6. The server modifies the plan

[1644] The generative AI model detects user frustrations and adjusts travel plans, for example, if a user isn't interested in historical sites, it suggests shopping spots instead.

[1645] 7. Display the revised plan on your device

[1646] The modified plan is sent to the terminal and displayed to the user.

[1647] 8. Users provide feedback

[1648] After the trip, the user enters feedback, and the emotion engine recognizes the emotions expressed and sends them to the server. The server uses this data to update the generative AI model and reflect it in the generation of the next travel plan.

[1649] By using this system, users can enjoy more personalized travel plans that reflect their individual preferences and feelings.

[1650] The processing flow will be explained below.

[1651] Step 1:

[1652] A user uses a terminal to input a request to generate a travel plan, which includes information such as the origin, destination, planned dates, desired activities and attractions, etc.

[1653] Step 2:

[1654] The device obtains the user's current location information by using the GPS function to obtain current location data.

[1655] Step 3:

[1656] The device sends the user's input data and current location information together to the server, allowing the server to understand the user's request and current location.

[1657] Step 4:

[1658] The server retrieves weather information using an external API. The server retrieves the weather forecast for the specified departure date and travel period.

[1659] Step 5:

[1660] The server obtains map information. The server collects the location information of the tourist spots and activities the user wishes to visit, as well as the geographical data of the surrounding areas.

[1661] Step 6:

[1662] The server obtains route information. The server collects data on the user's transportation method (e.g., train, bus, walking) and calculates the optimal route and travel time.

[1663] Step 7:

[1664] The AI ​​model on the server generates an optimal travel plan based on weather, map, and route information, taking into account the user's requests and weather conditions, and adjusting indoor and outdoor activities.

[1665] Step 8:

[1666] The server sends the generated travel plan to the user's terminal, which receives it and displays it in an easy-to-understand manner for the user.

[1667] Step 9:

[1668] When a user checks the travel plan created through their device, the emotion engine analyzes the user's facial expressions and voice to recognize their emotions, and this information is sent to the server in real time.

[1669] Step 10:

[1670] The generative AI model on the server uses the data sent from the emotion engine to adjust the plan if the user is dissatisfied. For example, if it determines that the user is not interested in a particular tourist spot, it will suggest another tourist spot.

[1671] Step 11:

[1672] The server sends the revised travel plan to the user's terminal, which receives it and displays the revised plan to the user.

[1673] Step 12:

[1674] The user acts according to the travel plan, and if changes are needed during the trip, the system can generate a new plan in real time.

[1675] Step 13:

[1676] After the trip is over, the user inputs feedback through the terminal. The emotion engine recognizes the user's emotions when giving feedback and sends the data including the emotions to the server.

[1677] Step 14:

[1678] The server updates the generative AI model based on the feedback data and reflects it in the next itinerary generation. This continuous feedback loop improves the accuracy of the generative AI model, enabling more personalized travel plans to be provided.

[1679] Example 2

[1680] 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."

[1681] Conventional travel plan generation systems have difficulty generating optimal travel plans that reflect individual user preferences and emotions. Furthermore, they provide uniform plans without considering user emotions, resulting in low user satisfaction. Therefore, there is a demand for a system that allows users to easily and efficiently create optimal travel plans based on their individual needs and emotions.

[1682] 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: means for receiving a travel plan generation request from a user; means for transmitting the user's current location information to the server along with the request; means for acquiring weather information, map information, and route information using an external API; means including a generative AI model that generates an optimal travel plan based on the generated weather information, map information, and route information; means for transmitting and displaying the generated travel plan to the user's terminal; means for recognizing the user's emotions when checking the travel plan using an emotion engine and adjusting the travel plan based on the emotions; means for receiving feedback from the user after the trip ends and transmitting it to the server; and means for updating the generative AI model based on the feedback and reflecting it in the next plan generation. This enables automatic generation and real-time adjustment of an optimal travel plan according to the user's individual requests and emotions.

[1683] A "request to create a travel plan" is data that includes a request or information that a user makes to the system to create a travel plan.

[1684] "User's current location information" refers to geographical data used to identify the user's location, and typically refers to GPS data.

[1685] The "server" is a computer system that receives user requests, communicates with external APIs, analyzes data, runs generative AI models, and generates travel plans and provides feedback to the user.

[1686] An "external API" is a program interface that allows a server to obtain data such as weather information, map information, and route information from external sources.

[1687] "Weather information" is data about local and destination weather forecasts and weather conditions.

[1688] "Map information" is data that includes geographical information about the destination and its surroundings, location information about tourist spots, and the like.

[1689] "Route information" is data relating to the means of transportation from the departure point to the destination, its operation schedule, and the required travel time.

[1690] A "generative AI model" is an artificial intelligence model that automatically generates optimal travel plans based on user requests and data obtained from external APIs.

[1691] An "emotion engine" is a technology that analyzes a user's facial expressions and tone of voice to recognize their emotions.

[1692] "Feedback" is data including evaluations and impressions of the travel plan provided by the user after the trip is completed.

[1693] The present invention is a system that allows a user to easily and efficiently create an optimal travel plan, and in particular incorporates an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention will be described below.

[1694] The system of the present invention receives a request from a user to create a travel plan, automatically generates an optimal travel plan, and provides it to the user. The system is broadly composed of a user terminal, a server, an external API, a generation AI model, and an emotion engine.

[1695] About user devices

[1696] User terminals include a variety of devices, such as smartphones, tablets, and PCs. These terminals have hardware such as GPS sensors, cameras, and microphones, and dedicated applications are installed to utilize these devices. Users input travel plan generation requests through the application and send them to the server.

[1697] About the Server

[1698] The server is a computer system that receives user requests, communicates with external APIs, analyzes data, runs generative AI models, and generates travel plans and provides feedback to users. The server has the following functions:

[1699] 1. Request reception and analysis: Receives the request data and current location information sent from the user terminal and analyzes them.

[1700] 2. Integration with external APIs: Obtain necessary information using external weather information APIs, map information APIs, and route information APIs. Examples include the OpenWeatherMap API, Google Maps API, and transport route APIs.

[1701] 3. Execution of the generative AI model: Based on the acquired weather, map, and route information, the generative AI model generates the optimal travel plan. An example of a generative AI model is OpenAI's GPT-4.

[1702] About the Emotion Engine

[1703] The emotion engine is a system that uses a camera and microphone to capture the user's facial expressions and voice when checking a travel plan on a device, and analyzes their emotions. This emotion data is sent to a server in real time and fed back to a generative AI model, which then automatically modifies the travel plan.

[1704] Specific examples

[1705] Travel plan generation and emotion engine utilization

[1706] 1. User: "I'd like to travel from Tokyo to Kyoto this weekend. I'm leaving next Saturday. I'd like to visit historical sites and museums."

[1707] 2. The device sends a request to the server:

[1708] The device sends the user's current location information and request data to the server using the HTTPS protocol.

[1709] 3. The server retrieves the relevant information:

[1710] The server calls the OpenWeatherMap API to get the weather forecast for Tokyo and Kyoto.

[1711] The server uses the Google Maps API to obtain map information and tourist information for Kyoto.

[1712] The server calls the transport route API to obtain information and travel time for the Shinkansen from Tokyo to Kyoto.

[1713] 4. The generative AI model in the server generates a plan:

[1714] The generative AI model generates a trip plan like this:

[1715] Departs Tokyo Station at 8:00, arrives Kyoto Station at 9:45 (by Shinkansen)

[1716] 10:00 Walk from Kyoto Station to Kiyomizu-dera Temple

[1717] 12:00 Lunch near Kiyomizu-dera Temple (with restaurant recommendations)

[1718] 14:00 Move to Kyoto National Museum (by bus)

[1719] 17:00 Hotel check-in

[1720] 5. Emotion engine recognizes user emotions:

[1721] While the user is checking their travel plans, the device's camera and microphone capture the user's facial expressions and voice, which are then analyzed by the emotion engine.

[1722] The analysis results are sent to the server in real time.

[1723] 6. The server modifies the plan:

[1724] If the generative AI model senses the user's frustration and determines that they are not particularly interested in "historical places," it will create a new plan that includes shopping spots instead.

[1725] 7. View the revised plan on your device:

[1726] The revised plan is sent to the terminal and displayed in an easy-to-understand user interface.

[1727] 8. Users provide feedback:

[1728] After the trip, the user enters feedback, and the emotion engine recognizes the emotions expressed at the time and sends them to the server. The generative AI model learns from this and reflects it in future plans.

[1729] In this way, the system of the present invention can generate personalized travel plans based on the user's needs and emotions, improving the overall travel experience.

[1730] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1731] Step 1: User requests to generate a travel plan

[1732] Input: A user launches a trip planning application and enters details such as origin, destination, planned dates, and desired activities and attractions.

[1733] Specific operation: When the user presses the "send" button, the terminal compiles this request information.

[1734] Step 2: The device sends its location and user input to the server

[1735] Input: User's current location (GPS data) and the request information entered in step 1.

[1736] Data processing: Create a single request packet containing the current location information and request data.

[1737] Specific operation: The terminal sends this request packet to the server using a secure communication protocol (e.g., HTTPS).

[1738] Output: A request packet is sent to the server.

[1739] Step 3: The server retrieves weather, map, and route information.

[1740] Input: The server parses the request packet and obtains the destination and origin information.

[1741] Data calculation: The server calls the weather API (e.g., general weather information API), map API (e.g., general map information API), and route information API (e.g., general traffic information API) to obtain each piece of information.

[1742] Specific operation: The weather information, map information, and route information obtained by the server are saved.

[1743] Output: Obtained weather information, map information, and route information.

[1744] Step 4: The generative AI model generates an optimal travel plan

[1745] Input: Weather information, map information, and route information stored by the server.

[1746] Data calculation: A generative AI model (e.g., a general generative AI model) installed on the server creates a prompt based on this information, and then generates a travel plan based on that.

[1747] How it works: The generative AI model automatically generates a travel plan including transportation options, travel time, recommended tourist spots, places to eat, etc.

[1748] Output: The generated itinerary.

[1749] Step 5: The emotion engine recognizes the user's emotion

[1750] Input: Facial and voice data captured while the user is reviewing the itinerary generated through the device.

[1751] Data calculation: The emotion engine analyzes facial expressions and tone of voice to detect the user's emotions.

[1752] Specific operation: Detects user dissatisfaction or satisfaction in real time and sends emotional data to the server.

[1753] Output: User emotion data.

[1754] Step 6: Modify your plan based on your emotions

[1755] Input: User emotion data, generated travel plan.

[1756] Data calculation: Based on emotion data, a generative AI model adjusts the contents of the travel plan.

[1757] Specific operation: For example, if the user is not interested in a particular tourist spot, a new tourist spot will be suggested.

[1758] Output: The revised itinerary.

[1759] Step 7: Send and display the revised plan on your device

[1760] Input: The modified itinerary in the server.

[1761] Specific operation: The server sends the modified plan to the user's terminal.

[1762] Output: The terminal displays the revised itinerary.

[1763] Step 8: Gather user feedback

[1764] Input: User feedback after the trip, captured facial and voice data.

[1765] Data computation: Combining feedback data and emotion data.

[1766] Specific operation: User feedback and emotional data are sent to the server, and the generative AI model uses this as training data.

[1767] Output: An updated generative AI model for future plan generation.

[1768] (Application example 2)

[1769] 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."

[1770] Conventional travel plan generation systems have had difficulty providing individual travel plans that fully reflect the user's preferences and feelings. This has resulted in the problem that it takes a lot of time and effort for users to plan a trip that satisfies them. In addition, travel plan revisions are not performed automatically, so users have to adjust multiple pieces of information themselves. It is necessary to provide a system that solves these problems and provides more personalized travel plans.

[1771] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for receiving a travel plan generation request from a user; means for transmitting the user's current location information to the server along with the request; means for acquiring weather information, map information, and route information using an external API; means including a generative AI model that generates an optimal travel plan based on the generated weather information, map information, and route information; means for transmitting and displaying the generated travel plan to the user's terminal; means including an emotion engine that analyzes the user's facial expressions and voice and recognizes the user's emotions; means for modifying the travel plan based on data obtained from the emotion engine; means for receiving feedback from the user after the trip and transmitting it to the server; and means for updating the generative AI model based on the feedback and reflecting it in the next plan generation. This makes it possible to automatically provide a highly satisfying customized travel plan that takes user emotions and feedback into consideration in real time.

[1772] Definitions of important words

[1773] The "means for receiving a request to create a travel plan" refers to a mechanism by which a user inputs requests and information necessary to create a travel plan.

[1774] The "means for transmitting current location information to a server" refers to a method for transmitting the user's current location information to a server via the Internet.

[1775] "Means of obtaining weather information, map information, and route information using external APIs" refers to methods of obtaining the necessary weather information, map information, and transportation route information via external data sources.

[1776] "Means including generative AI models" refers to a system that has an artificial intelligence algorithm that automatically generates optimal travel plans based on information received from users or data obtained through external APIs.

[1777] "Means for sending and displaying the generated itinerary on the user's device" refers to an interface for sending the itinerary created by the AI ​​model to the user's device and displaying it on that device.

[1778] "Means including an emotion engine" refers to a system used to analyze a user's facial expression and voice data and recognize their current emotional state.

[1779] "Means for modifying travel plans based on data obtained from the emotion engine" refers to a mechanism that uses the user's emotion data obtained by the emotion engine to modify the generated travel plan according to the user's requests.

[1780] "Means for receiving feedback from users after the trip has ended and transmitting it to a server" refers to a method for receiving impressions and evaluations from users after the trip has ended and transmitting that information to a server.

[1781] "Means for updating the generative AI model based on feedback and reflecting it in the next plan generation" refers to the learning process for improving the generative AI model based on feedback received from users and reflecting it in the next plan generation.

[1782] Description: Form for carrying out the invention

[1783] The present invention is a system that allows travelers to easily and efficiently create optimal travel plans, and in particular incorporates an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention will be described below.

[1784] System configuration

[1785] The system consists of the following components:

[1786] 1. User Device

[1787] The device on which the user requests the itinerary to be generated. This can be a smartphone, tablet, or computer.

[1788] 2. Server

[1789] The central processing unit processes data and runs AI models. It retrieves weather, map, and route information, generates travel plans, and modifies plans based on emotion data.

[1790] 3. External API

[1791] Interface to external services that provide weather, map, and route information.

[1792] 4. Generative AI Models

[1793] An algorithm that generates optimal travel plans based on user input and information from external APIs. OpenAI's GPT-3 is an example.

[1794] 5. Emotion Engine

[1795] A system that recognizes emotions by analyzing a user's facial expressions and voice. Emotion analysis frameworks such as DeepFace fall into this category.

[1796] Data Processing Flow

[1797] User terminal

[1798] When a user requests the creation of a travel plan, they input information such as the departure point, destination, desired activities, etc. The terminal then transmits this information along with the user's current location to the server.

[1799] server

[1800] The server uses external APIs to obtain weather, map, and route information. This data is updated in real time and always keeps the latest information. The generative AI model generates the optimal travel plan based on this data. The user's past travel history and ratings are also taken into consideration.

[1801] Generative AI Models

[1802] The generative AI model primarily uses natural language generation algorithms to generate optimal travel plans based on user requests, for example, using the following prompt:

[1803] Please suggest the best itinerary for a trip from Tokyo to Kyoto. The traveler wants to visit historical sites and museums.

[1804] Based on this prompt, a specific travel schedule is generated.

[1805] Emotion Engine

[1806] When the user checks the generated travel plan, the device's camera and microphone are used to analyze the user's facial expressions and voice, and the emotion engine recognizes the user's emotions. For example, if the user makes a dissatisfied expression, that data is sent to the server. The model in the server reflects this data and modifies the plan according to the user's emotions.

[1807] Specific examples

[1808] For example, if a user enters "I want to travel from Tokyo to Kyoto this weekend. I'll leave on Saturday. I want to visit historical sites and museums," the system will do the following:

[1809] 1. The user's request and current location information are sent to the server.

[1810] 2. The server obtains weather information, map information, and route information from an external API.

[1811] 3. The generative AI model generates a trip plan like this:

[1812] Departs Tokyo Station at 8:00, arrives Kyoto Station at 9:45 (by Shinkansen)

[1813] 10:00 Walk from Kyoto Station to Kiyomizu-dera Temple

[1814] 12:00 Lunch near Kiyomizu-dera Temple (with restaurant recommendations)

[1815] 14:00 Move to Kyoto National Museum (by bus)

[1816] 17:00 Hotel check-in

[1817] When the user reviews this plan, emotion recognition is performed, and if the emotion "unhappy" is detected, the plan may be modified as follows:

[1818] Suggestions for shopping spots and museum visits have been shortened, and cafe breaks have been added.

[1819] In this way, the present invention can provide a highly satisfying customized travel plan that is in line with the user's feelings and preferences.

[1820] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1821] Program processing flow

[1822] Step 1:

[1823] The user inputs a request to generate a travel plan using a terminal. The input includes information such as the departure point, destination, planned dates, desired activities and sightseeing spots, etc. This input data is necessary to generate a travel plan based on the user's preferences and wishes.

[1824] Step 2:

[1825] The device sends the user's current location information (GPS data) and the requested data to the server. Specifically, the device sends the user's current location information (GPS data) along with the departure point, destination, and desired activity entered by the user. This data is used by the server to obtain subsequent information and generate plans.

[1826] Step 3:

[1827] The server uses external APIs to obtain weather, map, and route information for the current location and destination. In this step, the server communicates with external services to obtain real-time weather forecasts, maps, and transportation options and travel times. By calling external APIs, the latest information is gathered.

[1828] Step 4:

[1829] The generative AI model automatically generates an optimal travel plan based on the weather, map, and route information it has acquired. In this step, the generative AI model installed on the server combines the user's request data with information collected from external APIs and creates a travel plan based on the prompt text.

[1830] Example: Suggest the best itinerary for a trip from Tokyo to Kyoto. The traveller wants to visit historical sites and museums.

[1831] Based on this, the generative AI model suggests specific schedules and places to visit.

[1832] Step 5:

[1833] The generated travel plan is sent to the user's terminal and displayed. The server sends the generated travel plan to the user's terminal and displays it on the terminal screen for the user to check. At this stage, the user can check the proposed plan in detail.

[1834] Step 6:

[1835] When a user checks their travel plans, the device captures their facial expressions and voice and analyzes them using an emotion engine. The device uses its built-in camera and microphone to collect the user's facial expressions and tone of voice in real time, and sends the data to a server for emotion analysis.

[1836] Step 7:

[1837] The emotion engine analyzes the user's emotions and sends them to the server. In this step, the emotion analysis results (e.g., "dissatisfaction" or "joy") are sent to the server, and the user's emotional state is stored as numerical data on the server.

[1838] Step 8:

[1839] The generative AI model modifies the travel plan based on the data sent from the emotion engine. If the user expresses dissatisfaction, the generative AI model adjusts the plan, for example, adding new tourist spots or changing existing ones.

[1840] Step 9:

[1841] The server retransmits the revised travel plan to the user's terminal and displays it. The server generates a new travel plan, transmits it to the user's terminal and redisplays it, so the user can check the revised plan.

[1842] Step 10:

[1843] After the user has completed the trip, they input their feedback via the device and send it to the server. The user inputs feedback such as their satisfaction with the trip, what they liked, and what needs to be improved through the device.

[1844] Step 11:

[1845] The server updates the generative AI model based on the user's feedback data and reflects it in the next plan generation.The server trains the generative AI model based on the received feedback and sentiment analysis results, thereby improving the accuracy of future travel plan generation.

[1846] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1847] 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.

[1848] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1849] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1850] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1851] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1852] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1853] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1854] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1855] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1856] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1857] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1858] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1859] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1860] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1861] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1862] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1863] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1864] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1865] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1866] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1867] The following is further disclosed regarding the above embodiment.

[1868] (Claim 1)

[1869] means for receiving a travel plan generation request from a user;

[1870] means for transmitting the user's current location information to a server together with the request;

[1871] A means of obtaining weather information, map information, and route information using external APIs;

[1872] A means including a generative AI model that generates an optimal travel plan based on the generated weather information, map information, and route information;

[1873] means for transmitting the generated travel plan to a user's terminal and displaying the same;

[1874] means for receiving feedback from the user after the trip is completed and transmitting the feedback to the server;

[1875] A means for updating the generation AI model based on the feedback and reflecting the feedback in the next plan generation;

[1876] A system including:

[1877] (Claim 2)

[1878] 10. The system of claim 1, wherein the generative AI model automatically selects and incorporates indoor and outdoor activities into a travel plan based on weather conditions.

[1879] (Claim 3)

[1880] 2. The system of claim 1, wherein the server includes a database containing users' past travel history and ratings as data used to generate travel plans.

[1881] "Example 1"

[1882] (Claim 1)

[1883] means for receiving a travel plan generation request from a user;

[1884] means for transmitting the user's current location information to a server together with the request;

[1885] A means for obtaining weather information, location information, and traffic information using external services;

[1886] A means including a generative AI model that generates an optimal travel plan based on the generated weather information, location information, and traffic information;

[1887] means for transmitting the generated travel plan to a user's terminal and displaying the same;

[1888] a means for receiving feedback from the user after the trip has ended and transmitting the feedback to the server;

[1889] A means for updating the generation AI model based on the feedback and reflecting the feedback in the next plan generation;

[1890] A system including:

[1891] (Claim 2)

[1892] 10. The system of claim 1, wherein the generative AI model automatically selects and incorporates indoor and outdoor activities into a travel plan based on weather conditions.

[1893] (Claim 3)

[1894] 2. The system of claim 1, wherein the server has records containing users' past travel history and ratings as information used to generate travel plans.

[1895] "Application Example 1"

[1896] (Claim 1)

[1897] means for receiving a travel plan generation request from a user;

[1898] means for transmitting the user's current location information to a server together with the request;

[1899] A means of obtaining weather information, map information, route information, shopping district and event information using external APIs;

[1900] A means including a generative AI model that generates an optimal travel plan based on the generated weather information, map information, route information, and shopping street and event information;

[1901] means for transmitting the generated travel plan to a user's terminal and displaying the same;

[1902] means for receiving feedback from the user after the trip is completed and transmitting the feedback to the server;

[1903] A means for updating the generation AI model based on the feedback and reflecting the feedback in the next plan generation;

[1904] A system including:

[1905] (Claim 2)

[1906] 10. The system of claim 1, wherein the generative AI model automatically selects and incorporates indoor and outdoor activities into a travel plan based on weather conditions and real-time special offers and sales information.

[1907] (Claim 3)

[1908] The system of claim 1, wherein the server has a database containing real-time event information in addition to a database containing the user's past travel history and ratings as data used to generate the travel plan.

[1909] "Example 2: Combining Emotion Engines"

[1910] (Claim 1)

[1911] means for receiving a travel plan generation request from a user;

[1912] means for transmitting the user's current location information to a server together with the request;

[1913] A means of obtaining weather information, map information, and route information using external APIs;

[1914] A means including a generative AI model that generates an optimal travel plan based on the generated weather information, map information, and route information;

[1915] means for transmitting the generated travel plan to a user's terminal and displaying the same;

[1916] means for recognizing a user's emotions when reviewing a travel plan using an emotion engine and adjusting the travel plan based on said emotions;

[1917] means for receiving feedback from the user after the trip is completed and transmitting the feedback to the server;

[1918] A means for updating the generation AI model based on the feedback and reflecting the feedback in the next plan generation;

[1919] A system including:

[1920] (Claim 2)

[1921] 10. The system of claim 1, wherein the generative AI model automatically selects and incorporates indoor and outdoor activities into a travel plan based on weather conditions.

[1922] (Claim 3)

[1923] 2. The system of claim 1, wherein the server includes a database containing users' past travel history and ratings as data used to generate travel plans.

[1924] "Application example 2 when combining emotion engines"

[1925] (Claim 1)

[1926] means for receiving a travel plan generation request from a user;

[1927] means for transmitting the user's current location information to a server together with the request;

[1928] A means of obtaining weather information, map information, and route information using external APIs;

[1929] A means including a generative AI model that generates an optimal travel plan based on the generated weather information, map information, and route information;

[1930] means for transmitting the generated travel plan to a user's terminal and displaying it;

[1931] means including an emotion engine that analyzes a user's facial expression and voice and recognizes the user's emotion;

[1932] a means for modifying the travel plan based on data obtained from the emotion engine;

[1933] means for receiving feedback from the user after the trip is completed and transmitting the feedback to the server;

[1934] A means for updating the generation AI model based on the feedback and reflecting the feedback in the next plan generation;

[1935] A system including:

[1936] (Claim 2)

[1937] 10. The system of claim 1, wherein the generative AI model automatically selects and incorporates indoor and outdoor activities into a travel plan based on weather conditions.

[1938] (Claim 3)

[1939] 2. The system of claim 1, wherein the server includes a database containing users' past travel history and ratings as data used to generate travel plans. [Explanation of symbols]

[1940] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving a travel plan generation request from a user; means for transmitting the user's current location information to a server together with the request; A means of obtaining weather information, map information, and route information using external APIs; A means including a generative AI model that generates an optimal travel plan based on the generated weather information, map information, and route information; means for transmitting the generated travel plan to a user's terminal and displaying the same; means for receiving feedback from the user after the trip is completed and transmitting the feedback to the server; A means for updating the generation AI model based on the feedback and reflecting the feedback in the next plan generation; A system including:

2. 10. The system of claim 1, wherein the generative AI model automatically selects and incorporates indoor and outdoor activities into a travel plan based on weather conditions.

3. 2. The system of claim 1, wherein the server includes a database containing users' past travel history and ratings as data used to generate travel plans.

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