System

Abstract

A system is provided.SOLUTION: A travel plan generating method comprising the steps of: inputting travel requirements such as a travel period, a budget, and a place to be visited by a traveler; generating an eco-friendly travel plan by utilizing a generated AI model based on an eco-friendly database; displaying the generated travel plan to the traveler in a bookmark format; and converting an environmental load into a numerical value and feeding back the numerical value to the traveler by using the generated AI model. A learning unit configured to improve accuracy of a next travel plan.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] Ecotourism, which takes environmental impact into consideration, has been gaining attention in recent years, but it is difficult for travelers to accurately grasp the environmental impact they are making during their trip. It is also not easy to find eco-friendly travel destinations, transportation options, accommodations, etc. Furthermore, there are few means to quantify and specifically understand travelers' environmental impact, resulting in a lack of concrete guidelines for achieving sustainable travel. The objective of this invention is to solve these problems and provide a system that allows travelers to plan and carry out environmentally friendly trips. [Means for solving the problem]

[0005] The present invention is a system that includes an input means for travelers to input travel requirements such as travel duration, budget, and places to visit, a generation means for receiving the input information and generating an eco-friendly travel plan using a generative AI model based on an eco-friendly database, a display means for displaying the generated travel plan to the traveler in bookmark form, an input means for inputting the means of transportation and activities used during the trip, a quantification means for quantifying the environmental impact based on the input behavioral data and feeding the result back to the traveler, and a learning means for updating the generative AI model using the collected behavioral data to improve the accuracy of the next travel plan. Furthermore, by including a suggestion means for suggesting eco-friendly means of transportation and tourist activities and allowing the traveler to select appropriate options, and a visualization means for quantifying the traveler's behavioral data as an environmental impact and visually displaying the result, it is possible to clearly understand the impact travelers have on the environment and provide concrete measures for realizing sustainable travel.

[0006] "Tourist" refers to an individual or group who plans and undertakes a trip.

[0007] "Duration of Trip" refers to the number of days a traveler plans to travel.

[0008] "Budget" refers to the amount of money a traveler plans to spend on their trip.

[0009] "Destinations" refers to the places and cities that a traveler plans to visit during their trip.

[0010] "Input means" refers to a device or interface through which a traveler inputs travel requirements and travel activities.

[0011] The "Eco-Friendly Database" refers to a database that collects and stores information on environmentally friendly transportation, accommodation, tourist attractions, etc.

[0012] "Generative AI model" refers to a model that uses artificial intelligence algorithms to create eco-friendly travel plans based on input information.

[0013] "Generator" refers to the device or software that uses a generative AI model to generate a travel plan based on information received from a traveler.

[0014] "Display means" refers to a device or interface for presenting the generated travel plan to a traveler.

[0015] "Environmental impact" refers to the impact or burden that travelers' actions have on the environment.

[0016] "Quantification means" refers to devices or software that express environmental impacts numerically based on traveler behavior data.

[0017] "Suggestion tool" refers to a device or software that suggests eco-friendly transportation methods and tourist activities to travelers.

[0018] "Learning tools" refers to devices or software that use collected behavioral data to update generative AI models and improve the accuracy of next-time travel plans.

[0019] "Visualization means" refers to a device or interface that visually displays the numerical results of environmental load. [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] This invention is a system that enables travelers to plan and execute environmentally friendly trips. The system generates eco-friendly itineraries based on travel requirements, such as travel duration, budget, and destinations, and records travel behavior, quantifies the environmental impact, and provides feedback to travelers to encourage sustainable travel.

[0042] First, users download the application and create an account. They enter basic information (name, age, place of residence, travel preferences), which is then sent to the server and stored in a database.

[0043] Next, the user inputs their specific travel requirements (trip duration, budget, and places to visit). The device sends this information to the server, which then uses the received information to reference an eco-friendly database and utilizes a generative AI model to generate a travel plan. The generated travel plan is then displayed on the device as a detailed schedule.

[0044] The user reviews the proposed plan and selects from eco-friendly transportation and sightseeing activities. This allows the traveler to be involved in the process of planning an environmentally conscious trip. The server records the selected options and stores them as travel data.

[0045] During a trip, users input the transportation methods they used and the activities they undertook (e.g., cycling, walking tours, environmental conservation activities) into their device. The device then sends this information to a server, which then quantifies the environmental impact based on the entered activity data. The results are fed back to the user. The environmental impact figures are displayed visually, allowing travelers to concretely understand the impact their actions have on the environment.

[0046] Additionally, the server uses the behavioral data collected during the trip to update the generative AI model, allowing it to generate more accurate and eco-friendly itineraries the next time you plan a trip.

[0047] For example, if a user inputs the travel requirements "length of stay: 3 days," "budget: 100,000 yen," and "place to visit: Kyoto," the server will generate the following travel plan:

[0048] Day 1: Arrive at Kyoto Station, go sightseeing by electric bicycle, and stay at an eco-certified inn

[0049] Day 2: Join a plastic waste-free tour and have dinner at a vegan restaurant

[0050] Day 3: Participate in nature conservation activities in Arashiyama, then return home by train

[0051] The user reviews this plan, selects each activity, and then records the actions taken during the trip. The server analyzes this data, quantifies the environmental impact of the trip, and provides feedback to the user. For example, the server might display a message like, "Your trip successfully reduced CO2 emissions by 30% compared to a normal trip." Based on this data, the server can provide even more accurate suggestions for the next trip.

[0052] The above is an embodiment of the present invention. The system of the present invention allows travelers to enjoy traveling while reducing the burden on the environment, and can contribute to spreading eco-friendly travel styles.

[0053] The processing flow will be explained below.

[0054] Step 1:

[0055] A user downloads the application and creates an account. They enter basic information such as their name, age, place of residence, and travel preferences. The device then sends this information to the server, which stores it in a database.

[0056] Step 2:

[0057] The user inputs specific travel requirements such as travel duration, budget, places to visit, etc. The device sends this information to the server.

[0058] Step 3:

[0059] The server receives the travel requirements and consults the eco-friendly database, based on which it uses a generative AI model to generate an eco-friendly itinerary.

[0060] Step 4:

[0061] The server sends the generated travel plan to the terminal, which displays the travel plan to the user in bookmark format.

[0062] Step 5:

[0063] The user reviews the proposed itinerary and selects from eco-friendly transport options and sightseeing activities, and the device sends the selected options to the server.

[0064] Step 6:

[0065] The server finalizes the itinerary including the selected options and stores it in a database. The terminal displays the finalized itinerary to the user.

[0066] Step 7:

[0067] During the trip, the user inputs the transportation method used and the activities performed (e.g., cycling, walking tours, environmental conservation activities) into the device, which then transmits this information to the server.

[0068] Step 8:

[0069] The server quantifies the environmental impact based on the input behavioral data, and the quantification results are sent to the terminal, which then visually displays them to the user.

[0070] Step 9:

[0071] The server updates the generative AI model using behavioral data collected during the trip, which improves the accuracy of future travel plans.

[0072] Step 10:

[0073] When the user plans another trip, the process repeats from step 2, providing a more accurate itinerary that reflects the data collected so far.

[0074] Example 1

[0075] 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."

[0076] To enable travelers to easily plan and carry out environmentally conscious trips, a system is needed that allows them to consistently create travel plans, record their activities, and provide feedback on their environmental impact. However, current travel planning systems lack the functionality to evaluate environmental impact and provide specific feedback. This makes it difficult for travelers to accurately understand the environmental impact of their actions, preventing them from making sufficient eco-friendly choices. It is essential to solve this issue and popularize sustainable travel styles.

[0077] 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.

[0078] In this invention, the server includes an input means for a traveler to input travel requirements such as travel period, budget, and places to visit, a generation means for receiving the input information and generating an eco-friendly travel plan using a generative AI model based on the environmental consideration information, and a display means for displaying the generated travel plan to the traveler in an itinerary format, thereby enabling travelers to easily create environmentally friendly travel plans and take eco-friendly trips.

[0079] The "input means" is a means for a traveler to input travel requirements such as travel period, budget, and places to visit.

[0080] The "generation means" is a means of receiving the input information and generating an eco-friendly travel plan using a generative AI model based on the environmental consideration information.

[0081] The "display means" is a means for displaying the generated travel plan to the traveler in the form of an itinerary.

[0082] "Communication means" refers to a means for transmitting input information to a server in real time and storing it in a database.

[0083] The "selection method" is a method that analyzes travel behavior data, quantifies the environmental impact in real time, and provides feedback.

[0084] "Proposal methods" are means of proposing environmentally friendly transportation methods and tourist activities and allowing travelers to select appropriate options.

[0085] The "quantification means" is a means for quantifying the environmental impact based on the inputted behavioral data and providing the results as feedback to the traveler.

[0086] The "learning method" is a method for updating the generative AI model using collected behavioral data to improve the accuracy of the next travel plan.

[0087] "Visualization means" is a means of quantifying traveler behavior data as environmental impact and visually displaying the results.

[0088] This invention is a system that enables travelers to plan and execute environmentally friendly trips. Specifically, travelers input their travel requirements, such as travel duration, budget, and destinations, and the system generates an eco-friendly itinerary based on that information. The system also records travel behavior, quantifies the environmental impact, and provides feedback to travelers to encourage sustainable travel. Detailed embodiments of this system are described below.

[0089] First, users download the application and create an account. This application is a program that runs on devices such as smartphones, tablets, and PCs. When creating an account, users enter basic information (name, age, place of residence, travel preferences). The information entered is sent from the device to a server and stored in a database. The server centrally manages individual traveler information and uses it as basic data for creating eco-friendly plans.

[0090] Next, the user enters their specific travel requirements (trip duration, budget, and places to visit) into the device. This information is analyzed using a generative AI model when it is sent from the device to the server. The generative AI model accesses an eco-friendly database and generates an optimal travel plan that meets the traveler's requirements. This plan includes environmentally friendly activities such as using electric bicycles and zero-plastic waste tours. The generated travel plan is displayed on the device in the form of a detailed schedule.

[0091] Users can review the displayed itinerary and select eco-friendly transportation and sightseeing activities, such as sightseeing by electric bicycle, staying at eco-certified accommodations, or eating at vegan restaurants. Once selections are complete, the selection data is sent from the device to a server and stored as travel data. This allows users to make environmentally conscious choices from the planning stage onwards.

[0092] During a trip, users enter the transportation methods they used and the activities they performed (e.g., cycling, walking tours, environmental conservation activities) into their device. This behavioral data is sent to a server in real time, and the server uses the data to quantify the environmental impact. The results are fed back to the user and displayed as concrete effects (e.g., "Your trip successfully reduced CO2 emissions by 30% compared to a normal trip"). Feedback is provided in the form of easy-to-understand visual graphs and messages.

[0093] Furthermore, after the trip, the server uses the collected behavioral data to update the generative AI model. This allows the server to provide more accurate eco-friendly travel plans the next time the trip is planned. For example, if you enter the prompts "stay length: 3 days," "budget: 100,000 yen," and "place to visit: Kyoto," the following specific travel plan will be generated:

[0094] Day 1: Arrive at Kyoto Station, go sightseeing by electric bicycle, and stay at an eco-certified inn

[0095] Day 2: Join a plastic waste-free tour and have dinner at a vegan restaurant

[0096] Day 3: Participate in nature conservation activities in Arashiyama, then return home by train

[0097] In this way, the system allows travelers to plan enjoyable trips while reducing their environmental impact, making it easy to make eco-friendly choices.

[0098] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0099] Step 1:

[0100] Users download the application and create an account.

[0101] What it does: Users enter basic information such as name, age, location, and travel preferences.

[0102] Input and Output:

[0103] Input: Basic information that the user types into the device

[0104] Output: Basic information sent from the terminal to the server and stored in the database

[0105] Step 2:

[0106] The user inputs specific travel requirements into the terminal.

[0107] Specific operation: The user enters the travel period, budget, and places to visit and presses the "Generate plan" button.

[0108] Input and Output:

[0109] Input: Travel duration, budget, and destinations entered by the user into the terminal

[0110] Output: Travel requirements sent from the device to the server and used by the generative AI model

[0111] Step 3:

[0112] The server receives the travel requirements and accesses the eco-friendly database to generate an optimal travel plan.

[0113] What it does: The server uses a generative AI model to generate a travel plan based on travel duration, budget, and places to visit.

[0114] Input and Output:

[0115] Input: Travel requirements and eco-friendly database information received by the server

[0116] Output: A detailed itinerary generated

[0117] Step 4:

[0118] The generated travel plan is displayed on the terminal.

[0119] Specific operation: The server returns the generated travel plan to the terminal and displays it in itinerary format on the terminal.

[0120] Input and Output:

[0121] Input: A generated travel plan sent from the server to the device

[0122] Output: The itinerary of the travel plan is displayed on the terminal.

[0123] Step 5:

[0124] Users can review their travel plans and choose eco-friendly transport and sightseeing activities.

[0125] Specific actions: The user looks at the displayed itinerary, clicks on each activity to select it, and presses the "Confirm" button.

[0126] Input and Output:

[0127] Input: Selected data that the user types into the terminal

[0128] Output: Selected data sent from the device to the server and stored

[0129] Step 6:

[0130] During the trip, the user inputs the means of transportation actually used and the actions taken into the terminal.

[0131] Specific operation: The user records the transportation used and activities participated in each day on the device and presses the "Save" button.

[0132] Input and Output:

[0133] Input: Behavioral data that the user enters into the device

[0134] Output: Behavioral data sent from the device to the server in real time and updated

[0135] Step 7:

[0136] The server quantifies the environmental impact based on the input behavioral data.

[0137] Specific operation: The server analyzes the behavioral data, calculates the environmental impact, and quantifies it.

[0138] Input and Output:

[0139] Input: Behavioral data received by the server

[0140] Output: Calculated environmental impact figures

[0141] Step 8:

[0142] The results of the environmental impact are fed back to the user.

[0143] Specific operation: The server sends feedback to the user based on the calculation results, for example, "Your trip has successfully reduced CO2 emissions by 30% compared to a normal trip."

[0144] Input and Output:

[0145] Input: Quantification results of environmental impact

[0146] Output: A feedback message is printed to the terminal.

[0147] Step 9:

[0148] The server uses the collected behavioral data to update the generative AI model.

[0149] Specific operation: The server reflects the behavioral data as learning data in the generative AI model and updates the model.

[0150] Input and Output:

[0151] Input: Behavioral data stored on the server

[0152] Output: An updated generative AI model

[0153] Step 10:

[0154] The next time a travel plan is generated, the updated generative AI model will be used.

[0155] What it does: The server generates a new itinerary using the latest generative AI model.

[0156] Input and Output:

[0157] Enter: Next Travel Requirement

[0158] Output: A new, more accurate itinerary

[0159] These are the specific processing steps of the system program, which allows users to plan and carry out enjoyable trips while reducing the burden on the environment.

[0160] (Application example 1)

[0161] 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."

[0162] Today's travelers lack concrete means and tools for planning and implementing environmentally conscious travel. Furthermore, there are limited systems that quantify the environmental impact of travel and promote sustainable travel styles. In addition, there is a need to promote environmentally conscious behavior in everyday life as well. In particular, there is a need for systems that reduce the environmental impact of food delivery services. Against this backdrop, there is a need for systems that reduce the environmental impact of travel and everyday life and promote sustainable behavior.

[0163] 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.

[0164] In this invention, the server includes an input means for travelers to input travel requirements such as travel duration, budget, and destinations; a generation means for receiving the input information and generating an eco-friendly travel plan using a generative AI model based on an eco-friendly database; and a display means for displaying the generated travel plan to travelers in bookmark form. The server also includes an input means for inputting the transportation methods and activities used during the trip, and a quantification means for quantifying the environmental impact based on the input behavioral data and providing feedback to the travelers. The server also includes a learning means for updating the generative AI model using collected behavioral data to improve the accuracy of the next travel plan, and an eco-friendly food delivery means for selecting food orders and delivery methods based on information registered by the user, quantifying the environmental impact, and providing feedback. This enables travelers and everyday users to plan and execute activities that reduce environmental impact and specifically understand the results.

[0165] "Travel requirements" refers to information necessary for travelers to plan their trip, such as the duration, budget, and places to visit.

[0166] "Input means" refers to an interface that allows travelers and users to input necessary information (such as travel requirements and activities) into the system.

[0167] "Generation means" is a function that generates eco-friendly travel plans and food delivery plans based on input information.

[0168] The "display means" is an interface for visually displaying the generated plan to the traveler or user.

[0169] The "quantification means" is a function that calculates the environmental impact based on the input behavioral data and provides the results as feedback to the user.

[0170] The "learning method" is a function that uses collected behavioral data to update the generative AI model and improve accuracy the next time a plan is generated.

[0171] The "Eco-Friendly Database" is a database that collects information on environmentally friendly travel and food delivery.

[0172] A "generative AI model" is an artificial intelligence model that generates eco-friendly plans based on input data.

[0173] "Eco-friendly food delivery methods" is a function that allows users to select food ordering and delivery methods that take environmental impact into consideration, and quantifies the environmental impact and provides feedback.

[0174] "Environmental impact" refers to the degree of negative impact that a certain action has on the global environment.

[0175] The present invention is a system for travelers and everyday users to plan, execute, and evaluate environmentally friendly actions. The system can be applied to both travel planning and food delivery. The following specific steps and tools are used to implement the invention:

[0176] System Configuration

[0177] The system includes the following main elements:

[0178] 1. Input means (traveler or user interface)

[0179] 2. Generator (AI model that generates eco-friendly plans)

[0180] 3. Display means (interface that visually displays the generated plan)

[0181] 4. Quantification method (function to calculate environmental impact and provide feedback)

[0182] 5. Learning methods (the ability to improve the AI ​​model based on collected data)

[0183] 6. Eco-Friendly Food Delivery Methods (Functionality to optimize food ordering and delivery methods)

[0184] Hardware and Software

[0185] Hardware: Smartphone (iOS, Android)

[0186] Software: Python, API server (Django or Flask), database (PostgreSQL)

[0187] Data processing and calculation

[0188] 1. Input method: Travelers or users input requirements such as travel duration, budget, places to visit, food orders and desired delivery method through a smartphone application.

[0189] 2. Generation means: The server receives the input information and uses a generative AI model to generate eco-friendly travel plans or food delivery.

[0190] 3. Display: The generated plan is displayed as a detailed schedule or delivery information on the traveler's or user's device.

[0191] 4. Quantification method: The user inputs the transportation method and activities used during the trip or delivery, and the server quantifies the environmental impact based on that data. The results are visually fed back to the user.

[0192] 5. Learning method: The server analyzes the collected behavioral data and updates the generative AI model, improving the accuracy of the next plan generation.

[0193] 6. Eco-friendly food delivery methods: Users can order food through the application in a way that minimizes the environmental impact. The server calculates the environmental impact based on the selected delivery method and provides feedback.

[0194] Specific examples

[0195] For travel plans:

[0196] The user inputs travel requirements such as "stay length 3 days," "budget 100,000 yen," and "place to visit: Kyoto." The server generates the following travel plan based on this information:

[0197] Day 1: Arrive at Kyoto Station, go sightseeing by electric bicycle, and stay at an environmentally certified accommodation facility

[0198] Day 2: Take a zero-plastic waste tour and have dinner at an eco-friendly restaurant

[0199] Day 3: Participate in nature conservation activities in Arashiyama, then return home by train

[0200] For eco-friendly food delivery:

[0201] A user orders a "vegan salad" via "bicycle delivery." The server receives the order, calculates the environmental impact (e.g., 0.0 kg of CO2), and provides feedback to the user.

[0202] Prompt Sentence Examples

[0203] Example prompts to use with generative AI models:

[0204] Prompt: Model recommendations for generating eco-friendly delivery orders and reducing the environmental impact of each option.

[0205] Example: The user selects "Bicycle", calculates the environmental impact of the order, and provides feedback to the user.

[0206] The above is an embodiment of the present invention. The system of the present invention allows travelers and everyday users to enjoy activities while reducing the environmental impact, contributing to the spread of eco-friendly lifestyles.

[0207] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0208] Step 1:

[0209] Users input travel requirements such as travel period, budget, and places to visit, as well as food delivery order information and delivery preferences through a smartphone application. The input information is sent from the device to the server.

[0210] Input: Travel requirements (duration, budget, places to visit), food delivery order information (type of food, delivery method)

[0211] Output: User information sent to the server

[0212] Step 2:

[0213] Based on the travel requirements or delivery order information received by the server, the server uses a generative AI model while referencing an eco-friendly database to generate an optimal travel plan or delivery plan. The server creates prompts for the generative AI model and inputs them into the model.

[0214] Input: Travel requirements or delivery order information, prompt text

[0215] Output: Generated travel or delivery plan

[0216] Step 3:

[0217] The generated travel and delivery plans are sent to the terminal and displayed to the user. The plans are visually displayed in bookmark format or as detailed order information.

[0218] Input: Generated plan

[0219] Output: Plan displayed on the terminal

[0220] Step 4:

[0221] While traveling or using food delivery services, users input the transportation method used and their activities into a smartphone application, and the input information is sent from the device to a server.

[0222] Input: Means of transportation used, activities

[0223] Output: Behavioral data sent to the server

[0224] Step 5:

[0225] The server quantifies the environmental impact based on the behavioral data received. The calculation is performed using a preset environmental impact coefficient for the means of transportation and the behavior.

[0226] Input: Behavioral data

[0227] Output: Quantified environmental impact data

[0228] Step 6:

[0229] The server sends the calculated environmental impact data to the user's terminal, allowing the user to visually check it.

[0230] Input: Quantified environmental impact data

[0231] Output: Feedback displayed on the terminal

[0232] Step 7:

[0233] The server uses collected behavioral data to update the generative AI model, improving accuracy when generating the next travel plan or delivery plan. Learning is performed based on behavioral data and environmental impact data.

[0234] Input: behavioral data, environmental impact data

[0235] Output: An updated generative AI model

[0236] 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.

[0237] This invention is a system that enables travelers to plan and execute eco-friendly trips, and in particular has the function of providing eco-friendly travel plans that reflect the user's emotional state.By incorporating an emotion engine, it is possible to analyze the user's emotional state in real time and provide an optimal plan based on that.

[0238] First, users download the application and create an account. They enter basic information such as name, age, place of residence, and travel preferences. This information is sent from the device to the server and stored in a database.

[0239] Next, the user inputs specific travel requirements, such as the duration of the trip, budget, and places to visit. The device sends this information to the server, which then references an eco-friendly database and uses a generative AI model to create an eco-friendly itinerary. The generated itinerary is then displayed to the user in bookmark form on the device.

[0240] A further feature of the present invention is the incorporation of an emotion engine, which can analyze the user's emotions and grasp their state in real time. For example, if the user is tired or stressed, the emotion engine can detect this and add relaxation activities or quiet tourist spots to the travel plan.

[0241] As a concrete example, if a user inputs the travel requirements of "stay length 3 days," "budget 100,000 yen," and "place to visit: Kyoto," and the emotion engine analyzes that "the user is looking for relaxation," the server will generate the following travel plan:

[0242] Day 1: Arrive at Kyoto Station, visit a tranquil temple by eco-friendly electric bicycle, and stay overnight at an eco-certified ryokan

[0243] Day 2: Join a stress-relieving yoga session and enjoy a healthy dinner at a vegan restaurant

[0244] Day 3: Nature walk and meditation in Arashiyama, then return home by train

[0245] During a trip, the user inputs the transportation method used and the actions taken into the device. The device then sends this information to the server, which then quantifies the environmental impact based on the input behavioral data. The results are then sent to the device and displayed visually to the user.

[0246] The emotion engine continues to run throughout the trip, monitoring the user's emotional state: if the user is feeling stressed, for example, the server will instantly adjust the plan and suggest options for relaxation activities or visiting quiet places.

[0247] After the trip is completed, the server updates the generative AI model using the collected behavioral and emotional data, improving the accuracy of future trip plans and providing users with more optimal, eco-friendly travel plans.

[0248] The system of the present invention allows users to plan environmentally friendly trips while taking their emotional state into consideration, thereby reducing stress and achieving a sustainable travel style. In this way, by taking into consideration both the emotions of travelers and the environmental impact, the present invention provides maximum satisfaction to travelers and promotes environmentally friendly travel.

[0249] The processing flow will be explained below.

[0250] Step 1:

[0251] A user downloads the application and creates an account. They enter basic information such as their name, age, place of residence, and travel preferences. The device then sends this information to the server, which stores it in a database.

[0252] Step 2:

[0253] The user inputs specific travel requirements such as travel duration, budget, places to visit, etc. The device sends this information to the server.

[0254] Step 3:

[0255] The server receives the travel requirements, consults the eco-friendly database, and uses a generative AI model to generate an eco-friendly travel plan.

[0256] Step 4:

[0257] The server sends the generated travel plan to the terminal, which displays the travel plan to the user in bookmark format.

[0258] Step 5:

[0259] The user reviews the proposed travel plan and selects from eco-friendly transportation and sightseeing activities, and the selection information is sent from the device to the server.

[0260] Step 6:

[0261] The server records the selected options and stores them in a database, while the emotion engine analyzes the user's emotions in real time and reflects them in the plan.

[0262] Step 7:

[0263] During the trip, the user inputs the transportation method used and the details of their activities into the device. The emotion engine analyzes the user's emotional state (stress, joy, etc.) through cameras and sensors. This data is sent from the device to the server.

[0264] Step 8:

[0265] The server quantifies the environmental impact based on the input behavioral and emotional data, and the quantification results are sent to the terminal, which then visually displays them to the user.

[0266] Step 9:

[0267] If the user feels stressed during the trip, the emotion engine will detect this and the server will instantly adjust the plan, suggesting options such as relaxation activities or quiet tourist spots, which the device will display to the user and prompt them to make a selection.

[0268] Step 10:

[0269] After the trip is over, the server uses the collected behavioral and emotional data to update the generative AI model, which improves the accuracy of the next itinerary.

[0270] Step 11:

[0271] When the user plans another trip, the process repeats from step 2. This time, the system provides more accurate eco-friendly travel plans that incorporate past data and sentiment analysis.

[0272] Example 2

[0273] 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."

[0274] Conventional travel planning systems have the problem of only considering the traveler's travel requirements and environmental impact, and are unable to adjust the plan to reflect the traveler's emotional state. In particular, since planning does not take into account emotional states such as stress and fatigue during the trip, it can be difficult for travelers to truly relax and enjoy the trip. Another issue is that the inability to adjust the plan in real time makes it difficult to respond quickly to unexpected situations. To solve these issues, a system is needed that analyzes the traveler's emotional state and provides a travel plan that reflects that.

[0275] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0276] In this invention, the server includes an input means for the traveler to input travel requirements such as travel duration, budget, and places to visit, a generation means for receiving the input information and generating an environmentally conscious travel plan using a generative model based on a database, and a display means for displaying the generated travel plan to the traveler in bookmark form.

[0277] This makes it possible to analyze the traveler's emotional state in real time and dynamically adjust the travel plan based on the results.In addition, by providing an input means for inputting the means of transportation used during the trip and the activities of the traveler, a quantification means for quantifying the environmental impact based on the input activity data and feeding the results back to the traveler, and a learning means for updating the generative model using the collected activity data and improving the accuracy of the next travel plan, it is possible to continue proposing the optimal eco-friendly travel plan for the traveler.

[0278] A "tourist" is someone who travels.

[0279] "Duration of Trip" means the number of days or hours over which the trip takes place.

[0280] "Budget" refers to the total amount of money used for travel.

[0281] "Destinations" are places or areas that a traveler visits during their trip.

[0282] "Travel requirements" refer to the conditions and desires that a traveler has when traveling.

[0283] "Input means" means a device or interface through which a traveler inputs information.

[0284] A "database" is a system that stores and manages data in an organized manner.

[0285] A "generative model" is an algorithm for generating data based on specific inputs.

[0286] "Environmentally friendly" means that the goal is to minimize the impact on the environment.

[0287] A "travel plan" is a detailed plan or schedule for a trip.

[0288] "Generation means" refers to a mechanism for generating a travel plan based on input information.

[0289] "Display means" refers to a device or interface that visually presents the generated travel plan to a traveler.

[0290] "Transportation" means any method or device of travel used during a trip.

[0291] "Behavioral content" refers to the specific activities and experiences that took place during the trip.

[0292] "Quantification means" refers to a means of expressing behavioral data and environmental impacts numerically.

[0293] "Emotion analysis means" refers to devices or algorithms that analyze the emotional state of travelers and utilize the results.

[0294] A "prompt sentence" is an instruction sentence to be input to a generative model.

[0295] A "learner" is a mechanism for improving a generative model based on collected data.

[0296] This invention is a system that enables travelers to plan and execute eco-friendly trips. The system has a function to provide eco-friendly travel plans that reflect the traveler's emotional state. To achieve this, the system is equipped with an emotion engine that analyzes the traveler's emotional state in real time and provides the optimal plan based on that analysis.

[0297] First, users need to download the application and create an account, entering basic information such as name, age, place of residence, travel preferences, etc. This information is then sent from the device to the server and stored in a database.

[0298] Next, the user inputs specific travel requirements, such as the duration of the trip, budget, and places to visit. The device sends this information to the server, which then references an eco-friendly database and uses a generative AI model to create an eco-friendly itinerary. The generated itinerary is then displayed to the user in bookmark form on the device.

[0299] The system of the present invention is characterized by its incorporation of an emotion engine, which analyzes the user's emotional state and can grasp that state in real time. For example, if the user is tired or stressed, the emotion engine can detect this and add relaxation activities or quiet tourist spots to the travel plan.

[0300] As a concrete example, if a user inputs the travel requirements of "stay length 3 days," "budget 100,000 yen," and "place to visit: Kyoto," and the emotion engine analyzes that "the user is looking for relaxation," the server will generate the following travel plan:

[0301] Day 1: Arrive at Kyoto Station, visit a tranquil temple by eco-friendly electric bicycle, and stay overnight at an eco-certified accommodation

[0302] Day 2: Join a stress-relieving yoga session and enjoy a healthy dinner at a vegan restaurant

[0303] Day 3: Nature walk and meditation in Arashiyama, then return home by train

[0304] During a trip, the user inputs the transportation method used and the actions taken into the device. The device then sends this information to the server. The server then quantifies the environmental impact based on the input behavioral data. The results are sent to the device and displayed visually to the user.

[0305] The emotion engine also continues to run while traveling, monitoring the user's emotional state: if the user is feeling stressed, for example, the server will instantly adjust the plan and suggest options for relaxation activities or visiting quiet places.

[0306] After the trip is over, the server updates the generative AI model using the collected behavioral and emotional data, which improves the accuracy of future trip plans and makes it possible to provide users with more optimal, eco-friendly travel plans.

[0307] Examples of prompts that could be fed into a generative AI model include:

[0308] User Basic Information:

[0309] Name: {name}

[0310] Age: {age}

[0311] Place of residence: {Place of residence}

[0312] Travel Preferences: {Travel Preferences}

[0313] Travel requirements:

[0314] Travel period: {Travel period}

[0315] Budget: {budget}

[0316] Place visited: {place visited}

[0317] Travel plan generation:

[0318] User's emotional state: {Emotional state}

[0319] By inputting these prompts into a generative AI model, the system can generate an optimal travel plan.

[0320] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0321] Step 1: User creates an account

[0322] Users download the application and create an account.

[0323] Input: Basic information such as name, age, location, and travel preferences

[0324] Specific behavior: The user enters their information into the registration form and presses the submit button.

[0325] Output: The registration information is sent to the server and stored in a database.

[0326] Data processing: The information is converted into JSON format and stored in the server database.

[0327] Step 2: User enters travel requirements

[0328] The user inputs specific travel requirements such as travel duration, budget, and places to visit.

[0329] Input: Travel requirements such as travel period, budget, and places to visit

[0330] Specific behavior: The user enters data into the travel requirements input screen within the application and presses the submit button.

[0331] Output: The travel requirements are sent to the server and stored in the Eco-Friendly Database.

[0332] Data processing: The information is converted into JSON format and stored in the server database.

[0333] Step 3: The server generates the travel plan

[0334] Based on the received travel requirements, the server references an eco-friendly database and generates a travel plan using a generative AI model.

[0335] Input: Travel Requirements

[0336] Specific operation: The server retrieves the relevant information from the database via a query, creates a prompt sentence, and inputs it into the generative AI model.

[0337] Output: Final itinerary

[0338] Data processing: The generative AI model analyzes the prompt and generates a travel plan in text format.

[0339] Step 4: Display your travel plans

[0340] The terminal displays the generated travel plan to the user in bookmark form.

[0341] Input: Travel Plan

[0342] Specific operation: The server sends the generated travel plan in JSON format to the terminal, which parses it and displays it.

[0343] Output: A visual representation of the itinerary to the user

[0344] Data processing: The terminal parses the JSON data and displays it in a user-friendly interface.

[0345] Step 5: Data collection during the trip

[0346] The user inputs the means of transportation used and the actions taken during the trip into the terminal.

[0347] Input: Transportation method, activities

[0348] Specific operation: The user enters the transportation method and details of the activity into a designated form within the app and submits it.

[0349] Output: Behavioral data sent to the server

[0350] Data processing: The information is converted into JSON format and stored in the server database.

[0351] Step 6: Use sentiment analysis tools

[0352] The server uses an emotion engine to analyze the user's emotional state in real time and dynamically adjust the travel plan.

[0353] Input: Emotion data (sensor information and metadata)

[0354] Specific operation: The server receives information from the emotion engine and makes adjustments such as adding relaxation activities to the travel plan.

[0355] Output: Dynamically adjusted itinerary

[0356] Data processing: Recalculate and update travel plan data based on the sentiment analysis results.

[0357] Step 7: Post-trip data update steps

[0358] After the trip ends, the server updates the generative AI model based on the collected behavioral and emotional data.

[0359] Input: Behavioral data, emotion data

[0360] Specific operation: After the trip ends, the server trains the generative AI model based on the collected data.

[0361] Output: An updated generative AI model

[0362] Data processing: Integrate collected data, generate new training datasets, and retrain generative AI models.

[0363] (Application example 2)

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

[0365] For modern travelers, achieving eco-friendly travel is important, but it is difficult to easily create an optimal plan that takes into account their emotional state and environmental impact during the trip. Additionally, while there is a demand for travel food choices that take environmental impact into consideration, there is no system that provides appropriate food delivery options based on emotional state. Therefore, a system is needed that allows travelers to enjoy a comfortable and environmentally conscious trip.

[0366] 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: an input means through which a traveler inputs travel requirements such as travel duration, budget, and places to visit; a generation means that receives the input information and generates an eco-friendly travel plan using a generative AI model based on an eco-friendly database; an emotion analysis means that analyzes the user's emotional state and provides an optimal travel plan; a display means that displays the generated travel plan to the traveler in bookmark form; an input means for inputting the means of transportation used during the trip and the activities of the user; a quantification means that quantifies the environmental impact based on the input activity data and provides feedback to the traveler; and a learning means that updates the generative AI model using collected activity data to improve the accuracy of the next travel plan. This makes it possible to provide convenient and eco-friendly travel and meal plans that correspond to the traveler's emotional state.

[0367] "Trip duration, budget, and places to visit" refers to the number of days a traveler plans to travel, the amount of money they have available, and the places they plan to go.

[0368] "Input means" refers to a device or interface through which a user provides information to a system.

[0369] "Receiving means" refers to a device or interface for receiving information input by a user on the server side.

[0370] An "eco-friendly database" refers to a database for managing and storing environmentally friendly information and data.

[0371] A "generative AI model" refers to an algorithm or system that uses artificial intelligence technology to automatically generate optimal plans and solutions from data.

[0372] "Generator" refers to a device or algorithm that generates an optimal plan or solution based on specified conditions and data.

[0373] "Emotion analysis means" refers to a device or system for analyzing a user's emotional state and assessing that state.

[0374] "Display means" refers to a device or interface for visually presenting the generated plan or information to the user.

[0375] "Quantification means" refers to devices or algorithms for calculating quantitative figures based on behavioral data.

[0376] "Learning tools" refers to devices and algorithms that update the AI ​​model based on collected data and improve the accuracy of the next generation plan.

[0377] "Suggestion means" refers to a device or system that presents appropriate choices and options to users and supports them in making optimal decisions.

[0378] "Food delivery plan" refers to a plan that plans and suggests the optimal way to deliver meals, taking into account the user's emotional state and environmental impact.

[0379] This system provides optimal eco-friendly travel and food delivery plans that take into account a traveler's emotional state and environmental concerns. Users download a dedicated application to their smartphone and create an account. This allows the system to create detailed travel plans and provide appropriate meal options based on the traveler's emotional state.

[0380] System Configuration

[0381] The system of the present invention comprises the following means:

[0382] Input means: An interface is provided as a smartphone application for users to input information such as the duration of the trip, budget, places to visit, and emotional state of the day.

[0383] Reception means: The server receives information entered by the user. This includes the ability to send and receive data from smartphones via API.

[0384] Eco-Friendly Database: A database for managing and storing environmentally friendly information and data, including, for example, a list of eco-friendly transportation options and tourist destinations.

[0385] Generative AI model: Automatically generates optimal travel plans based on input information and eco-friendly data. This model is trained using machine learning algorithms.

[0386] Generator: Includes an algorithm for generating eco-friendly itineraries using a generative AI model.

[0387] Sentiment analysis: Analyze the emotional state entered by the user and provide optimal travel plans or food delivery options in real time. For example, using a sentiment analysis engine.

[0388] Display method: The generated travel plan is displayed to the user in bookmark format on their smartphone, allowing them to easily check the plan.

[0389] Quantification method: Enter the transportation method and activities used during the trip and quantify the environmental impact based on that data. This includes an environmental impact calculation algorithm.

[0390] Learning method: The generative AI model is updated using collected behavioral data to improve the accuracy of the next trip plan. For example, the model is updated daily using a machine learning algorithm.

[0391] Recommendation tools: Suggest eco-friendly transport options, tourist activities, and suitable food delivery options. This includes a recommendation engine based on emotional state, among other things.

[0392] Processing flow explanation

[0393] Hardware and software used

[0394] Users access the application using their smartphones and enter the necessary information. The server uses APIs and a database management system (DBMS) to receive and process the data sent from the application, and runs generative AI models (e.g., TensorFlow, PyTorch) to generate eco-friendly travel plans.

[0395] Specific example explanation

[0396] For example, if a user inputs "Length of stay: 3 days," "Budget: 100,000 yen," "Place to visit: Kyoto," and "Emotional state: Seeking relaxation," the server will use its emotion analysis engine to analyze the user's emotional state, and the generative AI model will generate the following itinerary:

[0397] Day 1: Arrive at Kyoto Station and travel by eco-friendly public transport to visit a tranquil temple and stay overnight in an eco-certified accommodation.

[0398] Day 2: Attend a stress-relieving yoga session and have dinner at an organic restaurant.

[0399] Day 3: Nature walk and meditation activity in Arashiyama, then return home by public transport.

[0400] It also suggests "vegan dishes with a relaxing effect" as a food delivery plan based on the user's emotional state.

[0401] Prompt Sentence Examples

[0402] Examples of prompts based on the user's emotional state include:

[0403] "If the user's emotional state is relaxation, suggest eco-friendly travel itineraries and food delivery options. The user's preferences are vegan and they prefer organic food."

[0404] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0405] Step 1:

[0406] Using a smartphone application, users input details such as the duration of their trip, their budget, the places they plan to visit, and their emotional state for the day. The input data is then sent from the device to a server. The input data includes the traveler's planned travel range and their current emotional state.

[0407] Step 2:

[0408] The server analyzes the received travel requirements and emotional state. This involves matching the input data with an eco-friendly database and utilizing a generative AI model to generate a travel plan based on that. The input emotional state is also evaluated in real time using an emotion analysis engine. The input data are travel requirements and emotional data, and the output data are the analysis results and an initial plan.

[0409] Step 3:

[0410] The generated travel plan is sent back to the terminal from the server and displayed to the user in the form of a bookmark. This bookmark includes details of places to visit, accommodations, transportation, etc. The displayed data becomes the contents of the travel plan.

[0411] Step 4:

[0412] The user checks the travel plan and selects the transportation method and options for recording the activities they plan to use during the trip. The information selected by the user is then sent from the device to the server. The input data is the selected activities, and the output data is the record of the activities.

[0413] Step 5:

[0414] The server quantifies the environmental impact based on the travel behavior data. This process uses an environmental impact calculation algorithm to quantify the environmental impact of the transportation method used and the behavior. The input data is the behavior, and the output data is the numerical value of the environmental impact.

[0415] Step 6:

[0416] The quantified environmental impact is fed back to the device and displayed visually, allowing users to understand the impact their actions have had on the environment. The data displayed on the device is numerical information on the environmental impact.

[0417] Step 7:

[0418] The server updates the generative AI model using the collected behavioral and emotional data, thereby improving the accuracy of generating future travel plans. The input data is behavioral and emotional data, and the output data is the updated generative AI model.

[0419] Step 8:

[0420] If the user's emotional state changes during the trip planning process, the emotion analysis engine recognizes the change and reevaluates and adjusts the plan in real time based on the new emotional state. This ensures that the optimal plan is always provided based on the user's latest emotional state. The input data is the change in emotional state, and the output data is the adjusted trip plan.

[0421] Step 9:

[0422] This system proposes food delivery options based on the user's emotional state. The server analyzes the user's emotional state and proposes the optimal food delivery plan using a generative AI model. The input data is the emotional state, and the output data is the food delivery plan.

[0423] Through the above steps, users can enjoy optimal travel plans that take into account their emotional state and environmental impact during the trip, enabling them to enjoy a comfortable and sustainable trip.

[0424] 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.

[0425] 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.

[0426] 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.

[0427] [Second embodiment]

[0428] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0429] 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.

[0430] 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).

[0431] 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.

[0432] 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.

[0433] 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).

[0434] 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.

[0435] 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.

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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."

[0440] This invention is a system that enables travelers to plan and execute environmentally friendly trips. The system generates eco-friendly itineraries based on travel requirements, such as travel duration, budget, and destinations, and records travel behavior, quantifies the environmental impact, and provides feedback to travelers to encourage sustainable travel.

[0441] First, users download the application and create an account. They enter basic information (name, age, place of residence, travel preferences), which is then sent to the server and stored in a database.

[0442] Next, the user inputs their specific travel requirements (trip duration, budget, and places to visit). The device sends this information to the server, which then uses the received information to reference an eco-friendly database and utilizes a generative AI model to generate a travel plan. The generated travel plan is then displayed on the device as a detailed schedule.

[0443] The user reviews the proposed plan and selects from eco-friendly transportation and sightseeing activities. This allows the traveler to be involved in the process of planning an environmentally conscious trip. The server records the selected options and stores them as travel data.

[0444] During a trip, users input the transportation methods they used and the activities they undertook (e.g., cycling, walking tours, environmental conservation activities) into their device. The device then sends this information to a server, which then quantifies the environmental impact based on the entered activity data. The results are fed back to the user. The environmental impact figures are displayed visually, allowing travelers to concretely understand the impact their actions have on the environment.

[0445] Additionally, the server uses the behavioral data collected during the trip to update the generative AI model, allowing it to generate more accurate and eco-friendly itineraries the next time you plan a trip.

[0446] For example, if a user inputs the travel requirements "length of stay: 3 days," "budget: 100,000 yen," and "place to visit: Kyoto," the server will generate the following travel plan:

[0447] Day 1: Arrive at Kyoto Station, go sightseeing by electric bicycle, and stay at an eco-certified inn

[0448] Day 2: Join a plastic waste-free tour and have dinner at a vegan restaurant

[0449] Day 3: Participate in nature conservation activities in Arashiyama, then return home by train

[0450] The user reviews this plan, selects each activity, and then records the actions taken during the trip. The server analyzes this data, quantifies the environmental impact of the trip, and provides feedback to the user. For example, the server might display a message like, "Your trip successfully reduced CO2 emissions by 30% compared to a normal trip." Based on this data, the server can provide even more accurate suggestions for the next trip.

[0451] The above is an embodiment of the present invention. The system of the present invention allows travelers to enjoy traveling while reducing the burden on the environment, and can contribute to spreading eco-friendly travel styles.

[0452] The processing flow will be explained below.

[0453] Step 1:

[0454] A user downloads the application and creates an account. They enter basic information such as their name, age, place of residence, and travel preferences. The device then sends this information to the server, which stores it in a database.

[0455] Step 2:

[0456] The user inputs specific travel requirements such as travel duration, budget, places to visit, etc. The device sends this information to the server.

[0457] Step 3:

[0458] The server receives the travel requirements and consults the eco-friendly database, based on which it uses a generative AI model to generate an eco-friendly itinerary.

[0459] Step 4:

[0460] The server sends the generated travel plan to the terminal, which displays the travel plan to the user in bookmark format.

[0461] Step 5:

[0462] The user reviews the proposed itinerary and selects from eco-friendly transport options and sightseeing activities, and the device sends the selected options to the server.

[0463] Step 6:

[0464] The server finalizes the itinerary including the selected options and stores it in a database. The terminal displays the finalized itinerary to the user.

[0465] Step 7:

[0466] During the trip, the user inputs the transportation method used and the activities performed (e.g., cycling, walking tours, environmental conservation activities) into the device, which then transmits this information to the server.

[0467] Step 8:

[0468] The server quantifies the environmental impact based on the input behavioral data, and the quantification results are sent to the terminal, which then visually displays them to the user.

[0469] Step 9:

[0470] The server updates the generative AI model using behavioral data collected during the trip, which improves the accuracy of future travel plans.

[0471] Step 10:

[0472] When the user plans another trip, the process repeats from step 2, providing a more accurate itinerary that reflects the data collected so far.

[0473] Example 1

[0474] 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."

[0475] To enable travelers to easily plan and carry out environmentally conscious trips, a system is needed that allows them to consistently create travel plans, record their activities, and provide feedback on their environmental impact. However, current travel planning systems lack the functionality to evaluate environmental impact and provide specific feedback. This makes it difficult for travelers to accurately understand the environmental impact of their actions, preventing them from making sufficient eco-friendly choices. It is essential to solve this issue and popularize sustainable travel styles.

[0476] 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.

[0477] In this invention, the server includes an input means for a traveler to input travel requirements such as travel period, budget, and places to visit, a generation means for receiving the input information and generating an eco-friendly travel plan using a generative AI model based on the environmental consideration information, and a display means for displaying the generated travel plan to the traveler in an itinerary format, thereby enabling travelers to easily create environmentally friendly travel plans and take eco-friendly trips.

[0478] The "input means" is a means for a traveler to input travel requirements such as travel period, budget, and places to visit.

[0479] The "generation means" is a means of receiving the input information and generating an eco-friendly travel plan using a generative AI model based on the environmental consideration information.

[0480] The "display means" is a means for displaying the generated travel plan to the traveler in the form of an itinerary.

[0481] "Communication means" refers to a means for transmitting input information to a server in real time and storing it in a database.

[0482] The "selection method" is a method that analyzes travel behavior data, quantifies the environmental impact in real time, and provides feedback.

[0483] "Proposal methods" are means of proposing environmentally friendly transportation methods and tourist activities and allowing travelers to select appropriate options.

[0484] The "quantification means" is a means for quantifying the environmental impact based on the inputted behavioral data and providing the results as feedback to the traveler.

[0485] The "learning method" is a method for updating the generative AI model using collected behavioral data to improve the accuracy of the next travel plan.

[0486] "Visualization means" is a means of quantifying traveler behavior data as environmental impact and visually displaying the results.

[0487] This invention is a system that enables travelers to plan and execute environmentally friendly trips. Specifically, travelers input their travel requirements, such as travel duration, budget, and destinations, and the system generates an eco-friendly itinerary based on that information. The system also records travel behavior, quantifies the environmental impact, and provides feedback to travelers to encourage sustainable travel. Detailed embodiments of this system are described below.

[0488] First, users download the application and create an account. This application is a program that runs on devices such as smartphones, tablets, and PCs. When creating an account, users enter basic information (name, age, place of residence, travel preferences). The information entered is sent from the device to a server and stored in a database. The server centrally manages individual traveler information and uses it as basic data for creating eco-friendly plans.

[0489] Next, the user enters their specific travel requirements (trip duration, budget, and places to visit) into the device. This information is analyzed using a generative AI model when it is sent from the device to the server. The generative AI model accesses an eco-friendly database and generates an optimal travel plan that meets the traveler's requirements. This plan includes environmentally friendly activities such as using electric bicycles and zero-plastic waste tours. The generated travel plan is displayed on the device in the form of a detailed schedule.

[0490] Users can review the displayed itinerary and select eco-friendly transportation and sightseeing activities, such as sightseeing by electric bicycle, staying at eco-certified accommodations, or eating at vegan restaurants. Once selections are complete, the selection data is sent from the device to a server and stored as travel data. This allows users to make environmentally conscious choices from the planning stage onwards.

[0491] During a trip, users enter the transportation methods they used and the activities they performed (e.g., cycling, walking tours, environmental conservation activities) into their device. This behavioral data is sent to a server in real time, and the server uses the data to quantify the environmental impact. The results are fed back to the user and displayed as concrete effects (e.g., "Your trip successfully reduced CO2 emissions by 30% compared to a normal trip"). Feedback is provided in the form of easy-to-understand visual graphs and messages.

[0492] Furthermore, after the trip, the server uses the collected behavioral data to update the generative AI model. This allows the server to provide more accurate eco-friendly travel plans the next time the trip is planned. For example, if you enter the prompts "stay length: 3 days," "budget: 100,000 yen," and "place to visit: Kyoto," the following specific travel plan will be generated:

[0493] Day 1: Arrive at Kyoto Station, go sightseeing by electric bicycle, and stay at an eco-certified inn

[0494] Day 2: Join a plastic waste-free tour and have dinner at a vegan restaurant

[0495] Day 3: Participate in nature conservation activities in Arashiyama, then return home by train

[0496] In this way, the system allows travelers to plan enjoyable trips while reducing their environmental impact, making it easy to make eco-friendly choices.

[0497] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0498] Step 1:

[0499] Users download the application and create an account.

[0500] What it does: Users enter basic information such as name, age, location, and travel preferences.

[0501] Input and Output:

[0502] Input: Basic information that the user types into the device

[0503] Output: Basic information sent from the terminal to the server and stored in the database

[0504] Step 2:

[0505] The user inputs specific travel requirements into the terminal.

[0506] Specific operation: The user enters the travel period, budget, and places to visit and presses the "Generate plan" button.

[0507] Input and Output:

[0508] Input: Travel duration, budget, and destinations entered by the user into the terminal

[0509] Output: Travel requirements sent from the device to the server and used by the generative AI model

[0510] Step 3:

[0511] The server receives the travel requirements and accesses the eco-friendly database to generate an optimal travel plan.

[0512] What it does: The server uses a generative AI model to generate a travel plan based on travel duration, budget, and places to visit.

[0513] Input and Output:

[0514] Input: Travel requirements and eco-friendly database information received by the server

[0515] Output: A detailed itinerary generated

[0516] Step 4:

[0517] The generated travel plan is displayed on the terminal.

[0518] Specific operation: The server returns the generated travel plan to the terminal and displays it in itinerary format on the terminal.

[0519] Input and Output:

[0520] Input: A generated travel plan sent from the server to the device

[0521] Output: The itinerary of the travel plan is displayed on the terminal.

[0522] Step 5:

[0523] Users can review their travel plans and choose eco-friendly transport and sightseeing activities.

[0524] Specific actions: The user looks at the displayed itinerary, clicks on each activity to select it, and presses the "Confirm" button.

[0525] Input and Output:

[0526] Input: Selected data that the user types into the terminal

[0527] Output: Selected data sent from the device to the server and stored

[0528] Step 6:

[0529] During the trip, the user inputs the means of transportation actually used and the actions taken into the terminal.

[0530] Specific operation: The user records the transportation used and activities participated in each day on the device and presses the "Save" button.

[0531] Input and Output:

[0532] Input: Behavioral data that the user enters into the device

[0533] Output: Behavioral data sent from the device to the server in real time and updated

[0534] Step 7:

[0535] The server quantifies the environmental impact based on the input behavioral data.

[0536] Specific operation: The server analyzes the behavioral data, calculates the environmental impact, and quantifies it.

[0537] Input and Output:

[0538] Input: Behavioral data received by the server

[0539] Output: Calculated environmental impact figures

[0540] Step 8:

[0541] The results of the environmental impact are fed back to the user.

[0542] Specific operation: The server sends feedback to the user based on the calculation results, for example, "Your trip has successfully reduced CO2 emissions by 30% compared to a normal trip."

[0543] Input and Output:

[0544] Input: Quantification results of environmental impact

[0545] Output: A feedback message is printed to the terminal.

[0546] Step 9:

[0547] The server uses the collected behavioral data to update the generative AI model.

[0548] Specific operation: The server reflects the behavioral data as learning data in the generative AI model and updates the model.

[0549] Input and Output:

[0550] Input: Behavioral data stored on the server

[0551] Output: An updated generative AI model

[0552] Step 10:

[0553] The next time a travel plan is generated, the updated generative AI model will be used.

[0554] What it does: The server generates a new itinerary using the latest generative AI model.

[0555] Input and Output:

[0556] Enter: Next Travel Requirement

[0557] Output: A new, more accurate itinerary

[0558] These are the specific processing steps of the system program, which allows users to plan and carry out enjoyable trips while reducing the burden on the environment.

[0559] (Application example 1)

[0560] 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."

[0561] Today's travelers lack concrete means and tools for planning and implementing environmentally conscious travel. Furthermore, there are limited systems that quantify the environmental impact of travel and promote sustainable travel styles. In addition, there is a need to promote environmentally conscious behavior in everyday life as well. In particular, there is a need for systems that reduce the environmental impact of food delivery services. Against this backdrop, there is a need for systems that reduce the environmental impact of travel and everyday life and promote sustainable behavior.

[0562] 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.

[0563] In this invention, the server includes an input means for travelers to input travel requirements such as travel duration, budget, and destinations; a generation means for receiving the input information and generating an eco-friendly travel plan using a generative AI model based on an eco-friendly database; and a display means for displaying the generated travel plan to travelers in bookmark form. The server also includes an input means for inputting the transportation methods and activities used during the trip, and a quantification means for quantifying the environmental impact based on the input behavioral data and providing feedback to the travelers. The server also includes a learning means for updating the generative AI model using collected behavioral data to improve the accuracy of the next travel plan, and an eco-friendly food delivery means for selecting food orders and delivery methods based on information registered by the user, quantifying the environmental impact, and providing feedback. This enables travelers and everyday users to plan and execute activities that reduce environmental impact and specifically understand the results.

[0564] "Travel requirements" refers to information necessary for travelers to plan their trip, such as the duration, budget, and places to visit.

[0565] "Input means" refers to an interface that allows travelers and users to input necessary information (such as travel requirements and activities) into the system.

[0566] "Generation means" is a function that generates eco-friendly travel plans and food delivery plans based on input information.

[0567] The "display means" is an interface for visually displaying the generated plan to the traveler or user.

[0568] The "quantification means" is a function that calculates the environmental impact based on the input behavioral data and provides the results as feedback to the user.

[0569] The "learning method" is a function that uses collected behavioral data to update the generative AI model and improve accuracy the next time a plan is generated.

[0570] The "Eco-Friendly Database" is a database that collects information on environmentally friendly travel and food delivery.

[0571] A "generative AI model" is an artificial intelligence model that generates eco-friendly plans based on input data.

[0572] "Eco-friendly food delivery methods" is a function that allows users to select food ordering and delivery methods that take environmental impact into consideration, and quantifies the environmental impact and provides feedback.

[0573] "Environmental impact" refers to the degree of negative impact that a certain action has on the global environment.

[0574] The present invention is a system for travelers and everyday users to plan, execute, and evaluate environmentally friendly actions. The system can be applied to both travel planning and food delivery. The following specific steps and tools are used to implement the invention:

[0575] System Configuration

[0576] The system includes the following main elements:

[0577] 1. Input means (traveler or user interface)

[0578] 2. Generator (AI model that generates eco-friendly plans)

[0579] 3. Display means (interface that visually displays the generated plan)

[0580] 4. Quantification method (function to calculate environmental impact and provide feedback)

[0581] 5. Learning methods (the ability to improve the AI ​​model based on collected data)

[0582] 6. Eco-Friendly Food Delivery Methods (Functionality to optimize food ordering and delivery methods)

[0583] Hardware and Software

[0584] Hardware: Smartphone (iOS, Android)

[0585] Software: Python, API server (Django or Flask), database (PostgreSQL)

[0586] Data processing and calculation

[0587] 1. Input method: Travelers or users input requirements such as travel duration, budget, places to visit, food orders and desired delivery method through a smartphone application.

[0588] 2. Generation means: The server receives the input information and uses a generative AI model to generate eco-friendly travel plans or food delivery.

[0589] 3. Display: The generated plan is displayed as a detailed schedule or delivery information on the traveler's or user's device.

[0590] 4. Quantification method: The user inputs the transportation method and activities used during the trip or delivery, and the server quantifies the environmental impact based on that data. The results are visually fed back to the user.

[0591] 5. Learning method: The server analyzes the collected behavioral data and updates the generative AI model, improving the accuracy of the next plan generation.

[0592] 6. Eco-friendly food delivery methods: Users can order food through the application in a way that minimizes the environmental impact. The server calculates the environmental impact based on the selected delivery method and provides feedback.

[0593] Specific examples

[0594] For travel plans:

[0595] The user inputs travel requirements such as "stay length 3 days," "budget 100,000 yen," and "place to visit: Kyoto." The server generates the following travel plan based on this information:

[0596] Day 1: Arrive at Kyoto Station, go sightseeing by electric bicycle, and stay at an environmentally certified accommodation facility

[0597] Day 2: Take a zero-plastic waste tour and have dinner at an eco-friendly restaurant

[0598] Day 3: Participate in nature conservation activities in Arashiyama, then return home by train

[0599] For eco-friendly food delivery:

[0600] A user orders a "vegan salad" via "bicycle delivery." The server receives the order, calculates the environmental impact (e.g., 0.0 kg of CO2), and provides feedback to the user.

[0601] Prompt Sentence Examples

[0602] Example prompts to use with generative AI models:

[0603] Prompt: Model recommendations for generating eco-friendly delivery orders and reducing the environmental impact of each option.

[0604] Example: The user selects "Bicycle", calculates the environmental impact of the order, and provides feedback to the user.

[0605] The above is an embodiment of the present invention. The system of the present invention allows travelers and everyday users to enjoy activities while reducing the environmental impact, contributing to the spread of eco-friendly lifestyles.

[0606] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0607] Step 1:

[0608] Users input travel requirements such as travel period, budget, and places to visit, as well as food delivery order information and delivery preferences through a smartphone application. The input information is sent from the device to the server.

[0609] Input: Travel requirements (duration, budget, places to visit), food delivery order information (type of food, delivery method)

[0610] Output: User information sent to the server

[0611] Step 2:

[0612] Based on the travel requirements or delivery order information received by the server, the server uses a generative AI model while referencing an eco-friendly database to generate an optimal travel plan or delivery plan. The server creates prompts for the generative AI model and inputs them into the model.

[0613] Input: Travel requirements or delivery order information, prompt text

[0614] Output: Generated travel or delivery plan

[0615] Step 3:

[0616] The generated travel and delivery plans are sent to the terminal and displayed to the user. The plans are visually displayed in bookmark format or as detailed order information.

[0617] Input: Generated plan

[0618] Output: Plan displayed on the terminal

[0619] Step 4:

[0620] While traveling or using food delivery services, users input the transportation method used and their activities into a smartphone application, and the input information is sent from the device to a server.

[0621] Input: Means of transportation used, activities

[0622] Output: Behavioral data sent to the server

[0623] Step 5:

[0624] The server quantifies the environmental impact based on the behavioral data received. The calculation is performed using a preset environmental impact coefficient for the means of transportation and the behavior.

[0625] Input: Behavioral data

[0626] Output: Quantified environmental impact data

[0627] Step 6:

[0628] The server sends the calculated environmental impact data to the user's terminal, allowing the user to visually check it.

[0629] Input: Quantified environmental impact data

[0630] Output: Feedback displayed on the terminal

[0631] Step 7:

[0632] The server uses collected behavioral data to update the generative AI model, improving accuracy when generating the next travel plan or delivery plan. Learning is performed based on behavioral data and environmental impact data.

[0633] Input: behavioral data, environmental impact data

[0634] Output: An updated generative AI model

[0635] 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.

[0636] This invention is a system that enables travelers to plan and execute eco-friendly trips, and in particular has the function of providing eco-friendly travel plans that reflect the user's emotional state.By incorporating an emotion engine, it is possible to analyze the user's emotional state in real time and provide an optimal plan based on that.

[0637] First, users download the application and create an account. They enter basic information such as name, age, place of residence, and travel preferences. This information is sent from the device to the server and stored in a database.

[0638] Next, the user inputs specific travel requirements, such as the duration of the trip, budget, and places to visit. The device sends this information to the server, which then references an eco-friendly database and uses a generative AI model to create an eco-friendly itinerary. The generated itinerary is then displayed to the user in bookmark form on the device.

[0639] A further feature of the present invention is the incorporation of an emotion engine, which can analyze the user's emotions and grasp their state in real time. For example, if the user is tired or stressed, the emotion engine can detect this and add relaxation activities or quiet tourist spots to the travel plan.

[0640] As a concrete example, if a user inputs the travel requirements of "stay length 3 days," "budget 100,000 yen," and "place to visit: Kyoto," and the emotion engine analyzes that "the user is looking for relaxation," the server will generate the following travel plan:

[0641] Day 1: Arrive at Kyoto Station, visit a tranquil temple by eco-friendly electric bicycle, and stay overnight at an eco-certified ryokan

[0642] Day 2: Join a stress-relieving yoga session and enjoy a healthy dinner at a vegan restaurant

[0643] Day 3: Nature walk and meditation in Arashiyama, then return home by train

[0644] During a trip, the user inputs the transportation method used and the actions taken into the device. The device then sends this information to the server, which then quantifies the environmental impact based on the input behavioral data. The results are then sent to the device and displayed visually to the user.

[0645] The emotion engine continues to run throughout the trip, monitoring the user's emotional state: if the user is feeling stressed, for example, the server will instantly adjust the plan and suggest options for relaxation activities or visiting quiet places.

[0646] After the trip is completed, the server updates the generative AI model using the collected behavioral and emotional data, improving the accuracy of future trip plans and providing users with more optimal, eco-friendly travel plans.

[0647] The system of the present invention allows users to plan environmentally friendly trips while taking their emotional state into consideration, thereby reducing stress and achieving a sustainable travel style. In this way, by taking into consideration both the emotions of travelers and the environmental impact, the present invention provides maximum satisfaction to travelers and promotes environmentally friendly travel.

[0648] The processing flow will be explained below.

[0649] Step 1:

[0650] A user downloads the application and creates an account. They enter basic information such as their name, age, place of residence, and travel preferences. The device then sends this information to the server, which stores it in a database.

[0651] Step 2:

[0652] The user inputs specific travel requirements such as travel duration, budget, places to visit, etc. The device sends this information to the server.

[0653] Step 3:

[0654] The server receives the travel requirements, consults the eco-friendly database, and uses a generative AI model to generate an eco-friendly travel plan.

[0655] Step 4:

[0656] The server sends the generated travel plan to the terminal, which displays the travel plan to the user in bookmark format.

[0657] Step 5:

[0658] The user reviews the proposed travel plan and selects from eco-friendly transportation and sightseeing activities, and the selection information is sent from the device to the server.

[0659] Step 6:

[0660] The server records the selected options and stores them in a database, while the emotion engine analyzes the user's emotions in real time and reflects them in the plan.

[0661] Step 7:

[0662] During the trip, the user inputs the transportation method used and the details of their activities into the device. The emotion engine analyzes the user's emotional state (stress, joy, etc.) through cameras and sensors. This data is sent from the device to the server.

[0663] Step 8:

[0664] The server quantifies the environmental impact based on the input behavioral and emotional data, and the quantification results are sent to the terminal, which then visually displays them to the user.

[0665] Step 9:

[0666] If the user feels stressed during the trip, the emotion engine will detect this and the server will instantly adjust the plan, suggesting options such as relaxation activities or quiet tourist spots, which the device will display to the user and prompt them to make a selection.

[0667] Step 10:

[0668] After the trip is over, the server uses the collected behavioral and emotional data to update the generative AI model, which improves the accuracy of the next itinerary.

[0669] Step 11:

[0670] When the user plans another trip, the process repeats from step 2. This time, the system provides more accurate eco-friendly travel plans that incorporate past data and sentiment analysis.

[0671] Example 2

[0672] 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."

[0673] Conventional travel planning systems have the problem of only considering the traveler's travel requirements and environmental impact, and are unable to adjust the plan to reflect the traveler's emotional state. In particular, since planning does not take into account emotional states such as stress and fatigue during the trip, it can be difficult for travelers to truly relax and enjoy the trip. Another issue is that the inability to adjust the plan in real time makes it difficult to respond quickly to unexpected situations. To solve these issues, a system is needed that analyzes the traveler's emotional state and provides a travel plan that reflects that.

[0674] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0675] In this invention, the server includes an input means for the traveler to input travel requirements such as travel duration, budget, and places to visit, a generation means for receiving the input information and generating an environmentally conscious travel plan using a generative model based on a database, and a display means for displaying the generated travel plan to the traveler in bookmark form.

[0676] This makes it possible to analyze the traveler's emotional state in real time and dynamically adjust the travel plan based on the results.In addition, by providing an input means for inputting the means of transportation used during the trip and the activities of the traveler, a quantification means for quantifying the environmental impact based on the input activity data and feeding the results back to the traveler, and a learning means for updating the generative model using the collected activity data and improving the accuracy of the next travel plan, it is possible to continue proposing the optimal eco-friendly travel plan for the traveler.

[0677] A "tourist" is someone who travels.

[0678] "Duration of Trip" means the number of days or hours over which the trip takes place.

[0679] "Budget" refers to the total amount of money used for travel.

[0680] "Destinations" are places or areas that a traveler visits during their trip.

[0681] "Travel requirements" refer to the conditions and desires that a traveler has when traveling.

[0682] "Input means" means a device or interface through which a traveler inputs information.

[0683] A "database" is a system that stores and manages data in an organized manner.

[0684] A "generative model" is an algorithm for generating data based on specific inputs.

[0685] "Environmentally friendly" means that the goal is to minimize the impact on the environment.

[0686] A "travel plan" is a detailed plan or schedule for a trip.

[0687] "Generation means" refers to a mechanism for generating a travel plan based on input information.

[0688] "Display means" refers to a device or interface that visually presents the generated travel plan to a traveler.

[0689] "Transportation" means any method or device of travel used during a trip.

[0690] "Behavioral content" refers to the specific activities and experiences that took place during the trip.

[0691] "Quantification means" refers to a means of expressing behavioral data and environmental impacts numerically.

[0692] "Emotion analysis means" refers to devices or algorithms that analyze the emotional state of travelers and utilize the results.

[0693] A "prompt sentence" is an instruction sentence to be input to a generative model.

[0694] A "learner" is a mechanism for improving a generative model based on collected data.

[0695] This invention is a system that enables travelers to plan and execute eco-friendly trips. The system has a function to provide eco-friendly travel plans that reflect the traveler's emotional state. To achieve this, the system is equipped with an emotion engine that analyzes the traveler's emotional state in real time and provides the optimal plan based on that analysis.

[0696] First, users need to download the application and create an account, entering basic information such as name, age, place of residence, travel preferences, etc. This information is then sent from the device to the server and stored in a database.

[0697] Next, the user inputs specific travel requirements, such as the duration of the trip, budget, and places to visit. The device sends this information to the server, which then references an eco-friendly database and uses a generative AI model to create an eco-friendly itinerary. The generated itinerary is then displayed to the user in bookmark form on the device.

[0698] The system of the present invention is characterized by its incorporation of an emotion engine, which analyzes the user's emotional state and can grasp that state in real time. For example, if the user is tired or stressed, the emotion engine can detect this and add relaxation activities or quiet tourist spots to the travel plan.

[0699] As a concrete example, if a user inputs the travel requirements of "stay length 3 days," "budget 100,000 yen," and "place to visit: Kyoto," and the emotion engine analyzes that "the user is looking for relaxation," the server will generate the following travel plan:

[0700] Day 1: Arrive at Kyoto Station, visit a tranquil temple by eco-friendly electric bicycle, and stay overnight at an eco-certified accommodation

[0701] Day 2: Join a stress-relieving yoga session and enjoy a healthy dinner at a vegan restaurant

[0702] Day 3: Nature walk and meditation in Arashiyama, then return home by train

[0703] During a trip, the user inputs the transportation method used and the actions taken into the device. The device then sends this information to the server. The server then quantifies the environmental impact based on the input behavioral data. The results are sent to the device and displayed visually to the user.

[0704] The emotion engine also continues to run while traveling, monitoring the user's emotional state: if the user is feeling stressed, for example, the server will instantly adjust the plan and suggest options for relaxation activities or visiting quiet places.

[0705] After the trip is over, the server updates the generative AI model using the collected behavioral and emotional data, which improves the accuracy of future trip plans and makes it possible to provide users with more optimal, eco-friendly travel plans.

[0706] Examples of prompts that could be fed into a generative AI model include:

[0707] User Basic Information:

[0708] Name: {name}

[0709] Age: {age}

[0710] Place of residence: {Place of residence}

[0711] Travel Preferences: {Travel Preferences}

[0712] Travel requirements:

[0713] Travel period: {Travel period}

[0714] Budget: {budget}

[0715] Place visited: {place visited}

[0716] Travel plan generation:

[0717] User's emotional state: {Emotional state}

[0718] By inputting these prompts into a generative AI model, the system can generate an optimal travel plan.

[0719] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0720] Step 1: User creates an account

[0721] Users download the application and create an account.

[0722] Input: Basic information such as name, age, location, and travel preferences

[0723] Specific behavior: The user enters their information into the registration form and presses the submit button.

[0724] Output: The registration information is sent to the server and stored in a database.

[0725] Data processing: The information is converted into JSON format and stored in the server database.

[0726] Step 2: User enters travel requirements

[0727] The user inputs specific travel requirements such as travel duration, budget, and places to visit.

[0728] Input: Travel requirements such as travel period, budget, and places to visit

[0729] Specific behavior: The user enters data into the travel requirements input screen within the application and presses the submit button.

[0730] Output: The travel requirements are sent to the server and stored in the Eco-Friendly Database.

[0731] Data processing: The information is converted into JSON format and stored in the server database.

[0732] Step 3: The server generates the travel plan

[0733] Based on the received travel requirements, the server references an eco-friendly database and generates a travel plan using a generative AI model.

[0734] Input: Travel Requirements

[0735] Specific operation: The server retrieves the relevant information from the database via a query, creates a prompt sentence, and inputs it into the generative AI model.

[0736] Output: Final itinerary

[0737] Data processing: The generative AI model analyzes the prompt and generates a travel plan in text format.

[0738] Step 4: Display your travel plans

[0739] The terminal displays the generated travel plan to the user in bookmark form.

[0740] Input: Travel Plan

[0741] Specific operation: The server sends the generated travel plan in JSON format to the terminal, which parses it and displays it.

[0742] Output: A visual representation of the itinerary to the user

[0743] Data processing: The terminal parses the JSON data and displays it in a user-friendly interface.

[0744] Step 5: Data collection during the trip

[0745] The user inputs the means of transportation used and the actions taken during the trip into the terminal.

[0746] Input: Transportation method, activities

[0747] Specific operation: The user enters the transportation method and details of the activity into a designated form within the app and submits it.

[0748] Output: Behavioral data sent to the server

[0749] Data processing: The information is converted into JSON format and stored in the server database.

[0750] Step 6: Use sentiment analysis tools

[0751] The server uses an emotion engine to analyze the user's emotional state in real time and dynamically adjust the travel plan.

[0752] Input: Emotion data (sensor information and metadata)

[0753] Specific operation: The server receives information from the emotion engine and makes adjustments such as adding relaxation activities to the travel plan.

[0754] Output: Dynamically adjusted itinerary

[0755] Data processing: Recalculate and update travel plan data based on the sentiment analysis results.

[0756] Step 7: Post-trip data update steps

[0757] After the trip ends, the server updates the generative AI model based on the collected behavioral and emotional data.

[0758] Input: Behavioral data, emotion data

[0759] Specific operation: After the trip ends, the server trains the generative AI model based on the collected data.

[0760] Output: An updated generative AI model

[0761] Data processing: Integrate collected data, generate new training datasets, and retrain generative AI models.

[0762] (Application example 2)

[0763] 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."

[0764] For modern travelers, achieving eco-friendly travel is important, but it is difficult to easily create an optimal plan that takes into account their emotional state and environmental impact during the trip. Additionally, while there is a demand for travel food choices that take environmental impact into consideration, there is no system that provides appropriate food delivery options based on emotional state. Therefore, a system is needed that allows travelers to enjoy a comfortable and environmentally conscious trip.

[0765] 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: an input means through which a traveler inputs travel requirements such as travel duration, budget, and places to visit; a generation means that receives the input information and generates an eco-friendly travel plan using a generative AI model based on an eco-friendly database; an emotion analysis means that analyzes the user's emotional state and provides an optimal travel plan; a display means that displays the generated travel plan to the traveler in bookmark form; an input means for inputting the means of transportation used during the trip and the activities of the user; a quantification means that quantifies the environmental impact based on the input activity data and provides feedback to the traveler; and a learning means that updates the generative AI model using collected activity data to improve the accuracy of the next travel plan. This makes it possible to provide convenient and eco-friendly travel and meal plans that correspond to the traveler's emotional state.

[0766] "Trip duration, budget, and places to visit" refers to the number of days a traveler plans to travel, the amount of money they have available, and the places they plan to go.

[0767] "Input means" refers to a device or interface through which a user provides information to a system.

[0768] "Receiving means" refers to a device or interface for receiving information input by a user on the server side.

[0769] An "eco-friendly database" refers to a database for managing and storing environmentally friendly information and data.

[0770] A "generative AI model" refers to an algorithm or system that uses artificial intelligence technology to automatically generate optimal plans and solutions from data.

[0771] "Generator" refers to a device or algorithm that generates an optimal plan or solution based on specified conditions and data.

[0772] "Emotion analysis means" refers to a device or system for analyzing a user's emotional state and assessing that state.

[0773] "Display means" refers to a device or interface for visually presenting the generated plan or information to the user.

[0774] "Quantification means" refers to devices or algorithms for calculating quantitative figures based on behavioral data.

[0775] "Learning tools" refers to devices and algorithms that update the AI ​​model based on collected data and improve the accuracy of the next generation plan.

[0776] "Suggestion means" refers to a device or system that presents appropriate choices and options to users and supports them in making optimal decisions.

[0777] "Food delivery plan" refers to a plan that plans and suggests the optimal way to deliver meals, taking into account the user's emotional state and environmental impact.

[0778] This system provides optimal eco-friendly travel and food delivery plans that take into account a traveler's emotional state and environmental concerns. Users download a dedicated application to their smartphone and create an account. This allows the system to create detailed travel plans and provide appropriate meal options based on the traveler's emotional state.

[0779] System Configuration

[0780] The system of the present invention comprises the following means:

[0781] Input means: An interface is provided as a smartphone application for users to input information such as the duration of the trip, budget, places to visit, and emotional state of the day.

[0782] Reception means: The server receives information entered by the user. This includes the ability to send and receive data from smartphones via API.

[0783] Eco-Friendly Database: A database for managing and storing environmentally friendly information and data, including, for example, a list of eco-friendly transportation options and tourist destinations.

[0784] Generative AI model: Automatically generates optimal travel plans based on input information and eco-friendly data. This model is trained using machine learning algorithms.

[0785] Generator: Includes an algorithm for generating eco-friendly itineraries using a generative AI model.

[0786] Sentiment analysis: Analyze the emotional state entered by the user and provide optimal travel plans or food delivery options in real time. For example, using a sentiment analysis engine.

[0787] Display method: The generated travel plan is displayed to the user in bookmark format on their smartphone, allowing them to easily check the plan.

[0788] Quantification method: Enter the transportation method and activities used during the trip and quantify the environmental impact based on that data. This includes an environmental impact calculation algorithm.

[0789] Learning method: The generative AI model is updated using collected behavioral data to improve the accuracy of the next trip plan. For example, the model is updated daily using a machine learning algorithm.

[0790] Recommendation tools: Suggest eco-friendly transport options, tourist activities, and suitable food delivery options. This includes a recommendation engine based on emotional state, among other things.

[0791] Processing flow explanation

[0792] Hardware and software used

[0793] Users access the application using their smartphones and enter the necessary information. The server uses APIs and a database management system (DBMS) to receive and process the data sent from the application, and runs generative AI models (e.g., TensorFlow, PyTorch) to generate eco-friendly travel plans.

[0794] Specific example explanation

[0795] For example, if a user inputs "Length of stay: 3 days," "Budget: 100,000 yen," "Place to visit: Kyoto," and "Emotional state: Seeking relaxation," the server will use its emotion analysis engine to analyze the user's emotional state, and the generative AI model will generate the following itinerary:

[0796] Day 1: Arrive at Kyoto Station and travel by eco-friendly public transport to visit a tranquil temple and stay overnight in an eco-certified accommodation.

[0797] Day 2: Attend a stress-relieving yoga session and have dinner at an organic restaurant.

[0798] Day 3: Nature walk and meditation activity in Arashiyama, then return home by public transport.

[0799] It also suggests "vegan dishes with a relaxing effect" as a food delivery plan based on the user's emotional state.

[0800] Prompt Sentence Examples

[0801] Examples of prompts based on the user's emotional state include:

[0802] "If the user's emotional state is relaxation, suggest eco-friendly travel itineraries and food delivery options. The user's preferences are vegan and they prefer organic food."

[0803] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0804] Step 1:

[0805] Using a smartphone application, users input details such as the duration of their trip, their budget, the places they plan to visit, and their emotional state for the day. The input data is then sent from the device to a server. The input data includes the traveler's planned travel range and their current emotional state.

[0806] Step 2:

[0807] The server analyzes the received travel requirements and emotional state. This involves matching the input data with an eco-friendly database and utilizing a generative AI model to generate a travel plan based on that. The input emotional state is also evaluated in real time using an emotion analysis engine. The input data are travel requirements and emotional data, and the output data are the analysis results and an initial plan.

[0808] Step 3:

[0809] The generated travel plan is sent back to the terminal from the server and displayed to the user in the form of a bookmark. This bookmark includes details of places to visit, accommodations, transportation, etc. The displayed data becomes the contents of the travel plan.

[0810] Step 4:

[0811] The user checks the travel plan and selects the transportation method and options for recording the activities they plan to use during the trip. The information selected by the user is then sent from the device to the server. The input data is the selected activities, and the output data is the record of the activities.

[0812] Step 5:

[0813] The server quantifies the environmental impact based on the travel behavior data. This process uses an environmental impact calculation algorithm to quantify the environmental impact of the transportation method used and the behavior. The input data is the behavior, and the output data is the numerical value of the environmental impact.

[0814] Step 6:

[0815] The quantified environmental impact is fed back to the device and displayed visually, allowing users to understand the impact their actions have had on the environment. The data displayed on the device is numerical information on the environmental impact.

[0816] Step 7:

[0817] The server updates the generative AI model using the collected behavioral and emotional data, thereby improving the accuracy of generating future travel plans. The input data is behavioral and emotional data, and the output data is the updated generative AI model.

[0818] Step 8:

[0819] If the user's emotional state changes during the trip planning process, the emotion analysis engine recognizes the change and reevaluates and adjusts the plan in real time based on the new emotional state. This ensures that the optimal plan is always provided based on the user's latest emotional state. The input data is the change in emotional state, and the output data is the adjusted trip plan.

[0820] Step 9:

[0821] This system proposes food delivery options based on the user's emotional state. The server analyzes the user's emotional state and proposes the optimal food delivery plan using a generative AI model. The input data is the emotional state, and the output data is the food delivery plan.

[0822] Through the above steps, users can enjoy optimal travel plans that take into account their emotional state and environmental impact during the trip, enabling them to enjoy a comfortable and sustainable trip.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] [Third embodiment]

[0827] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0828] 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.

[0829] 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).

[0830] 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.

[0831] 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.

[0832] 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).

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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."

[0839] This invention is a system that enables travelers to plan and execute environmentally friendly trips. The system generates eco-friendly itineraries based on travel requirements, such as travel duration, budget, and destinations, and records travel behavior, quantifies the environmental impact, and provides feedback to travelers to encourage sustainable travel.

[0840] First, users download the application and create an account. They enter basic information (name, age, place of residence, travel preferences), which is then sent to the server and stored in a database.

[0841] Next, the user inputs their specific travel requirements (trip duration, budget, and places to visit). The device sends this information to the server, which then uses the received information to reference an eco-friendly database and utilizes a generative AI model to generate a travel plan. The generated travel plan is then displayed on the device as a detailed schedule.

[0842] The user reviews the proposed plan and selects from eco-friendly transportation and sightseeing activities. This allows the traveler to be involved in the process of planning an environmentally conscious trip. The server records the selected options and stores them as travel data.

[0843] During a trip, users input the transportation methods they used and the activities they undertook (e.g., cycling, walking tours, environmental conservation activities) into their device. The device then sends this information to a server, which then quantifies the environmental impact based on the entered activity data. The results are fed back to the user. The environmental impact figures are displayed visually, allowing travelers to concretely understand the impact their actions have on the environment.

[0844] Additionally, the server uses the behavioral data collected during the trip to update the generative AI model, allowing it to generate more accurate and eco-friendly itineraries the next time you plan a trip.

[0845] For example, if a user inputs the travel requirements "length of stay: 3 days," "budget: 100,000 yen," and "place to visit: Kyoto," the server will generate the following travel plan:

[0846] Day 1: Arrive at Kyoto Station, go sightseeing by electric bicycle, and stay at an eco-certified inn

[0847] Day 2: Join a plastic waste-free tour and have dinner at a vegan restaurant

[0848] Day 3: Participate in nature conservation activities in Arashiyama, then return home by train

[0849] The user reviews this plan, selects each activity, and then records the actions taken during the trip. The server analyzes this data, quantifies the environmental impact of the trip, and provides feedback to the user. For example, the server might display a message like, "Your trip successfully reduced CO2 emissions by 30% compared to a normal trip." Based on this data, the server can provide even more accurate suggestions for the next trip.

[0850] The above is an embodiment of the present invention. The system of the present invention allows travelers to enjoy traveling while reducing the burden on the environment, and can contribute to spreading eco-friendly travel styles.

[0851] The processing flow will be explained below.

[0852] Step 1:

[0853] A user downloads the application and creates an account. They enter basic information such as their name, age, place of residence, and travel preferences. The device then sends this information to the server, which stores it in a database.

[0854] Step 2:

[0855] The user inputs specific travel requirements such as travel duration, budget, places to visit, etc. The device sends this information to the server.

[0856] Step 3:

[0857] The server receives the travel requirements and consults the eco-friendly database, based on which it uses a generative AI model to generate an eco-friendly itinerary.

[0858] Step 4:

[0859] The server sends the generated travel plan to the terminal, which displays the travel plan to the user in bookmark format.

[0860] Step 5:

[0861] The user reviews the proposed itinerary and selects from eco-friendly transport options and sightseeing activities, and the device sends the selected options to the server.

[0862] Step 6:

[0863] The server finalizes the itinerary including the selected options and stores it in a database. The terminal displays the finalized itinerary to the user.

[0864] Step 7:

[0865] During the trip, the user inputs the transportation method used and the activities performed (e.g., cycling, walking tours, environmental conservation activities) into the device, which then transmits this information to the server.

[0866] Step 8:

[0867] The server quantifies the environmental impact based on the input behavioral data, and the quantification results are sent to the terminal, which then visually displays them to the user.

[0868] Step 9:

[0869] The server updates the generative AI model using behavioral data collected during the trip, which improves the accuracy of future travel plans.

[0870] Step 10:

[0871] When the user plans another trip, the process repeats from step 2, providing a more accurate itinerary that reflects the data collected so far.

[0872] Example 1

[0873] 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."

[0874] To enable travelers to easily plan and carry out environmentally conscious trips, a system is needed that allows them to consistently create travel plans, record their activities, and provide feedback on their environmental impact. However, current travel planning systems lack the functionality to evaluate environmental impact and provide specific feedback. This makes it difficult for travelers to accurately understand the environmental impact of their actions, preventing them from making sufficient eco-friendly choices. It is essential to solve this issue and popularize sustainable travel styles.

[0875] 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.

[0876] In this invention, the server includes an input means for a traveler to input travel requirements such as travel period, budget, and places to visit, a generation means for receiving the input information and generating an eco-friendly travel plan using a generative AI model based on the environmental consideration information, and a display means for displaying the generated travel plan to the traveler in an itinerary format, thereby enabling travelers to easily create environmentally friendly travel plans and take eco-friendly trips.

[0877] The "input means" is a means for a traveler to input travel requirements such as travel period, budget, and places to visit.

[0878] The "generation means" is a means of receiving the input information and generating an eco-friendly travel plan using a generative AI model based on the environmental consideration information.

[0879] The "display means" is a means for displaying the generated travel plan to the traveler in the form of an itinerary.

[0880] "Communication means" refers to a means for transmitting input information to a server in real time and storing it in a database.

[0881] The "selection method" is a method that analyzes travel behavior data, quantifies the environmental impact in real time, and provides feedback.

[0882] "Proposal methods" are means of proposing environmentally friendly transportation methods and tourist activities and allowing travelers to select appropriate options.

[0883] The "quantification means" is a means for quantifying the environmental impact based on the inputted behavioral data and providing the results as feedback to the traveler.

[0884] The "learning method" is a method for updating the generative AI model using collected behavioral data to improve the accuracy of the next travel plan.

[0885] "Visualization means" is a means of quantifying traveler behavior data as environmental impact and visually displaying the results.

[0886] This invention is a system that enables travelers to plan and execute environmentally friendly trips. Specifically, travelers input their travel requirements, such as travel duration, budget, and destinations, and the system generates an eco-friendly itinerary based on that information. The system also records travel behavior, quantifies the environmental impact, and provides feedback to travelers to encourage sustainable travel. Detailed embodiments of this system are described below.

[0887] First, users download the application and create an account. This application is a program that runs on devices such as smartphones, tablets, and PCs. When creating an account, users enter basic information (name, age, place of residence, travel preferences). The information entered is sent from the device to a server and stored in a database. The server centrally manages individual traveler information and uses it as basic data for creating eco-friendly plans.

[0888] Next, the user enters their specific travel requirements (trip duration, budget, and places to visit) into the device. This information is analyzed using a generative AI model when it is sent from the device to the server. The generative AI model accesses an eco-friendly database and generates an optimal travel plan that meets the traveler's requirements. This plan includes environmentally friendly activities such as using electric bicycles and zero-plastic waste tours. The generated travel plan is displayed on the device in the form of a detailed schedule.

[0889] Users can review the displayed itinerary and select eco-friendly transportation and sightseeing activities, such as sightseeing by electric bicycle, staying at eco-certified accommodations, or eating at vegan restaurants. Once selections are complete, the selection data is sent from the device to a server and stored as travel data. This allows users to make environmentally conscious choices from the planning stage onwards.

[0890] During a trip, users enter the transportation methods they used and the activities they performed (e.g., cycling, walking tours, environmental conservation activities) into their device. This behavioral data is sent to a server in real time, and the server uses the data to quantify the environmental impact. The results are fed back to the user and displayed as concrete effects (e.g., "Your trip successfully reduced CO2 emissions by 30% compared to a normal trip"). Feedback is provided in the form of easy-to-understand visual graphs and messages.

[0891] Furthermore, after the trip, the server uses the collected behavioral data to update the generative AI model. This allows the server to provide more accurate eco-friendly travel plans the next time the trip is planned. For example, if you enter the prompts "stay length: 3 days," "budget: 100,000 yen," and "place to visit: Kyoto," the following specific travel plan will be generated:

[0892] Day 1: Arrive at Kyoto Station, go sightseeing by electric bicycle, and stay at an eco-certified inn

[0893] Day 2: Join a plastic waste-free tour and have dinner at a vegan restaurant

[0894] Day 3: Participate in nature conservation activities in Arashiyama, then return home by train

[0895] In this way, the system allows travelers to plan enjoyable trips while reducing their environmental impact, making it easy to make eco-friendly choices.

[0896] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0897] Step 1:

[0898] Users download the application and create an account.

[0899] What it does: Users enter basic information such as name, age, location, and travel preferences.

[0900] Input and Output:

[0901] Input: Basic information that the user types into the device

[0902] Output: Basic information sent from the terminal to the server and stored in the database

[0903] Step 2:

[0904] The user inputs specific travel requirements into the terminal.

[0905] Specific operation: The user enters the travel period, budget, and places to visit and presses the "Generate plan" button.

[0906] Input and Output:

[0907] Input: Travel duration, budget, and destinations entered by the user into the terminal

[0908] Output: Travel requirements sent from the device to the server and used by the generative AI model

[0909] Step 3:

[0910] The server receives the travel requirements and accesses the eco-friendly database to generate an optimal travel plan.

[0911] What it does: The server uses a generative AI model to generate a travel plan based on travel duration, budget, and places to visit.

[0912] Input and Output:

[0913] Input: Travel requirements and eco-friendly database information received by the server

[0914] Output: A detailed itinerary generated

[0915] Step 4:

[0916] The generated travel plan is displayed on the terminal.

[0917] Specific operation: The server returns the generated travel plan to the terminal and displays it in itinerary format on the terminal.

[0918] Input and Output:

[0919] Input: A generated travel plan sent from the server to the device

[0920] Output: The itinerary of the travel plan is displayed on the terminal.

[0921] Step 5:

[0922] Users can review their travel plans and choose eco-friendly transport and sightseeing activities.

[0923] Specific actions: The user looks at the displayed itinerary, clicks on each activity to select it, and presses the "Confirm" button.

[0924] Input and Output:

[0925] Input: Selected data that the user types into the terminal

[0926] Output: Selected data sent from the device to the server and stored

[0927] Step 6:

[0928] During the trip, the user inputs the means of transportation actually used and the actions taken into the terminal.

[0929] Specific operation: The user records the transportation used and activities participated in each day on the device and presses the "Save" button.

[0930] Input and Output:

[0931] Input: Behavioral data that the user enters into the device

[0932] Output: Behavioral data sent from the device to the server in real time and updated

[0933] Step 7:

[0934] The server quantifies the environmental impact based on the input behavioral data.

[0935] Specific operation: The server analyzes the behavioral data, calculates the environmental impact, and quantifies it.

[0936] Input and Output:

[0937] Input: Behavioral data received by the server

[0938] Output: Calculated environmental impact figures

[0939] Step 8:

[0940] The results of the environmental impact are fed back to the user.

[0941] Specific operation: The server sends feedback to the user based on the calculation results, for example, "Your trip has successfully reduced CO2 emissions by 30% compared to a normal trip."

[0942] Input and Output:

[0943] Input: Quantification results of environmental impact

[0944] Output: A feedback message is printed to the terminal.

[0945] Step 9:

[0946] The server uses the collected behavioral data to update the generative AI model.

[0947] Specific operation: The server reflects the behavioral data as learning data in the generative AI model and updates the model.

[0948] Input and Output:

[0949] Input: Behavioral data stored on the server

[0950] Output: An updated generative AI model

[0951] Step 10:

[0952] The next time a travel plan is generated, the updated generative AI model will be used.

[0953] What it does: The server generates a new itinerary using the latest generative AI model.

[0954] Input and Output:

[0955] Enter: Next Travel Requirement

[0956] Output: A new, more accurate itinerary

[0957] These are the specific processing steps of the system program, which allows users to plan and carry out enjoyable trips while reducing the burden on the environment.

[0958] (Application example 1)

[0959] 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."

[0960] Today's travelers lack concrete means and tools for planning and implementing environmentally conscious travel. Furthermore, there are limited systems that quantify the environmental impact of travel and promote sustainable travel styles. In addition, there is a need to promote environmentally conscious behavior in everyday life as well. In particular, there is a need for systems that reduce the environmental impact of food delivery services. Against this backdrop, there is a need for systems that reduce the environmental impact of travel and everyday life and promote sustainable behavior.

[0961] 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.

[0962] In this invention, the server includes an input means for travelers to input travel requirements such as travel duration, budget, and destinations; a generation means for receiving the input information and generating an eco-friendly travel plan using a generative AI model based on an eco-friendly database; and a display means for displaying the generated travel plan to travelers in bookmark form. The server also includes an input means for inputting the transportation methods and activities used during the trip, and a quantification means for quantifying the environmental impact based on the input behavioral data and providing feedback to the travelers. The server also includes a learning means for updating the generative AI model using collected behavioral data to improve the accuracy of the next travel plan, and an eco-friendly food delivery means for selecting food orders and delivery methods based on information registered by the user, quantifying the environmental impact, and providing feedback. This enables travelers and everyday users to plan and execute activities that reduce environmental impact and specifically understand the results.

[0963] "Travel requirements" refers to information necessary for travelers to plan their trip, such as the duration, budget, and places to visit.

[0964] "Input means" refers to an interface that allows travelers and users to input necessary information (such as travel requirements and activities) into the system.

[0965] "Generation means" is a function that generates eco-friendly travel plans and food delivery plans based on input information.

[0966] The "display means" is an interface for visually displaying the generated plan to the traveler or user.

[0967] The "quantification means" is a function that calculates the environmental impact based on the input behavioral data and provides the results as feedback to the user.

[0968] The "learning method" is a function that uses collected behavioral data to update the generative AI model and improve accuracy the next time a plan is generated.

[0969] The "Eco-Friendly Database" is a database that collects information on environmentally friendly travel and food delivery.

[0970] A "generative AI model" is an artificial intelligence model that generates eco-friendly plans based on input data.

[0971] "Eco-friendly food delivery methods" is a function that allows users to select food ordering and delivery methods that take environmental impact into consideration, and quantifies the environmental impact and provides feedback.

[0972] "Environmental impact" refers to the degree of negative impact that a certain action has on the global environment.

[0973] The present invention is a system for travelers and everyday users to plan, execute, and evaluate environmentally friendly actions. The system can be applied to both travel planning and food delivery. The following specific steps and tools are used to implement the invention:

[0974] System Configuration

[0975] The system includes the following main elements:

[0976] 1. Input means (traveler or user interface)

[0977] 2. Generator (AI model that generates eco-friendly plans)

[0978] 3. Display means (interface that visually displays the generated plan)

[0979] 4. Quantification method (function to calculate environmental impact and provide feedback)

[0980] 5. Learning methods (the ability to improve the AI ​​model based on collected data)

[0981] 6. Eco-Friendly Food Delivery Methods (Functionality to optimize food ordering and delivery methods)

[0982] Hardware and Software

[0983] Hardware: Smartphone (iOS, Android)

[0984] Software: Python, API server (Django or Flask), database (PostgreSQL)

[0985] Data processing and calculation

[0986] 1. Input method: Travelers or users input requirements such as travel duration, budget, places to visit, food orders and desired delivery method through a smartphone application.

[0987] 2. Generation means: The server receives the input information and uses a generative AI model to generate eco-friendly travel plans or food delivery.

[0988] 3. Display: The generated plan is displayed as a detailed schedule or delivery information on the traveler's or user's device.

[0989] 4. Quantification method: The user inputs the transportation method and activities used during the trip or delivery, and the server quantifies the environmental impact based on that data. The results are visually fed back to the user.

[0990] 5. Learning method: The server analyzes the collected behavioral data and updates the generative AI model, improving the accuracy of the next plan generation.

[0991] 6. Eco-friendly food delivery methods: Users can order food through the application in a way that minimizes the environmental impact. The server calculates the environmental impact based on the selected delivery method and provides feedback.

[0992] Specific examples

[0993] For travel plans:

[0994] The user inputs travel requirements such as "stay length 3 days," "budget 100,000 yen," and "place to visit: Kyoto." The server generates the following travel plan based on this information:

[0995] Day 1: Arrive at Kyoto Station, go sightseeing by electric bicycle, and stay at an environmentally certified accommodation facility

[0996] Day 2: Take a zero-plastic waste tour and have dinner at an eco-friendly restaurant

[0997] Day 3: Participate in nature conservation activities in Arashiyama, then return home by train

[0998] For eco-friendly food delivery:

[0999] A user orders a "vegan salad" via "bicycle delivery." The server receives the order, calculates the environmental impact (e.g., 0.0 kg of CO2), and provides feedback to the user.

[1000] Prompt Sentence Examples

[1001] Example prompts to use with generative AI models:

[1002] Prompt: Model recommendations for generating eco-friendly delivery orders and reducing the environmental impact of each option.

[1003] Example: The user selects "Bicycle", calculates the environmental impact of the order, and provides feedback to the user.

[1004] The above is an embodiment of the present invention. The system of the present invention allows travelers and everyday users to enjoy activities while reducing the environmental impact, contributing to the spread of eco-friendly lifestyles.

[1005] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1006] Step 1:

[1007] Users input travel requirements such as travel period, budget, and places to visit, as well as food delivery order information and delivery preferences through a smartphone application. The input information is sent from the device to the server.

[1008] Input: Travel requirements (duration, budget, places to visit), food delivery order information (type of food, delivery method)

[1009] Output: User information sent to the server

[1010] Step 2:

[1011] Based on the travel requirements or delivery order information received by the server, the server uses a generative AI model while referencing an eco-friendly database to generate an optimal travel plan or delivery plan. The server creates prompts for the generative AI model and inputs them into the model.

[1012] Input: Travel requirements or delivery order information, prompt text

[1013] Output: Generated travel or delivery plan

[1014] Step 3:

[1015] The generated travel and delivery plans are sent to the terminal and displayed to the user. The plans are visually displayed in bookmark format or as detailed order information.

[1016] Input: Generated plan

[1017] Output: Plan displayed on the terminal

[1018] Step 4:

[1019] While traveling or using food delivery services, users input the transportation method used and their activities into a smartphone application, and the input information is sent from the device to a server.

[1020] Input: Means of transportation used, activities

[1021] Output: Behavioral data sent to the server

[1022] Step 5:

[1023] The server quantifies the environmental impact based on the behavioral data received. The calculation is performed using a preset environmental impact coefficient for the means of transportation and the behavior.

[1024] Input: Behavioral data

[1025] Output: Quantified environmental impact data

[1026] Step 6:

[1027] The server sends the calculated environmental impact data to the user's terminal, allowing the user to visually check it.

[1028] Input: Quantified environmental impact data

[1029] Output: Feedback displayed on the terminal

[1030] Step 7:

[1031] The server uses collected behavioral data to update the generative AI model, improving accuracy when generating the next travel plan or delivery plan. Learning is performed based on behavioral data and environmental impact data.

[1032] Input: behavioral data, environmental impact data

[1033] Output: An updated generative AI model

[1034] 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.

[1035] This invention is a system that enables travelers to plan and execute eco-friendly trips, and in particular has the function of providing eco-friendly travel plans that reflect the user's emotional state.By incorporating an emotion engine, it is possible to analyze the user's emotional state in real time and provide an optimal plan based on that.

[1036] First, users download the application and create an account. They enter basic information such as name, age, place of residence, and travel preferences. This information is sent from the device to the server and stored in a database.

[1037] Next, the user inputs specific travel requirements, such as the duration of the trip, budget, and places to visit. The device sends this information to the server, which then references an eco-friendly database and uses a generative AI model to create an eco-friendly itinerary. The generated itinerary is then displayed to the user in bookmark form on the device.

[1038] A further feature of the present invention is the incorporation of an emotion engine, which can analyze the user's emotions and grasp their state in real time. For example, if the user is tired or stressed, the emotion engine can detect this and add relaxation activities or quiet tourist spots to the travel plan.

[1039] As a concrete example, if a user inputs the travel requirements of "stay length 3 days," "budget 100,000 yen," and "place to visit: Kyoto," and the emotion engine analyzes that "the user is looking for relaxation," the server will generate the following travel plan:

[1040] Day 1: Arrive at Kyoto Station, visit a tranquil temple by eco-friendly electric bicycle, and stay overnight at an eco-certified ryokan

[1041] Day 2: Join a stress-relieving yoga session and enjoy a healthy dinner at a vegan restaurant

[1042] Day 3: Nature walk and meditation in Arashiyama, then return home by train

[1043] During a trip, the user inputs the transportation method used and the actions taken into the device. The device then sends this information to the server, which then quantifies the environmental impact based on the input behavioral data. The results are then sent to the device and displayed visually to the user.

[1044] The emotion engine continues to run throughout the trip, monitoring the user's emotional state: if the user is feeling stressed, for example, the server will instantly adjust the plan and suggest options for relaxation activities or visiting quiet places.

[1045] After the trip is completed, the server updates the generative AI model using the collected behavioral and emotional data, improving the accuracy of future trip plans and providing users with more optimal, eco-friendly travel plans.

[1046] The system of the present invention allows users to plan environmentally friendly trips while taking their emotional state into consideration, thereby reducing stress and achieving a sustainable travel style. In this way, by taking into consideration both the emotions of travelers and the environmental impact, the present invention provides maximum satisfaction to travelers and promotes environmentally friendly travel.

[1047] The processing flow will be explained below.

[1048] Step 1:

[1049] A user downloads the application and creates an account. They enter basic information such as their name, age, place of residence, and travel preferences. The device then sends this information to the server, which stores it in a database.

[1050] Step 2:

[1051] The user inputs specific travel requirements such as travel duration, budget, places to visit, etc. The device sends this information to the server.

[1052] Step 3:

[1053] The server receives the travel requirements, consults the eco-friendly database, and uses a generative AI model to generate an eco-friendly travel plan.

[1054] Step 4:

[1055] The server sends the generated travel plan to the terminal, which displays the travel plan to the user in bookmark format.

[1056] Step 5:

[1057] The user reviews the proposed travel plan and selects from eco-friendly transportation and sightseeing activities, and the selection information is sent from the device to the server.

[1058] Step 6:

[1059] The server records the selected options and stores them in a database, while the emotion engine analyzes the user's emotions in real time and reflects them in the plan.

[1060] Step 7:

[1061] During the trip, the user inputs the transportation method used and the details of their activities into the device. The emotion engine analyzes the user's emotional state (stress, joy, etc.) through cameras and sensors. This data is sent from the device to the server.

[1062] Step 8:

[1063] The server quantifies the environmental impact based on the input behavioral and emotional data, and the quantification results are sent to the terminal, which then visually displays them to the user.

[1064] Step 9:

[1065] If the user feels stressed during the trip, the emotion engine will detect this and the server will instantly adjust the plan, suggesting options such as relaxation activities or quiet tourist spots, which the device will display to the user and prompt them to make a selection.

[1066] Step 10:

[1067] After the trip is over, the server uses the collected behavioral and emotional data to update the generative AI model, which improves the accuracy of the next itinerary.

[1068] Step 11:

[1069] When the user plans another trip, the process repeats from step 2. This time, the system provides more accurate eco-friendly travel plans that incorporate past data and sentiment analysis.

[1070] Example 2

[1071] 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."

[1072] Conventional travel planning systems have the problem of only considering the traveler's travel requirements and environmental impact, and are unable to adjust the plan to reflect the traveler's emotional state. In particular, since planning does not take into account emotional states such as stress and fatigue during the trip, it can be difficult for travelers to truly relax and enjoy the trip. Another issue is that the inability to adjust the plan in real time makes it difficult to respond quickly to unexpected situations. To solve these issues, a system is needed that analyzes the traveler's emotional state and provides a travel plan that reflects that.

[1073] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1074] In this invention, the server includes an input means for the traveler to input travel requirements such as travel duration, budget, and places to visit, a generation means for receiving the input information and generating an environmentally conscious travel plan using a generative model based on a database, and a display means for displaying the generated travel plan to the traveler in bookmark form.

[1075] This makes it possible to analyze the traveler's emotional state in real time and dynamically adjust the travel plan based on the results.In addition, by providing an input means for inputting the means of transportation used during the trip and the activities of the traveler, a quantification means for quantifying the environmental impact based on the input activity data and feeding the results back to the traveler, and a learning means for updating the generative model using the collected activity data and improving the accuracy of the next travel plan, it is possible to continue proposing the optimal eco-friendly travel plan for the traveler.

[1076] A "tourist" is someone who travels.

[1077] "Duration of Trip" means the number of days or hours over which the trip takes place.

[1078] "Budget" refers to the total amount of money used for travel.

[1079] "Destinations" are places or areas that a traveler visits during their trip.

[1080] "Travel requirements" refer to the conditions and desires that a traveler has when traveling.

[1081] "Input means" means a device or interface through which a traveler inputs information.

[1082] A "database" is a system that stores and manages data in an organized manner.

[1083] A "generative model" is an algorithm for generating data based on specific inputs.

[1084] "Environmentally friendly" means that the goal is to minimize the impact on the environment.

[1085] A "travel plan" is a detailed plan or schedule for a trip.

[1086] "Generation means" refers to a mechanism for generating a travel plan based on input information.

[1087] "Display means" refers to a device or interface that visually presents the generated travel plan to a traveler.

[1088] "Transportation" means any method or device of travel used during a trip.

[1089] "Behavioral content" refers to the specific activities and experiences that took place during the trip.

[1090] "Quantification means" refers to a means of expressing behavioral data and environmental impacts numerically.

[1091] "Emotion analysis means" refers to devices or algorithms that analyze the emotional state of travelers and utilize the results.

[1092] A "prompt sentence" is an instruction sentence to be input to a generative model.

[1093] A "learner" is a mechanism for improving a generative model based on collected data.

[1094] This invention is a system that enables travelers to plan and execute eco-friendly trips. The system has a function to provide eco-friendly travel plans that reflect the traveler's emotional state. To achieve this, the system is equipped with an emotion engine that analyzes the traveler's emotional state in real time and provides the optimal plan based on that analysis.

[1095] First, users need to download the application and create an account, entering basic information such as name, age, place of residence, travel preferences, etc. This information is then sent from the device to the server and stored in a database.

[1096] Next, the user inputs specific travel requirements, such as the duration of the trip, budget, and places to visit. The device sends this information to the server, which then references an eco-friendly database and uses a generative AI model to create an eco-friendly itinerary. The generated itinerary is then displayed to the user in bookmark form on the device.

[1097] The system of the present invention is characterized by its incorporation of an emotion engine, which analyzes the user's emotional state and can grasp that state in real time. For example, if the user is tired or stressed, the emotion engine can detect this and add relaxation activities or quiet tourist spots to the travel plan.

[1098] As a concrete example, if a user inputs the travel requirements of "stay length 3 days," "budget 100,000 yen," and "place to visit: Kyoto," and the emotion engine analyzes that "the user is looking for relaxation," the server will generate the following travel plan:

[1099] Day 1: Arrive at Kyoto Station, visit a tranquil temple by eco-friendly electric bicycle, and stay overnight at an eco-certified accommodation

[1100] Day 2: Join a stress-relieving yoga session and enjoy a healthy dinner at a vegan restaurant

[1101] Day 3: Nature walk and meditation in Arashiyama, then return home by train

[1102] During a trip, the user inputs the transportation method used and the actions taken into the device. The device then sends this information to the server. The server then quantifies the environmental impact based on the input behavioral data. The results are sent to the device and displayed visually to the user.

[1103] The emotion engine also continues to run while traveling, monitoring the user's emotional state: if the user is feeling stressed, for example, the server will instantly adjust the plan and suggest options for relaxation activities or visiting quiet places.

[1104] After the trip is over, the server updates the generative AI model using the collected behavioral and emotional data, which improves the accuracy of future trip plans and makes it possible to provide users with more optimal, eco-friendly travel plans.

[1105] Examples of prompts that could be fed into a generative AI model include:

[1106] User Basic Information:

[1107] Name: {name}

[1108] Age: {age}

[1109] Place of residence: {Place of residence}

[1110] Travel Preferences: {Travel Preferences}

[1111] Travel requirements:

[1112] Travel period: {Travel period}

[1113] Budget: {budget}

[1114] Place visited: {place visited}

[1115] Travel plan generation:

[1116] User's emotional state: {Emotional state}

[1117] By inputting these prompts into a generative AI model, the system can generate an optimal travel plan.

[1118] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1119] Step 1: User creates an account

[1120] Users download the application and create an account.

[1121] Input: Basic information such as name, age, location, and travel preferences

[1122] Specific behavior: The user enters their information into the registration form and presses the submit button.

[1123] Output: The registration information is sent to the server and stored in a database.

[1124] Data processing: The information is converted into JSON format and stored in the server database.

[1125] Step 2: User enters travel requirements

[1126] The user inputs specific travel requirements such as travel duration, budget, and places to visit.

[1127] Input: Travel requirements such as travel period, budget, and places to visit

[1128] Specific behavior: The user enters data into the travel requirements input screen within the application and presses the submit button.

[1129] Output: The travel requirements are sent to the server and stored in the Eco-Friendly Database.

[1130] Data processing: The information is converted into JSON format and stored in the server database.

[1131] Step 3: The server generates the travel plan

[1132] Based on the received travel requirements, the server references an eco-friendly database and generates a travel plan using a generative AI model.

[1133] Input: Travel Requirements

[1134] Specific operation: The server retrieves the relevant information from the database via a query, creates a prompt sentence, and inputs it into the generative AI model.

[1135] Output: Final itinerary

[1136] Data processing: The generative AI model analyzes the prompt and generates a travel plan in text format.

[1137] Step 4: Display your travel plans

[1138] The terminal displays the generated travel plan to the user in bookmark form.

[1139] Input: Travel Plan

[1140] Specific operation: The server sends the generated travel plan in JSON format to the terminal, which parses it and displays it.

[1141] Output: A visual representation of the itinerary to the user

[1142] Data processing: The terminal parses the JSON data and displays it in a user-friendly interface.

[1143] Step 5: Data collection during the trip

[1144] The user inputs the means of transportation used and the actions taken during the trip into the terminal.

[1145] Input: Transportation method, activities

[1146] Specific operation: The user enters the transportation method and details of the activity into a designated form within the app and submits it.

[1147] Output: Behavioral data sent to the server

[1148] Data processing: The information is converted into JSON format and stored in the server database.

[1149] Step 6: Use sentiment analysis tools

[1150] The server uses an emotion engine to analyze the user's emotional state in real time and dynamically adjust the travel plan.

[1151] Input: Emotion data (sensor information and metadata)

[1152] Specific operation: The server receives information from the emotion engine and makes adjustments such as adding relaxation activities to the travel plan.

[1153] Output: Dynamically adjusted itinerary

[1154] Data processing: Recalculate and update travel plan data based on the sentiment analysis results.

[1155] Step 7: Post-trip data update steps

[1156] After the trip ends, the server updates the generative AI model based on the collected behavioral and emotional data.

[1157] Input: Behavioral data, emotion data

[1158] Specific operation: After the trip ends, the server trains the generative AI model based on the collected data.

[1159] Output: An updated generative AI model

[1160] Data processing: Integrate collected data, generate new training datasets, and retrain generative AI models.

[1161] (Application example 2)

[1162] 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."

[1163] For modern travelers, achieving eco-friendly travel is important, but it is difficult to easily create an optimal plan that takes into account their emotional state and environmental impact during the trip. Additionally, while there is a demand for travel food choices that take environmental impact into consideration, there is no system that provides appropriate food delivery options based on emotional state. Therefore, a system is needed that allows travelers to enjoy a comfortable and environmentally conscious trip.

[1164] 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: an input means through which a traveler inputs travel requirements such as travel duration, budget, and places to visit; a generation means that receives the input information and generates an eco-friendly travel plan using a generative AI model based on an eco-friendly database; an emotion analysis means that analyzes the user's emotional state and provides an optimal travel plan; a display means that displays the generated travel plan to the traveler in bookmark form; an input means for inputting the means of transportation used during the trip and the activities of the user; a quantification means that quantifies the environmental impact based on the input activity data and provides feedback to the traveler; and a learning means that updates the generative AI model using collected activity data to improve the accuracy of the next travel plan. This makes it possible to provide convenient and eco-friendly travel and meal plans that correspond to the traveler's emotional state.

[1165] "Trip duration, budget, and places to visit" refers to the number of days a traveler plans to travel, the amount of money they have available, and the places they plan to go.

[1166] "Input means" refers to a device or interface through which a user provides information to a system.

[1167] "Receiving means" refers to a device or interface for receiving information input by a user on the server side.

[1168] An "eco-friendly database" refers to a database for managing and storing environmentally friendly information and data.

[1169] A "generative AI model" refers to an algorithm or system that uses artificial intelligence technology to automatically generate optimal plans and solutions from data.

[1170] "Generator" refers to a device or algorithm that generates an optimal plan or solution based on specified conditions and data.

[1171] "Emotion analysis means" refers to a device or system for analyzing a user's emotional state and assessing that state.

[1172] "Display means" refers to a device or interface for visually presenting the generated plan or information to the user.

[1173] "Quantification means" refers to devices or algorithms for calculating quantitative figures based on behavioral data.

[1174] "Learning tools" refers to devices and algorithms that update the AI ​​model based on collected data and improve the accuracy of the next generation plan.

[1175] "Suggestion means" refers to a device or system that presents appropriate choices and options to users and supports them in making optimal decisions.

[1176] "Food delivery plan" refers to a plan that plans and suggests the optimal way to deliver meals, taking into account the user's emotional state and environmental impact.

[1177] This system provides optimal eco-friendly travel and food delivery plans that take into account a traveler's emotional state and environmental concerns. Users download a dedicated application to their smartphone and create an account. This allows the system to create detailed travel plans and provide appropriate meal options based on the traveler's emotional state.

[1178] System Configuration

[1179] The system of the present invention comprises the following means:

[1180] Input means: An interface is provided as a smartphone application for users to input information such as the duration of the trip, budget, places to visit, and emotional state of the day.

[1181] Reception means: The server receives information entered by the user. This includes the ability to send and receive data from smartphones via API.

[1182] Eco-Friendly Database: A database for managing and storing environmentally friendly information and data, including, for example, a list of eco-friendly transportation options and tourist destinations.

[1183] Generative AI model: Automatically generates optimal travel plans based on input information and eco-friendly data. This model is trained using machine learning algorithms.

[1184] Generator: Includes an algorithm for generating eco-friendly itineraries using a generative AI model.

[1185] Sentiment analysis: Analyze the emotional state entered by the user and provide optimal travel plans or food delivery options in real time. For example, using a sentiment analysis engine.

[1186] Display method: The generated travel plan is displayed to the user in bookmark format on their smartphone, allowing them to easily check the plan.

[1187] Quantification method: Enter the transportation method and activities used during the trip and quantify the environmental impact based on that data. This includes an environmental impact calculation algorithm.

[1188] Learning method: The generative AI model is updated using collected behavioral data to improve the accuracy of the next trip plan. For example, the model is updated daily using a machine learning algorithm.

[1189] Recommendation tools: Suggest eco-friendly transport options, tourist activities, and suitable food delivery options. This includes a recommendation engine based on emotional state, among other things.

[1190] Processing flow explanation

[1191] Hardware and software used

[1192] Users access the application using their smartphones and enter the necessary information. The server uses APIs and a database management system (DBMS) to receive and process the data sent from the application, and runs generative AI models (e.g., TensorFlow, PyTorch) to generate eco-friendly travel plans.

[1193] Specific example explanation

[1194] For example, if a user inputs "Length of stay: 3 days," "Budget: 100,000 yen," "Place to visit: Kyoto," and "Emotional state: Seeking relaxation," the server will use its emotion analysis engine to analyze the user's emotional state, and the generative AI model will generate the following itinerary:

[1195] Day 1: Arrive at Kyoto Station and travel by eco-friendly public transport to visit a tranquil temple and stay overnight in an eco-certified accommodation.

[1196] Day 2: Attend a stress-relieving yoga session and have dinner at an organic restaurant.

[1197] Day 3: Nature walk and meditation activity in Arashiyama, then return home by public transport.

[1198] It also suggests "vegan dishes with a relaxing effect" as a food delivery plan based on the user's emotional state.

[1199] Prompt Sentence Examples

[1200] Examples of prompts based on the user's emotional state include:

[1201] "If the user's emotional state is relaxation, suggest eco-friendly travel itineraries and food delivery options. The user's preferences are vegan and they prefer organic food."

[1202] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1203] Step 1:

[1204] Using a smartphone application, users input details such as the duration of their trip, their budget, the places they plan to visit, and their emotional state for the day. The input data is then sent from the device to a server. The input data includes the traveler's planned travel range and their current emotional state.

[1205] Step 2:

[1206] The server analyzes the received travel requirements and emotional state. This involves matching the input data with an eco-friendly database and utilizing a generative AI model to generate a travel plan based on that. The input emotional state is also evaluated in real time using an emotion analysis engine. The input data are travel requirements and emotional data, and the output data are the analysis results and an initial plan.

[1207] Step 3:

[1208] The generated travel plan is sent back to the terminal from the server and displayed to the user in the form of a bookmark. This bookmark includes details of places to visit, accommodations, transportation, etc. The displayed data becomes the contents of the travel plan.

[1209] Step 4:

[1210] The user checks the travel plan and selects the transportation method and options for recording the activities they plan to use during the trip. The information selected by the user is then sent from the device to the server. The input data is the selected activities, and the output data is the record of the activities.

[1211] Step 5:

[1212] The server quantifies the environmental impact based on the travel behavior data. This process uses an environmental impact calculation algorithm to quantify the environmental impact of the transportation method used and the behavior. The input data is the behavior, and the output data is the numerical value of the environmental impact.

[1213] Step 6:

[1214] The quantified environmental impact is fed back to the device and displayed visually, allowing users to understand the impact their actions have had on the environment. The data displayed on the device is numerical information on the environmental impact.

[1215] Step 7:

[1216] The server updates the generative AI model using the collected behavioral and emotional data, thereby improving the accuracy of generating future travel plans. The input data is behavioral and emotional data, and the output data is the updated generative AI model.

[1217] Step 8:

[1218] If the user's emotional state changes during the trip planning process, the emotion analysis engine recognizes the change and reevaluates and adjusts the plan in real time based on the new emotional state. This ensures that the optimal plan is always provided based on the user's latest emotional state. The input data is the change in emotional state, and the output data is the adjusted trip plan.

[1219] Step 9:

[1220] This system proposes food delivery options based on the user's emotional state. The server analyzes the user's emotional state and proposes the optimal food delivery plan using a generative AI model. The input data is the emotional state, and the output data is the food delivery plan.

[1221] Through the above steps, users can enjoy optimal travel plans that take into account their emotional state and environmental impact during the trip, enabling them to enjoy a comfortable and sustainable trip.

[1222] 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.

[1223] 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.

[1224] 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.

[1225] [Fourth embodiment]

[1226] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1227] 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.

[1228] 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).

[1229] 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.

[1230] 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.

[1231] 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).

[1232] 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.

[1233] 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.

[1234] 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.

[1235] 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.

[1236] 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.

[1237] 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.

[1238] 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."

[1239] This invention is a system that enables travelers to plan and execute environmentally friendly trips. The system generates eco-friendly itineraries based on travel requirements, such as travel duration, budget, and destinations, and records travel behavior, quantifies the environmental impact, and provides feedback to travelers to encourage sustainable travel.

[1240] First, users download the application and create an account. They enter basic information (name, age, place of residence, travel preferences), which is then sent to the server and stored in a database.

[1241] Next, the user inputs their specific travel requirements (trip duration, budget, and places to visit). The device sends this information to the server, which then uses the received information to reference an eco-friendly database and utilizes a generative AI model to generate a travel plan. The generated travel plan is then displayed on the device as a detailed schedule.

[1242] The user reviews the proposed plan and selects from eco-friendly transportation and sightseeing activities. This allows the traveler to be involved in the process of planning an environmentally conscious trip. The server records the selected options and stores them as travel data.

[1243] During a trip, users input the transportation methods they used and the activities they undertook (e.g., cycling, walking tours, environmental conservation activities) into their device. The device then sends this information to a server, which then quantifies the environmental impact based on the entered activity data. The results are fed back to the user. The environmental impact figures are displayed visually, allowing travelers to concretely understand the impact their actions have on the environment.

[1244] Additionally, the server uses the behavioral data collected during the trip to update the generative AI model, allowing it to generate more accurate and eco-friendly itineraries the next time you plan a trip.

[1245] For example, if a user inputs the travel requirements "length of stay: 3 days," "budget: 100,000 yen," and "place to visit: Kyoto," the server will generate the following travel plan:

[1246] Day 1: Arrive at Kyoto Station, go sightseeing by electric bicycle, and stay at an eco-certified inn

[1247] Day 2: Join a plastic waste-free tour and have dinner at a vegan restaurant

[1248] Day 3: Participate in nature conservation activities in Arashiyama, then return home by train

[1249] The user reviews this plan, selects each activity, and then records the actions taken during the trip. The server analyzes this data, quantifies the environmental impact of the trip, and provides feedback to the user. For example, the server might display a message like, "Your trip successfully reduced CO2 emissions by 30% compared to a normal trip." Based on this data, the server can provide even more accurate suggestions for the next trip.

[1250] The above is an embodiment of the present invention. The system of the present invention allows travelers to enjoy traveling while reducing the burden on the environment, and can contribute to spreading eco-friendly travel styles.

[1251] The processing flow will be explained below.

[1252] Step 1:

[1253] A user downloads the application and creates an account. They enter basic information such as their name, age, place of residence, and travel preferences. The device then sends this information to the server, which stores it in a database.

[1254] Step 2:

[1255] The user inputs specific travel requirements such as travel duration, budget, places to visit, etc. The device sends this information to the server.

[1256] Step 3:

[1257] The server receives the travel requirements and consults the eco-friendly database, based on which it uses a generative AI model to generate an eco-friendly itinerary.

[1258] Step 4:

[1259] The server sends the generated travel plan to the terminal, which displays the travel plan to the user in bookmark format.

[1260] Step 5:

[1261] The user reviews the proposed itinerary and selects from eco-friendly transport options and sightseeing activities, and the device sends the selected options to the server.

[1262] Step 6:

[1263] The server finalizes the itinerary including the selected options and stores it in a database. The terminal displays the finalized itinerary to the user.

[1264] Step 7:

[1265] During the trip, the user inputs the transportation method used and the activities performed (e.g., cycling, walking tours, environmental conservation activities) into the device, which then transmits this information to the server.

[1266] Step 8:

[1267] The server quantifies the environmental impact based on the input behavioral data, and the quantification results are sent to the terminal, which then visually displays them to the user.

[1268] Step 9:

[1269] The server updates the generative AI model using behavioral data collected during the trip, which improves the accuracy of future travel plans.

[1270] Step 10:

[1271] When the user plans another trip, the process repeats from step 2, providing a more accurate itinerary that reflects the data collected so far.

[1272] Example 1

[1273] 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."

[1274] To enable travelers to easily plan and carry out environmentally conscious trips, a system is needed that allows them to consistently create travel plans, record their activities, and provide feedback on their environmental impact. However, current travel planning systems lack the functionality to evaluate environmental impact and provide specific feedback. This makes it difficult for travelers to accurately understand the environmental impact of their actions, preventing them from making sufficient eco-friendly choices. It is essential to solve this issue and popularize sustainable travel styles.

[1275] 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.

[1276] In this invention, the server includes an input means for a traveler to input travel requirements such as travel period, budget, and places to visit, a generation means for receiving the input information and generating an eco-friendly travel plan using a generative AI model based on the environmental consideration information, and a display means for displaying the generated travel plan to the traveler in an itinerary format, thereby enabling travelers to easily create environmentally friendly travel plans and take eco-friendly trips.

[1277] The "input means" is a means for a traveler to input travel requirements such as travel period, budget, and places to visit.

[1278] The "generation means" is a means of receiving the input information and generating an eco-friendly travel plan using a generative AI model based on the environmental consideration information.

[1279] The "display means" is a means for displaying the generated travel plan to the traveler in the form of an itinerary.

[1280] "Communication means" refers to a means for transmitting input information to a server in real time and storing it in a database.

[1281] The "selection method" is a method that analyzes travel behavior data, quantifies the environmental impact in real time, and provides feedback.

[1282] "Proposal methods" are means of proposing environmentally friendly transportation methods and tourist activities and allowing travelers to select appropriate options.

[1283] The "quantification means" is a means for quantifying the environmental impact based on the inputted behavioral data and providing the results as feedback to the traveler.

[1284] The "learning method" is a method for updating the generative AI model using collected behavioral data to improve the accuracy of the next travel plan.

[1285] "Visualization means" is a means of quantifying traveler behavior data as environmental impact and visually displaying the results.

[1286] This invention is a system that enables travelers to plan and execute environmentally friendly trips. Specifically, travelers input their travel requirements, such as travel duration, budget, and destinations, and the system generates an eco-friendly itinerary based on that information. The system also records travel behavior, quantifies the environmental impact, and provides feedback to travelers to encourage sustainable travel. Detailed embodiments of this system are described below.

[1287] First, users download the application and create an account. This application is a program that runs on devices such as smartphones, tablets, and PCs. When creating an account, users enter basic information (name, age, place of residence, travel preferences). The information entered is sent from the device to a server and stored in a database. The server centrally manages individual traveler information and uses it as basic data for creating eco-friendly plans.

[1288] Next, the user enters their specific travel requirements (trip duration, budget, and places to visit) into the device. This information is analyzed using a generative AI model when it is sent from the device to the server. The generative AI model accesses an eco-friendly database and generates an optimal travel plan that meets the traveler's requirements. This plan includes environmentally friendly activities such as using electric bicycles and zero-plastic waste tours. The generated travel plan is displayed on the device in the form of a detailed schedule.

[1289] Users can review the displayed itinerary and select eco-friendly transportation and sightseeing activities, such as sightseeing by electric bicycle, staying at eco-certified accommodations, or eating at vegan restaurants. Once selections are complete, the selection data is sent from the device to a server and stored as travel data. This allows users to make environmentally conscious choices from the planning stage onwards.

[1290] During a trip, users enter the transportation methods they used and the activities they performed (e.g., cycling, walking tours, environmental conservation activities) into their device. This behavioral data is sent to a server in real time, and the server uses the data to quantify the environmental impact. The results are fed back to the user and displayed as concrete effects (e.g., "Your trip successfully reduced CO2 emissions by 30% compared to a normal trip"). Feedback is provided in the form of easy-to-understand visual graphs and messages.

[1291] Furthermore, after the trip, the server uses the collected behavioral data to update the generative AI model. This allows the server to provide more accurate eco-friendly travel plans the next time the trip is planned. For example, if you enter the prompts "stay length: 3 days," "budget: 100,000 yen," and "place to visit: Kyoto," the following specific travel plan will be generated:

[1292] Day 1: Arrive at Kyoto Station, go sightseeing by electric bicycle, and stay at an eco-certified inn

[1293] Day 2: Join a plastic waste-free tour and have dinner at a vegan restaurant

[1294] Day 3: Participate in nature conservation activities in Arashiyama, then return home by train

[1295] In this way, the system allows travelers to plan enjoyable trips while reducing their environmental impact, making it easy to make eco-friendly choices.

[1296] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1297] Step 1:

[1298] Users download the application and create an account.

[1299] What it does: Users enter basic information such as name, age, location, and travel preferences.

[1300] Input and Output:

[1301] Input: Basic information that the user types into the device

[1302] Output: Basic information sent from the terminal to the server and stored in the database

[1303] Step 2:

[1304] The user inputs specific travel requirements into the terminal.

[1305] Specific operation: The user enters the travel period, budget, and places to visit and presses the "Generate plan" button.

[1306] Input and Output:

[1307] Input: Travel duration, budget, and destinations entered by the user into the terminal

[1308] Output: Travel requirements sent from the device to the server and used by the generative AI model

[1309] Step 3:

[1310] The server receives the travel requirements and accesses the eco-friendly database to generate an optimal travel plan.

[1311] What it does: The server uses a generative AI model to generate a travel plan based on travel duration, budget, and places to visit.

[1312] Input and Output:

[1313] Input: Travel requirements and eco-friendly database information received by the server

[1314] Output: A detailed itinerary generated

[1315] Step 4:

[1316] The generated travel plan is displayed on the terminal.

[1317] Specific operation: The server returns the generated travel plan to the terminal and displays it in itinerary format on the terminal.

[1318] Input and Output:

[1319] Input: A generated travel plan sent from the server to the device

[1320] Output: The itinerary of the travel plan is displayed on the terminal.

[1321] Step 5:

[1322] Users can review their travel plans and choose eco-friendly transport and sightseeing activities.

[1323] Specific actions: The user looks at the displayed itinerary, clicks on each activity to select it, and presses the "Confirm" button.

[1324] Input and Output:

[1325] Input: Selected data that the user types into the terminal

[1326] Output: Selected data sent from the device to the server and stored

[1327] Step 6:

[1328] During the trip, the user inputs the means of transportation actually used and the actions taken into the terminal.

[1329] Specific operation: The user records the transportation used and activities participated in each day on the device and presses the "Save" button.

[1330] Input and Output:

[1331] Input: Behavioral data that the user enters into the device

[1332] Output: Behavioral data sent from the device to the server in real time and updated

[1333] Step 7:

[1334] The server quantifies the environmental impact based on the input behavioral data.

[1335] Specific operation: The server analyzes the behavioral data, calculates the environmental impact, and quantifies it.

[1336] Input and Output:

[1337] Input: Behavioral data received by the server

[1338] Output: Calculated environmental impact figures

[1339] Step 8:

[1340] The results of the environmental impact are fed back to the user.

[1341] Specific operation: The server sends feedback to the user based on the calculation results, for example, "Your trip has successfully reduced CO2 emissions by 30% compared to a normal trip."

[1342] Input and Output:

[1343] Input: Quantification results of environmental impact

[1344] Output: A feedback message is printed to the terminal.

[1345] Step 9:

[1346] The server uses the collected behavioral data to update the generative AI model.

[1347] Specific operation: The server reflects the behavioral data as learning data in the generative AI model and updates the model.

[1348] Input and Output:

[1349] Input: Behavioral data stored on the server

[1350] Output: An updated generative AI model

[1351] Step 10:

[1352] The next time a travel plan is generated, the updated generative AI model will be used.

[1353] What it does: The server generates a new itinerary using the latest generative AI model.

[1354] Input and Output:

[1355] Enter: Next Travel Requirement

[1356] Output: A new, more accurate itinerary

[1357] These are the specific processing steps of the system program, which allows users to plan and carry out enjoyable trips while reducing the burden on the environment.

[1358] (Application example 1)

[1359] 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."

[1360] Today's travelers lack concrete means and tools for planning and implementing environmentally conscious travel. Furthermore, there are limited systems that quantify the environmental impact of travel and promote sustainable travel styles. In addition, there is a need to promote environmentally conscious behavior in everyday life as well. In particular, there is a need for systems that reduce the environmental impact of food delivery services. Against this backdrop, there is a need for systems that reduce the environmental impact of travel and everyday life and promote sustainable behavior.

[1361] 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.

[1362] In this invention, the server includes an input means for travelers to input travel requirements such as travel duration, budget, and destinations; a generation means for receiving the input information and generating an eco-friendly travel plan using a generative AI model based on an eco-friendly database; and a display means for displaying the generated travel plan to travelers in bookmark form. The server also includes an input means for inputting the transportation methods and activities used during the trip, and a quantification means for quantifying the environmental impact based on the input behavioral data and providing feedback to the travelers. The server also includes a learning means for updating the generative AI model using collected behavioral data to improve the accuracy of the next travel plan, and an eco-friendly food delivery means for selecting food orders and delivery methods based on information registered by the user, quantifying the environmental impact, and providing feedback. This enables travelers and everyday users to plan and execute activities that reduce environmental impact and specifically understand the results.

[1363] "Travel requirements" refers to information necessary for travelers to plan their trip, such as the duration, budget, and places to visit.

[1364] "Input means" refers to an interface that allows travelers and users to input necessary information (such as travel requirements and activities) into the system.

[1365] "Generation means" is a function that generates eco-friendly travel plans and food delivery plans based on input information.

[1366] The "display means" is an interface for visually displaying the generated plan to the traveler or user.

[1367] The "quantification means" is a function that calculates the environmental impact based on the input behavioral data and provides the results as feedback to the user.

[1368] The "learning method" is a function that uses collected behavioral data to update the generative AI model and improve accuracy the next time a plan is generated.

[1369] The "Eco-Friendly Database" is a database that collects information on environmentally friendly travel and food delivery.

[1370] A "generative AI model" is an artificial intelligence model that generates eco-friendly plans based on input data.

[1371] "Eco-friendly food delivery methods" is a function that allows users to select food ordering and delivery methods that take environmental impact into consideration, and quantifies the environmental impact and provides feedback.

[1372] "Environmental impact" refers to the degree of negative impact that a certain action has on the global environment.

[1373] The present invention is a system for travelers and everyday users to plan, execute, and evaluate environmentally friendly actions. The system can be applied to both travel planning and food delivery. The following specific steps and tools are used to implement the invention:

[1374] System Configuration

[1375] The system includes the following main elements:

[1376] 1. Input means (traveler or user interface)

[1377] 2. Generator (AI model that generates eco-friendly plans)

[1378] 3. Display means (interface that visually displays the generated plan)

[1379] 4. Quantification method (function to calculate environmental impact and provide feedback)

[1380] 5. Learning methods (the ability to improve the AI ​​model based on collected data)

[1381] 6. Eco-Friendly Food Delivery Methods (Functionality to optimize food ordering and delivery methods)

[1382] Hardware and Software

[1383] Hardware: Smartphone (iOS, Android)

[1384] Software: Python, API server (Django or Flask), database (PostgreSQL)

[1385] Data processing and calculation

[1386] 1. Input method: Travelers or users input requirements such as travel duration, budget, places to visit, food orders and desired delivery method through a smartphone application.

[1387] 2. Generation means: The server receives the input information and uses a generative AI model to generate eco-friendly travel plans or food delivery.

[1388] 3. Display: The generated plan is displayed as a detailed schedule or delivery information on the traveler's or user's device.

[1389] 4. Quantification method: The user inputs the transportation method and activities used during the trip or delivery, and the server quantifies the environmental impact based on that data. The results are visually fed back to the user.

[1390] 5. Learning method: The server analyzes the collected behavioral data and updates the generative AI model, improving the accuracy of the next plan generation.

[1391] 6. Eco-friendly food delivery methods: Users can order food through the application in a way that minimizes the environmental impact. The server calculates the environmental impact based on the selected delivery method and provides feedback.

[1392] Specific examples

[1393] For travel plans:

[1394] The user inputs travel requirements such as "stay length 3 days," "budget 100,000 yen," and "place to visit: Kyoto." The server generates the following travel plan based on this information:

[1395] Day 1: Arrive at Kyoto Station, go sightseeing by electric bicycle, and stay at an environmentally certified accommodation facility

[1396] Day 2: Take a zero-plastic waste tour and have dinner at an eco-friendly restaurant

[1397] Day 3: Participate in nature conservation activities in Arashiyama, then return home by train

[1398] For eco-friendly food delivery:

[1399] A user orders a "vegan salad" via "bicycle delivery." The server receives the order, calculates the environmental impact (e.g., 0.0 kg of CO2), and provides feedback to the user.

[1400] Prompt Sentence Examples

[1401] Example prompts to use with generative AI models:

[1402] Prompt: Model recommendations for generating eco-friendly delivery orders and reducing the environmental impact of each option.

[1403] Example: The user selects "Bicycle", calculates the environmental impact of the order, and provides feedback to the user.

[1404] The above is an embodiment of the present invention. The system of the present invention allows travelers and everyday users to enjoy activities while reducing the environmental impact, contributing to the spread of eco-friendly lifestyles.

[1405] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1406] Step 1:

[1407] Users input travel requirements such as travel period, budget, and places to visit, as well as food delivery order information and delivery preferences through a smartphone application. The input information is sent from the device to the server.

[1408] Input: Travel requirements (duration, budget, places to visit), food delivery order information (type of food, delivery method)

[1409] Output: User information sent to the server

[1410] Step 2:

[1411] Based on the travel requirements or delivery order information received by the server, the server uses a generative AI model while referencing an eco-friendly database to generate an optimal travel plan or delivery plan. The server creates prompts for the generative AI model and inputs them into the model.

[1412] Input: Travel requirements or delivery order information, prompt text

[1413] Output: Generated travel or delivery plan

[1414] Step 3:

[1415] The generated travel and delivery plans are sent to the terminal and displayed to the user. The plans are visually displayed in bookmark format or as detailed order information.

[1416] Input: Generated plan

[1417] Output: Plan displayed on the terminal

[1418] Step 4:

[1419] While traveling or using food delivery services, users input the transportation method used and their activities into a smartphone application, and the input information is sent from the device to a server.

[1420] Input: Means of transportation used, activities

[1421] Output: Behavioral data sent to the server

[1422] Step 5:

[1423] The server quantifies the environmental impact based on the behavioral data received. The calculation is performed using a preset environmental impact coefficient for the means of transportation and the behavior.

[1424] Input: Behavioral data

[1425] Output: Quantified environmental impact data

[1426] Step 6:

[1427] The server sends the calculated environmental impact data to the user's terminal, allowing the user to visually check it.

[1428] Input: Quantified environmental impact data

[1429] Output: Feedback displayed on the terminal

[1430] Step 7:

[1431] The server uses collected behavioral data to update the generative AI model, improving accuracy when generating the next travel plan or delivery plan. Learning is performed based on behavioral data and environmental impact data.

[1432] Input: behavioral data, environmental impact data

[1433] Output: An updated generative AI model

[1434] 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.

[1435] This invention is a system that enables travelers to plan and execute eco-friendly trips, and in particular has the function of providing eco-friendly travel plans that reflect the user's emotional state.By incorporating an emotion engine, it is possible to analyze the user's emotional state in real time and provide an optimal plan based on that.

[1436] First, users download the application and create an account. They enter basic information such as name, age, place of residence, and travel preferences. This information is sent from the device to the server and stored in a database.

[1437] Next, the user inputs specific travel requirements, such as the duration of the trip, budget, and places to visit. The device sends this information to the server, which then references an eco-friendly database and uses a generative AI model to create an eco-friendly itinerary. The generated itinerary is then displayed to the user in bookmark form on the device.

[1438] A further feature of the present invention is the incorporation of an emotion engine, which can analyze the user's emotions and grasp their state in real time. For example, if the user is tired or stressed, the emotion engine can detect this and add relaxation activities or quiet tourist spots to the travel plan.

[1439] As a concrete example, if a user inputs the travel requirements of "stay length 3 days," "budget 100,000 yen," and "place to visit: Kyoto," and the emotion engine analyzes that "the user is looking for relaxation," the server will generate the following travel plan:

[1440] Day 1: Arrive at Kyoto Station, visit a tranquil temple by eco-friendly electric bicycle, and stay overnight at an eco-certified ryokan

[1441] Day 2: Join a stress-relieving yoga session and enjoy a healthy dinner at a vegan restaurant

[1442] Day 3: Nature walk and meditation in Arashiyama, then return home by train

[1443] During a trip, the user inputs the transportation method used and the actions taken into the device. The device then sends this information to the server, which then quantifies the environmental impact based on the input behavioral data. The results are then sent to the device and displayed visually to the user.

[1444] The emotion engine continues to run throughout the trip, monitoring the user's emotional state: if the user is feeling stressed, for example, the server will instantly adjust the plan and suggest options for relaxation activities or visiting quiet places.

[1445] After the trip is completed, the server updates the generative AI model using the collected behavioral and emotional data, improving the accuracy of future trip plans and providing users with more optimal, eco-friendly travel plans.

[1446] The system of the present invention allows users to plan environmentally friendly trips while taking their emotional state into consideration, thereby reducing stress and achieving a sustainable travel style. In this way, by taking into consideration both the emotions of travelers and the environmental impact, the present invention provides maximum satisfaction to travelers and promotes environmentally friendly travel.

[1447] The processing flow will be explained below.

[1448] Step 1:

[1449] A user downloads the application and creates an account. They enter basic information such as their name, age, place of residence, and travel preferences. The device then sends this information to the server, which stores it in a database.

[1450] Step 2:

[1451] The user inputs specific travel requirements such as travel duration, budget, places to visit, etc. The device sends this information to the server.

[1452] Step 3:

[1453] The server receives the travel requirements, consults the eco-friendly database, and uses a generative AI model to generate an eco-friendly travel plan.

[1454] Step 4:

[1455] The server sends the generated travel plan to the terminal, which displays the travel plan to the user in bookmark format.

[1456] Step 5:

[1457] The user reviews the proposed travel plan and selects from eco-friendly transportation and sightseeing activities, and the selection information is sent from the device to the server.

[1458] Step 6:

[1459] The server records the selected options and stores them in a database, while the emotion engine analyzes the user's emotions in real time and reflects them in the plan.

[1460] Step 7:

[1461] During the trip, the user inputs the transportation method used and the details of their activities into the device. The emotion engine analyzes the user's emotional state (stress, joy, etc.) through cameras and sensors. This data is sent from the device to the server.

[1462] Step 8:

[1463] The server quantifies the environmental impact based on the input behavioral and emotional data, and the quantification results are sent to the terminal, which then visually displays them to the user.

[1464] Step 9:

[1465] If the user feels stressed during the trip, the emotion engine will detect this and the server will instantly adjust the plan, suggesting options such as relaxation activities or quiet tourist spots, which the device will display to the user and prompt them to make a selection.

[1466] Step 10:

[1467] After the trip is over, the server uses the collected behavioral and emotional data to update the generative AI model, which improves the accuracy of the next itinerary.

[1468] Step 11:

[1469] When the user plans another trip, the process repeats from step 2. This time, the system provides more accurate eco-friendly travel plans that incorporate past data and sentiment analysis.

[1470] Example 2

[1471] 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."

[1472] Conventional travel planning systems have the problem of only considering the traveler's travel requirements and environmental impact, and are unable to adjust the plan to reflect the traveler's emotional state. In particular, since planning does not take into account emotional states such as stress and fatigue during the trip, it can be difficult for travelers to truly relax and enjoy the trip. Another issue is that the inability to adjust the plan in real time makes it difficult to respond quickly to unexpected situations. To solve these issues, a system is needed that analyzes the traveler's emotional state and provides a travel plan that reflects that.

[1473] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1474] In this invention, the server includes an input means for the traveler to input travel requirements such as travel duration, budget, and places to visit, a generation means for receiving the input information and generating an environmentally conscious travel plan using a generative model based on a database, and a display means for displaying the generated travel plan to the traveler in bookmark form.

[1475] This makes it possible to analyze the traveler's emotional state in real time and dynamically adjust the travel plan based on the results.In addition, by providing an input means for inputting the means of transportation used during the trip and the activities of the traveler, a quantification means for quantifying the environmental impact based on the input activity data and feeding the results back to the traveler, and a learning means for updating the generative model using the collected activity data and improving the accuracy of the next travel plan, it is possible to continue proposing the optimal eco-friendly travel plan for the traveler.

[1476] A "tourist" is someone who travels.

[1477] "Duration of Trip" means the number of days or hours over which the trip takes place.

[1478] "Budget" refers to the total amount of money used for travel.

[1479] "Destinations" are places or areas that a traveler visits during their trip.

[1480] "Travel requirements" refer to the conditions and desires that a traveler has when traveling.

[1481] "Input means" means a device or interface through which a traveler inputs information.

[1482] A "database" is a system that stores and manages data in an organized manner.

[1483] A "generative model" is an algorithm for generating data based on specific inputs.

[1484] "Environmentally friendly" means that the goal is to minimize the impact on the environment.

[1485] A "travel plan" is a detailed plan or schedule for a trip.

[1486] "Generation means" refers to a mechanism for generating a travel plan based on input information.

[1487] "Display means" refers to a device or interface that visually presents the generated travel plan to a traveler.

[1488] "Transportation" means any method or device of travel used during a trip.

[1489] "Behavioral content" refers to the specific activities and experiences that took place during the trip.

[1490] "Quantification means" refers to a means of expressing behavioral data and environmental impacts numerically.

[1491] "Emotion analysis means" refers to devices or algorithms that analyze the emotional state of travelers and utilize the results.

[1492] A "prompt sentence" is an instruction sentence to be input to a generative model.

[1493] A "learner" is a mechanism for improving a generative model based on collected data.

[1494] This invention is a system that enables travelers to plan and execute eco-friendly trips. The system has a function to provide eco-friendly travel plans that reflect the traveler's emotional state. To achieve this, the system is equipped with an emotion engine that analyzes the traveler's emotional state in real time and provides the optimal plan based on that analysis.

[1495] First, users need to download the application and create an account, entering basic information such as name, age, place of residence, travel preferences, etc. This information is then sent from the device to the server and stored in a database.

[1496] Next, the user inputs specific travel requirements, such as the duration of the trip, budget, and places to visit. The device sends this information to the server, which then references an eco-friendly database and uses a generative AI model to create an eco-friendly itinerary. The generated itinerary is then displayed to the user in bookmark form on the device.

[1497] The system of the present invention is characterized by its incorporation of an emotion engine, which analyzes the user's emotional state and can grasp that state in real time. For example, if the user is tired or stressed, the emotion engine can detect this and add relaxation activities or quiet tourist spots to the travel plan.

[1498] As a concrete example, if a user inputs the travel requirements of "stay length 3 days," "budget 100,000 yen," and "place to visit: Kyoto," and the emotion engine analyzes that "the user is looking for relaxation," the server will generate the following travel plan:

[1499] Day 1: Arrive at Kyoto Station, visit a tranquil temple by eco-friendly electric bicycle, and stay overnight at an eco-certified accommodation

[1500] Day 2: Join a stress-relieving yoga session and enjoy a healthy dinner at a vegan restaurant

[1501] Day 3: Nature walk and meditation in Arashiyama, then return home by train

[1502] During a trip, the user inputs the transportation method used and the actions taken into the device. The device then sends this information to the server. The server then quantifies the environmental impact based on the input behavioral data. The results are sent to the device and displayed visually to the user.

[1503] The emotion engine also continues to run while traveling, monitoring the user's emotional state: if the user is feeling stressed, for example, the server will instantly adjust the plan and suggest options for relaxation activities or visiting quiet places.

[1504] After the trip is over, the server updates the generative AI model using the collected behavioral and emotional data, which improves the accuracy of future trip plans and makes it possible to provide users with more optimal, eco-friendly travel plans.

[1505] Examples of prompts that could be fed into a generative AI model include:

[1506] User Basic Information:

[1507] Name: {name}

[1508] Age: {age}

[1509] Place of residence: {Place of residence}

[1510] Travel Preferences: {Travel Preferences}

[1511] Travel requirements:

[1512] Travel period: {Travel period}

[1513] Budget: {budget}

[1514] Place visited: {place visited}

[1515] Travel plan generation:

[1516] User's emotional state: {Emotional state}

[1517] By inputting these prompts into a generative AI model, the system can generate an optimal travel plan.

[1518] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1519] Step 1: User creates an account

[1520] Users download the application and create an account.

[1521] Input: Basic information such as name, age, location, and travel preferences

[1522] Specific behavior: The user enters their information into the registration form and presses the submit button.

[1523] Output: The registration information is sent to the server and stored in a database.

[1524] Data processing: The information is converted into JSON format and stored in the server database.

[1525] Step 2: User enters travel requirements

[1526] The user inputs specific travel requirements such as travel duration, budget, and places to visit.

[1527] Input: Travel requirements such as travel period, budget, and places to visit

[1528] Specific behavior: The user enters data into the travel requirements input screen within the application and presses the submit button.

[1529] Output: The travel requirements are sent to the server and stored in the Eco-Friendly Database.

[1530] Data processing: The information is converted into JSON format and stored in the server database.

[1531] Step 3: The server generates the travel plan

[1532] Based on the received travel requirements, the server references an eco-friendly database and generates a travel plan using a generative AI model.

[1533] Input: Travel Requirements

[1534] Specific operation: The server retrieves the relevant information from the database via a query, creates a prompt sentence, and inputs it into the generative AI model.

[1535] Output: Final itinerary

[1536] Data processing: The generative AI model analyzes the prompt and generates a travel plan in text format.

[1537] Step 4: Display your travel plans

[1538] The terminal displays the generated travel plan to the user in bookmark form.

[1539] Input: Travel Plan

[1540] Specific operation: The server sends the generated travel plan in JSON format to the terminal, which parses it and displays it.

[1541] Output: A visual representation of the itinerary to the user

[1542] Data processing: The terminal parses the JSON data and displays it in a user-friendly interface.

[1543] Step 5: Data collection during the trip

[1544] The user inputs the means of transportation used and the actions taken during the trip into the terminal.

[1545] Input: Transportation method, activities

[1546] Specific operation: The user enters the transportation method and details of the activity into a designated form within the app and submits it.

[1547] Output: Behavioral data sent to the server

[1548] Data processing: The information is converted into JSON format and stored in the server database.

[1549] Step 6: Use sentiment analysis tools

[1550] The server uses an emotion engine to analyze the user's emotional state in real time and dynamically adjust the travel plan.

[1551] Input: Emotion data (sensor information and metadata)

[1552] Specific operation: The server receives information from the emotion engine and makes adjustments such as adding relaxation activities to the travel plan.

[1553] Output: Dynamically adjusted itinerary

[1554] Data processing: Recalculate and update travel plan data based on the sentiment analysis results.

[1555] Step 7: Post-trip data update steps

[1556] After the trip ends, the server updates the generative AI model based on the collected behavioral and emotional data.

[1557] Input: Behavioral data, emotion data

[1558] Specific operation: After the trip ends, the server trains the generative AI model based on the collected data.

[1559] Output: An updated generative AI model

[1560] Data processing: Integrate collected data, generate new training datasets, and retrain generative AI models.

[1561] (Application example 2)

[1562] 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."

[1563] For modern travelers, achieving eco-friendly travel is important, but it is difficult to easily create an optimal plan that takes into account their emotional state and environmental impact during the trip. Additionally, while there is a demand for travel food choices that take environmental impact into consideration, there is no system that provides appropriate food delivery options based on emotional state. Therefore, a system is needed that allows travelers to enjoy a comfortable and environmentally conscious trip.

[1564] 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: an input means through which a traveler inputs travel requirements such as travel duration, budget, and places to visit; a generation means that receives the input information and generates an eco-friendly travel plan using a generative AI model based on an eco-friendly database; an emotion analysis means that analyzes the user's emotional state and provides an optimal travel plan; a display means that displays the generated travel plan to the traveler in bookmark form; an input means for inputting the means of transportation used during the trip and the activities of the user; a quantification means that quantifies the environmental impact based on the input activity data and provides feedback to the traveler; and a learning means that updates the generative AI model using collected activity data to improve the accuracy of the next travel plan. This makes it possible to provide convenient and eco-friendly travel and meal plans that correspond to the traveler's emotional state.

[1565] "Trip duration, budget, and places to visit" refers to the number of days a traveler plans to travel, the amount of money they have available, and the places they plan to go.

[1566] "Input means" refers to a device or interface through which a user provides information to a system.

[1567] "Receiving means" refers to a device or interface for receiving information input by a user on the server side.

[1568] An "eco-friendly database" refers to a database for managing and storing environmentally friendly information and data.

[1569] A "generative AI model" refers to an algorithm or system that uses artificial intelligence technology to automatically generate optimal plans and solutions from data.

[1570] "Generator" refers to a device or algorithm that generates an optimal plan or solution based on specified conditions and data.

[1571] "Emotion analysis means" refers to a device or system for analyzing a user's emotional state and assessing that state.

[1572] "Display means" refers to a device or interface for visually presenting the generated plan or information to the user.

[1573] "Quantification means" refers to devices or algorithms for calculating quantitative figures based on behavioral data.

[1574] "Learning tools" refers to devices and algorithms that update the AI ​​model based on collected data and improve the accuracy of the next generation plan.

[1575] "Suggestion means" refers to a device or system that presents appropriate choices and options to users and supports them in making optimal decisions.

[1576] "Food delivery plan" refers to a plan that plans and suggests the optimal way to deliver meals, taking into account the user's emotional state and environmental impact.

[1577] This system provides optimal eco-friendly travel and food delivery plans that take into account a traveler's emotional state and environmental concerns. Users download a dedicated application to their smartphone and create an account. This allows the system to create detailed travel plans and provide appropriate meal options based on the traveler's emotional state.

[1578] System Configuration

[1579] The system of the present invention comprises the following means:

[1580] Input means: An interface is provided as a smartphone application for users to input information such as the duration of the trip, budget, places to visit, and emotional state of the day.

[1581] Reception means: The server receives information entered by the user. This includes the ability to send and receive data from smartphones via API.

[1582] Eco-Friendly Database: A database for managing and storing environmentally friendly information and data, including, for example, a list of eco-friendly transportation options and tourist destinations.

[1583] Generative AI model: Automatically generates optimal travel plans based on input information and eco-friendly data. This model is trained using machine learning algorithms.

[1584] Generator: Includes an algorithm for generating eco-friendly itineraries using a generative AI model.

[1585] Sentiment analysis: Analyze the emotional state entered by the user and provide optimal travel plans or food delivery options in real time. For example, using a sentiment analysis engine.

[1586] Display method: The generated travel plan is displayed to the user in bookmark format on their smartphone, allowing them to easily check the plan.

[1587] Quantification method: Enter the transportation method and activities used during the trip and quantify the environmental impact based on that data. This includes an environmental impact calculation algorithm.

[1588] Learning method: The generative AI model is updated using collected behavioral data to improve the accuracy of the next trip plan. For example, the model is updated daily using a machine learning algorithm.

[1589] Recommendation tools: Suggest eco-friendly transport options, tourist activities, and suitable food delivery options. This includes a recommendation engine based on emotional state, among other things.

[1590] Processing flow explanation

[1591] Hardware and software used

[1592] Users access the application using their smartphones and enter the necessary information. The server uses APIs and a database management system (DBMS) to receive and process the data sent from the application, and runs generative AI models (e.g., TensorFlow, PyTorch) to generate eco-friendly travel plans.

[1593] Specific example explanation

[1594] For example, if a user inputs "Length of stay: 3 days," "Budget: 100,000 yen," "Place to visit: Kyoto," and "Emotional state: Seeking relaxation," the server will use its emotion analysis engine to analyze the user's emotional state, and the generative AI model will generate the following itinerary:

[1595] Day 1: Arrive at Kyoto Station and travel by eco-friendly public transport to visit a tranquil temple and stay overnight in an eco-certified accommodation.

[1596] Day 2: Attend a stress-relieving yoga session and have dinner at an organic restaurant.

[1597] Day 3: Nature walk and meditation activity in Arashiyama, then return home by public transport.

[1598] It also suggests "vegan dishes with a relaxing effect" as a food delivery plan based on the user's emotional state.

[1599] Prompt Sentence Examples

[1600] Examples of prompts based on the user's emotional state include:

[1601] "If the user's emotional state is relaxation, suggest eco-friendly travel itineraries and food delivery options. The user's preferences are vegan and they prefer organic food."

[1602] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1603] Step 1:

[1604] Using a smartphone application, users input details such as the duration of their trip, their budget, the places they plan to visit, and their emotional state for the day. The input data is then sent from the device to a server. The input data includes the traveler's planned travel range and their current emotional state.

[1605] Step 2:

[1606] The server analyzes the received travel requirements and emotional state. This involves matching the input data with an eco-friendly database and utilizing a generative AI model to generate a travel plan based on that. The input emotional state is also evaluated in real time using an emotion analysis engine. The input data are travel requirements and emotional data, and the output data are the analysis results and an initial plan.

[1607] Step 3:

[1608] The generated travel plan is sent back to the terminal from the server and displayed to the user in the form of a bookmark. This bookmark includes details of places to visit, accommodations, transportation, etc. The displayed data becomes the contents of the travel plan.

[1609] Step 4:

[1610] The user checks the travel plan and selects the transportation method and options for recording the activities they plan to use during the trip. The information selected by the user is then sent from the device to the server. The input data is the selected activities, and the output data is the record of the activities.

[1611] Step 5:

[1612] The server quantifies the environmental impact based on the travel behavior data. This process uses an environmental impact calculation algorithm to quantify the environmental impact of the transportation method used and the behavior. The input data is the behavior, and the output data is the numerical value of the environmental impact.

[1613] Step 6:

[1614] The quantified environmental impact is fed back to the device and displayed visually, allowing users to understand the impact their actions have had on the environment. The data displayed on the device is numerical information on the environmental impact.

[1615] Step 7:

[1616] The server updates the generative AI model using the collected behavioral and emotional data, thereby improving the accuracy of generating future travel plans. The input data is behavioral and emotional data, and the output data is the updated generative AI model.

[1617] Step 8:

[1618] If the user's emotional state changes during the trip planning process, the emotion analysis engine recognizes the change and reevaluates and adjusts the plan in real time based on the new emotional state. This ensures that the optimal plan is always provided based on the user's latest emotional state. The input data is the change in emotional state, and the output data is the adjusted trip plan.

[1619] Step 9:

[1620] This system proposes food delivery options based on the user's emotional state. The server analyzes the user's emotional state and proposes the optimal food delivery plan using a generative AI model. The input data is the emotional state, and the output data is the food delivery plan.

[1621] Through the above steps, users can enjoy optimal travel plans that take into account their emotional state and environmental impact during the trip, enabling them to enjoy a comfortable and sustainable trip.

[1622] 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.

[1623] 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.

[1624] 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.

[1625] 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.

[1626] 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.

[1627] 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.

[1628] 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).

[1629] 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.

[1630] 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."

[1631] 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.

[1632] 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).

[1633] 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.

[1634] 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.

[1635] 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.

[1636] 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.

[1637] 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.

[1638] 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.

[1639] 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.

[1640] 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.

[1641] 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.

[1642] 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.

[1643] The following is further disclosed regarding the above embodiment.

[1644] (Claim 1)

[1645] an input means for a traveler to input travel requirements such as travel period, budget, and places to visit;

[1646] A generating means for receiving the input information and generating an eco-friendly travel plan using a generating AI model based on the eco-friendly database;

[1647] a display means for displaying the generated travel plan to the traveler in bookmark form;

[1648] an input means for inputting the means of transportation and activities used during the trip;

[1649] A quantification means for quantifying the environmental impact based on the input behavioral data and providing feedback on the results to the traveler;

[1650] A learning method that uses collected behavioral data to update the generative AI model and improve the accuracy of the next trip plan.

[1651] A system including:

[1652] (Claim 2)

[1653] 10. The system of claim 1, further comprising a suggestion means for suggesting eco-friendly transportation options and tourist activities and allowing the traveler to select appropriate options.

[1654] (Claim 3)

[1655] 2. The system according to claim 1, further comprising visualization means for quantifying traveler behavior data as an environmental impact and visually displaying the results.

[1656] "Example 1"

[1657] (Claim 1)

[1658] an input means for a traveler to input travel requirements such as travel period, budget, and places to visit;

[1659] A generation means for receiving the input information and generating an eco-friendly travel plan using a generation AI model based on the environmental consideration information;

[1660] a display means for displaying the generated travel plan to the traveler in an itinerary format;

[1661] an input means for inputting the means of transportation and activities used during the trip;

[1662] A quantification means for quantifying the environmental impact based on the input behavioral data and providing feedback on the results to the traveler;

[1663] A learning method that uses collected behavioral data to update the generative AI model and improve the accuracy of the next trip plan.

[1664] A communication means for transmitting the input information to a server in real time and storing it in a database;

[1665] An analytical method that analyzes travel behavior data and quantifies the environmental impact in real time and provides feedback.

[1666] Choices of environmentally friendly transportation and tourism activities;

[1667] A system including:

[1668] (Claim 2)

[1669] 10. The system according to claim 1, further comprising a suggestion means for suggesting environmentally friendly transportation options and tourist activities and allowing the traveler to select an appropriate option.

[1670] (Claim 3)

[1671] 2. The system according to claim 1, further comprising a visualization means for quantifying traveler behavior data as an environmental impact and visually displaying the results.

[1672] "Application Example 1"

[1673] (Claim 1)

[1674] an input means for a traveler to input travel requirements such as travel period, budget, and places to visit;

[1675] A generating means for receiving the input information and generating an eco-friendly travel plan using a generating AI model based on the eco-friendly database;

[1676] a display means for displaying the generated travel plan to the traveler in bookmark form;

[1677] an input means for inputting the means of transportation and activities used during the trip;

[1678] A quantification means for quantifying the environmental impact based on the input behavioral data and providing feedback on the results to the traveler;

[1679] A learning method that uses collected behavioral data to update the generative AI model and improve the accuracy of the next trip plan.

[1680] An eco-friendly food delivery service that selects food orders and delivery methods based on the information registered by users, and quantifies the environmental impact and provides feedback.

[1681] A system including:

[1682] (Claim 2)

[1683] 10. The system of claim 1, further comprising a suggestion means for suggesting eco-friendly transportation options and tourist activities and allowing the traveler to select appropriate options.

[1684] (Claim 3)

[1685] 2. The system according to claim 1, further comprising visualization means for quantifying traveler behavior data as an environmental impact and visually displaying the results.

[1686] "Example 2: Combining Emotion Engines"

[1687] (Claim 1)

[1688] an input means for a traveler to input travel requirements such as travel period, budget, and places to visit;

[1689] A generation means receives the input information and generates an environmentally friendly travel plan using a generative model based on the database;

[1690] a display means for displaying the generated travel plan to the traveler in bookmark form;

[1691] an input means for inputting the means of transportation and activities used during the trip;

[1692] A quantification means for quantifying the environmental impact based on the input behavioral data and providing feedback on the results to the traveler;

[1693] A learning method that uses collected behavioral data to update the generative model and improve the accuracy of the next trip plan.

[1694] an emotion analysis means for analyzing the emotional state of a traveler in real time and dynamically adjusting the travel plan based on the results;

[1695] A system including:

[1696] (Claim 2)

[1697] 10. The system of claim 1, further comprising a suggestion means for suggesting environmentally friendly transportation options and tourist activities and allowing the traveler to select appropriate options.

[1698] (Claim 3)

[1699] 2. The system according to claim 1, further comprising visualization means for quantifying traveler behavior data as an environmental impact and visually displaying the results.

[1700] "Application example 2 when combining emotion engines"

[1701] (Claim 1)

[1702] an input means for a traveler to input travel requirements such as travel period, budget, and places to visit;

[1703] A generating means for receiving the input information and generating an eco-friendly travel plan using a generating AI model based on the eco-friendly database;

[1704] An emotion analysis means for analyzing the user's emotional state and providing an optimal travel plan;

[1705] a display means for displaying the generated travel plan to the traveler in bookmark form;

[1706] an input means for inputting the means of transportation and activities used during the trip;

[1707] A quantification means for quantifying the environmental impact based on the input behavioral data and providing feedback on the results to the traveler;

[1708] A learning method that uses collected behavioral data to update the generative AI model and improve the accuracy of the next trip plan.

[1709] A system including:

[1710] (Claim 2)

[1711] 10. The system of claim 1, further comprising a suggestion means for suggesting eco-friendly transportation options and tourist activities and allowing the traveler to select appropriate options.

[1712] (Claim 3)

[1713] 10. The system of claim 1, further comprising means for suggesting suitable food delivery options based on the traveler's emotional state and quantifying the environmental impact of the food delivery plan selected by the traveler. [Explanation of symbols]

[1714] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. an input means for a traveler to input travel requirements such as travel period, budget, and places to visit; A generating means for receiving the input information and generating an eco-friendly travel plan using a generating AI model based on the eco-friendly database; a display means for displaying the generated travel plan to the traveler in bookmark form; an input means for inputting the means of transportation and activities used during the trip; A quantification means for quantifying the environmental impact based on the input behavioral data and providing feedback on the results to the traveler; A learning method that uses collected behavioral data to update the generative AI model and improve the accuracy of the next trip plan. A system including:

2. The system of claim 1 , further comprising a suggestion means for suggesting eco-friendly transportation options and tourist activities and allowing the traveler to select appropriate options.

3. 2. The system according to claim 1, further comprising visualization means for quantifying traveler behavior data as an environmental load and visually displaying the results.

Images