system

Abstract

We provide systems that improve the quality of the tourist experience. [Solution] A system having the following functions to solve the challenges of the tourism industry: means for collecting data on tourist destinations, accommodations and means of transportation; means for analyzing the collected data and calculating the optimal allocation of personnel, materials and ingredients; means for analyzing the workload at accommodations and identifying processes that can be automated; means for generating an optimal travel plan based on individual traveler profile data; means for proposing transportation and routes with low carbon dioxide emissions; means for notifying the user terminal of the generated travel plan; and means for collecting user feedback and reflecting it in optimizing the travel plan.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is 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 an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, the tourism industry has faced various challenges such as labor shortages, delays in digitalization, and the realization of sustainable tourism. Also, the optimal allocation of resources required for tourist destinations, accommodation facilities, and transportation, as well as the reduction of carbon dioxide emissions, are important issues. Furthermore, there is also a demand for proposing an optimal travel plan for individual travelers and collecting feedback after the trip and reflecting it in the next plan. The purpose of the present invention is to provide a system that solves these various challenges and improves the quality of the tourism experience.

Means for Solving the Problems

[0005] This invention provides a system with the following functions to solve the challenges of the tourism industry: It includes means for collecting data on tourist destinations, accommodations, and transportation, and for analyzing this data to calculate the optimal allocation of personnel, materials, and ingredients. It also includes means for analyzing the workload at accommodations and identifying processes that can be automated. Furthermore, it includes means for generating optimal travel plans based on individual traveler profile data and proposing transportation methods and routes with low carbon dioxide emissions. The generated travel plan is notified to the user terminal, and the system includes means for collecting post-trip feedback and incorporating it into future plans. In this way, this invention can solve the multifaceted challenges of the tourism industry and provide an efficient and sustainable tourism experience.

[0006] A "tourist destination" is a place that tourists visit and where tourist resources such as natural landscapes, cultural properties, and museums exist.

[0007] "Accommodation facilities" are places that provide facilities for travelers to stay, and include hotels, inns, and private accommodations.

[0008] "Transportation" refers to methods used to move people or goods from one point to another, and includes means such as trains, buses, taxis, and airplanes.

[0009] "Data collection" refers to the process of acquiring information about tourist destinations, accommodations, and transportation methods, and storing it in a database.

[0010] "Data analysis" is the process of analyzing collected data and extracting meaningful information and trends.

[0011] "Optimal allocation of personnel" refers to a plan for efficiently deploying the necessary personnel to tourist destinations and accommodations.

[0012] "Optimal allocation of materials" refers to a plan for efficiently distributing the necessary materials and equipment for tourist destinations and accommodations.

[0013] "Optimal allocation of ingredients" refers to a plan for efficiently distributing ingredients supplied to tourist destinations and accommodations.

[0014] "Workload analysis" is the process of evaluating business processes at accommodations and tourist destinations and identifying the workload in order to improve work efficiency.

[0015] An "automatable process" refers to a part of a manually performed business process that can be automated using robots or software.

[0016] "Profile data" refers to data that includes personal information such as the traveler's age, gender, preferences, budget, and purpose of travel.

[0017] A "travel plan" is a trip plan that includes selecting tourist destinations, booking accommodations, and arranging transportation.

[0018] "Transportation with low carbon dioxide emissions" refers to eco-friendly modes of transport such as electric vehicles, hybrid vehicles, and bicycles, which reduce the burden on the environment.

[0019] A "device" refers to a device used by a user to check their travel plan or enter feedback, and includes smartphones, tablets, and personal computers.

[0020] "Feedback" refers to evaluations that include opinions and impressions about the experiences travelers had during their trip. [Brief explanation of the drawing]

[0021] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4]It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0022] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0023] First, the language used in the following description will be explained.

[0024] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0025] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0026] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0027] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0028] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0029] [First Embodiment]

[0030] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0031] As shown in Figure 1, the 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.

[0032] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0033] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0034] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0035] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0036] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0037] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0039] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0040] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0041] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0042] This invention is a system designed to solve challenges in the tourism industry. Its purpose is to collect and analyze data on tourist destinations, accommodations, and transportation, and to generate and notify users of optimal travel plans. This system is primarily composed of servers, terminals, and users.

[0043] Data collection

[0044] 1. Obtaining tourist information

[0045] The server sends a GET request to the API endpoint of the tourist destination.

[0046] The server parses the returned JSON data and extracts detailed information such as the name of the tourist attraction, location information, rating, and opening hours.

[0047] The server stores this information in a database.

[0048] 2. Acquisition of weather data

[0049] The server accesses the API of a weather data provider service to obtain the weekly weather forecast for the target area.

[0050] The server analyzes the weather information it receives, links it to tourist destination information, and stores it in a database.

[0051] 3. Acquisition of traffic condition data

[0052] The server calls the API of the traffic information service to obtain real-time traffic condition data.

[0053] The server analyzes this information and updates the database with delay information and operating status for each mode of transport (trains, buses, taxis, etc.).

[0054] Data Analysis

[0055] 1. Demand forecasting and optimization

[0056] The server uses AI models (e.g., time-series forecasting models) based on past tourism data to predict demand for the following month.

[0057] The server calculates the optimal allocation of personnel, materials, and ingredients, optimizing resource management.

[0058] 2. Workload analysis

[0059] The server analyzes operational data from the accommodation facility (for example, the frequency of room cleaning and the response time for front desk staff).

[0060] The server identifies business processes that can be automated and simulates the effects of automation using robots and software.

[0061] Plan creation

[0062] 1. Generating an individualized optimization plan

[0063] The server generates a travel plan based on the user's profile data (age, length of stay, budget, preferences, etc.).

[0064] The server uses an AI model (e.g., a recommendation system) to select tourist spots, accommodations, and restaurants based on the user's preferences.

[0065] 2. Creating a Sustainable Tourism Plan

[0066] The server calculates transportation methods and routes with the lowest carbon emissions.

[0067] The server generates plans that include eco-friendly accommodations and restaurants.

[0068] User notifications

[0069] 1. Plan notification

[0070] The server generates a travel plan and sends it to the terminal in JSON format.

[0071] The device displays notifications to the user as push alerts.

[0072] 2. Displaying plan details

[0073] The device displays detailed plan information (transportation, accommodation, sightseeing spots, etc.) to the user via the UI.

[0074] Execution Management

[0075] 1. Plan Review and Approval

[0076] The user reviews the plan on their device and chooses whether or not to proceed.

[0077] The terminal sends the user's selection to the server.

[0078] 2. Notification to relevant organizations

[0079] The server notifies accommodation providers, transportation companies, tour guides, and other relevant parties of the reservation information.

[0080] The server verifies that the notification was received successfully and sends a reservation confirmation to the user.

[0081] Feedback processing

[0082] 1. Gathering feedback

[0083] After the trip ends, the device displays a satisfaction survey to the user.

[0084] The device collects user feedback and sends it to the server.

[0085] 2. Analysis of Feedback

[0086] The server stores the feedback in a database and analyzes it using an AI model.

[0087] The server uses the analysis results to improve the plan generation algorithm and optimize it.

[0088] Specific example

[0089] Data collection examples

[0090] The server retrieves information about Tokyo Tower from a tourist destination API.

[0091] The server retrieves the weekly weather forecast for Tokyo from a weather data provider.

[0092] The server retrieves real-time train operation information from a traffic information service.

[0093] Data analysis example

[0094] The server uses an AI model to predict the demand for Tokyo tourism in the following month.

[0095] The server analyzes operational data from accommodation facilities and simulates the effects of introducing cleaning robots.

[0096] Plan creation example

[0097] The server generates a 3-day Kyoto and Nara travel plan based on profile data indicating a couple's trip.

[0098] The server creates sustainable plans that include train travel and eco-friendly accommodations.

[0099] User notification example

[0100] The server generates a travel plan and sends it to the device.

[0101] The device will notify the user via push notification and display the detailed plan.

[0102] Execution Management Example

[0103] The user reviews the plan and selects to proceed on their device.

[0104] The server notifies accommodations and transportation providers of the reservation information and sends a reservation confirmation to the user.

[0105] Feedback Processing Example

[0106] The device displays a post-trip feedback survey to the user and sends it to the server.

[0107] The server analyzes the feedback and incorporates it into the next plan generation.

[0108] As a result, the system of the present invention can solve the multifaceted challenges of the tourism industry and provide efficient and sustainable tourism experiences.

[0109] The following describes the processing flow.

[0110] Step 1:

[0111] The server sends a GET request to the tourist destination's API endpoint. The information retrieved may include the name of the tourist destination, its location, its rating, and its opening hours.

[0112] Step 2:

[0113] The server parses the returned JSON data and extracts detailed information about the tourist destination. The extracted data includes the name of the tourist destination, location information, rating, opening hours, etc.

[0114] Step 3:

[0115] The server saves detailed information about tourist destinations to a database. The saved data is used in subsequent analysis processes.

[0116] Step 4:

[0117] The server accesses the weather data service's API. It sends a GET request to retrieve the weekly weather forecast for the target area.

[0118] Step 5:

[0119] The server analyzes the weather information it has acquired. The analyzed weather information is then linked to tourist destination information and stored in a database.

[0120] Step 6:

[0121] The server calls the API of a traffic information service to obtain real-time traffic data for the target area. The information obtained includes delay information, service status, and more.

[0122] Step 7:

[0123] The server analyzes traffic data and stores delay information and operational status for each mode of transport in a database.

[0124] Step 8:

[0125] The server uses historical tourism data and an AI model (e.g., a time-series forecasting model) to predict demand for the following month. The prediction results are stored in a database.

[0126] Step 9:

[0127] The server calculates the optimal allocation of personnel, materials, and ingredients based on demand forecast data. The calculation results are then applied to tourist destinations and accommodations.

[0128] Step 10:

[0129] The server analyzes operational data from the accommodation facility. This operational data includes information such as the frequency of room cleaning and the response time for front desk staff.

[0130] Step 11:

[0131] The server identifies business processes that can be automated and simulates the effectiveness of automation using robots and software. The simulation results are stored in a database.

[0132] Step 12:

[0133] The server generates the optimal travel plan based on the user's profile data (age, length of stay, budget, preferences, etc.). An AI model (recommendation system) is used for this generation.

[0134] Step 13:

[0135] The server calculates transportation options and routes with low carbon emissions. The calculation results also include eco-friendly accommodations and restaurants.

[0136] Step 14:

[0137] The server generates a travel plan and sends it to the user's device in JSON format. The generated plan includes tourist destinations, accommodations, transportation options, and environmentally friendly choices.

[0138] Step 15:

[0139] The device displays travel plan notifications to the user as push alerts. The user can then check the notifications.

[0140] Step 16:

[0141] The device displays detailed plan information to the user (transportation, accommodation, sightseeing spots, etc.). The user reviews the plan details.

[0142] Step 17:

[0143] The user reviews the plan on their device and chooses whether to proceed. The result of the selection is sent from the device to the server.

[0144] Step 18:

[0145] The server notifies accommodation providers, transportation providers, tour guides, etc., of the booking information. After each provider confirms the booking, they send that information to the user.

[0146] Step 19:

[0147] After the trip ends, the device displays a satisfaction survey to the user. The user then answers the survey.

[0148] Step 20:

[0149] The device sends the feedback collected from the user to the server. The server stores the feedback in a database.

[0150] Step 21:

[0151] The server analyzes the feedback and uses an AI model to optimize the next plan generation algorithm. The analysis results are then incorporated into the next plan creation process.

[0152] (Example 1)

[0153] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0154] In the tourism industry, the lack of sufficient integration in data collection and analysis of tourist destinations, accommodations, and transportation methods makes it difficult to provide travelers with optimal travel plans. Furthermore, conventional systems have difficulty in demand forecasting and identifying tasks that can be automated, making it impossible to achieve efficient resource allocation and propose eco-friendly travel plans. In addition, there has been a lack of mechanisms to effectively collect user feedback and utilize it to optimize future travel plans. The objective of this invention is to solve these problems.

[0155] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0156] In this invention, the server includes means for collecting data on tourist destinations, accommodations, and means of transportation; means for analyzing the collected data and calculating the optimal allocation of personnel, materials, and ingredients; means for analyzing the workload at accommodations and identifying processes that can be automated; means for generating an optimal travel plan based on individual traveler profile data; means for suggesting transportation and routes with low carbon dioxide emissions; means for notifying the user terminal of the generated travel plan; means for collecting user feedback and reflecting it in optimizing the travel plan; means for obtaining data on tourist destinations, weather, and traffic conditions via API; means for storing and analyzing the acquired data in a database; means for forecasting demand using an AI model based on past tourism data; means for generating an optimal travel plan using user profile data and an AI model; means for adjusting the generated travel plan to take eco-friendly elements into consideration; means for sending the travel plan in JSON format to the user terminal and providing push notifications; means for displaying a questionnaire to collect user satisfaction after the trip; means for storing the collected feedback in a database and analyzing it using an AI model; and means for optimizing the travel plan generation algorithm based on the analysis results. This makes it possible to efficiently and integrally execute a series of processes in the tourism industry, from data collection to plan generation and feedback analysis.

[0157] A "tourist destination" is a geographical location that travelers visit for their own purposes, and is primarily a spot with cultural, historical, or natural attractions.

[0158] "Accommodation facilities" refer to buildings and facilities that provide temporary accommodation for travelers, and include hotels, inns, and guesthouses.

[0159] "Transportation" refers to the vehicles and routes used by travelers to reach their destination, and includes trains, buses, taxis, and airplanes.

[0160] "Data collection" is the process of obtaining information about tourist destinations, accommodations, and transportation from various sources.

[0161] "Analysis" is a technique for processing collected data to make it easier to understand and extract meaningful information.

[0162] "Resource allocation" refers to devising methods for efficiently distributing personnel, materials, and ingredients.

[0163] "Workload" is an indicator that shows the quantity and quality of work performed by employees within an accommodation facility.

[0164] "Automable processes" refer to business processes that can be automated using robots or software.

[0165] "Profile data" refers to data that represents individual information about travelers, such as age, length of stay, budget, and preferences.

[0166] A "travel plan" is a schedule and activity plan for a trip that is proposed to travelers.

[0167] "Carbon dioxide emissions" refer to the amount of carbon dioxide released into the atmosphere by means of transportation and other activities.

[0168] "Eco-friendly" refers to characteristics that indicate environmentally friendly and sustainable methods and products.

[0169] A "push notification" is a notification message that is sent instantly from a server to a device.

[0170] "Feedback" refers to information about opinions and satisfaction levels collected from travelers.

[0171] "API" stands for Application Programming Interface, and it is a standardized method for exchanging data between different software systems.

[0172] "JSON format" is an abbreviation for JavaScript (registered trademark) Object Notation, a format used for data exchange that represents data in text format.

[0173] An "AI model" is an algorithm or computational model that uses artificial intelligence technology to perform data analysis and prediction.

[0174] A "survey" is a research method used to collect opinions and information based on a specific question format.

[0175] This invention is a system designed to solve challenges in the tourism industry. Its purpose is to collect and analyze data on tourist destinations, accommodations, and transportation, and to generate and notify users of optimal travel plans. This system is primarily composed of servers, terminals, and users.

[0176] Data collection

[0177] The server retrieves data on tourist destinations, weather, and traffic conditions via APIs. For example, it obtains detailed information such as the name, location, rating, and opening hours of tourist destinations from services that provide tourist destination information. It obtains weekly weather forecasts for the target area from weather data providers and real-time traffic data from traffic information providers. This data is returned in JSON format, which the server parses and stores the necessary information in a database. This allows for the integrated management of detailed information on tourist destinations, accommodations, and transportation options.

[0178] Data Analysis

[0179] Based on the collected data, the server uses AI models (e.g., time-series forecasting models) with historical tourism data to predict demand for the following month. It also uses profile data and AI models (e.g., recommendation systems) to generate personalized travel plans. Furthermore, the server analyzes operational data from accommodations to identify business processes that can be automated. For example, it can simulate the introduction of cleaning robots to reduce the workload of cleaning operations.

[0180] Plan creation

[0181] The server generates an optimal travel plan based on the user's profile data (age, length of stay, budget, preferences, etc.). The generated plan is adjusted to take eco-friendly factors into consideration, prioritizing transportation methods and routes with low carbon emissions. Eco-friendly accommodations and restaurants are also selected. The generated travel plan is converted to JSON format and sent to the user's device.

[0182] User notifications

[0183] The server notifies the user's device of the generated travel plan. The device receives this data and notifies the user via push notification. For example, a message such as "A new travel plan has been suggested!" might appear on the smartphone. The user can then review the plan details and choose to proceed through their device.

[0184] Feedback processing

[0185] After the trip ends, the device displays a satisfaction survey to the user. The user's feedback is sent to the server and stored in a database. The server analyzes this feedback using an AI model and uses it to optimize the algorithm for generating the next travel plan. For example, it can provide specific insights such as, "Satisfaction improved by changing the order of sightseeing spots in the travel plan."

[0186] Specific examples and prompt statements

[0187] As a concrete example, the prompt message used by the server to retrieve information about Tokyo Tower from a tourist destination API is as follows:

[0188] "Please obtain information about Tokyo Tower."

[0189] Furthermore, the prompt message for predicting the demand for Tokyo tourism in the following month using an AI model is as follows:

[0190] "Please predict the demand for tourism in Tokyo next month."

[0191] The following is an example of a prompt message used to notify the user of the generated travel plan.

[0192] "A new travel plan has been suggested! Please check the details."

[0193] As described above, the system of the present invention efficiently and integrally executes a series of processes in the tourism industry, from data collection to plan generation and feedback analysis. This makes it possible to provide travelers with optimal travel plans and improve their tourism experience.

[0194] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0195] Step 1:

[0196] Obtain tourist information

[0197] The server sends a GET request to the tourist destination's API endpoint. The input includes the URL of the tourist destination API and necessary parameters (e.g., API key). The server executes the GET request and receives JSON data about the tourist destination. This JSON data is parsed to extract detailed information such as the tourist destination's name, location, rating, and opening hours, and stored in the database. Specifically, an HTTP request is sent to the URL of the tourist destination API.

[0198] Step 2:

[0199] Acquisition of weather data

[0200] The server accesses the weather data provider's API to retrieve the weekly weather forecast for the target area. The input includes the weather data provider's API URL and a region specification parameter. The server executes a GET request and retrieves the weather data in JSON format. This data is then parsed, linked to tourist destination information, and stored in a database. Specifically, the server accesses the weather data API endpoint and retrieves regional information.

[0201] Step 3:

[0202] Acquisition of traffic condition data

[0203] The server calls the API of a traffic information service to obtain real-time traffic data. The input includes the URL of the traffic information API and necessary parameters (e.g., region, mode of transport). The server executes a GET request and retrieves traffic information in JSON format. This data is then parsed and stored in the database. Specifically, the server accesses the traffic information API to obtain delay information and service status.

[0204] Step 4:

[0205] Demand forecasting and optimization

[0206] The server uses an AI model (e.g., a time-series forecasting model) based on historical tourism data to predict demand for the following month. Historical tourism data is used as input. The data is fed into the AI ​​model, and the demand forecast results are output. Based on these results, the optimal allocation of personnel, materials, and ingredients is calculated. Specifically, the time-series forecasting model is executed, and the forecast results are reflected in resource management.

[0207] Step 5:

[0208] Work load analysis

[0209] The server collects and analyzes operational data from accommodation facilities (e.g., room cleaning frequency and front desk service response time). The operational data from the accommodation facilities is used as input. The server analyzes the data and identifies business processes that can be automated. Specifically, it uses data analysis tools to visualize business processes and simulate automation.

[0210] Step 6:

[0211] Generating Individually Optimized Plans

[0212] The server generates a travel plan based on the user's profile data (age, length of stay, budget, preferences, etc.). User profile data is used as input. The data is input into an AI model (recommendation system) to output the optimal travel plan. Specifically, the recommendation system is used to select tourist spots, accommodations, and restaurants that are suitable for the user.

[0213] Step 7:

[0214] Display of sustainable tourism plans

[0215] The server calculates transportation methods and routes with low carbon emissions. Transportation and route data are used as input. The server adjusts the plan considering eco-friendly factors and selects eco-friendly accommodations and restaurants. Specifically, it generates plans prioritizing sustainable transportation and accommodation options.

[0216] Step 8:

[0217] Plan notification

[0218] The server generates a travel plan and sends it to the device in JSON format. The input is the generated travel plan. The server converts this data to JSON and sends it to the user's device. The device receives this data and sends a push notification. Specifically, the smartphone displays a notification saying, "A new travel plan has been suggested!"

[0219] Step 9:

[0220] View plan details

[0221] The device displays detailed plan information to the user. The input is travel plan data received from a server. The device analyzes this data and displays it in the UI. Specifically, it displays detailed information about transportation, accommodation, and tourist attractions in a list format within the app.

[0222] Step 10:

[0223] Feedback Collection

[0224] After the trip ends, the device displays a satisfaction survey to the user. The end time of the trip plan is used as input. The device displays the survey to the user and collects responses. Specifically, the app displays a form asking, "Please tell us how satisfied you were with your trip."

[0225] Step 11:

[0226] Feedback analysis

[0227] The server stores feedback in a database and analyzes it using an AI model. User feedback data is used as input. The server analyzes the feedback and uses it to optimize the plan generation algorithm. Specifically, it performs sentiment analysis and uses it to generate the next plan.

[0228] The above is a detailed explanation of the system's processing steps and operation.

[0229] (Application Example 1)

[0230] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0231] In the tourism industry, travelers face challenges in creating and executing efficient and comfortable travel plans. In particular, they require flexible arrangements for meals at tourist destinations, as well as adaptability to weather and traffic conditions. Furthermore, providing environmentally friendly options is essential for sustainable tourism. In addition, personalized planning tailored to individual traveler preferences is required, and incorporating user feedback into future plans is crucial.

[0232] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0233] In this invention, the server includes means for collecting data on tourist destinations, accommodations, and transportation; means for analyzing the collected data and calculating the optimal allocation of personnel, materials, and ingredients; means for analyzing the workload at accommodations and identifying processes that can be automated; means for generating an optimal travel plan based on individual traveler profile data; means for suggesting transportation and routes with low carbon dioxide emissions; means for delivering meals from local restaurants based on the travel plan; means for calculating the optimal delivery time based on weather information and traffic conditions; means for notifying the user terminal of the generated travel plan; and means for collecting user feedback and reflecting it in optimizing the travel plan. This enables the creation and execution of efficient and sustainable travel plans.

[0234] A "tourist destination" is a place that travelers visit, such as a natural landscape, historical buildings, or cultural facilities.

[0235] "Accommodation facilities" refer to facilities such as hotels, inns, and guesthouses where travelers can stay temporarily.

[0236] "Transportation" refers to the means of getting around that travelers use, such as trains, buses, taxis, and airplanes.

[0237] "Means of data collection" refers to the hardware and software used to acquire information about tourist destinations, accommodations, and transportation.

[0238] "Means for calculating the optimal allocation of personnel, materials, and food supplies" refers to an analytical system that uses collected data to calculate the efficient allocation of human resources, materials, and food supplies in tourist destinations and accommodation facilities.

[0239] "A means of analyzing workload and identifying processes that can be automated" refers to a system for analyzing the workload at accommodation facilities and identifying tasks that can be automated.

[0240] "Profile data" refers to information about individual travelers, such as age, length of stay, budget, and preferences.

[0241] "A means of generating the optimal travel plan" refers to a system that plans the most suitable sightseeing routes, accommodations, and activities for travelers based on their profile data.

[0242] "Means for proposing transportation methods and routes with low carbon dioxide emissions" refers to a system that calculates and proposes transportation methods and travel routes that are environmentally conscious and reduce carbon dioxide emissions.

[0243] "Methods for having meals delivered from local restaurants" refers to a system for ordering and having meals delivered from local restaurants based on a travel plan.

[0244] "A means of calculating the optimal delivery time based on weather information and traffic conditions" refers to a system that takes weather forecasts and traffic conditions into consideration to optimally adjust the delivery time of meals.

[0245] "Means for notifying user devices of generated travel plans" refers to a system for delivering generated travel plans to travelers' devices such as smartphones and tablets.

[0246] "Means for collecting feedback and using it to optimize travel plans" refers to a system that collects travelers' evaluations and opinions and uses them to improve and optimize future travel plans.

[0247] This invention is a system that solves problems in the tourism industry and is composed primarily of a server, terminals, and users. The specific form of this system is described below.

[0248] Data collection

[0249] 1. Obtaining tourist information

[0250] The server sends a GET request to the tourist destination's API endpoint and retrieves detailed information about the tourist destination (name, location, rating, opening hours, etc.) in JSON format. This information is then stored in the database.

[0251] 2. Acquisition of weather data

[0252] The server retrieves weekly weather forecasts from a weather data provider service, analyzes the data, and stores it in a database linked to tourist destination information.

[0253] 3. Acquisition of traffic condition data

[0254] The server retrieves real-time traffic information from traffic information services and updates the database with delay information and operating status for each mode of transport.

[0255] Data Analysis

[0256] 1. Demand forecasting and optimization

[0257] The server uses historical tourism data to feed into an AI model (e.g., a time-series forecasting model) to predict future demand. It also calculates the optimal allocation of personnel, materials, and ingredients.

[0258] 2. Workload analysis

[0259] The server analyzes operational data from accommodations (such as room cleaning frequency and front desk service response times) to identify business processes that can be automated.

[0260] Plan creation

[0261] 1. Generating an individualized optimization plan

[0262] The server generates a travel plan based on the user's profile data (age, length of stay, budget, preferences, etc.). The generated plan appropriately includes sightseeing spots, accommodations, and restaurants.

[0263] 2. Creating a Sustainable Tourism Plan

[0264] The server calculates transportation options and routes with low carbon emissions and creates plans that include eco-friendly accommodations and restaurants.

[0265] 3. Generating a meal delivery plan based on the travel plan.

[0266] The server collects restaurant information around tourist destinations and creates the optimal delivery plan based on user preferences, weather information, and traffic conditions.

[0267] User notifications

[0268] 1. Plan notification

[0269] The server sends the generated travel plan and delivery plan to the device in JSON format. The device then displays the notification to the user as a push alert.

[0270] 2. Displaying plan details

[0271] The device displays detailed plan information (transportation, accommodation, sightseeing spots, delivery options, etc.) to the user via its UI.

[0272] Feedback processing

[0273] 1. Gathering feedback

[0274] After the trip ends, the device displays a satisfaction survey to the user and collects feedback. The collected data is sent to a server.

[0275] 2. Analysis of Feedback

[0276] The server stores the feedback in a database and performs analysis using an AI model. Based on the analysis results, it optimizes the algorithm for generating the next plan.

[0277] Specific example

[0278] Data collection examples

[0279] The server obtains information about city A from a tourist destination API and the weekly weather forecast for city A from a weather data service. It also obtains real-time traffic information from a traffic information service.

[0280] Data analysis example

[0281] The server predicts tourism demand in city A based on historical data and analyzes operational data from accommodation facilities to simulate the effects of introducing cleaning robots.

[0282] Plan creation example

[0283] The server generates a three-day travel plan based on the profile of a couple's trip. This plan includes eco-friendly accommodation facilities and sustainable travel routes. Also, based on the user's preferences and weather information, food delivery options are added.

[0284] Example of a prompt sentence

[0285] "When sightseeing in City A, please create a plan to deliver meals from local recommended restaurants considering the optimal delivery time. A detailed plan based on the user's preferences, weather information, and traffic conditions is required."

[0286] The flow of specific processing in Application Example 1 will be described using FIG. 12.

[0287] Step 1:

[0288] Obtaining tourist destination information

[0289] The server sends a GET request to the API endpoint of the tourist destination. The input data is the tourist destination ID. The server analyzes the returned JSON data, extracts detailed information such as the name, location information, evaluation, opening hours, etc. of the tourist destination, and saves it in the database. The output is the detailed information of the tourist destination.

[0290] Step 2:

[0291] Obtaining weather data

[0292] The server accesses the API of the weather data service to obtain the weekly weather forecast for the target area. The input data is the location information of the tourist destination. The server analyzes the obtained weather information, associates it with the tourist destination information, and saves it in the database. The output is the weekly weather forecast information.

[0293] Step 3:

[0294] Obtaining traffic condition data

[0295] The server calls an API from a traffic information service to obtain real-time traffic data. The input data is travel route information to tourist destinations. The server analyzes this information and updates the database with delay information and operating status for each mode of transport. The output is traffic information.

[0296] Step 4:

[0297] Retrieving User Profiles

[0298] The server retrieves user profile information. The input data is the user ID. The server retrieves profile information (age, length of stay, budget, preferences, etc.) from the database. The output is the user profile data.

[0299] Step 5:

[0300] Demand forecasting and resource allocation

[0301] The server uses historical tourism data to predict next month's demand based on an AI model. The input data is historical tourism data. The server calculates the optimal allocation of personnel, materials, and ingredients, optimizing resource management. The output is demand forecast data and resource allocation plan.

[0302] Step 6:

[0303] Workload analysis

[0304] The server analyzes the operational data of the accommodation facility and identifies processes that can be automated. The input data is the operational data of the accommodation facility. The server performs automation simulations and evaluates their effectiveness. The output is the business processes that can be automated and the simulation results.

[0305] Step 7:

[0306] Travel plan generation

[0307] The server generates an optimal travel plan using an AI model based on the user's profile data. The input data is profile data and tourist destination information. The server creates a travel plan including tourist spots, accommodation facilities, and restaurants. The output is a travel plan.

[0308] Step 8:

[0309] Generation of Delivery Plan

[0310] The server generates a plan to deliver meals from local restaurants based on the travel plan. The input data is tourist destination information and the user's preference information. The server calculates the optimal delivery time based on weather information and traffic conditions. The output is a delivery plan.

[0311] Step 9:

[0312] Notification of Plan

[0313] The server sends the generated travel plan and delivery plan to the terminal in JSON format. The input data is the travel plan and the delivery plan. The terminal displays the notification to the user as a push alert. The output is a notification to the user.

[0314] Step 10:

[0315] Collection and Analysis of Feedback

[0316] After the trip, the terminal displays a satisfaction questionnaire to the user and collects feedback. The input data is the user's feedback. The server saves the collected feedback in the database, analyzes it using an AI model, and optimizes the next plan generation algorithm. The output is the analysis result and the optimized algorithm.

[0317] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0318] This invention is a system designed to solve challenges in the tourism industry. It aims to collect and analyze data on tourist destinations, accommodations, and transportation methods, and to generate and notify users of optimal travel plans by combining this data with an emotion engine that recognizes user emotions. This system is primarily composed of a server, terminals, and users.

[0319] Data collection

[0320] 1. Obtaining tourist information

[0321] The server sends a GET request to the tourist destination's API endpoint. The information retrieved may include the name of the tourist destination, its location, its rating, and its opening hours.

[0322] The server parses the returned JSON data and extracts detailed information about the tourist destination. The extracted data includes the name of the tourist destination, location information, rating, opening hours, etc.

[0323] The server saves detailed information about tourist destinations to a database. The saved data is used in subsequent analysis processes.

[0324] 2. Acquisition of weather data

[0325] The server accesses the weather data provider's API and sends a GET request to retrieve the weekly weather forecast for the target area.

[0326] The server analyzes the weather information it has acquired. The analyzed weather information is then linked to tourist destination information and stored in a database.

[0327] 3. Acquisition of traffic condition data

[0328] The server calls the API of a traffic information service to obtain real-time traffic data for the target area. The information obtained includes delay information, service status, and more.

[0329] The server analyzes traffic data and stores delay information and operational status for each mode of transport in a database.

[0330] Data Analysis

[0331] 1. Demand forecasting and optimization

[0332] The server uses historical tourism data and an AI model (e.g., a time-series forecasting model) to predict demand for the following month. The prediction results are stored in a database.

[0333] The server calculates the optimal allocation of personnel, materials, and ingredients based on demand forecast data. The calculation results are then applied to tourist destinations and accommodations.

[0334] 2. Workload analysis

[0335] The server analyzes operational data from the accommodation facility (for example, the frequency of room cleaning and the response time for front desk staff).

[0336] The server identifies business processes that can be automated and simulates the effectiveness of automation using robots and software. The simulation results are stored in a database.

[0337] Plan creation

[0338] 1. Generating an individualized optimization plan

[0339] The server generates travel plans based on the user's profile data (age, length of stay, budget, preferences, etc.). An AI model (recommendation system) is used for this generation.

[0340] The server uses an emotion engine to collect user emotion data and incorporate it into the generation and optimization of travel plans.

[0341] 2. Creating a Sustainable Tourism Plan

[0342] The server calculates transportation options and routes with low carbon emissions. The calculation results also include eco-friendly accommodations and restaurants.

[0343] User notifications

[0344] 1. Plan notification

[0345] The server generates a travel plan and sends it to the user's device in JSON format. The generated plan includes tourist destinations, accommodations, transportation options, and environmentally friendly choices.

[0346] The device displays travel plan notifications to the user as push alerts. The user can then check the notifications.

[0347] 2. Displaying plan details

[0348] The device displays detailed plan information to the user (transportation, accommodation, sightseeing spots, etc.). The user reviews the plan details.

[0349] Execution Management

[0350] 1. Plan Review and Approval

[0351] The user reviews the plan on their device and chooses whether to proceed. The result of the selection is sent from the device to the server.

[0352] 2. Notification to relevant organizations

[0353] The server notifies accommodation providers, transportation providers, tour guides, etc., of the booking information. After each provider confirms the booking, they send that information to the user.

[0354] Feedback processing

[0355] 1. Gathering feedback

[0356] After the trip ends, the device displays a satisfaction survey to the user. The user then answers the survey.

[0357] The device sends the feedback collected from the user to the server. The server stores the feedback in a database.

[0358] 2. Analysis of Feedback

[0359] The server analyzes the feedback and uses an AI model to optimize the next plan generation algorithm. The analysis results are then incorporated into the next plan creation process.

[0360] The server uses an emotion engine to analyze emotional data along with feedback and incorporate it into the next plan.

[0361] Specific example

[0362] Data collection examples

[0363] The server retrieves information about Tokyo Tower from a tourist destination API.

[0364] The server retrieves the weekly weather forecast for Tokyo from a weather data provider.

[0365] The server retrieves real-time train operation information from a traffic information service.

[0366] Data analysis example

[0367] The server uses an AI model to predict the demand for Tokyo tourism in the following month.

[0368] The server analyzes operational data from accommodation facilities and simulates the effects of introducing cleaning robots.

[0369] Plan creation example

[0370] The server generates a 3-day Kyoto and Nara travel plan based on profile data indicating a couple's trip.

[0371] The server creates sustainable plans that include train travel and eco-friendly accommodations.

[0372] The server uses an emotion engine to optimize plans by taking user sentiment data into account.

[0373] User notification example

[0374] The server generates a travel plan and sends it to the device.

[0375] The device will notify the user via push notification and display the detailed plan.

[0376] Execution Management Example

[0377] The user reviews the plan and selects to proceed on their device.

[0378] The server notifies accommodations and transportation providers of the reservation information and sends a reservation confirmation to the user.

[0379] Feedback Processing Example

[0380] The device displays a post-trip feedback survey to the user and sends it to the server.

[0381] The server analyzes feedback and sentiment data and incorporates it into generating the next plan.

[0382] As a result, the system of the present invention can solve the multifaceted challenges of the tourism industry and provide an efficient, emotionally resonant, and sustainable tourism experience.

[0383] The following describes the processing flow.

[0384] Step 1:

[0385] The server sends a GET request to the tourist destination's API endpoint. The information retrieved may include the name of the tourist destination, its location, its rating, and its opening hours.

[0386] Step 2:

[0387] The server parses the returned JSON data and extracts detailed information about the tourist destination. The extracted data includes the name of the tourist destination, location information, rating, opening hours, etc.

[0388] Step 3:

[0389] The server saves detailed information about tourist destinations to a database. The saved data is used in subsequent analysis processes.

[0390] Step 4:

[0391] The server accesses the weather data service's API. It sends a GET request to retrieve the weekly weather forecast for the target area.

[0392] Step 5:

[0393] The server analyzes the weather information it has acquired. The analyzed weather information is then linked to tourist destination information and stored in a database.

[0394] Step 6:

[0395] The server calls the API of a traffic information service to obtain real-time traffic data for the target area. The information obtained includes delay information, service status, and more.

[0396] Step 7:

[0397] The server analyzes traffic data and stores delay information and operational status for each mode of transport in a database.

[0398] Step 8:

[0399] The server uses historical tourism data and an AI model (e.g., a time-series forecasting model) to predict demand for the following month. The prediction results are stored in a database.

[0400] Step 9:

[0401] The server calculates the optimal allocation of personnel, materials, and ingredients based on demand forecast data. The calculation results are then applied to tourist destinations and accommodations.

[0402] Step 10:

[0403] The server analyzes operational data from the accommodation facility. This operational data includes information such as the frequency of room cleaning and the response time for front desk staff.

[0404] Step 11:

[0405] The server identifies business processes that can be automated and simulates the effectiveness of automation using robots and software. The simulation results are stored in a database.

[0406] Step 12:

[0407] The server generates the optimal travel plan based on the user's profile data (age, length of stay, budget, preferences, etc.). An AI model (recommendation system) is used for this generation.

[0408] Step 13:

[0409] The server uses an emotion engine to collect and analyze user emotion data, which is then used to generate and optimize travel plans. This emotion data is based on real-time user feedback and the user's emotional state at the time of plan generation.

[0410] Step 14:

[0411] The server calculates transportation options and routes with low carbon emissions. The calculation results also include eco-friendly accommodations and restaurants.

[0412] Step 15:

[0413] The server generates a travel plan and sends it to the user's device in JSON format. The generated plan includes tourist destinations, accommodations, transportation options, and environmentally friendly choices.

[0414] Step 16:

[0415] The device displays travel plan notifications to the user as push alerts. The user can then check the notifications.

[0416] Step 17:

[0417] The device displays detailed plan information to the user (transportation, accommodation, sightseeing spots, etc.). The user reviews the plan details.

[0418] Step 18:

[0419] The user reviews the plan on their device and chooses whether to proceed. The result of the selection is sent from the device to the server.

[0420] Step 19:

[0421] The server notifies accommodation providers, transportation providers, tour guides, etc., of the booking information. After each provider confirms the booking, they send that information to the user.

[0422] Step 20:

[0423] After the trip ends, the device displays a satisfaction survey to the user. The user then answers the survey.

[0424] Step 21:

[0425] The device sends the feedback collected from the user to the server. The server stores the feedback in a database.

[0426] Step 22:

[0427] The server analyzes the feedback and uses an AI model to optimize the next plan generation algorithm. The analysis results are then incorporated into the next plan creation process.

[0428] Step 23:

[0429] The server uses an emotion engine to analyze emotional data along with feedback, and incorporates it into future plans. This emotional data includes not only satisfaction levels but also user stress levels and excitement levels.

[0430] As a result, the system of the present invention can solve the multifaceted challenges of the tourism industry and provide an efficient, emotionally resonant, and sustainable tourism experience.

[0431] (Example 2)

[0432] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal".

[0433] In today's tourism industry, there is a demand for providing optimal travel plans that meet the diverse needs of tourists. Furthermore, managing the uncertainty of demand forecasts, workload, and efficient allocation of insufficient resources are crucial challenges. In addition, there is a growing demand for environmentally conscious and sustainable tourism, as well as the provision of travel experiences that consider the emotions of users. However, there are limited systems that can comprehensively address all of these issues.

[0434] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0435] In this invention, the server includes means for collecting information on tourist destinations, weather data, and traffic data; means for analyzing the collected data and storing detailed information on tourist destinations, weather information, and traffic information in a database; and means for using an AI model based on past tourism data to forecast demand and calculate the optimal allocation of personnel, materials, and ingredients. This makes it possible to provide travel plans that best meet the diverse needs of tourists. Furthermore, by predicting and optimizing the workload, efficient use of resources is achieved, enabling the provision of environmentally conscious and sustainable travel experiences.

[0436] "Information about tourist destinations" refers to detailed data such as the name of the tourist destination, its location, rating, and opening hours.

[0437] "Weather data" refers to data related to weather conditions in a specific region, such as weather forecasts, temperature, precipitation, and wind speed.

[0438] "Traffic status data" refers to real-time data on the operation status, delay information, and route information of public transportation (e.g., trains, buses, airplanes, etc.).

[0439] A "database" is a collection of structured data that stores collected and analyzed data, and that can be searched and retrieved as needed.

[0440] An "AI model" refers to algorithms and computational methods that use artificial intelligence technology, and includes, for example, time series forecasting models and recommendation systems.

[0441] "Demand forecasting" is the process of predicting future demand by analyzing past data.

[0442] "Optimal allocation of personnel, materials, and ingredients" refers to the operation of efficiently distributing resources based on demand forecasts.

[0443] "Workload" refers to the burden of tasks related to labor and service provision at accommodation facilities.

[0444] An "automatable process" is a stage in a repetitive task or process that can be performed by machines or software without human intervention.

[0445] "Individual traveler profile data" refers to personal attribute information such as the traveler's age, length of stay, budget, and preferences.

[0446] "Low-carbon emission modes of transport" refer to eco-friendly modes of transport that aim to minimize their impact on the environment.

[0447] "Suggesting a route" refers to the process of calculating and presenting the optimal travel path to the traveler.

[0448] A "user terminal" refers to an electronic device such as a smartphone, tablet, or personal computer, which is used by a user to obtain information and operate it through its interface.

[0449] "Feedback" refers to information provided by users, such as ratings, opinions, and survey responses.

[0450] "Generating" refers to the operation of creating new information or results based on a specific algorithm or rule.

[0451] This invention is a system that solves problems in the tourism industry. It aims to collect and analyze data on tourist destinations, accommodations, and transportation methods, and to generate and notify users of optimal travel plans by combining this with an emotion engine that recognizes user emotions. This system is mainly composed of a server, terminals, and users.

[0452] Data collection

[0453] Obtain tourist information

[0454] The server sends a GET request to the tourist destination API endpoint. It retrieves information such as the name, location, rating, and opening hours of the tourist destination. It is desirable that this tourist destination API endpoint be a commonly used API.

[0455] The system analyzes the JSON data acquired by the server and extracts detailed information about tourist destinations. For example, it can retrieve information about the tourist destination "Tokyo Tower" and save it to a database.

[0456] Acquisition of weather data

[0457] The server accesses the weather data provider's API and sends a GET request to retrieve the weekly weather forecast for the target area. For example, it retrieves the weekly weather forecast for "Tokyo" from the weather forecast API.

[0458] The server analyzes the data, links it to tourist destination information, and then stores it in the database.

[0459] Acquisition of traffic condition data

[0460] The server calls the API of a traffic information service to obtain real-time traffic data for the target area (e.g., delay information, service status, etc.). Using a traffic information API is recommended.

[0461] The server analyzes the data and stores delay information and operating status for each mode of transport in a database. For example, it retrieves the operating status of trains in Tokyo.

[0462] Data Analysis

[0463] Demand forecasting and optimization

[0464] The server uses a time-series forecasting model based on past tourism data to predict demand for the following month and stores the data in a database. Applying an AI model is crucial.

[0465] The server calculates the optimal allocation of personnel, materials, and ingredients based on demand forecast data. For example, it forecasts the demand for tourism in Tokyo for the following month and performs calculations based on the results.

[0466] Work load analysis

[0467] The server collects and analyzes operational data from accommodation facilities (e.g., room cleaning frequency and front desk service response time).

[0468] The server identifies business processes that can be automated and simulates the effects of automation using robots and software. For example, it evaluates the effects of introducing cleaning robots.

[0469] Plan creation

[0470] Generating Individually Optimized Plans

[0471] The server retrieves user profile data (e.g., age, length of stay, budget, preferences, etc.) and uses an AI model (recommendation system) to generate the optimal travel plan.

[0472] The server uses an emotion engine to collect user emotion data and incorporate it into generating and optimizing travel plans. For example, it can create a 3-day Kyoto and Nara travel plan based on a couple's travel profile.

[0473] Display of sustainable tourism plans

[0474] The server calculates transportation options and routes with low carbon emissions and creates sustainable plans that include eco-friendly accommodations and restaurants. For example, it can create a plan that includes train travel and eco-friendly accommodations.

[0475] User notifications

[0476] Plan notification

[0477] The server generates a travel plan and sends it to the user's device in JSON format.

[0478] The device will display a push alert notifying the user of the travel plan. For example, it might display, "A 3-day travel plan for Kyoto has been generated."

[0479] View plan details

[0480] The device displays detailed plan information to the user (transportation, accommodation, sightseeing spots, etc.). For example, it might display "Day 1: Travel from Tokyo to Kyoto and check into an eco-hotel."

[0481] Execution Management

[0482] Plan review and approval

[0483] The user reviews the plan on their device and chooses whether or not to proceed.

[0484] The device sends the user's selection results to the server. For example, the user approves a travel plan.

[0485] Notification to relevant organizations

[0486] The server notifies accommodation providers, transportation providers, tour guides, and others of the reservation information.

[0487] The server retrieves reservation confirmation information from each institution and sends it to the user. For example, it notifies the user of confirmation information from accommodations where reservations have been confirmed.

[0488] Feedback processing

[0489] Feedback Collection

[0490] After the trip ends, the device displays a satisfaction survey to the user, who then answers the survey.

[0491] The device sends user feedback to the server, which then stores the feedback data in a database. For example, a user might answer a survey asking, "How was your overall satisfaction with your trip?"

[0492] Feedback analysis

[0493] The server analyzes the feedback data and uses an AI model to optimize the next plan generation algorithm.

[0494] The server uses an emotion engine to analyze feedback and emotional data and incorporate it into generating future plans. For example, it might consider the user's emotional data when creating the next travel plan.

[0495] Example prompt statements

[0496] "Please have the server retrieve tourist information about Tokyo Tower and save it to the database."

[0497] "Please retrieve Tokyo weather data from a weather forecast API and link it with tourist information."

[0498] "Please generate a 3-day Kyoto travel plan based on the user's profile data."

[0499] As a result, the system of the present invention can solve the multifaceted challenges of the tourism industry and provide an efficient, emotionally resonant, and sustainable tourism experience.

[0500] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0501] Step 1: Obtain tourist information

[0502] The server sends a GET request to the tourist destination API endpoint. For example, it might send a request with the input "Tourist Destination Name = Tokyo Tower".

[0503] The server receives the returned JSON data and analyzes and extracts detailed information such as the name of the tourist attraction, location information, rating, and opening hours.

[0504] The server analyzes tourist destination information and saves it to the database. Specifically, it executes an INSERT query on the appropriate table in the database.

[0505] Step 2: Obtain weather data

[0506] The server sends a GET request to the weather data service's API. For example, it might send a request with the input "Region=Tokyo".

[0507] The server receives JSON data of the weekly weather forecast and analyzes the weather conditions (e.g., weather, temperature, precipitation).

[0508] The server analyzes weather data and stores it in a database, linking it to tourist destination information. Specifically, it links the primary key of the tourist destination with the weather data.

[0509] Step 3: Obtain traffic condition data

[0510] The server sends a GET request to the API of the traffic information service. For example, it might send a request with the input "Region = Tokyo, Mode of Transportation = Train".

[0511] The server receives the traffic data in JSON format and analyzes delay information and service status.

[0512] The server analyzes traffic data and saves it to a database. Specifically, it saves information for each mode of transport to the corresponding table.

[0513] Step 4: Demand forecasting and optimization

[0514] The server retrieves historical tourism data from the database. For example, it might retrieve data based on input such as "Tourist destination = Tokyo, Period = Past 3 years".

[0515] The server uses an AI model (e.g., a time series forecasting model) to predict demand for the following month. It performs data processing and time series analysis based on the input data.

[0516] The server saves the prediction results to a database and uses that data to calculate the optimal allocation of personnel, materials, and ingredients. Specifically, it applies a resource optimization algorithm.

[0517] Step 5: Workload Analysis

[0518] The server collects operational data from accommodation facilities. For example, it retrieves data with the input "Accommodation facility name = Hotel A".

[0519] The server analyzes factors such as room cleaning frequency and front desk response times to identify business processes that can be automated.

[0520] The server simulates the effects of automation using robots and software, and stores the results in a database. Specifically, it applies an automation simulation algorithm.

[0521] Step 6: Generating an individualized optimization plan

[0522] The server retrieves the user's profile data. For example, it can retrieve data with input such as "User ID=1234, Age=30, Length of Stay=3 days, Budget=50,000 yen, Interests=History".

[0523] The server uses an AI model (recommendation system) to generate the optimal travel plan. It applies data processing and recommendation algorithms based on the input profile data.

[0524] The server uses an emotion engine to collect user emotion data and incorporate it into the travel plan. Specifically, it applies an emotion analysis algorithm.

[0525] Step 7: Create a Sustainable Tourism Plan

[0526] The server calculates eco-friendly modes of transport and routes. For example, it calculates based on the input "Destination = Kyoto, Departure = Tokyo".

[0527] The server creates travel plans that include eco-friendly accommodations and restaurants. Specifically, it applies an environmental impact assessment algorithm.

[0528] Step 8: Plan Notification

[0529] The server generates a travel plan and sends it to the user's terminal in JSON format. For example, it might be sent with the input "User ID=1234".

[0530] The device will notify the user of the generated travel plan via push alert. Specifically, this will be done using the notification API.

[0531] Step 9: View plan details

[0532] The device displays detailed plan information to the user. For example, it retrieves and displays details based on the input "Plan ID=5678".

[0533] The user views the details of the displayed travel plan. Specifically, this involves displaying the details on the interface.

[0534] Step 10: Plan Review and Approval

[0535] The user reviews the plan on their device and chooses whether to proceed. For example, they might select it by entering "Plan ID=5678, Select=Approve".

[0536] The terminal sends the user's selection results to the server. Specifically, it sends the approval result to the server via a POST request.

[0537] Step 11: Notification to relevant organizations

[0538] The server notifies accommodations, transportation providers, guide services, etc., of the reservation information. For example, it will notify based on input such as "Accommodation = Hotel A, Transportation = Shinkansen (bullet train)".

[0539] The server retrieves reservation confirmation information from each institution and notifies the user. Specifically, it sends the confirmation information to the terminal.

[0540] Step 12: Gathering Feedback

[0541] After the trip ends, the device displays a satisfaction survey to the user. For example, the survey will be displayed when the user enters "Trip ID=7890".

[0542] The user answers the survey, and the device sends that data to the server. Specifically, the survey data is sent to the server via a POST request.

[0543] Step 13: Analyzing Feedback

[0544] The server retrieves feedback data from the database. For example, it retrieves data with the input "Travel ID=7890".

[0545] The server uses an AI model to analyze and optimize the next plan generation algorithm. Specifically, it applies a feedback analysis algorithm.

[0546] The server uses an emotion engine to analyze feedback and emotional data, and incorporates this into the generation of the next plan. Specifically, it performs emotional data analysis.

[0547] In this way, each processing step works in conjunction with the others, and the system as a whole solves a variety of challenges in the tourism industry.

[0548] (Application Example 2)

[0549] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0550] In the modern tourism industry, a challenge exists in that travelers find it difficult to create efficient and personalized travel plans. Furthermore, there is a lack of systems that can flexibly adjust plans in response to changes in weather and traffic conditions, as well as the emotional state of travelers. Additionally, while sustainability in the tourism industry is a pressing need, technologies for automatically generating travel plans that reflect this need are limited.

[0551] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting data on tourist destinations, accommodations, and means of transportation; means for analyzing the collected data and calculating the optimal allocation of personnel, materials, and ingredients; means for analyzing the workload at accommodations and identifying processes that can be automated; means for generating an optimal travel plan based on individual traveler profile data; means for collecting user sentiment data and reflecting it in the travel plan; means for suggesting transportation methods and routes with low carbon dioxide emissions; means for notifying the user terminal of the generated travel plan in a prompt format; means for collecting user feedback and reflecting it in optimizing the travel plan; and means for displaying the tourist plan in conjunction with real-time location information. This makes it possible to provide an efficient, sustainable travel plan that is considerate of the user's feelings.

[0552] "Tourist information" refers to detailed information about tourist attractions, such as their name, location, rating, and opening hours.

[0553] "Accommodation information" refers to data such as the name, location, price range, and availability of accommodations, including hotels and hot spring inns.

[0554] "Transportation information" refers to information such as the operating status, delay information, price range, and travel time of public transportation such as buses, trains, and taxis.

[0555] "Emotional data" refers to data related to emotional states such as joy, sadness, and surprise, obtained through user facial analysis and text analysis.

[0556] "Prompt format" refers to a format that presents recommended actions or information in short text messages.

[0557] "Real-time location information" refers to information that instantly obtains and updates the user's current geographical location.

[0558] A "personalized travel plan" refers to a travel schedule and sightseeing route optimized based on each user's profile data and emotional data.

[0559] "Feedback" refers to opinions and impressions, such as satisfaction levels and areas for improvement, that users provide after completing a trip.

[0560] "Sustainable transportation" refers to environmentally friendly modes of transport that reduce carbon dioxide emissions (e.g., electric vehicles and bicycles).

[0561] The system for implementing this invention collects data on tourist destinations, accommodations, and transportation, and based on this data, generates and notifies users of optimal travel plans. The system mainly consists of a server, user terminals, and users. The following describes how this system works.

[0562] Data collection

[0563] The server sends GET requests to the API endpoints of tourist destinations to retrieve detailed information such as the name, location, rating, and opening hours of the tourist destinations. The retrieved information is parsed in JSON format and stored in the database. Similarly, the server accesses the API of a weather data service to retrieve the weekly weather forecast for the target area, parses it, and stores it in the database. Furthermore, the server calls the API of a traffic information service to retrieve real-time traffic data for the target area and stores delay information and service status in the database.

[0564] The hardware used will consist of a Linux® server and a database server. The software will utilize Python and Flask for API communication and data analysis.

[0565] emotion recognition

[0566] When a user points their face at the camera using a smartphone or smart glasses, their face is captured using OpenCV. The captured face image is input into a TENSORFLOW® emotion recognition model to identify the user's emotion (joy, sadness, surprise, etc.). This emotion data is sent to a server and used to generate travel plans.

[0567] Plan generation

[0568] The server combines collected tourist destination, weather, and transportation information with user profile data (age, length of stay, budget, preferences) and sentiment data to generate the optimal travel plan. This is done using an AI model (recommendation system). The generated plan is sent to the user's device in JSON format.

[0569] User notifications

[0570] Sightseeing plans are sent via push notifications to smartphones or smart glasses. Users can check these notifications and view detailed plans within the application. For example, for a user in Kyoto City, real-time location information can be used to suggest the most suitable sightseeing route and recommended spots on the spot.

[0571] Feedback Collection

[0572] After the trip, users provide feedback through a satisfaction survey. This feedback data, along with sentiment data, is sent to the server and used to optimize future travel plans.

[0573] Specific examples and prompt statements

[0574] As a concrete example, let's consider the case where the user is in Kyoto City.

[0575] Example of a prompt:

[0576] Based on the user's current location in Kyoto City, retrieve weather information and real-time traffic information for the following tourist spots and generate a recommended sightseeing plan. Suggest the most optimal route, especially if the user's emotion is "joyful."

[0577] Following this prompt, the server collects information on Kyoto's tourist attractions, weather, and traffic, recognizes that the user's emotion is "joy," generates an optimal sightseeing plan, and notifies the user's device. This allows the user to enjoy an efficient and emotionally responsive travel experience.

[0578] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0579] Step 1: Data Collection

[0580] The server sends GET requests to the API endpoints of tourist destinations to retrieve information such as the name, location, rating, and opening hours of the tourist destinations. The server parses the returned JSON data and saves the detailed information of the tourist destinations to the database. Similarly, it sends GET requests to the API of a weather data provider to retrieve weekly weather forecasts and saves the parsed weather information in the database, linked to the tourist destination information. It also calls the API of a traffic information provider to retrieve real-time traffic data and saves delay information and service status to the database.

[0581] Input: Endpoints for the tourist destination API, weather data API, and traffic information API.

[0582] Output: Database containing acquired tourist destination information, weather information, and traffic data.

[0583] Step 2: Emotion Recognition

[0584] The user uses a smartphone or smart glasses to point their face at the camera. The device's camera captures an image of the user's face and sends it to the server. The server uses a TensorFlow emotion recognition model to analyze the face image and identify the user's emotion (e.g., joy, sadness, surprise). This emotion data is stored for travel plan generation.

[0585] Input: User's face image

[0586] Output: Recognized emotion data

[0587] Step 3: Enter profile data

[0588] Users enter their profile data through the application. This profile data includes age, length of stay, budget, and preferences. The device sends this data to the server for storage.

[0589] Input: User's age, length of stay, budget, preferences

[0590] Output: Saved profile data

[0591] Step 4: Plan Generation

[0592] The server uses an AI model to generate the optimal travel plan based on collected tourist destination information, weather information, transportation information, user profile data, and sentiment data. This analyzes each piece of data and recommends the most suitable tourist spots, modes of transportation, and accommodations for the user.

[0593] Input: Tourist information, weather information, traffic information, profile data, sentiment data

[0594] Output: Generated travel plan

[0595] Step 5: User Notifications

[0596] The server sends the generated travel plan to the user's device in JSON format. The device uses push notifications to inform the user of the travel plan and display the detailed plan. The user can check the notification and view the detailed plan within the application.

[0597] Input: Generated travel plan (JSON format)

[0598] Output: Display of user device notifications and detailed plans

[0599] Step 6: Gathering Feedback

[0600] After the trip ends, the device displays a satisfaction survey to the user. The user answers the survey and enters feedback data into the device. The device sends this feedback data to a server for storage. The server analyzes the feedback data and uses it to generate the next trip plan.

[0601] Input: User feedback data

[0602] Output: Saved and analyzed feedback data

[0603] This enables the system to provide efficient, sustainable, and user-friendly travel plans.

[0604] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0605] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0606] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0607] [Second Embodiment]

[0608] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0609] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0610] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0611] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0612] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0613] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0614] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0615] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0616] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0617] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0618] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0619] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0620] This invention is a system designed to solve challenges in the tourism industry. Its purpose is to collect and analyze data on tourist destinations, accommodations, and transportation, and to generate and notify users of optimal travel plans. This system is primarily composed of servers, terminals, and users.

[0621] Data collection

[0622] 1. Obtaining tourist information

[0623] The server sends a GET request to the API endpoint of the tourist destination.

[0624] The server parses the returned JSON data and extracts detailed information such as the name of the tourist attraction, location information, rating, and opening hours.

[0625] The server stores this information in a database.

[0626] 2. Acquisition of weather data

[0627] The server accesses the API of a weather data provider service to obtain the weekly weather forecast for the target area.

[0628] The server analyzes the weather information it receives, links it to tourist destination information, and stores it in a database.

[0629] 3. Acquisition of traffic condition data

[0630] The server calls the API of the traffic information service to obtain real-time traffic condition data.

[0631] The server analyzes this information and updates the database with delay information and operating status for each mode of transport (trains, buses, taxis, etc.).

[0632] Data Analysis

[0633] 1. Demand forecasting and optimization

[0634] The server uses AI models (e.g., time-series forecasting models) based on past tourism data to predict demand for the following month.

[0635] The server calculates the optimal allocation of personnel, materials, and ingredients, optimizing resource management.

[0636] 2. Workload analysis

[0637] The server analyzes operational data from the accommodation facility (for example, the frequency of room cleaning and the response time for front desk staff).

[0638] The server identifies business processes that can be automated and simulates the effects of automation using robots and software.

[0639] Plan creation

[0640] 1. Generating an individualized optimization plan

[0641] The server generates a travel plan based on the user's profile data (age, length of stay, budget, preferences, etc.).

[0642] The server uses an AI model (e.g., a recommendation system) to select tourist spots, accommodations, and restaurants based on the user's preferences.

[0643] 2. Creating a Sustainable Tourism Plan

[0644] The server calculates transportation methods and routes with the lowest carbon emissions.

[0645] The server generates plans that include eco-friendly accommodations and restaurants.

[0646] User notifications

[0647] 1. Plan notification

[0648] The server generates a travel plan and sends it to the terminal in JSON format.

[0649] The device displays notifications to the user as push alerts.

[0650] 2. Displaying plan details

[0651] The device displays detailed plan information (transportation, accommodation, sightseeing spots, etc.) to the user via the UI.

[0652] Execution Management

[0653] 1. Plan Review and Approval

[0654] The user reviews the plan on their device and chooses whether or not to proceed.

[0655] The terminal sends the user's selection to the server.

[0656] 2. Notification to relevant organizations

[0657] The server notifies accommodation providers, transportation companies, tour guides, and other relevant parties of the reservation information.

[0658] The server verifies that the notification was received successfully and sends a reservation confirmation to the user.

[0659] Feedback processing

[0660] 1. Gathering feedback

[0661] After the trip ends, the device displays a satisfaction survey to the user.

[0662] The device collects user feedback and sends it to the server.

[0663] 2. Analysis of Feedback

[0664] The server stores the feedback in a database and analyzes it using an AI model.

[0665] The server uses the analysis results to improve the plan generation algorithm and optimize it.

[0666] Specific example

[0667] Data collection examples

[0668] The server retrieves information about Tokyo Tower from a tourist destination API.

[0669] The server retrieves the weekly weather forecast for Tokyo from a weather data provider.

[0670] The server retrieves real-time train operation information from a traffic information service.

[0671] Data analysis example

[0672] The server uses an AI model to predict the demand for Tokyo tourism in the following month.

[0673] The server analyzes operational data from accommodation facilities and simulates the effects of introducing cleaning robots.

[0674] Plan creation example

[0675] The server generates a 3-day Kyoto and Nara travel plan based on profile data indicating a couple's trip.

[0676] The server creates sustainable plans that include train travel and eco-friendly accommodations.

[0677] User notification example

[0678] The server generates a travel plan and sends it to the device.

[0679] The device will notify the user via push notification and display the detailed plan.

[0680] Execution Management Example

[0681] The user reviews the plan and selects to proceed on their device.

[0682] The server notifies accommodations and transportation providers of the reservation information and sends a reservation confirmation to the user.

[0683] Feedback Processing Example

[0684] The device displays a post-trip feedback survey to the user and sends it to the server.

[0685] The server analyzes the feedback and incorporates it into the next plan generation.

[0686] As a result, the system of the present invention can solve the multifaceted challenges of the tourism industry and provide efficient and sustainable tourism experiences.

[0687] The following describes the processing flow.

[0688] Step 1:

[0689] The server sends a GET request to the tourist destination's API endpoint. The information retrieved may include the name of the tourist destination, its location, its rating, and its opening hours.

[0690] Step 2:

[0691] The server parses the returned JSON data and extracts detailed information about the tourist destination. The extracted data includes the name of the tourist destination, location information, rating, opening hours, etc.

[0692] Step 3:

[0693] The server saves detailed information about tourist destinations to a database. The saved data is used in subsequent analysis processes.

[0694] Step 4:

[0695] The server accesses the weather data service's API. It sends a GET request to retrieve the weekly weather forecast for the target area.

[0696] Step 5:

[0697] The server analyzes the weather information it has acquired. The analyzed weather information is then linked to tourist destination information and stored in a database.

[0698] Step 6:

[0699] The server calls the API of a traffic information service to obtain real-time traffic data for the target area. The information obtained includes delay information, service status, and more.

[0700] Step 7:

[0701] The server analyzes traffic data and stores delay information and operational status for each mode of transport in a database.

[0702] Step 8:

[0703] The server uses historical tourism data and an AI model (e.g., a time-series forecasting model) to predict demand for the following month. The prediction results are stored in a database.

[0704] Step 9:

[0705] The server calculates the optimal allocation of personnel, materials, and ingredients based on demand forecast data. The calculation results are then applied to tourist destinations and accommodations.

[0706] Step 10:

[0707] The server analyzes operational data from the accommodation facility. This operational data includes information such as the frequency of room cleaning and the response time for front desk staff.

[0708] Step 11:

[0709] The server identifies business processes that can be automated and simulates the effectiveness of automation using robots and software. The simulation results are stored in a database.

[0710] Step 12:

[0711] The server generates the optimal travel plan based on the user's profile data (age, length of stay, budget, preferences, etc.). An AI model (recommendation system) is used for this generation.

[0712] Step 13:

[0713] The server calculates transportation options and routes with low carbon emissions. The calculation results also include eco-friendly accommodations and restaurants.

[0714] Step 14:

[0715] The server generates a travel plan and sends it to the user's device in JSON format. The generated plan includes tourist destinations, accommodations, transportation options, and environmentally friendly choices.

[0716] Step 15:

[0717] The device displays travel plan notifications to the user as push alerts. The user can then check the notifications.

[0718] Step 16:

[0719] The device displays detailed plan information to the user (transportation, accommodation, sightseeing spots, etc.). The user reviews the plan details.

[0720] Step 17:

[0721] The user reviews the plan on their device and chooses whether to proceed. The result of the selection is sent from the device to the server.

[0722] Step 18:

[0723] The server notifies accommodation providers, transportation providers, tour guides, etc., of the booking information. After each provider confirms the booking, they send that information to the user.

[0724] Step 19:

[0725] After the trip ends, the device displays a satisfaction survey to the user. The user then answers the survey.

[0726] Step 20:

[0727] The device sends the feedback collected from the user to the server. The server stores the feedback in a database.

[0728] Step 21:

[0729] The server analyzes the feedback and uses an AI model to optimize the next plan generation algorithm. The analysis results are then incorporated into the next plan creation process.

[0730] (Example 1)

[0731] Next, we will describe Example 1. 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."

[0732] In the tourism industry, the lack of sufficient integration in data collection and analysis of tourist destinations, accommodations, and transportation methods makes it difficult to provide travelers with optimal travel plans. Furthermore, conventional systems have difficulty in demand forecasting and identifying tasks that can be automated, making it impossible to achieve efficient resource allocation and propose eco-friendly travel plans. In addition, there has been a lack of mechanisms to effectively collect user feedback and utilize it to optimize future travel plans. The objective of this invention is to solve these problems.

[0733] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0734] In this invention, the server includes means for collecting data on tourist destinations, accommodations, and means of transportation; means for analyzing the collected data and calculating the optimal allocation of personnel, materials, and ingredients; means for analyzing the workload at accommodations and identifying processes that can be automated; means for generating an optimal travel plan based on individual traveler profile data; means for suggesting transportation and routes with low carbon dioxide emissions; means for notifying the user terminal of the generated travel plan; means for collecting user feedback and reflecting it in optimizing the travel plan; means for obtaining data on tourist destinations, weather, and traffic conditions via API; means for storing and analyzing the acquired data in a database; means for forecasting demand using an AI model based on past tourism data; means for generating an optimal travel plan using user profile data and an AI model; means for adjusting the generated travel plan to take eco-friendly elements into consideration; means for sending the travel plan in JSON format to the user terminal and providing push notifications; means for displaying a questionnaire to collect user satisfaction after the trip; means for storing the collected feedback in a database and analyzing it using an AI model; and means for optimizing the travel plan generation algorithm based on the analysis results. This makes it possible to efficiently and integrally execute a series of processes in the tourism industry, from data collection to plan generation and feedback analysis.

[0735] A "tourist destination" is a geographical location that travelers visit for their own purposes, and is primarily a spot with cultural, historical, or natural attractions.

[0736] "Accommodation facilities" refer to buildings and facilities that provide temporary accommodation for travelers, and include hotels, inns, and guesthouses.

[0737] "Transportation" refers to the vehicles and routes used by travelers to reach their destination, and includes trains, buses, taxis, and airplanes.

[0738] "Data collection" is the process of obtaining information about tourist destinations, accommodations, and transportation from various sources.

[0739] "Analysis" is a technique for processing collected data to make it easier to understand and extract meaningful information.

[0740] "Resource allocation" refers to devising methods for efficiently distributing personnel, materials, and ingredients.

[0741] "Workload" is an indicator that shows the quantity and quality of work performed by employees within an accommodation facility.

[0742] "Automable processes" refer to business processes that can be automated using robots or software.

[0743] "Profile data" refers to data that represents individual information about travelers, such as age, length of stay, budget, and preferences.

[0744] A "travel plan" is a schedule and activity plan for a trip that is proposed to travelers.

[0745] "Carbon dioxide emissions" refer to the amount of carbon dioxide released into the atmosphere by means of transportation and other activities.

[0746] "Eco-friendly" refers to characteristics that indicate environmentally friendly and sustainable methods and products.

[0747] A "push notification" is a notification message that is sent instantly from a server to a device.

[0748] "Feedback" refers to information about opinions and satisfaction levels collected from travelers.

[0749] "API" stands for Application Programming Interface, and it is a standardized method for exchanging data between different software systems.

[0750] "JSON format" is an abbreviation for JavaScript Object Notation, and it is a format used for data exchange that represents data in text format.

[0751] An "AI model" is an algorithm or computational model that uses artificial intelligence technology to perform data analysis and prediction.

[0752] A "survey" is a research method used to collect opinions and information based on a specific question format.

[0753] This invention is a system designed to solve challenges in the tourism industry. Its purpose is to collect and analyze data on tourist destinations, accommodations, and transportation, and to generate and notify users of optimal travel plans. This system is primarily composed of servers, terminals, and users.

[0754] Data collection

[0755] The server retrieves data on tourist destinations, weather, and traffic conditions via APIs. For example, it obtains detailed information such as the name, location, rating, and opening hours of tourist destinations from services that provide tourist destination information. It obtains weekly weather forecasts for the target area from weather data providers and real-time traffic data from traffic information providers. This data is returned in JSON format, which the server parses and stores the necessary information in a database. This allows for the integrated management of detailed information on tourist destinations, accommodations, and transportation options.

[0756] Data Analysis

[0757] Based on the collected data, the server uses AI models (e.g., time-series forecasting models) with historical tourism data to predict demand for the following month. It also uses profile data and AI models (e.g., recommendation systems) to generate personalized travel plans. Furthermore, the server analyzes operational data from accommodations to identify business processes that can be automated. For example, it can simulate the introduction of cleaning robots to reduce the workload of cleaning operations.

[0758] Plan creation

[0759] The server generates an optimal travel plan based on the user's profile data (age, length of stay, budget, preferences, etc.). The generated plan is adjusted to take eco-friendly factors into consideration, prioritizing transportation methods and routes with low carbon emissions. Eco-friendly accommodations and restaurants are also selected. The generated travel plan is converted to JSON format and sent to the user's device.

[0760] User notifications

[0761] The server notifies the user's device of the generated travel plan. The device receives this data and notifies the user via push notification. For example, a message such as "A new travel plan has been suggested!" might appear on the smartphone. The user can then review the plan details and choose to proceed through their device.

[0762] Feedback processing

[0763] After the trip ends, the device displays a satisfaction survey to the user. The user's feedback is sent to the server and stored in a database. The server analyzes this feedback using an AI model and uses it to optimize the algorithm for generating the next travel plan. For example, it can provide specific insights such as, "Satisfaction improved by changing the order of sightseeing spots in the travel plan."

[0764] Specific examples and prompt statements

[0765] As a concrete example, the prompt message used by the server to retrieve information about Tokyo Tower from a tourist destination API is as follows:

[0766] "Please obtain information about Tokyo Tower."

[0767] Furthermore, the prompt message for predicting the demand for Tokyo tourism in the following month using an AI model is as follows:

[0768] "Please predict the demand for tourism in Tokyo next month."

[0769] The following is an example of a prompt message used to notify the user of the generated travel plan.

[0770] "A new travel plan has been suggested! Please check the details."

[0771] As described above, the system of the present invention efficiently and integrally executes a series of processes in the tourism industry, from data collection to plan generation and feedback analysis. This makes it possible to provide travelers with optimal travel plans and improve their tourism experience.

[0772] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0773] Step 1:

[0774] Obtain tourist information

[0775] The server sends a GET request to the tourist destination's API endpoint. The input includes the URL of the tourist destination API and necessary parameters (e.g., API key). The server executes the GET request and receives JSON data about the tourist destination. This JSON data is parsed to extract detailed information such as the tourist destination's name, location, rating, and opening hours, and stored in the database. Specifically, an HTTP request is sent to the URL of the tourist destination API.

[0776] Step 2:

[0777] Acquisition of weather data

[0778] The server accesses the weather data provider's API to retrieve the weekly weather forecast for the target area. The input includes the weather data provider's API URL and a region specification parameter. The server executes a GET request and retrieves the weather data in JSON format. This data is then parsed, linked to tourist destination information, and stored in a database. Specifically, the server accesses the weather data API endpoint and retrieves regional information.

[0779] Step 3:

[0780] Acquisition of traffic condition data

[0781] The server calls the API of a traffic information service to obtain real-time traffic data. The input includes the URL of the traffic information API and necessary parameters (e.g., region, mode of transport). The server executes a GET request and retrieves traffic information in JSON format. This data is then parsed and stored in the database. Specifically, the server accesses the traffic information API to obtain delay information and service status.

[0782] Step 4:

[0783] Demand forecasting and optimization

[0784] The server uses an AI model (e.g., a time-series forecasting model) based on historical tourism data to predict demand for the following month. Historical tourism data is used as input. The data is fed into the AI ​​model, and the demand forecast results are output. Based on these results, the optimal allocation of personnel, materials, and ingredients is calculated. Specifically, the time-series forecasting model is executed, and the forecast results are reflected in resource management.

[0785] Step 5:

[0786] Work load analysis

[0787] The server collects and analyzes operational data from accommodation facilities (e.g., room cleaning frequency and front desk service response time). The operational data from the accommodation facilities is used as input. The server analyzes the data and identifies business processes that can be automated. Specifically, it uses data analysis tools to visualize business processes and simulate automation.

[0788] Step 6:

[0789] Generating Individually Optimized Plans

[0790] The server generates a travel plan based on the user's profile data (age, length of stay, budget, preferences, etc.). User profile data is used as input. The data is input into an AI model (recommendation system) to output the optimal travel plan. Specifically, the recommendation system is used to select tourist spots, accommodations, and restaurants that are suitable for the user.

[0791] Step 7:

[0792] Display of sustainable tourism plans

[0793] The server calculates transportation methods and routes with low carbon emissions. Transportation and route data are used as input. The server adjusts the plan considering eco-friendly factors and selects eco-friendly accommodations and restaurants. Specifically, it generates plans prioritizing sustainable transportation and accommodation options.

[0794] Step 8:

[0795] Plan notification

[0796] The server generates a travel plan and sends it to the device in JSON format. The input is the generated travel plan. The server converts this data to JSON and sends it to the user's device. The device receives this data and sends a push notification. Specifically, the smartphone displays a notification saying, "A new travel plan has been suggested!"

[0797] Step 9:

[0798] View plan details

[0799] The device displays detailed plan information to the user. The input is travel plan data received from a server. The device analyzes this data and displays it in the UI. Specifically, it displays detailed information about transportation, accommodation, and tourist attractions in a list format within the app.

[0800] Step 10:

[0801] Feedback Collection

[0802] After the trip ends, the device displays a satisfaction survey to the user. The end time of the trip plan is used as input. The device displays the survey to the user and collects responses. Specifically, the app displays a form asking, "Please tell us how satisfied you were with your trip."

[0803] Step 11:

[0804] Feedback analysis

[0805] The server stores feedback in a database and analyzes it using an AI model. User feedback data is used as input. The server analyzes the feedback and uses it to optimize the plan generation algorithm. Specifically, it performs sentiment analysis and uses it to generate the next plan.

[0806] The above is a detailed explanation of the system's processing steps and operation.

[0807] (Application Example 1)

[0808] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0809] In the tourism industry, travelers face challenges in creating and executing efficient and comfortable travel plans. In particular, they require flexible arrangements for meals at tourist destinations, as well as adaptability to weather and traffic conditions. Furthermore, providing environmentally friendly options is essential for sustainable tourism. In addition, personalized planning tailored to individual traveler preferences is required, and incorporating user feedback into future plans is crucial.

[0810] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0811] In this invention, the server includes means for collecting data on tourist destinations, accommodations, and transportation; means for analyzing the collected data and calculating the optimal allocation of personnel, materials, and ingredients; means for analyzing the workload at accommodations and identifying processes that can be automated; means for generating an optimal travel plan based on individual traveler profile data; means for suggesting transportation and routes with low carbon dioxide emissions; means for delivering meals from local restaurants based on the travel plan; means for calculating the optimal delivery time based on weather information and traffic conditions; means for notifying the user terminal of the generated travel plan; and means for collecting user feedback and reflecting it in optimizing the travel plan. This enables the creation and execution of efficient and sustainable travel plans.

[0812] A "tourist destination" is a place that travelers visit, such as a natural landscape, historical buildings, or cultural facilities.

[0813] "Accommodation facilities" refer to facilities such as hotels, inns, and guesthouses where travelers can stay temporarily.

[0814] "Transportation" refers to the means of getting around that travelers use, such as trains, buses, taxis, and airplanes.

[0815] "Means of data collection" refers to the hardware and software used to acquire information about tourist destinations, accommodations, and transportation.

[0816] "Means for calculating the optimal allocation of personnel, materials, and food supplies" refers to an analytical system that uses collected data to calculate the efficient allocation of human resources, materials, and food supplies in tourist destinations and accommodation facilities.

[0817] "A means of analyzing workload and identifying processes that can be automated" refers to a system for analyzing the workload at accommodation facilities and identifying tasks that can be automated.

[0818] "Profile data" refers to information about individual travelers, such as age, length of stay, budget, and preferences.

[0819] "A means of generating the optimal travel plan" refers to a system that plans the most suitable sightseeing routes, accommodations, and activities for travelers based on their profile data.

[0820] "Means for proposing transportation methods and routes with low carbon dioxide emissions" refers to a system that calculates and proposes transportation methods and travel routes that are environmentally conscious and reduce carbon dioxide emissions.

[0821] "Methods for having meals delivered from local restaurants" refers to a system for ordering and having meals delivered from local restaurants based on a travel plan.

[0822] "A means of calculating the optimal delivery time based on weather information and traffic conditions" refers to a system that takes weather forecasts and traffic conditions into consideration to optimally adjust the delivery time of meals.

[0823] "Means for notifying user devices of generated travel plans" refers to a system for delivering generated travel plans to travelers' devices such as smartphones and tablets.

[0824] "Means for collecting feedback and using it to optimize travel plans" refers to a system that collects travelers' evaluations and opinions and uses them to improve and optimize future travel plans.

[0825] This invention is a system that solves problems in the tourism industry and is composed primarily of a server, terminals, and users. The specific form of this system is described below.

[0826] Data collection

[0827] 1. Obtaining tourist information

[0828] The server sends a GET request to the tourist destination's API endpoint and retrieves detailed information about the tourist destination (name, location, rating, opening hours, etc.) in JSON format. This information is then stored in the database.

[0829] 2. Acquisition of weather data

[0830] The server retrieves weekly weather forecasts from a weather data provider service, analyzes the data, and stores it in a database linked to tourist destination information.

[0831] 3. Acquisition of traffic condition data

[0832] The server retrieves real-time traffic information from traffic information services and updates the database with delay information and operating status for each mode of transport.

[0833] Data Analysis

[0834] 1. Demand forecasting and optimization

[0835] The server uses historical tourism data to feed into an AI model (e.g., a time-series forecasting model) to predict future demand. It also calculates the optimal allocation of personnel, materials, and ingredients.

[0836] 2. Workload analysis

[0837] The server analyzes operational data from accommodations (such as room cleaning frequency and front desk service response times) to identify business processes that can be automated.

[0838] Plan creation

[0839] 1. Generating an individualized optimization plan

[0840] The server generates a travel plan based on the user's profile data (age, length of stay, budget, preferences, etc.). The generated plan appropriately includes sightseeing spots, accommodations, and restaurants.

[0841] 2. Creating a Sustainable Tourism Plan

[0842] The server calculates transportation options and routes with low carbon emissions and creates plans that include eco-friendly accommodations and restaurants.

[0843] 3. Generating a meal delivery plan based on the travel plan.

[0844] The server collects restaurant information around tourist destinations and creates the optimal delivery plan based on user preferences, weather information, and traffic conditions.

[0845] User notifications

[0846] 1. Plan notification

[0847] The server sends the generated travel plan and delivery plan to the device in JSON format. The device then displays the notification to the user as a push alert.

[0848] 2. Displaying plan details

[0849] The device displays detailed plan information (transportation, accommodation, sightseeing spots, delivery options, etc.) to the user via its UI.

[0850] Feedback processing

[0851] 1. Gathering feedback

[0852] After the trip ends, the device displays a satisfaction survey to the user and collects feedback. The collected data is sent to a server.

[0853] 2. Analysis of Feedback

[0854] The server stores the feedback in a database and performs analysis using an AI model. Based on the analysis results, it optimizes the algorithm for generating the next plan.

[0855] Specific example

[0856] Data collection examples

[0857] The server obtains information about city A from a tourist destination API and the weekly weather forecast for city A from a weather data service. It also obtains real-time traffic information from a traffic information service.

[0858] Data analysis example

[0859] The server predicts tourism demand in city A based on historical data and analyzes operational data from accommodation facilities to simulate the effects of introducing cleaning robots.

[0860] Plan creation example

[0861] The server generates a 3-day travel plan based on the profile of a couple traveling together. This plan includes eco-friendly accommodations and sustainable travel routes. It also adds restaurant delivery options based on the user's preferences and weather information.

[0862] Example of a prompt

[0863] "When sightseeing in City A, please create a plan that includes ordering food delivery from recommended local restaurants, taking into account the optimal delivery time. A detailed plan based on user preferences, weather information, and traffic conditions is required."

[0864] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0865] Step 1:

[0866] Obtain tourist information

[0867] The server sends a GET request to the tourist destination's API endpoint. The input data is the tourist destination ID. The server parses the returned JSON data, extracts detailed information such as the tourist destination's name, location, rating, and opening hours, and saves it to the database. The output is the detailed information of the tourist destination.

[0868] Step 2:

[0869] Acquisition of weather data

[0870] The server accesses the API of a weather data service to obtain the weekly weather forecast for the target area. The input data is location information of tourist destinations. The server analyzes the obtained weather information, links it to the tourist destination information, and stores it in a database. The output is the weekly weather forecast information.

[0871] Step 3:

[0872] Acquisition of traffic condition data

[0873] The server calls an API from a traffic information service to obtain real-time traffic data. The input data is travel route information to tourist destinations. The server analyzes this information and updates the database with delay information and operating status for each mode of transport. The output is traffic information.

[0874] Step 4:

[0875] Retrieving User Profiles

[0876] The server retrieves user profile information. The input data is the user ID. The server retrieves profile information (age, length of stay, budget, preferences, etc.) from the database. The output is the user profile data.

[0877] Step 5:

[0878] Demand forecasting and resource allocation

[0879] The server uses historical tourism data to predict next month's demand based on an AI model. The input data is historical tourism data. The server calculates the optimal allocation of personnel, materials, and ingredients, optimizing resource management. The output is demand forecast data and resource allocation plan.

[0880] Step 6:

[0881] Workload analysis

[0882] The server analyzes the operational data of the accommodation facility and identifies processes that can be automated. The input data is the operational data of the accommodation facility. The server performs automation simulations and evaluates their effectiveness. The output is the business processes that can be automated and the simulation results.

[0883] Step 7:

[0884] Travel plan generation

[0885] The server uses an AI model to generate an optimal travel plan based on the user's profile data. Input data includes profile data and tourist destination information. The server creates a travel plan that includes tourist spots, accommodations, and restaurants. The output is the travel plan.

[0886] Step 8:

[0887] Generating a delivery plan

[0888] The server generates a delivery plan for meals from local restaurants based on the travel plan. Input data includes tourist information and user preferences. The server calculates the optimal delivery time based on weather and traffic conditions. The output is the delivery plan.

[0889] Step 9:

[0890] Plan notification

[0891] The server generates a travel plan and a delivery plan and sends them to the terminal in JSON format. The input data is the travel plan and the delivery plan. The terminal displays the notification to the user as a push alert. The output is the notification to the user.

[0892] Step 10:

[0893] Gathering and analyzing feedback

[0894] After the trip ends, the device displays a satisfaction survey to the user and collects feedback. The input data is the user's feedback. The server stores the collected feedback in a database, analyzes it using an AI model, and optimizes the algorithm for generating the next trip plan. The output is the analysis results and the optimized algorithm.

[0895] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0896] This invention is a system designed to solve challenges in the tourism industry. It aims to collect and analyze data on tourist destinations, accommodations, and transportation methods, and to generate and notify users of optimal travel plans by combining this data with an emotion engine that recognizes user emotions. This system is primarily composed of a server, terminals, and users.

[0897] Data collection

[0898] 1. Obtaining tourist information

[0899] The server sends a GET request to the tourist destination's API endpoint. The information retrieved may include the name of the tourist destination, its location, its rating, and its opening hours.

[0900] The server parses the returned JSON data and extracts detailed information about the tourist destination. The extracted data includes the name of the tourist destination, location information, rating, opening hours, etc.

[0901] The server saves detailed information about tourist destinations to a database. The saved data is used in subsequent analysis processes.

[0902] 2. Acquisition of weather data

[0903] The server accesses the weather data provider's API and sends a GET request to retrieve the weekly weather forecast for the target area.

[0904] The server analyzes the weather information it has acquired. The analyzed weather information is then linked to tourist destination information and stored in a database.

[0905] 3. Acquisition of traffic condition data

[0906] The server calls the API of a traffic information service to obtain real-time traffic data for the target area. The information obtained includes delay information, service status, and more.

[0907] The server analyzes traffic data and stores delay information and operational status for each mode of transport in a database.

[0908] Data Analysis

[0909] 1. Demand forecasting and optimization

[0910] The server uses historical tourism data and an AI model (e.g., a time-series forecasting model) to predict demand for the following month. The prediction results are stored in a database.

[0911] The server calculates the optimal allocation of personnel, materials, and ingredients based on demand forecast data. The calculation results are then applied to tourist destinations and accommodations.

[0912] 2. Workload analysis

[0913] The server analyzes operational data from the accommodation facility (for example, the frequency of room cleaning and the response time for front desk staff).

[0914] The server identifies business processes that can be automated and simulates the effectiveness of automation using robots and software. The simulation results are stored in a database.

[0915] Plan creation

[0916] 1. Generating an individualized optimization plan

[0917] The server generates travel plans based on the user's profile data (age, length of stay, budget, preferences, etc.). An AI model (recommendation system) is used for this generation.

[0918] The server uses an emotion engine to collect user emotion data and incorporate it into the generation and optimization of travel plans.

[0919] 2. Creating a Sustainable Tourism Plan

[0920] The server calculates transportation options and routes with low carbon emissions. The calculation results also include eco-friendly accommodations and restaurants.

[0921] User notifications

[0922] 1. Plan notification

[0923] The server generates a travel plan and sends it to the user's device in JSON format. The generated plan includes tourist destinations, accommodations, transportation options, and environmentally friendly choices.

[0924] The device displays travel plan notifications to the user as push alerts. The user can then check the notifications.

[0925] 2. Displaying plan details

[0926] The device displays detailed plan information to the user (transportation, accommodation, sightseeing spots, etc.). The user reviews the plan details.

[0927] Execution Management

[0928] 1. Plan Review and Approval

[0929] The user reviews the plan on their device and chooses whether to proceed. The result of the selection is sent from the device to the server.

[0930] 2. Notification to relevant organizations

[0931] The server notifies accommodation providers, transportation providers, tour guides, etc., of the booking information. After each provider confirms the booking, they send that information to the user.

[0932] Feedback processing

[0933] 1. Gathering feedback

[0934] After the trip ends, the device displays a satisfaction survey to the user. The user then answers the survey.

[0935] The device sends the feedback collected from the user to the server. The server stores the feedback in a database.

[0936] 2. Analysis of Feedback

[0937] The server analyzes the feedback and uses an AI model to optimize the next plan generation algorithm. The analysis results are then incorporated into the next plan creation process.

[0938] The server uses an emotion engine to analyze emotional data along with feedback and incorporate it into the next plan.

[0939] Specific example

[0940] Data collection examples

[0941] The server retrieves information about Tokyo Tower from a tourist destination API.

[0942] The server retrieves the weekly weather forecast for Tokyo from a weather data provider.

[0943] The server retrieves real-time train operation information from a traffic information service.

[0944] Data analysis example

[0945] The server uses an AI model to predict the demand for Tokyo tourism in the following month.

[0946] The server analyzes operational data from accommodation facilities and simulates the effects of introducing cleaning robots.

[0947] Plan creation example

[0948] The server generates a 3-day Kyoto and Nara travel plan based on profile data indicating a couple's trip.

[0949] The server creates sustainable plans that include train travel and eco-friendly accommodations.

[0950] The server uses an emotion engine to optimize plans by taking user sentiment data into account.

[0951] User notification example

[0952] The server generates a travel plan and sends it to the device.

[0953] The device will notify the user via push notification and display the detailed plan.

[0954] Execution Management Example

[0955] The user reviews the plan and selects to proceed on their device.

[0956] The server notifies accommodations and transportation providers of the reservation information and sends a reservation confirmation to the user.

[0957] Feedback Processing Example

[0958] The device displays a post-trip feedback survey to the user and sends it to the server.

[0959] The server analyzes feedback and sentiment data and incorporates it into generating the next plan.

[0960] As a result, the system of the present invention can solve the multifaceted challenges of the tourism industry and provide an efficient, emotionally resonant, and sustainable tourism experience.

[0961] The following describes the processing flow.

[0962] Step 1:

[0963] The server sends a GET request to the tourist destination's API endpoint. The information retrieved may include the name of the tourist destination, its location, its rating, and its opening hours.

[0964] Step 2:

[0965] The server parses the returned JSON data and extracts detailed information about the tourist destination. The extracted data includes the name of the tourist destination, location information, rating, opening hours, etc.

[0966] Step 3:

[0967] The server saves detailed information about tourist destinations to a database. The saved data is used in subsequent analysis processes.

[0968] Step 4:

[0969] The server accesses the weather data service's API. It sends a GET request to retrieve the weekly weather forecast for the target area.

[0970] Step 5:

[0971] The server analyzes the weather information it has acquired. The analyzed weather information is then linked to tourist destination information and stored in a database.

[0972] Step 6:

[0973] The server calls the API of a traffic information service to obtain real-time traffic data for the target area. The information obtained includes delay information, service status, and more.

[0974] Step 7:

[0975] The server analyzes traffic data and stores delay information and operational status for each mode of transport in a database.

[0976] Step 8:

[0977] The server uses historical tourism data and an AI model (e.g., a time-series forecasting model) to predict demand for the following month. The prediction results are stored in a database.

[0978] Step 9:

[0979] The server calculates the optimal allocation of personnel, materials, and ingredients based on demand forecast data. The calculation results are then applied to tourist destinations and accommodations.

[0980] Step 10:

[0981] The server analyzes operational data from the accommodation facility. This operational data includes information such as the frequency of room cleaning and the response time for front desk staff.

[0982] Step 11:

[0983] The server identifies business processes that can be automated and simulates the effectiveness of automation using robots and software. The simulation results are stored in a database.

[0984] Step 12:

[0985] The server generates the optimal travel plan based on the user's profile data (age, length of stay, budget, preferences, etc.). An AI model (recommendation system) is used for this generation.

[0986] Step 13:

[0987] The server uses an emotion engine to collect and analyze user emotion data, which is then used to generate and optimize travel plans. This emotion data is based on real-time user feedback and the user's emotional state at the time of plan generation.

[0988] Step 14:

[0989] The server calculates transportation options and routes with low carbon emissions. The calculation results also include eco-friendly accommodations and restaurants.

[0990] Step 15:

[0991] The server generates a travel plan and sends it to the user's device in JSON format. The generated plan includes tourist destinations, accommodations, transportation options, and environmentally friendly choices.

[0992] Step 16:

[0993] The device displays travel plan notifications to the user as push alerts. The user can then check the notifications.

[0994] Step 17:

[0995] The device displays detailed plan information to the user (transportation, accommodation, sightseeing spots, etc.). The user reviews the plan details.

[0996] Step 18:

[0997] The user reviews the plan on their device and chooses whether to proceed. The result of the selection is sent from the device to the server.

[0998] Step 19:

[0999] The server notifies accommodation providers, transportation providers, tour guides, etc., of the booking information. After each provider confirms the booking, they send that information to the user.

[1000] Step 20:

[1001] After the trip ends, the device displays a satisfaction survey to the user. The user then answers the survey.

[1002] Step 21:

[1003] The device sends the feedback collected from the user to the server. The server stores the feedback in a database.

[1004] Step 22:

[1005] The server analyzes the feedback and uses an AI model to optimize the next plan generation algorithm. The analysis results are then incorporated into the next plan creation process.

[1006] Step 23:

[1007] The server uses an emotion engine to analyze emotional data along with feedback, and incorporates it into future plans. This emotional data includes not only satisfaction levels but also user stress levels and excitement levels.

[1008] As a result, the system of the present invention can solve the multifaceted challenges of the tourism industry and provide an efficient, emotionally resonant, and sustainable tourism experience.

[1009] (Example 2)

[1010] Next, we will describe Example 2. 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".

[1011] In today's tourism industry, there is a demand for providing optimal travel plans that meet the diverse needs of tourists. Furthermore, managing the uncertainty of demand forecasts, workload, and efficient allocation of insufficient resources are crucial challenges. In addition, there is a growing demand for environmentally conscious and sustainable tourism, as well as the provision of travel experiences that consider the emotions of users. However, there are limited systems that can comprehensively address all of these issues.

[1012] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1013] In this invention, the server includes means for collecting information on tourist destinations, weather data, and traffic data; means for analyzing the collected data and storing detailed information on tourist destinations, weather information, and traffic information in a database; and means for using an AI model based on past tourism data to forecast demand and calculate the optimal allocation of personnel, materials, and ingredients. This makes it possible to provide travel plans that best meet the diverse needs of tourists. Furthermore, by predicting and optimizing the workload, efficient use of resources is achieved, enabling the provision of environmentally conscious and sustainable travel experiences.

[1014] "Information about tourist destinations" refers to detailed data such as the name of the tourist destination, its location, rating, and opening hours.

[1015] "Weather data" refers to data related to weather conditions in a specific region, such as weather forecasts, temperature, precipitation, and wind speed.

[1016] "Traffic status data" refers to real-time data on the operation status, delay information, and route information of public transportation (e.g., trains, buses, airplanes, etc.).

[1017] A "database" is a collection of structured data that stores collected and analyzed data, and that can be searched and retrieved as needed.

[1018] An "AI model" refers to algorithms and computational methods that use artificial intelligence technology, and includes, for example, time series forecasting models and recommendation systems.

[1019] "Demand forecasting" is the process of predicting future demand by analyzing past data.

[1020] "Optimal allocation of personnel, materials, and ingredients" refers to the operation of efficiently distributing resources based on demand forecasts.

[1021] "Workload" refers to the burden of tasks related to labor and service provision at accommodation facilities.

[1022] An "automatable process" is a stage in a repetitive task or process that can be performed by machines or software without human intervention.

[1023] "Individual traveler profile data" refers to personal attribute information such as the traveler's age, length of stay, budget, and preferences.

[1024] "Low-carbon emission modes of transport" refer to eco-friendly modes of transport that aim to minimize their impact on the environment.

[1025] "Suggesting a route" refers to the process of calculating and presenting the optimal travel path to the traveler.

[1026] A "user terminal" refers to an electronic device such as a smartphone, tablet, or personal computer, which is used by a user to obtain information and operate it through its interface.

[1027] "Feedback" refers to information provided by users, such as ratings, opinions, and survey responses.

[1028] "Generating" refers to the operation of creating new information or results based on a specific algorithm or rule.

[1029] This invention is a system that solves problems in the tourism industry. It aims to collect and analyze data on tourist destinations, accommodations, and transportation methods, and to generate and notify users of optimal travel plans by combining this with an emotion engine that recognizes user emotions. This system is mainly composed of a server, terminals, and users.

[1030] Data collection

[1031] Obtain tourist information

[1032] The server sends a GET request to the tourist destination API endpoint. It retrieves information such as the name, location, rating, and opening hours of the tourist destination. It is desirable that this tourist destination API endpoint be a commonly used API.

[1033] The system analyzes the JSON data acquired by the server and extracts detailed information about tourist destinations. For example, it can retrieve information about the tourist destination "Tokyo Tower" and save it to a database.

[1034] Acquisition of weather data

[1035] The server accesses the weather data provider's API and sends a GET request to retrieve the weekly weather forecast for the target area. For example, it retrieves the weekly weather forecast for "Tokyo" from the weather forecast API.

[1036] The server analyzes the data, links it to tourist destination information, and then stores it in the database.

[1037] Acquisition of traffic condition data

[1038] The server calls the API of a traffic information service to obtain real-time traffic data for the target area (e.g., delay information, service status, etc.). Using a traffic information API is recommended.

[1039] The server analyzes the data and stores delay information and operating status for each mode of transport in a database. For example, it retrieves the operating status of trains in Tokyo.

[1040] Data Analysis

[1041] Demand forecasting and optimization

[1042] The server uses a time-series forecasting model based on past tourism data to predict demand for the following month and stores the data in a database. Applying an AI model is crucial.

[1043] The server calculates the optimal allocation of personnel, materials, and ingredients based on demand forecast data. For example, it forecasts the demand for tourism in Tokyo for the following month and performs calculations based on the results.

[1044] Work load analysis

[1045] The server collects and analyzes operational data from accommodation facilities (e.g., room cleaning frequency and front desk service response time).

[1046] The server identifies business processes that can be automated and simulates the effects of automation using robots and software. For example, it evaluates the effects of introducing cleaning robots.

[1047] Plan creation

[1048] Generating Individually Optimized Plans

[1049] The server retrieves user profile data (e.g., age, length of stay, budget, preferences, etc.) and uses an AI model (recommendation system) to generate the optimal travel plan.

[1050] The server uses an emotion engine to collect user emotion data and incorporate it into generating and optimizing travel plans. For example, it can create a 3-day Kyoto and Nara travel plan based on a couple's travel profile.

[1051] Display of sustainable tourism plans

[1052] The server calculates transportation options and routes with low carbon emissions and creates sustainable plans that include eco-friendly accommodations and restaurants. For example, it can create a plan that includes train travel and eco-friendly accommodations.

[1053] User notifications

[1054] Plan notification

[1055] The server generates a travel plan and sends it to the user's device in JSON format.

[1056] The device will display a push alert notifying the user of the travel plan. For example, it might display, "A 3-day travel plan for Kyoto has been generated."

[1057] View plan details

[1058] The device displays detailed plan information to the user (transportation, accommodation, sightseeing spots, etc.). For example, it might display "Day 1: Travel from Tokyo to Kyoto and check into an eco-hotel."

[1059] Execution Management

[1060] Plan review and approval

[1061] The user reviews the plan on their device and chooses whether or not to proceed.

[1062] The device sends the user's selection results to the server. For example, the user approves a travel plan.

[1063] Notification to relevant organizations

[1064] The server notifies accommodation providers, transportation providers, tour guides, and others of the reservation information.

[1065] The server retrieves reservation confirmation information from each institution and sends it to the user. For example, it notifies the user of confirmation information from accommodations where reservations have been confirmed.

[1066] Feedback processing

[1067] Feedback Collection

[1068] After the trip ends, the device displays a satisfaction survey to the user, who then answers the survey.

[1069] The device sends user feedback to the server, which then stores the feedback data in a database. For example, a user might answer a survey asking, "How was your overall satisfaction with your trip?"

[1070] Feedback analysis

[1071] The server analyzes the feedback data and uses an AI model to optimize the next plan generation algorithm.

[1072] The server uses an emotion engine to analyze feedback and emotional data and incorporate it into generating future plans. For example, it might consider the user's emotional data when creating the next travel plan.

[1073] Example prompt statements

[1074] "Please have the server retrieve tourist information about Tokyo Tower and save it to the database."

[1075] "Please retrieve Tokyo weather data from a weather forecast API and link it with tourist information."

[1076] "Please generate a 3-day Kyoto travel plan based on the user's profile data."

[1077] As a result, the system of the present invention can solve the multifaceted challenges of the tourism industry and provide an efficient, emotionally resonant, and sustainable tourism experience.

[1078] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1079] Step 1: Obtain tourist information

[1080] The server sends a GET request to the tourist destination API endpoint. For example, it might send a request with the input "Tourist Destination Name = Tokyo Tower".

[1081] The server receives the returned JSON data and analyzes and extracts detailed information such as the name of the tourist attraction, location information, rating, and opening hours.

[1082] The server analyzes tourist destination information and saves it to the database. Specifically, it executes an INSERT query on the appropriate table in the database.

[1083] Step 2: Obtain weather data

[1084] The server sends a GET request to the weather data service's API. For example, it might send a request with the input "Region=Tokyo".

[1085] The server receives JSON data of the weekly weather forecast and analyzes the weather conditions (e.g., weather, temperature, precipitation).

[1086] The server analyzes weather data and stores it in a database, linking it to tourist destination information. Specifically, it links the primary key of the tourist destination with the weather data.

[1087] Step 3: Obtain traffic condition data

[1088] The server sends a GET request to the API of the traffic information service. For example, it might send a request with the input "Region = Tokyo, Mode of Transportation = Train".

[1089] The server receives the traffic data in JSON format and analyzes delay information and service status.

[1090] The server analyzes traffic data and saves it to a database. Specifically, it saves information for each mode of transport to the corresponding table.

[1091] Step 4: Demand forecasting and optimization

[1092] The server retrieves historical tourism data from the database. For example, it might retrieve data based on input such as "Tourist destination = Tokyo, Period = Past 3 years".

[1093] The server uses an AI model (e.g., a time series forecasting model) to predict demand for the following month. It performs data processing and time series analysis based on the input data.

[1094] The server saves the prediction results to a database and uses that data to calculate the optimal allocation of personnel, materials, and ingredients. Specifically, it applies a resource optimization algorithm.

[1095] Step 5: Workload Analysis

[1096] The server collects operational data from accommodation facilities. For example, it retrieves data with the input "Accommodation facility name = Hotel A".

[1097] The server analyzes factors such as room cleaning frequency and front desk response times to identify business processes that can be automated.

[1098] The server simulates the effects of automation using robots and software, and stores the results in a database. Specifically, it applies an automation simulation algorithm.

[1099] Step 6: Generating an individualized optimization plan

[1100] The server retrieves the user's profile data. For example, it can retrieve data with input such as "User ID=1234, Age=30, Length of Stay=3 days, Budget=50,000 yen, Interests=History".

[1101] The server uses an AI model (recommendation system) to generate the optimal travel plan. It applies data processing and recommendation algorithms based on the input profile data.

[1102] The server uses an emotion engine to collect user emotion data and incorporate it into the travel plan. Specifically, it applies an emotion analysis algorithm.

[1103] Step 7: Create a Sustainable Tourism Plan

[1104] The server calculates eco-friendly modes of transport and routes. For example, it calculates based on the input "Destination = Kyoto, Departure = Tokyo".

[1105] The server creates travel plans that include eco-friendly accommodations and restaurants. Specifically, it applies an environmental impact assessment algorithm.

[1106] Step 8: Plan Notification

[1107] The server generates a travel plan and sends it to the user's terminal in JSON format. For example, it might be sent with the input "User ID=1234".

[1108] The device will notify the user of the generated travel plan via push alert. Specifically, this will be done using the notification API.

[1109] Step 9: View plan details

[1110] The device displays detailed plan information to the user. For example, it retrieves and displays details based on the input "Plan ID=5678".

[1111] The user views the details of the displayed travel plan. Specifically, this involves displaying the details on the interface.

[1112] Step 10: Plan Review and Approval

[1113] The user reviews the plan on their device and chooses whether to proceed. For example, they might select it by entering "Plan ID=5678, Select=Approve".

[1114] The terminal sends the user's selection results to the server. Specifically, it sends the approval result to the server via a POST request.

[1115] Step 11: Notification to relevant organizations

[1116] The server notifies accommodations, transportation providers, guide services, etc., of the reservation information. For example, it will notify based on input such as "Accommodation = Hotel A, Transportation = Shinkansen (bullet train)".

[1117] The server retrieves reservation confirmation information from each institution and notifies the user. Specifically, it sends the confirmation information to the terminal.

[1118] Step 12: Gathering Feedback

[1119] After the trip ends, the device displays a satisfaction survey to the user. For example, the survey will be displayed when the user enters "Trip ID=7890".

[1120] The user answers the survey, and the device sends that data to the server. Specifically, the survey data is sent to the server via a POST request.

[1121] Step 13: Analyzing Feedback

[1122] The server retrieves feedback data from the database. For example, it retrieves data with the input "Travel ID=7890".

[1123] The server uses an AI model to analyze and optimize the next plan generation algorithm. Specifically, it applies a feedback analysis algorithm.

[1124] The server uses an emotion engine to analyze feedback and emotional data, and incorporates this into the generation of the next plan. Specifically, it performs emotional data analysis.

[1125] In this way, each processing step works in conjunction with the others, and the system as a whole solves a variety of challenges in the tourism industry.

[1126] (Application Example 2)

[1127] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[1128] In the modern tourism industry, a challenge exists in that travelers find it difficult to create efficient and personalized travel plans. Furthermore, there is a lack of systems that can flexibly adjust plans in response to changes in weather and traffic conditions, as well as the emotional state of travelers. Additionally, while sustainability in the tourism industry is a pressing need, technologies for automatically generating travel plans that reflect this need are limited.

[1129] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting data on tourist destinations, accommodations, and means of transportation; means for analyzing the collected data and calculating the optimal allocation of personnel, materials, and ingredients; means for analyzing the workload at accommodations and identifying processes that can be automated; means for generating an optimal travel plan based on individual traveler profile data; means for collecting user sentiment data and reflecting it in the travel plan; means for suggesting transportation methods and routes with low carbon dioxide emissions; means for notifying the user terminal of the generated travel plan in a prompt format; means for collecting user feedback and reflecting it in optimizing the travel plan; and means for displaying the tourist plan in conjunction with real-time location information. This makes it possible to provide an efficient, sustainable travel plan that is considerate of the user's feelings.

[1130] "Tourist information" refers to detailed information about tourist attractions, such as their name, location, rating, and opening hours.

[1131] "Accommodation information" refers to data such as the name, location, price range, and availability of accommodations, including hotels and hot spring inns.

[1132] "Transportation information" refers to information such as the operating status, delay information, price range, and travel time of public transportation such as buses, trains, and taxis.

[1133] "Emotional data" refers to data related to emotional states such as joy, sadness, and surprise, obtained through user facial analysis and text analysis.

[1134] "Prompt format" refers to a format that presents recommended actions or information in short text messages.

[1135] "Real-time location information" refers to information that instantly obtains and updates the user's current geographical location.

[1136] A "personalized travel plan" refers to a travel schedule and sightseeing route optimized based on each user's profile data and emotional data.

[1137] "Feedback" refers to opinions and impressions, such as satisfaction levels and areas for improvement, that users provide after completing a trip.

[1138] "Sustainable transportation" refers to environmentally friendly modes of transport that reduce carbon dioxide emissions (e.g., electric vehicles and bicycles).

[1139] The system for implementing this invention collects data on tourist destinations, accommodations, and transportation, and based on this data, generates and notifies users of optimal travel plans. The system mainly consists of a server, user terminals, and users. The following describes how this system works.

[1140] Data collection

[1141] The server sends GET requests to the API endpoints of tourist destinations to retrieve detailed information such as the name, location, rating, and opening hours of the tourist destinations. The retrieved information is parsed in JSON format and stored in the database. Similarly, the server accesses the API of a weather data service to retrieve the weekly weather forecast for the target area, parses it, and stores it in the database. Furthermore, the server calls the API of a traffic information service to retrieve real-time traffic data for the target area and stores delay information and service status in the database.

[1142] The hardware used will consist of Linux servers and database servers. The software will utilize Python and Flask for API communication and data analysis.

[1143] emotion recognition

[1144] When a user points their face at the camera using a smartphone or smart glasses, their face is captured using OpenCV. The captured face image is input into a TensorFlow emotion recognition model to identify the user's emotion (joy, sadness, surprise, etc.). This emotion data is sent to a server and used to generate travel plans.

[1145] Plan generation

[1146] The server combines collected tourist destination, weather, and transportation information with user profile data (age, length of stay, budget, preferences) and sentiment data to generate the optimal travel plan. This is done using an AI model (recommendation system). The generated plan is sent to the user's device in JSON format.

[1147] User notifications

[1148] Sightseeing plans are sent via push notifications to smartphones or smart glasses. Users can check these notifications and view detailed plans within the application. For example, for a user in Kyoto City, real-time location information can be used to suggest the most suitable sightseeing route and recommended spots on the spot.

[1149] Feedback Collection

[1150] After the trip, users provide feedback through a satisfaction survey. This feedback data, along with sentiment data, is sent to the server and used to optimize future travel plans.

[1151] Specific examples and prompt statements

[1152] As a concrete example, let's consider the case where the user is in Kyoto City.

[1153] Example of a prompt:

[1154] Based on the user's current location in Kyoto City, retrieve weather information and real-time traffic information for the following tourist spots and generate a recommended sightseeing plan. Suggest the most optimal route, especially if the user's emotion is "joyful."

[1155] Following this prompt, the server collects information on Kyoto's tourist attractions, weather, and traffic, recognizes that the user's emotion is "joy," generates an optimal sightseeing plan, and notifies the user's device. This allows the user to enjoy an efficient and emotionally responsive travel experience.

[1156] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1157] Step 1: Data Collection

[1158] The server sends GET requests to the API endpoints of tourist destinations to retrieve information such as the name, location, rating, and opening hours of the tourist destinations. The server parses the returned JSON data and saves the detailed information of the tourist destinations to the database. Similarly, it sends GET requests to the API of a weather data provider to retrieve weekly weather forecasts and saves the parsed weather information in the database, linked to the tourist destination information. It also calls the API of a traffic information provider to retrieve real-time traffic data and saves delay information and service status to the database.

[1159] Input: Endpoints for the tourist destination API, weather data API, and traffic information API.

[1160] Output: Database containing acquired tourist destination information, weather information, and traffic data.

[1161] Step 2: Emotion Recognition

[1162] The user uses a smartphone or smart glasses to point their face at the camera. The device's camera captures an image of the user's face and sends it to the server. The server uses a TensorFlow emotion recognition model to analyze the face image and identify the user's emotion (e.g., joy, sadness, surprise). This emotion data is stored for travel plan generation.

[1163] Input: User's face image

[1164] Output: Recognized emotion data

[1165] Step 3: Enter profile data

[1166] Users enter their profile data through the application. This profile data includes age, length of stay, budget, and preferences. The device sends this data to the server for storage.

[1167] Input: User's age, length of stay, budget, preferences

[1168] Output: Saved profile data

[1169] Step 4: Plan Generation

[1170] The server uses an AI model to generate the optimal travel plan based on collected tourist destination information, weather information, transportation information, user profile data, and sentiment data. This analyzes each piece of data and recommends the most suitable tourist spots, modes of transportation, and accommodations for the user.

[1171] Input: Tourist information, weather information, traffic information, profile data, sentiment data

[1172] Output: Generated travel plan

[1173] Step 5: User Notifications

[1174] The server sends the generated travel plan to the user's device in JSON format. The device uses push notifications to inform the user of the travel plan and display the detailed plan. The user can check the notification and view the detailed plan within the application.

[1175] Input: Generated travel plan (JSON format)

[1176] Output: Display of user device notifications and detailed plans

[1177] Step 6: Gathering Feedback

[1178] After the trip ends, the device displays a satisfaction survey to the user. The user answers the survey and enters feedback data into the device. The device sends this feedback data to a server for storage. The server analyzes the feedback data and uses it to generate the next trip plan.

[1179] Input: User feedback data

[1180] Output: Saved and analyzed feedback data

[1181] This enables the system to provide efficient, sustainable, and user-friendly travel plans.

[1182] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1183] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1184] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[1185] [Third Embodiment]

[1186] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[1187] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1188] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1189] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[1190] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1191] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1192] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1193] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1194] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[1195] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1196] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1197] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[1198] This invention is a system designed to solve challenges in the tourism industry. Its purpose is to collect and analyze data on tourist destinations, accommodations, and transportation, and to generate and notify users of optimal travel plans. This system is primarily composed of servers, terminals, and users.

[1199] Data collection

[1200] 1. Obtaining tourist information

[1201] The server sends a GET request to the API endpoint of the tourist destination.

[1202] The server parses the returned JSON data and extracts detailed information such as the name of the tourist attraction, location information, rating, and opening hours.

[1203] The server stores this information in a database.

[1204] 2. Acquisition of weather data

[1205] The server accesses the API of a weather data provider service to obtain the weekly weather forecast for the target area.

[1206] The server analyzes the weather information it receives, links it to tourist destination information, and stores it in a database.

[1207] 3. Acquisition of traffic condition data

[1208] The server calls the API of the traffic information service to obtain real-time traffic condition data.

[1209] The server analyzes this information and updates the database with delay information and operating status for each mode of transport (trains, buses, taxis, etc.).

[1210] Data Analysis

[1211] 1. Demand forecasting and optimization

[1212] The server uses AI models (e.g., time-series forecasting models) based on past tourism data to predict demand for the following month.

[1213] The server calculates the optimal allocation of personnel, materials, and ingredients, optimizing resource management.

[1214] 2. Workload analysis

[1215] The server analyzes operational data from the accommodation facility (for example, the frequency of room cleaning and the response time for front desk staff).

[1216] The server identifies business processes that can be automated and simulates the effects of automation using robots and software.

[1217] Plan creation

[1218] 1. Generating an individualized optimization plan

[1219] The server generates a travel plan based on the user's profile data (age, length of stay, budget, preferences, etc.).

[1220] The server uses an AI model (e.g., a recommendation system) to select tourist spots, accommodations, and restaurants based on the user's preferences.

[1221] 2. Creating a Sustainable Tourism Plan

[1222] The server calculates transportation methods and routes with the lowest carbon emissions.

[1223] The server generates plans that include eco-friendly accommodations and restaurants.

[1224] User notifications

[1225] 1. Plan notification

[1226] The server generates a travel plan and sends it to the terminal in JSON format.

[1227] The device displays notifications to the user as push alerts.

[1228] 2. Displaying plan details

[1229] The device displays detailed plan information (transportation, accommodation, sightseeing spots, etc.) to the user via the UI.

[1230] Execution Management

[1231] 1. Plan Review and Approval

[1232] The user reviews the plan on their device and chooses whether or not to proceed.

[1233] The terminal sends the user's selection to the server.

[1234] 2. Notification to relevant organizations

[1235] The server notifies accommodation providers, transportation companies, tour guides, and other relevant parties of the reservation information.

[1236] The server verifies that the notification was received successfully and sends a reservation confirmation to the user.

[1237] Feedback processing

[1238] 1. Gathering feedback

[1239] After the trip ends, the device displays a satisfaction survey to the user.

[1240] The device collects user feedback and sends it to the server.

[1241] 2. Analysis of Feedback

[1242] The server stores the feedback in a database and analyzes it using an AI model.

[1243] The server uses the analysis results to improve the plan generation algorithm and optimize it.

[1244] Specific example

[1245] Data collection examples

[1246] The server retrieves information about Tokyo Tower from a tourist destination API.

[1247] The server retrieves the weekly weather forecast for Tokyo from a weather data provider.

[1248] The server retrieves real-time train operation information from a traffic information service.

[1249] Data analysis example

[1250] The server uses an AI model to predict the demand for Tokyo tourism in the following month.

[1251] The server analyzes operational data from accommodation facilities and simulates the effects of introducing cleaning robots.

[1252] Plan creation example

[1253] The server generates a 3-day Kyoto and Nara travel plan based on profile data indicating a couple's trip.

[1254] The server creates sustainable plans that include train travel and eco-friendly accommodations.

[1255] User notification example

[1256] The server generates a travel plan and sends it to the device.

[1257] The device will notify the user via push notification and display the detailed plan.

[1258] Execution Management Example

[1259] The user reviews the plan and selects to proceed on their device.

[1260] The server notifies accommodations and transportation providers of the reservation information and sends a reservation confirmation to the user.

[1261] Feedback Processing Example

[1262] The device displays a post-trip feedback survey to the user and sends it to the server.

[1263] The server analyzes the feedback and incorporates it into the next plan generation.

[1264] As a result, the system of the present invention can solve the multifaceted challenges of the tourism industry and provide efficient and sustainable tourism experiences.

[1265] The following describes the processing flow.

[1266] Step 1:

[1267] The server sends a GET request to the tourist destination's API endpoint. The information retrieved may include the name of the tourist destination, its location, its rating, and its opening hours.

[1268] Step 2:

[1269] The server parses the returned JSON data and extracts detailed information about the tourist destination. The extracted data includes the name of the tourist destination, location information, rating, opening hours, etc.

[1270] Step 3:

[1271] The server saves detailed information about tourist destinations to a database. The saved data is used in subsequent analysis processes.

[1272] Step 4:

[1273] The server accesses the weather data service's API. It sends a GET request to retrieve the weekly weather forecast for the target area.

[1274] Step 5:

[1275] The server analyzes the weather information it has acquired. The analyzed weather information is then linked to tourist destination information and stored in a database.

[1276] Step 6:

[1277] The server calls the API of a traffic information service to obtain real-time traffic data for the target area. The information obtained includes delay information, service status, and more.

[1278] Step 7:

[1279] The server analyzes traffic data and stores delay information and operational status for each mode of transport in a database.

[1280] Step 8:

[1281] The server uses historical tourism data and an AI model (e.g., a time-series forecasting model) to predict demand for the following month. The prediction results are stored in a database.

[1282] Step 9:

[1283] The server calculates the optimal allocation of personnel, materials, and ingredients based on demand forecast data. The calculation results are then applied to tourist destinations and accommodations.

[1284] Step 10:

[1285] The server analyzes operational data from the accommodation facility. This operational data includes information such as the frequency of room cleaning and the response time for front desk staff.

[1286] Step 11:

[1287] The server identifies business processes that can be automated and simulates the effectiveness of automation using robots and software. The simulation results are stored in a database.

[1288] Step 12:

[1289] The server generates the optimal travel plan based on the user's profile data (age, length of stay, budget, preferences, etc.). An AI model (recommendation system) is used for this generation.

[1290] Step 13:

[1291] The server calculates transportation options and routes with low carbon emissions. The calculation results also include eco-friendly accommodations and restaurants.

[1292] Step 14:

[1293] The server generates a travel plan and sends it to the user's device in JSON format. The generated plan includes tourist destinations, accommodations, transportation options, and environmentally friendly choices.

[1294] Step 15:

[1295] The device displays travel plan notifications to the user as push alerts. The user can then check the notifications.

[1296] Step 16:

[1297] The device displays detailed plan information to the user (transportation, accommodation, sightseeing spots, etc.). The user reviews the plan details.

[1298] Step 17:

[1299] The user reviews the plan on their device and chooses whether to proceed. The result of the selection is sent from the device to the server.

[1300] Step 18:

[1301] The server notifies accommodation providers, transportation providers, tour guides, etc., of the booking information. After each provider confirms the booking, they send that information to the user.

[1302] Step 19:

[1303] After the trip ends, the device displays a satisfaction survey to the user. The user then answers the survey.

[1304] Step 20:

[1305] The device sends the feedback collected from the user to the server. The server stores the feedback in a database.

[1306] Step 21:

[1307] The server analyzes the feedback and uses an AI model to optimize the next plan generation algorithm. The analysis results are then incorporated into the next plan creation process.

[1308] (Example 1)

[1309] Next, we will describe Example 1. 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."

[1310] In the tourism industry, the lack of sufficient integration in data collection and analysis of tourist destinations, accommodations, and transportation methods makes it difficult to provide travelers with optimal travel plans. Furthermore, conventional systems have difficulty in demand forecasting and identifying tasks that can be automated, making it impossible to achieve efficient resource allocation and propose eco-friendly travel plans. In addition, there has been a lack of mechanisms to effectively collect user feedback and utilize it to optimize future travel plans. The objective of this invention is to solve these problems.

[1311] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1312] In this invention, the server includes means for collecting data on tourist destinations, accommodations, and means of transportation; means for analyzing the collected data and calculating the optimal allocation of personnel, materials, and ingredients; means for analyzing the workload at accommodations and identifying processes that can be automated; means for generating an optimal travel plan based on individual traveler profile data; means for suggesting transportation and routes with low carbon dioxide emissions; means for notifying the user terminal of the generated travel plan; means for collecting user feedback and reflecting it in optimizing the travel plan; means for obtaining data on tourist destinations, weather, and traffic conditions via API; means for storing and analyzing the acquired data in a database; means for forecasting demand using an AI model based on past tourism data; means for generating an optimal travel plan using user profile data and an AI model; means for adjusting the generated travel plan to take eco-friendly elements into consideration; means for sending the travel plan in JSON format to the user terminal and providing push notifications; means for displaying a questionnaire to collect user satisfaction after the trip; means for storing the collected feedback in a database and analyzing it using an AI model; and means for optimizing the travel plan generation algorithm based on the analysis results. This makes it possible to efficiently and integrally execute a series of processes in the tourism industry, from data collection to plan generation and feedback analysis.

[1313] A "tourist destination" is a geographical location that travelers visit for their own purposes, and is primarily a spot with cultural, historical, or natural attractions.

[1314] "Accommodation facilities" refer to buildings and facilities that provide temporary accommodation for travelers, and include hotels, inns, and guesthouses.

[1315] "Transportation" refers to the vehicles and routes used by travelers to reach their destination, and includes trains, buses, taxis, and airplanes.

[1316] "Data collection" is the process of obtaining information about tourist destinations, accommodations, and transportation from various sources.

[1317] "Analysis" is a technique for processing collected data to make it easier to understand and extract meaningful information.

[1318] "Resource allocation" refers to devising methods for efficiently distributing personnel, materials, and ingredients.

[1319] "Workload" is an indicator that shows the quantity and quality of work performed by employees within an accommodation facility.

[1320] "Automable processes" refer to business processes that can be automated using robots or software.

[1321] "Profile data" refers to data that represents individual information about travelers, such as age, length of stay, budget, and preferences.

[1322] A "travel plan" is a schedule and activity plan for a trip that is proposed to travelers.

[1323] "Carbon dioxide emissions" refer to the amount of carbon dioxide released into the atmosphere by means of transportation and other activities.

[1324] "Eco-friendly" refers to characteristics that indicate environmentally friendly and sustainable methods and products.

[1325] A "push notification" is a notification message that is sent instantly from a server to a device.

[1326] "Feedback" refers to information about opinions and satisfaction levels collected from travelers.

[1327] "API" stands for Application Programming Interface, and it is a standardized method for exchanging data between different software systems.

[1328] "JSON format" is an abbreviation for JavaScript Object Notation, and it is a format used for data exchange that represents data in text format.

[1329] An "AI model" is an algorithm or computational model that uses artificial intelligence technology to perform data analysis and prediction.

[1330] A "survey" is a research method used to collect opinions and information based on a specific question format.

[1331] This invention is a system designed to solve challenges in the tourism industry. Its purpose is to collect and analyze data on tourist destinations, accommodations, and transportation, and to generate and notify users of optimal travel plans. This system is primarily composed of servers, terminals, and users.

[1332] Data collection

[1333] The server retrieves data on tourist destinations, weather, and traffic conditions via APIs. For example, it obtains detailed information such as the name, location, rating, and opening hours of tourist destinations from services that provide tourist destination information. It obtains weekly weather forecasts for the target area from weather data providers and real-time traffic data from traffic information providers. This data is returned in JSON format, which the server parses and stores the necessary information in a database. This allows for the integrated management of detailed information on tourist destinations, accommodations, and transportation options.

[1334] Data Analysis

[1335] Based on the collected data, the server uses AI models (e.g., time-series forecasting models) with historical tourism data to predict demand for the following month. It also uses profile data and AI models (e.g., recommendation systems) to generate personalized travel plans. Furthermore, the server analyzes operational data from accommodations to identify business processes that can be automated. For example, it can simulate the introduction of cleaning robots to reduce the workload of cleaning operations.

[1336] Plan creation

[1337] The server generates an optimal travel plan based on the user's profile data (age, length of stay, budget, preferences, etc.). The generated plan is adjusted to take eco-friendly factors into consideration, prioritizing transportation methods and routes with low carbon emissions. Eco-friendly accommodations and restaurants are also selected. The generated travel plan is converted to JSON format and sent to the user's device.

[1338] User notifications

[1339] The server notifies the user's device of the generated travel plan. The device receives this data and notifies the user via push notification. For example, a message such as "A new travel plan has been suggested!" might appear on the smartphone. The user can then review the plan details and choose to proceed through their device.

[1340] Feedback processing

[1341] After the trip ends, the device displays a satisfaction survey to the user. The user's feedback is sent to the server and stored in a database. The server analyzes this feedback using an AI model and uses it to optimize the algorithm for generating the next travel plan. For example, it can provide specific insights such as, "Satisfaction improved by changing the order of sightseeing spots in the travel plan."

[1342] Specific examples and prompt statements

[1343] As a concrete example, the prompt message used by the server to retrieve information about Tokyo Tower from a tourist destination API is as follows:

[1344] "Please obtain information about Tokyo Tower."

[1345] Furthermore, the prompt message for predicting the demand for Tokyo tourism in the following month using an AI model is as follows:

[1346] "Please predict the demand for tourism in Tokyo next month."

[1347] The following is an example of a prompt message used to notify the user of the generated travel plan.

[1348] "A new travel plan has been suggested! Please check the details."

[1349] As described above, the system of the present invention efficiently and integrally executes a series of processes in the tourism industry, from data collection to plan generation and feedback analysis. This makes it possible to provide travelers with optimal travel plans and improve their tourism experience.

[1350] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1351] Step 1:

[1352] Obtain tourist information

[1353] The server sends a GET request to the tourist destination's API endpoint. The input includes the URL of the tourist destination API and necessary parameters (e.g., API key). The server executes the GET request and receives JSON data about the tourist destination. This JSON data is parsed to extract detailed information such as the tourist destination's name, location, rating, and opening hours, and stored in the database. Specifically, an HTTP request is sent to the URL of the tourist destination API.

[1354] Step 2:

[1355] Acquisition of weather data

[1356] The server accesses the weather data provider's API to retrieve the weekly weather forecast for the target area. The input includes the weather data provider's API URL and a region specification parameter. The server executes a GET request and retrieves the weather data in JSON format. This data is then parsed, linked to tourist destination information, and stored in a database. Specifically, the server accesses the weather data API endpoint and retrieves regional information.

[1357] Step 3:

[1358] Acquisition of traffic condition data

[1359] The server calls the API of a traffic information service to obtain real-time traffic data. The input includes the URL of the traffic information API and necessary parameters (e.g., region, mode of transport). The server executes a GET request and retrieves traffic information in JSON format. This data is then parsed and stored in the database. Specifically, the server accesses the traffic information API to obtain delay information and service status.

[1360] Step 4:

[1361] Demand forecasting and optimization

[1362] The server uses an AI model (e.g., a time-series forecasting model) based on historical tourism data to predict demand for the following month. Historical tourism data is used as input. The data is fed into the AI ​​model, and the demand forecast results are output. Based on these results, the optimal allocation of personnel, materials, and ingredients is calculated. Specifically, the time-series forecasting model is executed, and the forecast results are reflected in resource management.

[1363] Step 5:

[1364] Work load analysis

[1365] The server collects and analyzes operational data from accommodation facilities (e.g., room cleaning frequency and front desk service response time). The operational data from the accommodation facilities is used as input. The server analyzes the data and identifies business processes that can be automated. Specifically, it uses data analysis tools to visualize business processes and simulate automation.

[1366] Step 6:

[1367] Generating Individually Optimized Plans

[1368] The server generates a travel plan based on the user's profile data (age, length of stay, budget, preferences, etc.). User profile data is used as input. The data is input into an AI model (recommendation system) to output the optimal travel plan. Specifically, the recommendation system is used to select tourist spots, accommodations, and restaurants that are suitable for the user.

[1369] Step 7:

[1370] Display of sustainable tourism plans

[1371] The server calculates transportation methods and routes with low carbon emissions. Transportation and route data are used as input. The server adjusts the plan considering eco-friendly factors and selects eco-friendly accommodations and restaurants. Specifically, it generates plans prioritizing sustainable transportation and accommodation options.

[1372] Step 8:

[1373] Plan notification

[1374] The server generates a travel plan and sends it to the device in JSON format. The input is the generated travel plan. The server converts this data to JSON and sends it to the user's device. The device receives this data and sends a push notification. Specifically, the smartphone displays a notification saying, "A new travel plan has been suggested!"

[1375] Step 9:

[1376] View plan details

[1377] The device displays detailed plan information to the user. The input is travel plan data received from a server. The device analyzes this data and displays it in the UI. Specifically, it displays detailed information about transportation, accommodation, and tourist attractions in a list format within the app.

[1378] Step 10:

[1379] Feedback Collection

[1380] After the trip ends, the device displays a satisfaction survey to the user. The end time of the trip plan is used as input. The device displays the survey to the user and collects responses. Specifically, the app displays a form asking, "Please tell us how satisfied you were with your trip."

[1381] Step 11:

[1382] Feedback analysis

[1383] The server stores feedback in a database and analyzes it using an AI model. User feedback data is used as input. The server analyzes the feedback and uses it to optimize the plan generation algorithm. Specifically, it performs sentiment analysis and uses it to generate the next plan.

[1384] The above is a detailed explanation of the system's processing steps and operation.

[1385] (Application Example 1)

[1386] Next, we will explain Application Example 1. In the following explanation, 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."

[1387] In the tourism industry, travelers face challenges in creating and executing efficient and comfortable travel plans. In particular, they require flexible arrangements for meals at tourist destinations, as well as adaptability to weather and traffic conditions. Furthermore, providing environmentally friendly options is essential for sustainable tourism. In addition, personalized planning tailored to individual traveler preferences is required, and incorporating user feedback into future plans is crucial.

[1388] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1389] In this invention, the server includes means for collecting data on tourist destinations, accommodations, and transportation; means for analyzing the collected data and calculating the optimal allocation of personnel, materials, and ingredients; means for analyzing the workload at accommodations and identifying processes that can be automated; means for generating an optimal travel plan based on individual traveler profile data; means for suggesting transportation and routes with low carbon dioxide emissions; means for delivering meals from local restaurants based on the travel plan; means for calculating the optimal delivery time based on weather information and traffic conditions; means for notifying the user terminal of the generated travel plan; and means for collecting user feedback and reflecting it in optimizing the travel plan. This enables the creation and execution of efficient and sustainable travel plans.

[1390] A "tourist destination" is a place that travelers visit, such as a natural landscape, historical buildings, or cultural facilities.

[1391] "Accommodation facilities" refer to facilities such as hotels, inns, and guesthouses where travelers can stay temporarily.

[1392] "Transportation" refers to the means of getting around that travelers use, such as trains, buses, taxis, and airplanes.

[1393] "Means of data collection" refers to the hardware and software used to acquire information about tourist destinations, accommodations, and transportation.

[1394] "Means for calculating the optimal allocation of personnel, materials, and food supplies" refers to an analytical system that uses collected data to calculate the efficient allocation of human resources, materials, and food supplies in tourist destinations and accommodation facilities.

[1395] "A means of analyzing workload and identifying processes that can be automated" refers to a system for analyzing the workload at accommodation facilities and identifying tasks that can be automated.

[1396] "Profile data" refers to information about individual travelers, such as age, length of stay, budget, and preferences.

[1397] "A means of generating the optimal travel plan" refers to a system that plans the most suitable sightseeing routes, accommodations, and activities for travelers based on their profile data.

[1398] "Means for proposing transportation methods and routes with low carbon dioxide emissions" refers to a system that calculates and proposes transportation methods and travel routes that are environmentally conscious and reduce carbon dioxide emissions.

[1399] "Methods for having meals delivered from local restaurants" refers to a system for ordering and having meals delivered from local restaurants based on a travel plan.

[1400] "A means of calculating the optimal delivery time based on weather information and traffic conditions" refers to a system that takes weather forecasts and traffic conditions into consideration to optimally adjust the delivery time of meals.

[1401] "Means for notifying user devices of generated travel plans" refers to a system for delivering generated travel plans to travelers' devices such as smartphones and tablets.

[1402] "Means for collecting feedback and using it to optimize travel plans" refers to a system that collects travelers' evaluations and opinions and uses them to improve and optimize future travel plans.

[1403] This invention is a system that solves problems in the tourism industry and is composed primarily of a server, terminals, and users. The specific form of this system is described below.

[1404] Data collection

[1405] 1. Obtaining tourist information

[1406] The server sends a GET request to the tourist destination's API endpoint and retrieves detailed information about the tourist destination (name, location, rating, opening hours, etc.) in JSON format. This information is then stored in the database.

[1407] 2. Acquisition of weather data

[1408] The server retrieves weekly weather forecasts from a weather data provider service, analyzes the data, and stores it in a database linked to tourist destination information.

[1409] 3. Acquisition of traffic condition data

[1410] The server retrieves real-time traffic information from traffic information services and updates the database with delay information and operating status for each mode of transport.

[1411] Data Analysis

[1412] 1. Demand forecasting and optimization

[1413] The server uses historical tourism data to feed into an AI model (e.g., a time-series forecasting model) to predict future demand. It also calculates the optimal allocation of personnel, materials, and ingredients.

[1414] 2. Workload analysis

[1415] The server analyzes operational data from accommodations (such as room cleaning frequency and front desk service response times) to identify business processes that can be automated.

[1416] Plan creation

[1417] 1. Generating an individualized optimization plan

[1418] The server generates a travel plan based on the user's profile data (age, length of stay, budget, preferences, etc.). The generated plan appropriately includes sightseeing spots, accommodations, and restaurants.

[1419] 2. Creating a Sustainable Tourism Plan

[1420] The server calculates transportation options and routes with low carbon emissions and creates plans that include eco-friendly accommodations and restaurants.

[1421] 3. Generating a meal delivery plan based on the travel plan.

[1422] The server collects restaurant information around tourist destinations and creates the optimal delivery plan based on user preferences, weather information, and traffic conditions.

[1423] User notifications

[1424] 1. Plan notification

[1425] The server sends the generated travel plan and delivery plan to the device in JSON format. The device then displays the notification to the user as a push alert.

[1426] 2. Displaying plan details

[1427] The device displays detailed plan information (transportation, accommodation, sightseeing spots, delivery options, etc.) to the user via its UI.

[1428] Feedback processing

[1429] 1. Gathering feedback

[1430] After the trip ends, the device displays a satisfaction survey to the user and collects feedback. The collected data is sent to a server.

[1431] 2. Analysis of Feedback

[1432] The server stores the feedback in a database and performs analysis using an AI model. Based on the analysis results, it optimizes the algorithm for generating the next plan.

[1433] Specific example

[1434] Data collection examples

[1435] The server obtains information about city A from a tourist destination API and the weekly weather forecast for city A from a weather data service. It also obtains real-time traffic information from a traffic information service.

[1436] Data analysis example

[1437] The server predicts tourism demand in city A based on historical data and analyzes operational data from accommodation facilities to simulate the effects of introducing cleaning robots.

[1438] Plan creation example

[1439] The server generates a 3-day travel plan based on the profile of a couple traveling together. This plan includes eco-friendly accommodations and sustainable travel routes. It also adds restaurant delivery options based on the user's preferences and weather information.

[1440] Example of a prompt

[1441] "When sightseeing in City A, please create a plan that includes ordering food delivery from recommended local restaurants, taking into account the optimal delivery time. A detailed plan based on user preferences, weather information, and traffic conditions is required."

[1442] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1443] Step 1:

[1444] Obtain tourist information

[1445] The server sends a GET request to the tourist destination's API endpoint. The input data is the tourist destination ID. The server parses the returned JSON data, extracts detailed information such as the tourist destination's name, location, rating, and opening hours, and saves it to the database. The output is the detailed information of the tourist destination.

[1446] Step 2:

[1447] Acquisition of weather data

[1448] The server accesses the API of a weather data service to obtain the weekly weather forecast for the target area. The input data is location information of tourist destinations. The server analyzes the obtained weather information, links it to the tourist destination information, and stores it in a database. The output is the weekly weather forecast information.

[1449] Step 3:

[1450] Acquisition of traffic condition data

[1451] The server calls an API from a traffic information service to obtain real-time traffic data. The input data is travel route information to tourist destinations. The server analyzes this information and updates the database with delay information and operating status for each mode of transport. The output is traffic information.

[1452] Step 4:

[1453] Retrieving User Profiles

[1454] The server retrieves user profile information. The input data is the user ID. The server retrieves profile information (age, length of stay, budget, preferences, etc.) from the database. The output is the user profile data.

[1455] Step 5:

[1456] Demand forecasting and resource allocation

[1457] The server uses historical tourism data to predict next month's demand based on an AI model. The input data is historical tourism data. The server calculates the optimal allocation of personnel, materials, and ingredients, optimizing resource management. The output is demand forecast data and resource allocation plan.

[1458] Step 6:

[1459] Workload analysis

[1460] The server analyzes the operational data of the accommodation facility and identifies processes that can be automated. The input data is the operational data of the accommodation facility. The server performs automation simulations and evaluates their effectiveness. The output is the business processes that can be automated and the simulation results.

[1461] Step 7:

[1462] Travel plan generation

[1463] The server uses an AI model to generate an optimal travel plan based on the user's profile data. Input data includes profile data and tourist destination information. The server creates a travel plan that includes tourist spots, accommodations, and restaurants. The output is the travel plan.

[1464] Step 8:

[1465] Generating a delivery plan

[1466] The server generates a delivery plan for meals from local restaurants based on the travel plan. Input data includes tourist information and user preferences. The server calculates the optimal delivery time based on weather and traffic conditions. The output is the delivery plan.

[1467] Step 9:

[1468] Plan notification

[1469] The server generates a travel plan and a delivery plan and sends them to the terminal in JSON format. The input data is the travel plan and the delivery plan. The terminal displays the notification to the user as a push alert. The output is the notification to the user.

[1470] Step 10:

[1471] Gathering and analyzing feedback

[1472] After the trip ends, the device displays a satisfaction survey to the user and collects feedback. The input data is the user's feedback. The server stores the collected feedback in a database, analyzes it using an AI model, and optimizes the algorithm for generating the next trip plan. The output is the analysis results and the optimized algorithm.

[1473] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1474] This invention is a system designed to solve challenges in the tourism industry. It aims to collect and analyze data on tourist destinations, accommodations, and transportation methods, and to generate and notify users of optimal travel plans by combining this data with an emotion engine that recognizes user emotions. This system is primarily composed of a server, terminals, and users.

[1475] Data collection

[1476] 1. Obtaining tourist information

[1477] The server sends a GET request to the tourist destination's API endpoint. The information retrieved may include the name of the tourist destination, its location, its rating, and its opening hours.

[1478] The server parses the returned JSON data and extracts detailed information about the tourist destination. The extracted data includes the name of the tourist destination, location information, rating, opening hours, etc.

[1479] The server saves detailed information about tourist destinations to a database. The saved data is used in subsequent analysis processes.

[1480] 2. Acquisition of weather data

[1481] The server accesses the weather data provider's API and sends a GET request to retrieve the weekly weather forecast for the target area.

[1482] The server analyzes the weather information it has acquired. The analyzed weather information is then linked to tourist destination information and stored in a database.

[1483] 3. Acquisition of traffic condition data

[1484] The server calls the API of a traffic information service to obtain real-time traffic data for the target area. The information obtained includes delay information, service status, and more.

[1485] The server analyzes traffic data and stores delay information and operational status for each mode of transport in a database.

[1486] Data Analysis

[1487] 1. Demand forecasting and optimization

[1488] The server uses historical tourism data and an AI model (e.g., a time-series forecasting model) to predict demand for the following month. The prediction results are stored in a database.

[1489] The server calculates the optimal allocation of personnel, materials, and ingredients based on demand forecast data. The calculation results are then applied to tourist destinations and accommodations.

[1490] 2. Workload analysis

[1491] The server analyzes operational data from the accommodation facility (for example, the frequency of room cleaning and the response time for front desk staff).

[1492] The server identifies business processes that can be automated and simulates the effectiveness of automation using robots and software. The simulation results are stored in a database.

[1493] Plan creation

[1494] 1. Generating an individualized optimization plan

[1495] The server generates travel plans based on the user's profile data (age, length of stay, budget, preferences, etc.). An AI model (recommendation system) is used for this generation.

[1496] The server uses an emotion engine to collect user emotion data and incorporate it into the generation and optimization of travel plans.

[1497] 2. Creating a Sustainable Tourism Plan

[1498] The server calculates transportation options and routes with low carbon emissions. The calculation results also include eco-friendly accommodations and restaurants.

[1499] User notifications

[1500] 1. Plan notification

[1501] The server generates a travel plan and sends it to the user's device in JSON format. The generated plan includes tourist destinations, accommodations, transportation options, and environmentally friendly choices.

[1502] The device displays travel plan notifications to the user as push alerts. The user can then check the notifications.

[1503] 2. Displaying plan details

[1504] The device displays detailed plan information to the user (transportation, accommodation, sightseeing spots, etc.). The user reviews the plan details.

[1505] Execution Management

[1506] 1. Plan Review and Approval

[1507] The user reviews the plan on their device and chooses whether to proceed. The result of the selection is sent from the device to the server.

[1508] 2. Notification to relevant organizations

[1509] The server notifies accommodation providers, transportation providers, tour guides, etc., of the booking information. After each provider confirms the booking, they send that information to the user.

[1510] Feedback processing

[1511] 1. Gathering feedback

[1512] After the trip ends, the device displays a satisfaction survey to the user. The user then answers the survey.

[1513] The device sends the feedback collected from the user to the server. The server stores the feedback in a database.

[1514] 2. Analysis of Feedback

[1515] The server analyzes the feedback and uses an AI model to optimize the next plan generation algorithm. The analysis results are then incorporated into the next plan creation process.

[1516] The server uses an emotion engine to analyze emotional data along with feedback and incorporate it into the next plan.

[1517] Specific example

[1518] Data collection examples

[1519] The server retrieves information about Tokyo Tower from a tourist destination API.

[1520] The server retrieves the weekly weather forecast for Tokyo from a weather data provider.

[1521] The server retrieves real-time train operation information from a traffic information service.

[1522] Data analysis example

[1523] The server uses an AI model to predict the demand for Tokyo tourism in the following month.

[1524] The server analyzes operational data from accommodation facilities and simulates the effects of introducing cleaning robots.

[1525] Plan creation example

[1526] The server generates a 3-day Kyoto and Nara travel plan based on profile data indicating a couple's trip.

[1527] The server creates sustainable plans that include train travel and eco-friendly accommodations.

[1528] The server uses an emotion engine to optimize plans by taking user sentiment data into account.

[1529] User notification example

[1530] The server generates a travel plan and sends it to the device.

[1531] The device will notify the user via push notification and display the detailed plan.

[1532] Execution Management Example

[1533] The user reviews the plan and selects to proceed on their device.

[1534] The server notifies accommodations and transportation providers of the reservation information and sends a reservation confirmation to the user.

[1535] Feedback Processing Example

[1536] The device displays a post-trip feedback survey to the user and sends it to the server.

[1537] The server analyzes feedback and sentiment data and incorporates it into generating the next plan.

[1538] As a result, the system of the present invention can solve the multifaceted challenges of the tourism industry and provide an efficient, emotionally resonant, and sustainable tourism experience.

[1539] The following describes the processing flow.

[1540] Step 1:

[1541] The server sends a GET request to the tourist destination's API endpoint. The information retrieved may include the name of the tourist destination, its location, its rating, and its opening hours.

[1542] Step 2:

[1543] The server parses the returned JSON data and extracts detailed information about the tourist destination. The extracted data includes the name of the tourist destination, location information, rating, opening hours, etc.

[1544] Step 3:

[1545] The server saves detailed information about tourist destinations to a database. The saved data is used in subsequent analysis processes.

[1546] Step 4:

[1547] The server accesses the weather data service's API. It sends a GET request to retrieve the weekly weather forecast for the target area.

[1548] Step 5:

[1549] The server analyzes the weather information it has acquired. The analyzed weather information is then linked to tourist destination information and stored in a database.

[1550] Step 6:

[1551] The server calls the API of a traffic information service to obtain real-time traffic data for the target area. The information obtained includes delay information, service status, and more.

[1552] Step 7:

[1553] The server analyzes traffic data and stores delay information and operational status for each mode of transport in a database.

[1554] Step 8:

[1555] The server uses historical tourism data and an AI model (e.g., a time-series forecasting model) to predict demand for the following month. The prediction results are stored in a database.

[1556] Step 9:

[1557] The server calculates the optimal allocation of personnel, materials, and ingredients based on demand forecast data. The calculation results are then applied to tourist destinations and accommodations.

[1558] Step 10:

[1559] The server analyzes operational data from the accommodation facility. This operational data includes information such as the frequency of room cleaning and the response time for front desk staff.

[1560] Step 11:

[1561] The server identifies business processes that can be automated and simulates the effectiveness of automation using robots and software. The simulation results are stored in a database.

[1562] Step 12:

[1563] The server generates the optimal travel plan based on the user's profile data (age, length of stay, budget, preferences, etc.). An AI model (recommendation system) is used for this generation.

[1564] Step 13:

[1565] The server uses an emotion engine to collect and analyze user emotion data, which is then used to generate and optimize travel plans. This emotion data is based on real-time user feedback and the user's emotional state at the time of plan generation.

[1566] Step 14:

[1567] The server calculates transportation options and routes with low carbon emissions. The calculation results also include eco-friendly accommodations and restaurants.

[1568] Step 15:

[1569] The server generates a travel plan and sends it to the user's device in JSON format. The generated plan includes tourist destinations, accommodations, transportation options, and environmentally friendly choices.

[1570] Step 16:

[1571] The device displays travel plan notifications to the user as push alerts. The user can then check the notifications.

[1572] Step 17:

[1573] The device displays detailed plan information to the user (transportation, accommodation, sightseeing spots, etc.). The user reviews the plan details.

[1574] Step 18:

[1575] The user reviews the plan on their device and chooses whether to proceed. The result of the selection is sent from the device to the server.

[1576] Step 19:

[1577] The server notifies accommodation providers, transportation providers, tour guides, etc., of the booking information. After each provider confirms the booking, they send that information to the user.

[1578] Step 20:

[1579] After the trip ends, the device displays a satisfaction survey to the user. The user then answers the survey.

[1580] Step 21:

[1581] The device sends the feedback collected from the user to the server. The server stores the feedback in a database.

[1582] Step 22:

[1583] The server analyzes the feedback and uses an AI model to optimize the next plan generation algorithm. The analysis results are then incorporated into the next plan creation process.

[1584] Step 23:

[1585] The server uses an emotion engine to analyze emotional data along with feedback, and incorporates it into future plans. This emotional data includes not only satisfaction levels but also user stress levels and excitement levels.

[1586] As a result, the system of the present invention can solve the multifaceted challenges of the tourism industry and provide an efficient, emotionally resonant, and sustainable tourism experience.

[1587] (Example 2)

[1588] Next, we will describe Example 2. 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."

[1589] In today's tourism industry, there is a demand for providing optimal travel plans that meet the diverse needs of tourists. Furthermore, managing the uncertainty of demand forecasts, workload, and efficient allocation of insufficient resources are crucial challenges. In addition, there is a growing demand for environmentally conscious and sustainable tourism, as well as the provision of travel experiences that consider the emotions of users. However, there are limited systems that can comprehensively address all of these issues.

[1590] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1591] In this invention, the server includes means for collecting information on tourist destinations, weather data, and traffic data; means for analyzing the collected data and storing detailed information on tourist destinations, weather information, and traffic information in a database; and means for using an AI model based on past tourism data to forecast demand and calculate the optimal allocation of personnel, materials, and ingredients. This makes it possible to provide travel plans that best meet the diverse needs of tourists. Furthermore, by predicting and optimizing the workload, efficient use of resources is achieved, enabling the provision of environmentally conscious and sustainable travel experiences.

[1592] "Information about tourist destinations" refers to detailed data such as the name of the tourist destination, its location, rating, and opening hours.

[1593] "Weather data" refers to data related to weather conditions in a specific region, such as weather forecasts, temperature, precipitation, and wind speed.

[1594] "Traffic status data" refers to real-time data on the operation status, delay information, and route information of public transportation (e.g., trains, buses, airplanes, etc.).

[1595] A "database" is a collection of structured data that stores collected and analyzed data, and that can be searched and retrieved as needed.

[1596] An "AI model" refers to algorithms and computational methods that use artificial intelligence technology, and includes, for example, time series forecasting models and recommendation systems.

[1597] "Demand forecasting" is the process of predicting future demand by analyzing past data.

[1598] "Optimal allocation of personnel, materials, and ingredients" refers to the operation of efficiently distributing resources based on demand forecasts.

[1599] "Workload" refers to the burden of tasks related to labor and service provision at accommodation facilities.

[1600] An "automatable process" is a stage in a repetitive task or process that can be performed by machines or software without human intervention.

[1601] "Individual traveler profile data" refers to personal attribute information such as the traveler's age, length of stay, budget, and preferences.

[1602] "Low-carbon emission modes of transport" refer to eco-friendly modes of transport that aim to minimize their impact on the environment.

[1603] "Suggesting a route" refers to the process of calculating and presenting the optimal travel path to the traveler.

[1604] A "user terminal" refers to an electronic device such as a smartphone, tablet, or personal computer, which is used by a user to obtain information and operate it through its interface.

[1605] "Feedback" refers to information provided by users, such as ratings, opinions, and survey responses.

[1606] "Generating" refers to the operation of creating new information or results based on a specific algorithm or rule.

[1607] This invention is a system that solves problems in the tourism industry. It aims to collect and analyze data on tourist destinations, accommodations, and transportation methods, and to generate and notify users of optimal travel plans by combining this with an emotion engine that recognizes user emotions. This system is mainly composed of a server, terminals, and users.

[1608] Data collection

[1609] Obtain tourist information

[1610] The server sends a GET request to the tourist destination API endpoint. It retrieves information such as the name, location, rating, and opening hours of the tourist destination. It is desirable that this tourist destination API endpoint be a commonly used API.

[1611] The system analyzes the JSON data acquired by the server and extracts detailed information about tourist destinations. For example, it can retrieve information about the tourist destination "Tokyo Tower" and save it to a database.

[1612] Acquisition of weather data

[1613] The server accesses the weather data provider's API and sends a GET request to retrieve the weekly weather forecast for the target area. For example, it retrieves the weekly weather forecast for "Tokyo" from the weather forecast API.

[1614] The server analyzes the data, links it to tourist destination information, and then stores it in the database.

[1615] Acquisition of traffic condition data

[1616] The server calls the API of a traffic information service to obtain real-time traffic data for the target area (e.g., delay information, service status, etc.). Using a traffic information API is recommended.

[1617] The server analyzes the data and stores delay information and operating status for each mode of transport in a database. For example, it retrieves the operating status of trains in Tokyo.

[1618] Data Analysis

[1619] Demand forecasting and optimization

[1620] The server uses a time-series forecasting model based on past tourism data to predict demand for the following month and stores the data in a database. Applying an AI model is crucial.

[1621] The server calculates the optimal allocation of personnel, materials, and ingredients based on demand forecast data. For example, it forecasts the demand for tourism in Tokyo for the following month and performs calculations based on the results.

[1622] Work load analysis

[1623] The server collects and analyzes operational data from accommodation facilities (e.g., room cleaning frequency and front desk service response time).

[1624] The server identifies business processes that can be automated and simulates the effects of automation using robots and software. For example, it evaluates the effects of introducing cleaning robots.

[1625] Plan creation

[1626] Generating Individually Optimized Plans

[1627] The server retrieves user profile data (e.g., age, length of stay, budget, preferences, etc.) and uses an AI model (recommendation system) to generate the optimal travel plan.

[1628] The server uses an emotion engine to collect user emotion data and incorporate it into generating and optimizing travel plans. For example, it can create a 3-day Kyoto and Nara travel plan based on a couple's travel profile.

[1629] Display of sustainable tourism plans

[1630] The server calculates transportation options and routes with low carbon emissions and creates sustainable plans that include eco-friendly accommodations and restaurants. For example, it can create a plan that includes train travel and eco-friendly accommodations.

[1631] User notifications

[1632] Plan notification

[1633] The server generates a travel plan and sends it to the user's device in JSON format.

[1634] The device will display a push alert notifying the user of the travel plan. For example, it might display, "A 3-day travel plan for Kyoto has been generated."

[1635] View plan details

[1636] The device displays detailed plan information to the user (transportation, accommodation, sightseeing spots, etc.). For example, it might display "Day 1: Travel from Tokyo to Kyoto and check into an eco-hotel."

[1637] Execution Management

[1638] Plan review and approval

[1639] The user reviews the plan on their device and chooses whether or not to proceed.

[1640] The device sends the user's selection results to the server. For example, the user approves a travel plan.

[1641] Notification to relevant organizations

[1642] The server notifies accommodation providers, transportation providers, tour guides, and others of the reservation information.

[1643] The server retrieves reservation confirmation information from each institution and sends it to the user. For example, it notifies the user of confirmation information from accommodations where reservations have been confirmed.

[1644] Feedback processing

[1645] Feedback Collection

[1646] After the trip ends, the device displays a satisfaction survey to the user, who then answers the survey.

[1647] The device sends user feedback to the server, which then stores the feedback data in a database. For example, a user might answer a survey asking, "How was your overall satisfaction with your trip?"

[1648] Feedback analysis

[1649] The server analyzes the feedback data and uses an AI model to optimize the next plan generation algorithm.

[1650] The server uses an emotion engine to analyze feedback and emotional data and incorporate it into generating future plans. For example, it might consider the user's emotional data when creating the next travel plan.

[1651] Example prompt statements

[1652] "Please have the server retrieve tourist information about Tokyo Tower and save it to the database."

[1653] "Please retrieve Tokyo weather data from a weather forecast API and link it with tourist information."

[1654] "Please generate a 3-day Kyoto travel plan based on the user's profile data."

[1655] As a result, the system of the present invention can solve the multifaceted challenges of the tourism industry and provide an efficient, emotionally resonant, and sustainable tourism experience.

[1656] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1657] Step 1: Obtain tourist information

[1658] The server sends a GET request to the tourist destination API endpoint. For example, it might send a request with the input "Tourist Destination Name = Tokyo Tower".

[1659] The server receives the returned JSON data and analyzes and extracts detailed information such as the name of the tourist attraction, location information, rating, and opening hours.

[1660] The server analyzes tourist destination information and saves it to the database. Specifically, it executes an INSERT query on the appropriate table in the database.

[1661] Step 2: Obtain weather data

[1662] The server sends a GET request to the weather data service's API. For example, it might send a request with the input "Region=Tokyo".

[1663] The server receives JSON data of the weekly weather forecast and analyzes the weather conditions (e.g., weather, temperature, precipitation).

[1664] The server analyzes weather data and stores it in a database, linking it to tourist destination information. Specifically, it links the primary key of the tourist destination with the weather data.

[1665] Step 3: Obtain traffic condition data

[1666] The server sends a GET request to the API of the traffic information service. For example, it might send a request with the input "Region = Tokyo, Mode of Transportation = Train".

[1667] The server receives the traffic data in JSON format and analyzes delay information and service status.

[1668] The server analyzes traffic data and saves it to a database. Specifically, it saves information for each mode of transport to the corresponding table.

[1669] Step 4: Demand forecasting and optimization

[1670] The server retrieves historical tourism data from the database. For example, it might retrieve data based on input such as "Tourist destination = Tokyo, Period = Past 3 years".

[1671] The server uses an AI model (e.g., a time series forecasting model) to predict demand for the following month. It performs data processing and time series analysis based on the input data.

[1672] The server saves the prediction results to a database and uses that data to calculate the optimal allocation of personnel, materials, and ingredients. Specifically, it applies a resource optimization algorithm.

[1673] Step 5: Workload Analysis

[1674] The server collects operational data from accommodation facilities. For example, it retrieves data with the input "Accommodation facility name = Hotel A".

[1675] The server analyzes factors such as room cleaning frequency and front desk response times to identify business processes that can be automated.

[1676] The server simulates the effects of automation using robots and software, and stores the results in a database. Specifically, it applies an automation simulation algorithm.

[1677] Step 6: Generating an individualized optimization plan

[1678] The server retrieves the user's profile data. For example, it can retrieve data with input such as "User ID=1234, Age=30, Length of Stay=3 days, Budget=50,000 yen, Interests=History".

[1679] The server uses an AI model (recommendation system) to generate the optimal travel plan. It applies data processing and recommendation algorithms based on the input profile data.

[1680] The server uses an emotion engine to collect user emotion data and incorporate it into the travel plan. Specifically, it applies an emotion analysis algorithm.

[1681] Step 7: Create a Sustainable Tourism Plan

[1682] The server calculates eco-friendly modes of transport and routes. For example, it calculates based on the input "Destination = Kyoto, Departure = Tokyo".

[1683] The server creates travel plans that include eco-friendly accommodations and restaurants. Specifically, it applies an environmental impact assessment algorithm.

[1684] Step 8: Plan Notification

[1685] The server generates a travel plan and sends it to the user's terminal in JSON format. For example, it might be sent with the input "User ID=1234".

[1686] The device will notify the user of the generated travel plan via push alert. Specifically, this will be done using the notification API.

[1687] Step 9: View plan details

[1688] The device displays detailed plan information to the user. For example, it retrieves and displays details based on the input "Plan ID=5678".

[1689] The user views the details of the displayed travel plan. Specifically, this involves displaying the details on the interface.

[1690] Step 10: Plan Review and Approval

[1691] The user reviews the plan on their device and chooses whether to proceed. For example, they might select it by entering "Plan ID=5678, Select=Approve".

[1692] The terminal sends the user's selection results to the server. Specifically, it sends the approval result to the server via a POST request.

[1693] Step 11: Notification to relevant organizations

[1694] The server notifies accommodations, transportation providers, guide services, etc., of the reservation information. For example, it will notify based on input such as "Accommodation = Hotel A, Transportation = Shinkansen (bullet train)".

[1695] The server retrieves reservation confirmation information from each institution and notifies the user. Specifically, it sends the confirmation information to the terminal.

[1696] Step 12: Gathering Feedback

[1697] After the trip ends, the device displays a satisfaction survey to the user. For example, the survey will be displayed when the user enters "Trip ID=7890".

[1698] The user answers the survey, and the device sends that data to the server. Specifically, the survey data is sent to the server via a POST request.

[1699] Step 13: Analyzing Feedback

[1700] The server retrieves feedback data from the database. For example, it retrieves data with the input "Travel ID=7890".

[1701] The server uses an AI model to analyze and optimize the next plan generation algorithm. Specifically, it applies a feedback analysis algorithm.

[1702] The server uses an emotion engine to analyze feedback and emotional data, and incorporates this into the generation of the next plan. Specifically, it performs emotional data analysis.

[1703] In this way, each processing step works in conjunction with the others, and the system as a whole solves a variety of challenges in the tourism industry.

[1704] (Application Example 2)

[1705] Next, we will explain application example 2. In the following explanation, 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."

[1706] In the modern tourism industry, a challenge exists in that travelers find it difficult to create efficient and personalized travel plans. Furthermore, there is a lack of systems that can flexibly adjust plans in response to changes in weather and traffic conditions, as well as the emotional state of travelers. Additionally, while sustainability in the tourism industry is a pressing need, technologies for automatically generating travel plans that reflect this need are limited.

[1707] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting data on tourist destinations, accommodations, and means of transportation; means for analyzing the collected data and calculating the optimal allocation of personnel, materials, and ingredients; means for analyzing the workload at accommodations and identifying processes that can be automated; means for generating an optimal travel plan based on individual traveler profile data; means for collecting user sentiment data and reflecting it in the travel plan; means for suggesting transportation methods and routes with low carbon dioxide emissions; means for notifying the user terminal of the generated travel plan in a prompt format; means for collecting user feedback and reflecting it in optimizing the travel plan; and means for displaying the tourist plan in conjunction with real-time location information. This makes it possible to provide an efficient, sustainable travel plan that is considerate of the user's feelings.

[1708] "Tourist information" refers to detailed information about tourist attractions, such as their name, location, rating, and opening hours.

[1709] "Accommodation information" refers to data such as the name, location, price range, and availability of accommodations, including hotels and hot spring inns.

[1710] "Transportation information" refers to information such as the operating status, delay information, price range, and travel time of public transportation such as buses, trains, and taxis.

[1711] "Emotional data" refers to data related to emotional states such as joy, sadness, and surprise, obtained through user facial analysis and text analysis.

[1712] "Prompt format" refers to a format that presents recommended actions or information in short text messages.

[1713] "Real-time location information" refers to information that instantly obtains and updates the user's current geographical location.

[1714] A "personalized travel plan" refers to a travel schedule and sightseeing route optimized based on each user's profile data and emotional data.

[1715] "Feedback" refers to opinions and impressions, such as satisfaction levels and areas for improvement, that users provide after completing a trip.

[1716] "Sustainable transportation" refers to environmentally friendly modes of transport that reduce carbon dioxide emissions (e.g., electric vehicles and bicycles).

[1717] The system for implementing this invention collects data on tourist destinations, accommodations, and transportation, and based on this data, generates and notifies users of optimal travel plans. The system mainly consists of a server, user terminals, and users. The following describes how this system works.

[1718] Data collection

[1719] The server sends GET requests to the API endpoints of tourist destinations to retrieve detailed information such as the name, location, rating, and opening hours of the tourist destinations. The retrieved information is parsed in JSON format and stored in the database. Similarly, the server accesses the API of a weather data service to retrieve the weekly weather forecast for the target area, parses it, and stores it in the database. Furthermore, the server calls the API of a traffic information service to retrieve real-time traffic data for the target area and stores delay information and service status in the database.

[1720] The hardware used will consist of Linux servers and database servers. The software will utilize Python and Flask for API communication and data analysis.

[1721] emotion recognition

[1722] When a user points their face at the camera using a smartphone or smart glasses, their face is captured using OpenCV. The captured face image is input into a TensorFlow emotion recognition model to identify the user's emotion (joy, sadness, surprise, etc.). This emotion data is sent to a server and used to generate travel plans.

[1723] Plan generation

[1724] The server combines collected tourist destination, weather, and transportation information with user profile data (age, length of stay, budget, preferences) and sentiment data to generate the optimal travel plan. This is done using an AI model (recommendation system). The generated plan is sent to the user's device in JSON format.

[1725] User notifications

[1726] Sightseeing plans are sent via push notifications to smartphones or smart glasses. Users can check these notifications and view detailed plans within the application. For example, for a user in Kyoto City, real-time location information can be used to suggest the most suitable sightseeing route and recommended spots on the spot.

[1727] Feedback Collection

[1728] After the trip, users provide feedback through a satisfaction survey. This feedback data, along with sentiment data, is sent to the server and used to optimize future travel plans.

[1729] Specific examples and prompt statements

[1730] As a concrete example, let's consider the case where the user is in Kyoto City.

[1731] Example of a prompt:

[1732] Based on the user's current location in Kyoto City, retrieve weather information and real-time traffic information for the following tourist spots and generate a recommended sightseeing plan. Suggest the most optimal route, especially if the user's emotion is "joyful."

[1733] Following this prompt, the server collects information on Kyoto's tourist attractions, weather, and traffic, recognizes that the user's emotion is "joy," generates an optimal sightseeing plan, and notifies the user's device. This allows the user to enjoy an efficient and emotionally responsive travel experience.

[1734] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1735] Step 1: Data Collection

[1736] The server sends GET requests to the API endpoints of tourist destinations to retrieve information such as the name, location, rating, and opening hours of the tourist destinations. The server parses the returned JSON data and saves the detailed information of the tourist destinations to the database. Similarly, it sends GET requests to the API of a weather data provider to retrieve weekly weather forecasts and saves the parsed weather information in the database, linked to the tourist destination information. It also calls the API of a traffic information provider to retrieve real-time traffic data and saves delay information and service status to the database.

[1737] Input: Endpoints for the tourist destination API, weather data API, and traffic information API.

[1738] Output: Database containing acquired tourist destination information, weather information, and traffic data.

[1739] Step 2: Emotion Recognition

[1740] The user uses a smartphone or smart glasses to point their face at the camera. The device's camera captures an image of the user's face and sends it to the server. The server uses a TensorFlow emotion recognition model to analyze the face image and identify the user's emotion (e.g., joy, sadness, surprise). This emotion data is stored for travel plan generation.

[1741] Input: User's face image

[1742] Output: Recognized emotion data

[1743] Step 3: Enter profile data

[1744] Users enter their profile data through the application. This profile data includes age, length of stay, budget, and preferences. The device sends this data to the server for storage.

[1745] Input: User's age, length of stay, budget, preferences

[1746] Output: Saved profile data

[1747] Step 4: Plan Generation

[1748] The server uses an AI model to generate the optimal travel plan based on collected tourist destination information, weather information, transportation information, user profile data, and sentiment data. This analyzes each piece of data and recommends the most suitable tourist spots, modes of transportation, and accommodations for the user.

[1749] Input: Tourist information, weather information, traffic information, profile data, sentiment data

[1750] Output: Generated travel plan

[1751] Step 5: User Notifications

[1752] The server sends the generated travel plan to the user's device in JSON format. The device uses push notifications to inform the user of the travel plan and display the detailed plan. The user can check the notification and view the detailed plan within the application.

[1753] Input: Generated travel plan (JSON format)

[1754] Output: Display of user device notifications and detailed plans

[1755] Step 6: Gathering Feedback

[1756] After the trip ends, the device displays a satisfaction survey to the user. The user answers the survey and enters feedback data into the device. The device sends this feedback data to a server for storage. The server analyzes the feedback data and uses it to generate the next trip plan.

[1757] Input: User feedback data

[1758] Output: Saved and analyzed feedback data

[1759] This enables the system to provide efficient, sustainable, and user-friendly travel plans.

[1760] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1761] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1762] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1763] [Fourth Embodiment]

[1764] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1765] As shown in Figure 7, the 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.

[1766] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1767] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1768] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1769] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1770] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1771] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1772] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1773] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[1774] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1775] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1776] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1777] This invention is a system designed to solve challenges in the tourism industry. Its purpose is to collect and analyze data on tourist destinations, accommodations, and transportation, and to generate and notify users of optimal travel plans. This system is primarily composed of servers, terminals, and users.

[1778] Data collection

[1779] 1. Obtaining tourist information

[1780] The server sends a GET request to the API endpoint of the tourist destination.

[1781] The server parses the returned JSON data and extracts detailed information such as the name of the tourist attraction, location information, rating, and opening hours.

[1782] The server stores this information in a database.

[1783] 2. Acquisition of weather data

[1784] The server accesses the API of a weather data provider service to obtain the weekly weather forecast for the target area.

[1785] The server analyzes the weather information it receives, links it to tourist destination information, and stores it in a database.

[1786] 3. Acquisition of traffic condition data

[1787] The server calls the API of the traffic information service to obtain real-time traffic condition data.

[1788] The server analyzes this information and updates the database with delay information and operating status for each mode of transport (trains, buses, taxis, etc.).

[1789] Data Analysis

[1790] 1. Demand forecasting and optimization

[1791] The server uses AI models (e.g., time-series forecasting models) based on past tourism data to predict demand for the following month.

[1792] The server calculates the optimal allocation of personnel, materials, and ingredients, optimizing resource management.

[1793] 2. Workload analysis

[1794] The server analyzes operational data from the accommodation facility (for example, the frequency of room cleaning and the response time for front desk staff).

[1795] The server identifies business processes that can be automated and simulates the effects of automation using robots and software.

[1796] Plan creation

[1797] 1. Generating an individualized optimization plan

[1798] The server generates a travel plan based on the user's profile data (age, length of stay, budget, preferences, etc.).

[1799] The server uses an AI model (e.g., a recommendation system) to select tourist spots, accommodations, and restaurants based on the user's preferences.

[1800] 2. Creating a Sustainable Tourism Plan

[1801] The server calculates transportation methods and routes with the lowest carbon emissions.

[1802] The server generates plans that include eco-friendly accommodations and restaurants.

[1803] User notifications

[1804] 1. Plan notification

[1805] The server generates a travel plan and sends it to the terminal in JSON format.

[1806] The device displays notifications to the user as push alerts.

[1807] 2. Displaying plan details

[1808] The device displays detailed plan information (transportation, accommodation, sightseeing spots, etc.) to the user via the UI.

[1809] Execution Management

[1810] 1. Plan Review and Approval

[1811] The user reviews the plan on their device and chooses whether or not to proceed.

[1812] The terminal sends the user's selection to the server.

[1813] 2. Notification to relevant organizations

[1814] The server notifies accommodation providers, transportation companies, tour guides, and other relevant parties of the reservation information.

[1815] The server verifies that the notification was received successfully and sends a reservation confirmation to the user.

[1816] Feedback processing

[1817] 1. Gathering feedback

[1818] After the trip ends, the device displays a satisfaction survey to the user.

[1819] The device collects user feedback and sends it to the server.

[1820] 2. Analysis of Feedback

[1821] The server stores the feedback in a database and analyzes it using an AI model.

[1822] The server uses the analysis results to improve the plan generation algorithm and optimize it.

[1823] Specific example

[1824] Data collection examples

[1825] The server retrieves information about Tokyo Tower from a tourist destination API.

[1826] The server retrieves the weekly weather forecast for Tokyo from a weather data provider.

[1827] The server retrieves real-time train operation information from a traffic information service.

[1828] Data analysis example

[1829] The server uses an AI model to predict the demand for Tokyo tourism in the following month.

[1830] The server analyzes operational data from accommodation facilities and simulates the effects of introducing cleaning robots.

[1831] Plan creation example

[1832] The server generates a 3-day Kyoto and Nara travel plan based on profile data indicating a couple's trip.

[1833] The server creates sustainable plans that include train travel and eco-friendly accommodations.

[1834] User notification example

[1835] The server generates a travel plan and sends it to the device.

[1836] The device will notify the user via push notification and display the detailed plan.

[1837] Execution Management Example

[1838] The user reviews the plan and selects to proceed on their device.

[1839] The server notifies accommodations and transportation providers of the reservation information and sends a reservation confirmation to the user.

[1840] Feedback Processing Example

[1841] The device displays a post-trip feedback survey to the user and sends it to the server.

[1842] The server analyzes the feedback and incorporates it into the next plan generation.

[1843] As a result, the system of the present invention can solve the multifaceted challenges of the tourism industry and provide efficient and sustainable tourism experiences.

[1844] The following describes the processing flow.

[1845] Step 1:

[1846] The server sends a GET request to the tourist destination's API endpoint. The information retrieved may include the name of the tourist destination, its location, its rating, and its opening hours.

[1847] Step 2:

[1848] The server parses the returned JSON data and extracts detailed information about the tourist destination. The extracted data includes the name of the tourist destination, location information, rating, opening hours, etc.

[1849] Step 3:

[1850] The server saves detailed information about tourist destinations to a database. The saved data is used in subsequent analysis processes.

[1851] Step 4:

[1852] The server accesses the weather data service's API. It sends a GET request to retrieve the weekly weather forecast for the target area.

[1853] Step 5:

[1854] The server analyzes the weather information it has acquired. The analyzed weather information is then linked to tourist destination information and stored in a database.

[1855] Step 6:

[1856] The server calls the API of a traffic information service to obtain real-time traffic data for the target area. The information obtained includes delay information, service status, and more.

[1857] Step 7:

[1858] The server analyzes traffic data and stores delay information and operational status for each mode of transport in a database.

[1859] Step 8:

[1860] The server uses historical tourism data and an AI model (e.g., a time-series forecasting model) to predict demand for the following month. The prediction results are stored in a database.

[1861] Step 9:

[1862] The server calculates the optimal allocation of personnel, materials, and ingredients based on demand forecast data. The calculation results are then applied to tourist destinations and accommodations.

[1863] Step 10:

[1864] The server analyzes operational data from the accommodation facility. This operational data includes information such as the frequency of room cleaning and the response time for front desk staff.

[1865] Step 11:

[1866] The server identifies business processes that can be automated and simulates the effectiveness of automation using robots and software. The simulation results are stored in a database.

[1867] Step 12:

[1868] The server generates the optimal travel plan based on the user's profile data (age, length of stay, budget, preferences, etc.). An AI model (recommendation system) is used for this generation.

[1869] Step 13:

[1870] The server calculates transportation options and routes with low carbon emissions. The calculation results also include eco-friendly accommodations and restaurants.

[1871] Step 14:

[1872] The server generates a travel plan and sends it to the user's device in JSON format. The generated plan includes tourist destinations, accommodations, transportation options, and environmentally friendly choices.

[1873] Step 15:

[1874] The device displays travel plan notifications to the user as push alerts. The user can then check the notifications.

[1875] Step 16:

[1876] The device displays detailed plan information to the user (transportation, accommodation, sightseeing spots, etc.). The user reviews the plan details.

[1877] Step 17:

[1878] The user reviews the plan on their device and chooses whether to proceed. The result of the selection is sent from the device to the server.

[1879] Step 18:

[1880] The server notifies accommodation providers, transportation providers, tour guides, etc., of the booking information. After each provider confirms the booking, they send that information to the user.

[1881] Step 19:

[1882] After the trip ends, the device displays a satisfaction survey to the user. The user then answers the survey.

[1883] Step 20:

[1884] The device sends the feedback collected from the user to the server. The server stores the feedback in a database.

[1885] Step 21:

[1886] The server analyzes the feedback and uses an AI model to optimize the next plan generation algorithm. The analysis results are then incorporated into the next plan creation process.

[1887] (Example 1)

[1888] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1889] In the tourism industry, the lack of sufficient integration in data collection and analysis of tourist destinations, accommodations, and transportation methods makes it difficult to provide travelers with optimal travel plans. Furthermore, conventional systems have difficulty in demand forecasting and identifying tasks that can be automated, making it impossible to achieve efficient resource allocation and propose eco-friendly travel plans. In addition, there has been a lack of mechanisms to effectively collect user feedback and utilize it to optimize future travel plans. The objective of this invention is to solve these problems.

[1890] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1891] In this invention, the server includes means for collecting data on tourist destinations, accommodations, and means of transportation; means for analyzing the collected data and calculating the optimal allocation of personnel, materials, and ingredients; means for analyzing the workload at accommodations and identifying processes that can be automated; means for generating an optimal travel plan based on individual traveler profile data; means for suggesting transportation and routes with low carbon dioxide emissions; means for notifying the user terminal of the generated travel plan; means for collecting user feedback and reflecting it in optimizing the travel plan; means for obtaining data on tourist destinations, weather, and traffic conditions via API; means for storing and analyzing the acquired data in a database; means for forecasting demand using an AI model based on past tourism data; means for generating an optimal travel plan using user profile data and an AI model; means for adjusting the generated travel plan to take eco-friendly elements into consideration; means for sending the travel plan in JSON format to the user terminal and providing push notifications; means for displaying a questionnaire to collect user satisfaction after the trip; means for storing the collected feedback in a database and analyzing it using an AI model; and means for optimizing the travel plan generation algorithm based on the analysis results. This makes it possible to efficiently and integrally execute a series of processes in the tourism industry, from data collection to plan generation and feedback analysis.

[1892] A "tourist destination" is a geographical location that travelers visit for their own purposes, and is primarily a spot with cultural, historical, or natural attractions.

[1893] "Accommodation facilities" refer to buildings and facilities that provide temporary accommodation for travelers, and include hotels, inns, and guesthouses.

[1894] "Transportation" refers to the vehicles and routes used by travelers to reach their destination, and includes trains, buses, taxis, and airplanes.

[1895] "Data collection" is the process of obtaining information about tourist destinations, accommodations, and transportation from various sources.

[1896] "Analysis" is a technique for processing collected data to make it easier to understand and extract meaningful information.

[1897] "Resource allocation" refers to devising methods for efficiently distributing personnel, materials, and ingredients.

[1898] "Workload" is an indicator that shows the quantity and quality of work performed by employees within an accommodation facility.

[1899] "Automable processes" refer to business processes that can be automated using robots or software.

[1900] "Profile data" refers to data that represents individual information about travelers, such as age, length of stay, budget, and preferences.

[1901] A "travel plan" is a schedule and activity plan for a trip that is proposed to travelers.

[1902] "Carbon dioxide emissions" refer to the amount of carbon dioxide released into the atmosphere by means of transportation and other activities.

[1903] "Eco-friendly" refers to characteristics that indicate environmentally friendly and sustainable methods and products.

[1904] A "push notification" is a notification message that is sent instantly from a server to a device.

[1905] "Feedback" refers to information about opinions and satisfaction levels collected from travelers.

[1906] "API" stands for Application Programming Interface, and it is a standardized method for exchanging data between different software systems.

[1907] "JSON format" is an abbreviation for JavaScript Object Notation, and it is a format used for data exchange that represents data in text format.

[1908] An "AI model" is an algorithm or computational model that uses artificial intelligence technology to perform data analysis and prediction.

[1909] A "survey" is a research method used to collect opinions and information based on a specific question format.

[1910] This invention is a system designed to solve challenges in the tourism industry. Its purpose is to collect and analyze data on tourist destinations, accommodations, and transportation, and to generate and notify users of optimal travel plans. This system is primarily composed of servers, terminals, and users.

[1911] Data collection

[1912] The server retrieves data on tourist destinations, weather, and traffic conditions via APIs. For example, it obtains detailed information such as the name, location, rating, and opening hours of tourist destinations from services that provide tourist destination information. It obtains weekly weather forecasts for the target area from weather data providers and real-time traffic data from traffic information providers. This data is returned in JSON format, which the server parses and stores the necessary information in a database. This allows for the integrated management of detailed information on tourist destinations, accommodations, and transportation options.

[1913] Data Analysis

[1914] Based on the collected data, the server uses AI models (e.g., time-series forecasting models) with historical tourism data to predict demand for the following month. It also uses profile data and AI models (e.g., recommendation systems) to generate personalized travel plans. Furthermore, the server analyzes operational data from accommodations to identify business processes that can be automated. For example, it can simulate the introduction of cleaning robots to reduce the workload of cleaning operations.

[1915] Plan creation

[1916] The server generates an optimal travel plan based on the user's profile data (age, length of stay, budget, preferences, etc.). The generated plan is adjusted to take eco-friendly factors into consideration, prioritizing transportation methods and routes with low carbon emissions. Eco-friendly accommodations and restaurants are also selected. The generated travel plan is converted to JSON format and sent to the user's device.

[1917] User notifications

[1918] The server notifies the user's device of the generated travel plan. The device receives this data and notifies the user via push notification. For example, a message such as "A new travel plan has been suggested!" might appear on the smartphone. The user can then review the plan details and choose to proceed through their device.

[1919] Feedback processing

[1920] After the trip ends, the device displays a satisfaction survey to the user. The user's feedback is sent to the server and stored in a database. The server analyzes this feedback using an AI model and uses it to optimize the algorithm for generating the next travel plan. For example, it can provide specific insights such as, "Satisfaction improved by changing the order of sightseeing spots in the travel plan."

[1921] Specific examples and prompt statements

[1922] As a concrete example, the prompt message used by the server to retrieve information about Tokyo Tower from a tourist destination API is as follows:

[1923] "Please obtain information about Tokyo Tower."

[1924] Furthermore, the prompt message for predicting the demand for Tokyo tourism in the following month using an AI model is as follows:

[1925] "Please predict the demand for tourism in Tokyo next month."

[1926] The following is an example of a prompt message used to notify the user of the generated travel plan.

[1927] "A new travel plan has been suggested! Please check the details."

[1928] As described above, the system of the present invention efficiently and integrally executes a series of processes in the tourism industry, from data collection to plan generation and feedback analysis. This makes it possible to provide travelers with optimal travel plans and improve their tourism experience.

[1929] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1930] Step 1:

[1931] Obtain tourist information

[1932] The server sends a GET request to the tourist destination's API endpoint. The input includes the URL of the tourist destination API and necessary parameters (e.g., API key). The server executes the GET request and receives JSON data about the tourist destination. This JSON data is parsed to extract detailed information such as the tourist destination's name, location, rating, and opening hours, and stored in the database. Specifically, an HTTP request is sent to the URL of the tourist destination API.

[1933] Step 2:

[1934] Acquisition of weather data

[1935] The server accesses the weather data provider's API to retrieve the weekly weather forecast for the target area. The input includes the weather data provider's API URL and a region specification parameter. The server executes a GET request and retrieves the weather data in JSON format. This data is then parsed, linked to tourist destination information, and stored in a database. Specifically, the server accesses the weather data API endpoint and retrieves regional information.

[1936] Step 3:

[1937] Acquisition of traffic condition data

[1938] The server calls the API of a traffic information service to obtain real-time traffic data. The input includes the URL of the traffic information API and necessary parameters (e.g., region, mode of transport). The server executes a GET request and retrieves traffic information in JSON format. This data is then parsed and stored in the database. Specifically, the server accesses the traffic information API to obtain delay information and service status.

[1939] Step 4:

[1940] Demand forecasting and optimization

[1941] The server uses an AI model (e.g., a time-series forecasting model) based on historical tourism data to predict demand for the following month. Historical tourism data is used as input. The data is fed into the AI ​​model, and the demand forecast results are output. Based on these results, the optimal allocation of personnel, materials, and ingredients is calculated. Specifically, the time-series forecasting model is executed, and the forecast results are reflected in resource management.

[1942] Step 5:

[1943] Work load analysis

[1944] The server collects and analyzes operational data from accommodation facilities (e.g., room cleaning frequency and front desk service response time). The operational data from the accommodation facilities is used as input. The server analyzes the data and identifies business processes that can be automated. Specifically, it uses data analysis tools to visualize business processes and simulate automation.

[1945] Step 6:

[1946] Generating Individually Optimized Plans

[1947] The server generates a travel plan based on the user's profile data (age, length of stay, budget, preferences, etc.). User profile data is used as input. The data is input into an AI model (recommendation system) to output the optimal travel plan. Specifically, the recommendation system is used to select tourist spots, accommodations, and restaurants that are suitable for the user.

[1948] Step 7:

[1949] Display of sustainable tourism plans

[1950] The server calculates transportation methods and routes with low carbon emissions. Transportation and route data are used as input. The server adjusts the plan considering eco-friendly factors and selects eco-friendly accommodations and restaurants. Specifically, it generates plans prioritizing sustainable transportation and accommodation options.

[1951] Step 8:

[1952] Plan notification

[1953] The server generates a travel plan and sends it to the device in JSON format. The input is the generated travel plan. The server converts this data to JSON and sends it to the user's device. The device receives this data and sends a push notification. Specifically, the smartphone displays a notification saying, "A new travel plan has been suggested!"

[1954] Step 9:

[1955] View plan details

[1956] The device displays detailed plan information to the user. The input is travel plan data received from a server. The device analyzes this data and displays it in the UI. Specifically, it displays detailed information about transportation, accommodation, and tourist attractions in a list format within the app.

[1957] Step 10:

[1958] Feedback Collection

[1959] After the trip ends, the device displays a satisfaction survey to the user. The end time of the trip plan is used as input. The device displays the survey to the user and collects responses. Specifically, the app displays a form asking, "Please tell us how satisfied you were with your trip."

[1960] Step 11:

[1961] Feedback analysis

[1962] The server stores feedback in a database and analyzes it using an AI model. User feedback data is used as input. The server analyzes the feedback and uses it to optimize the plan generation algorithm. Specifically, it performs sentiment analysis and uses it to generate the next plan.

[1963] The above is a detailed explanation of the system's processing steps and operation.

[1964] (Application Example 1)

[1965] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1966] In the tourism industry, travelers face challenges in creating and executing efficient and comfortable travel plans. In particular, they require flexible arrangements for meals at tourist destinations, as well as adaptability to weather and traffic conditions. Furthermore, providing environmentally friendly options is essential for sustainable tourism. In addition, personalized planning tailored to individual traveler preferences is required, and incorporating user feedback into future plans is crucial.

[1967] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1968] In this invention, the server includes means for collecting data on tourist destinations, accommodations, and transportation; means for analyzing the collected data and calculating the optimal allocation of personnel, materials, and ingredients; means for analyzing the workload at accommodations and identifying processes that can be automated; means for generating an optimal travel plan based on individual traveler profile data; means for suggesting transportation and routes with low carbon dioxide emissions; means for delivering meals from local restaurants based on the travel plan; means for calculating the optimal delivery time based on weather information and traffic conditions; means for notifying the user terminal of the generated travel plan; and means for collecting user feedback and reflecting it in optimizing the travel plan. This enables the creation and execution of efficient and sustainable travel plans.

[1969] A "tourist destination" is a place that travelers visit, such as a natural landscape, historical buildings, or cultural facilities.

[1970] "Accommodation facilities" refer to facilities such as hotels, inns, and guesthouses where travelers can stay temporarily.

[1971] "Transportation" refers to the means of getting around that travelers use, such as trains, buses, taxis, and airplanes.

[1972] "Means of data collection" refers to the hardware and software used to acquire information about tourist destinations, accommodations, and transportation.

[1973] "Means for calculating the optimal allocation of personnel, materials, and food supplies" refers to an analytical system that uses collected data to calculate the efficient allocation of human resources, materials, and food supplies in tourist destinations and accommodation facilities.

[1974] "A means of analyzing workload and identifying processes that can be automated" refers to a system for analyzing the workload at accommodation facilities and identifying tasks that can be automated.

[1975] "Profile data" refers to information about individual travelers, such as age, length of stay, budget, and preferences.

[1976] "A means of generating the optimal travel plan" refers to a system that plans the most suitable sightseeing routes, accommodations, and activities for travelers based on their profile data.

[1977] "Means for proposing transportation methods and routes with low carbon dioxide emissions" refers to a system that calculates and proposes transportation methods and travel routes that are environmentally conscious and reduce carbon dioxide emissions.

[1978] "Methods for having meals delivered from local restaurants" refers to a system for ordering and having meals delivered from local restaurants based on a travel plan.

[1979] "A means of calculating the optimal delivery time based on weather information and traffic conditions" refers to a system that takes weather forecasts and traffic conditions into consideration to optimally adjust the delivery time of meals.

[1980] "Means for notifying user devices of generated travel plans" refers to a system for delivering generated travel plans to travelers' devices such as smartphones and tablets.

[1981] "Means for collecting feedback and using it to optimize travel plans" refers to a system that collects travelers' evaluations and opinions and uses them to improve and optimize future travel plans.

[1982] This invention is a system that solves problems in the tourism industry and is composed primarily of a server, terminals, and users. The specific form of this system is described below.

[1983] Data collection

[1984] 1. Obtaining tourist information

[1985] The server sends a GET request to the tourist destination's API endpoint and retrieves detailed information about the tourist destination (name, location, rating, opening hours, etc.) in JSON format. This information is then stored in the database.

[1986] 2. Acquisition of weather data

[1987] The server retrieves weekly weather forecasts from a weather data provider service, analyzes the data, and stores it in a database linked to tourist destination information.

[1988] 3. Acquisition of traffic condition data

[1989] The server retrieves real-time traffic information from traffic information services and updates the database with delay information and operating status for each mode of transport.

[1990] Data Analysis

[1991] 1. Demand forecasting and optimization

[1992] The server uses historical tourism data to feed into an AI model (e.g., a time-series forecasting model) to predict future demand. It also calculates the optimal allocation of personnel, materials, and ingredients.

[1993] 2. Workload analysis

[1994] The server analyzes operational data from accommodations (such as room cleaning frequency and front desk service response times) to identify business processes that can be automated.

[1995] Plan creation

[1996] 1. Generating an individualized optimization plan

[1997] The server generates a travel plan based on the user's profile data (age, length of stay, budget, preferences, etc.). The generated plan appropriately includes sightseeing spots, accommodations, and restaurants.

[1998] 2. Creating a Sustainable Tourism Plan

[1999] The server calculates transportation options and routes with low carbon emissions and creates plans that include eco-friendly accommodations and restaurants.

[2000] 3. Generating a meal delivery plan based on the travel plan.

[2001] The server collects restaurant information around tourist destinations and creates the optimal delivery plan based on user preferences, weather information, and traffic conditions.

[2002] User notifications

[2003] 1. Plan notification

[2004] The server sends the generated travel plan and delivery plan to the device in JSON format. The device then displays the notification to the user as a push alert.

[2005] 2. Displaying plan details

[2006] The device displays detailed plan information (transportation, accommodation, sightseeing spots, delivery options, etc.) to the user via its UI.

[2007] Feedback processing

[2008] 1. Gathering feedback

[2009] After the trip ends, the device displays a satisfaction survey to the user and collects feedback. The collected data is sent to a server.

[2010] 2. Analysis of Feedback

[2011] The server stores the feedback in a database and performs analysis using an AI model. Based on the analysis results, it optimizes the algorithm for generating the next plan.

[2012] Specific example

[2013] Data collection examples

[2014] The server obtains information about city A from a tourist destination API and the weekly weather forecast for city A from a weather data service. It also obtains real-time traffic information from a traffic information service.

[2015] Data analysis example

[2016] The server predicts tourism demand in city A based on historical data and analyzes operational data from accommodation facilities to simulate the effects of introducing cleaning robots.

[2017] Plan creation example

[2018] The server generates a 3-day travel plan based on the profile of a couple traveling together. This plan includes eco-friendly accommodations and sustainable travel routes. It also adds restaurant delivery options based on the user's preferences and weather information.

[2019] Example of a prompt

[2020] "When sightseeing in City A, please create a plan that includes ordering food delivery from recommended local restaurants, taking into account the optimal delivery time. A detailed plan based on user preferences, weather information, and traffic conditions is required."

[2021] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[2022] Step 1:

[2023] Obtain tourist information

[2024] The server sends a GET request to the tourist destination's API endpoint. The input data is the tourist destination ID. The server parses the returned JSON data, extracts detailed information such as the tourist destination's name, location, rating, and opening hours, and saves it to the database. The output is the detailed information of the tourist destination. 【2025...

Claims

1. To solve the challenges facing the tourism industry, a system with the following functions is provided: Means for collecting data on tourist destinations, accommodations, and transportation, A means for analyzing collected data and calculating the optimal allocation of personnel, materials, and ingredients, A means of analyzing the workload at accommodation facilities and identifying processes that can be automated, A means of generating an optimal travel plan based on individual traveler profile data, Means for proposing transportation methods and routes with low carbon dioxide emissions, A means of notifying the user terminal of the generated travel plan, A means of collecting user feedback and incorporating it into optimizing travel plans, A system that includes this.

2. The system according to claim 1, characterized in that the profile data includes the traveler's age, length of stay, budget, and preferences.

3. The system according to claim 1, further comprising means for automatically notifying accommodation providers, transportation providers, and tour guides of reservation information.

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