System

Abstract

A system is provided.SOLUTION: A system comprising: means for obtaining location information and travel plan information from a user terminal; means for obtaining and analyzing weather forecast data to predict abnormal weather; means for searching for the user's current location and nearby accommodation facilities and transportation means, and presenting optimal options; means for notifying the user terminal of a suggestion regarding recommended accommodation facilities and transportation means; and means for automatically making reservations for accommodation facilities and transportation means based on the user's selection.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Modern travelers often encounter unexpected extreme weather conditions during their trips, which can require them to suddenly change their accommodations or transportation options. However, there is a lack of an effective system for quickly and appropriately adjusting travel plans when extreme weather occurs. In particular, there is a need for a platform that can instantly suggest the best accommodations and transportation options based on the user's current location and streamline the booking process. [Means for solving the problem]

[0005] In order to solve the above problems, the present invention provides the following means: A system including means for acquiring location information and travel plan information from a user terminal, means for acquiring and analyzing weather forecast data to predict extreme weather, means for searching for accommodations and transportation options in and around the user's current location and presenting optimal options, means for notifying the user terminal of suggestions regarding recommended accommodations and transportation options, and means for automatically reserving accommodations and transportation options based on the user's selections, thereby enabling a quick and appropriate response to unexpected extreme weather during travel.

[0006] "User device" refers to a portable device carried by a traveler, such as a smartphone, tablet, or laptop.

[0007] "Location Information" means your current geographic coordinates obtained using GPS technology or other location determination techniques.

[0008] "Travel plan information" refers to detailed travel plans such as departure point, destination, itinerary, means of transportation, and accommodations that are set or entered in advance by a traveler.

[0009] "Extreme weather" refers to extreme weather phenomena such as heavy rain, typhoons, heavy snow, and storms that deviate from normal weather patterns.

[0010] "Weather forecast data" refers to weather forecast information for each region provided by meteorological observation agencies and weather information services.

[0011] "Accommodation facilities" refers to facilities such as hotels, inns, guesthouses, and lodgings where travelers can stay temporarily.

[0012] "Transportation" refers to the means of transportation that travelers use to get around, such as bullet trains, buses, taxis, rental cars, and airplanes.

[0013] "Suggestions" refers to recommendations that inform users about the best accommodations and transportation options.

[0014] "Reservation process" refers to the process of completing the necessary procedures online to secure accommodation and transportation. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] The embodiment of the present invention will be described as an autonomous AI platform that proposes optimal accommodations and transportation methods based on the user's current location and flexibly changes the itinerary in response to unexpected abnormal weather conditions during travel. The program processing of this system will be explained below in natural language.

[0037] Overall system overview

[0038] This system automatically performs a series of processes: collecting information from user devices, predicting extreme weather, suggesting optimal accommodations and transportation options, notifying users, and completing reservations. The system mainly consists of the following components: user devices, a central server, and an external weather forecast database.

[0039] Program processing

[0040] 1. Obtaining user information

[0041] Device: When a user travels, the device uses GPS to obtain the user's current location and inputs the user's travel plan (start point, destination, and itinerary) into the device.

[0042] Terminal: Sends the acquired current location and travel plan information to a central server.

[0043] 2. Predicting extreme weather

[0044] Server: Obtains weather forecast data from an external weather database.

[0045] Server: Analyzes the acquired data and predicts unexpected abnormal weather (e.g., typhoons, heavy rain, heavy snow, etc.) along the user's travel route.

[0046] 3. Find the best accommodation and transportation

[0047] Server: Based on the results of the abnormal weather forecast, searches for accommodation and transportation options near the user's current location and destination.

[0048] Server: Selects the best accommodation based on availability, price, property ratings, and location.

[0049] Server: Checks the availability and timetables of transportation options such as bullet trains, buses, taxis, and rental cars, and selects the most suitable means of transportation.

[0050] 4. User Suggestions and Notifications

[0051] Server: Generates a list of suggestions for the best accommodations and transportation options.

[0052] Server: Sends this proposal list to the user's terminal and notifies them.

[0053] On device: Show a notification to the user so they can review the suggestion.

[0054] 5. Reservation procedure support

[0055] User: Choose from suggested accommodations and transportation options.

[0056] Terminal: Sends user selections to a central server.

[0057] Server: Automatically initiates the booking process for the accommodation and transportation selected by the user.

[0058] Server: Processes the booking confirmation and payment information and sends the booking confirmation to the user.

[0059] Specific examples

[0060] Let's take the example of a user traveling from Tokyo to Osaka when a typhoon is suddenly predicted to form near Nagoya.

[0061] 1. Obtaining user information

[0062] Device: The user's current location is determined to be Nagoya via GPS.

[0063] Terminal: The travel plan, which includes the fact that the user is on the way from Tokyo to Osaka, and the itinerary, is transmitted to the server.

[0064] 2. Predicting extreme weather

[0065] Server: Obtains forecasts of approaching typhoons from a weather forecast database.

[0066] Server: Predicts that the typhoon will have a major impact on Nagoya.

[0067] 3. Find the best accommodation and transportation

[0068] Server: Search affiliated accommodations and transportation options around Nagoya.

[0069] Server: Check that there is a room available at the hotel in front of Nagoya Station and select that the price is 10,000 yen.

[0070] Server: Check whether there are seats available on the early morning Shinkansen train.

[0071] 4. User Suggestions and Notifications

[0072] Server: Creates an optimal list of suggestions including hotels in front of Nagoya Station and early morning Shinkansen trains, and notifies the device.

[0073] Device: Display a notification to the user advising them to stay overnight in Nagoya and take an early flight due to the typhoon.

[0074] 5. Reservation procedure support

[0075] User: Select a hotel and an early morning Shinkansen flight.

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

[0077] Server: Automates the booking process for the selected accommodation and transportation and sends confirmation to the user.

[0078] In this way, the system of the present invention can support the user's trip smoothly even if unexpected abnormal weather occurs during the trip.

[0079] The processing flow will be explained below.

[0080] Step 1:

[0081] Device: Before the user starts their journey, they launch an application on their device and use GPS to obtain the user's current location.

[0082] Terminal: where the user enters travel plan information such as origin, destination, and travel dates.

[0083] Terminal: Sends the acquired current location and travel plan information to the server.

[0084] Step 2:

[0085] Server: Accesses an external weather forecast database (e.g., a weather forecast API) to retrieve weather forecast data for the user's travel route and destination.

[0086] Server: Analyzes the acquired weather forecast data and evaluates the possibility of abnormal weather (e.g., typhoons, heavy rain, heavy snow, etc.) occurring.

[0087] Step 3:

[0088] Server: Based on the analysis results, determine whether unexpected extreme weather will affect the user's travel route or destination.

[0089] Server: If an impact is anticipated, determine the scope and timeframe of the impact.

[0090] Step 4:

[0091] Server: Finds the best accommodation and transportation options, taking into account the user's current location and the expected impact of extreme weather.

[0092] Server: Check availability and pricing information of partner hotels to select the best accommodation.

[0093] Server: Checks the timetables and availability of available transportation options (such as bullet trains, buses, taxis, and rental cars) and selects the most suitable mode of transportation.

[0094] Step 5:

[0095] Server: Aggregates and ranks the best accommodation and transportation options.

[0096] Server: Creates a proposal list based on this information and sends it to the user's device.

[0097] Step 6:

[0098] Device: Receives the suggestion list and displays a notification to the user.

[0099] Terminal: A message such as "The typhoon may cause travel disruptions. There are vacancies at the XX Hotel in front of Nagoya Station (10,000 yen / night). You can also take the early morning Shinkansen." will be displayed.

[0100] User: Decide on a choice from suggested accommodations and transportation options.

[0101] Step 7:

[0102] Terminal: Sends the user's selections to the server.

[0103] Server: Automates the booking process for the selected accommodation and transportation.

[0104] Server: Processes the necessary payment information and generates the reservation confirmation.

[0105] Step 8:

[0106] Server: Sends reservation confirmation information to the user's terminal.

[0107] Terminal: Display reservation confirmation information to the user.

[0108] Through these steps, the system can respond quickly and effectively to unexpected extreme weather events that occur during travel, allowing users to flexibly change their itinerary.

[0109] Example 1

[0110] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0111] When unexpected extreme weather occurs during a trip, it can be difficult for users to quickly find the best accommodation and transportation. A platform is needed to solve this problem and make travel schedule changes smoothly.

[0112] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0113] In this invention, the server includes means for acquiring location information and travel plan information from the user terminal, means for acquiring and analyzing weather data to predict abnormal weather, and means for searching for accommodations and transportation options near the user's current location and destination and presenting optimal options. This allows the user to quickly find optimal accommodations and transportation options and smoothly change their travel schedule even if unexpected abnormal weather occurs during their trip.

[0114] "User device" refers to an electronic device that can be operated by a user, including a smartphone, tablet, laptop, etc.

[0115] "Location information" refers to geographic coordinate data that indicates a user's current location and is obtained using technologies such as GPS.

[0116] "Travel Plan Information" means data that includes the origin, destination, dates, and other related information for a trip that a user is planning.

[0117] "Abnormal weather" refers to weather phenomena that deviate significantly from normal weather conditions, including typhoons, heavy rain, and heavy snow.

[0118] "Weather Data" means atmospheric data, including weather forecasts and historical weather records.

[0119] "Accommodation" means a facility that provides a place for users to stay, including hotels, motels, inns, etc.

[0120] "Transportation" refers to the means used by users to travel, including trains, buses, taxis, rental cars, etc.

[0121] "Searching" means finding information in a database or on the Internet based on specific criteria.

[0122] "Notification" means sending information to a user terminal to notify the user.

[0123] "Reservation" means the process of reserving a particular service or product in advance, and may include payment information.

[0124] "Security protocols" are the standards for communication methods used to ensure data security, including SSL / TLS.

[0125] "Satellite positioning technology" is a technology that uses artificial satellites to measure specific locations on Earth.

[0126] "Weather Information API" refers to an application program interface for obtaining weather data, and is used to obtain information from external weather information services.

[0127] This invention will be described as an autonomous AI platform that suggests optimal accommodation and transportation options based on the user's current location and flexibly changes the itinerary in response to unexpected abnormal weather conditions while traveling.

[0128] The system consists of a user terminal, a central server, and an external weather database. Its main processes are obtaining user information, forecasting extreme weather, searching for optimal accommodation and transportation options, notifying users, and completing reservation procedures.

[0129] Hardware and software used

[0130] User device: Electronic devices such as smartphones, tablets, and laptops that use GPS modules to obtain location information.

[0131] Central server: Web server, database server. Analyzes weather data using the LSTM model as a machine learning model.

[0132] External weather database: An API that provides weather information (e.g., Weather API).

[0133] Data processing and calculation

[0134] 1. Obtaining user information

[0135] Device: The user's current location is acquired using GPS. The user inputs their travel plan (start point, destination, and itinerary).

[0136] Device: Sends location and travel plan information to a central server. Data is transmitted using a secure protocol (e.g., SSL / TLS).

[0137] 2. Predicting extreme weather

[0138] Server: Obtains weather forecast data from an external weather database and stores it in the weather database.

[0139] Server: Uses LSTM model to calculate the probability of occurrence of extreme weather events. Performs data analysis to identify the probability along the user's travel route.

[0140] 3. Find the best accommodation and transportation

[0141] Server: Searches for accommodation and transportation based on the results of abnormal weather forecasts. Accommodation information is obtained from an external API (e.g., accommodation information API), and transportation information is obtained from the API in charge (e.g., transportation information API).

[0142] Server: Selects the best accommodation based on availability, price, and ratings of the acquired accommodations, and also selects the transportation method.

[0143] 4. User Suggestions and Notifications

[0144] Server: Creates a list of best accommodation and transportation suggestions in HTML format.

[0145] Server: Generates a notification message and pushes it to the user's device (e.g., using Firebase Cloud Messaging).

[0146] On your device: Show the notification you received to the user so they can review the suggestion.

[0147] 5. Reservation procedure support

[0148] User: Choose from suggested accommodations and transportation options.

[0149] Terminal: Sends user selections to a central server.

[0150] Server: Automates the booking process for the selected accommodation and transportation and sends confirmation to the user. Processes payment details using a secure payment gateway (e.g. payment API).

[0151] Specific examples

[0152] Let's take the example of a user traveling from Tokyo to Osaka when a typhoon is suddenly predicted to form near Nagoya.

[0153] 1. Obtaining user information

[0154] Device: The user's current location is determined to be Nagoya via GPS.

[0155] Terminal: The travel plan, which includes the fact that the user is on the way from Tokyo to Osaka, and the itinerary, is transmitted to the server.

[0156] 2. Predicting extreme weather

[0157] Server: Obtains forecasts of approaching typhoons from a weather forecast database.

[0158] Server: Predicts that the typhoon will have a major impact on Nagoya.

[0159] 3. Find the best accommodation and transportation

[0160] Server: Search affiliated accommodations and transportation options around Nagoya.

[0161] Server: Check that there is a room available at the hotel in front of Nagoya Station and select that the price is 10,000 yen.

[0162] Server: Check whether there are seats available on the early morning Shinkansen train.

[0163] 4. User Suggestions and Notifications

[0164] Server: Creates an optimal list of suggestions including hotels in front of Nagoya Station and early morning Shinkansen trains, and notifies the device.

[0165] Device: Display a notification to the user advising them to stay overnight in Nagoya and take an early flight due to the typhoon.

[0166] 5. Reservation procedure support

[0167] User: Select a hotel and an early morning Shinkansen flight.

[0168] Terminal: Sends the user's selection to the server.

[0169] Server: Automates the booking process for the selected accommodation and transportation and sends confirmation to the user.

[0170] Example prompts to input to the generative AI model

[0171] Example prompt:

[0172] "If a user traveling from Tokyo to Osaka suddenly arrives in Nagoya and a typhoon is predicted to approach, please explain in detail each processing step of the system that will suggest appropriate accommodation and transportation options to the user and automatically complete the reservation process."

[0173] The above is a specific embodiment for carrying out the present invention.

[0174] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0175] Step 1: Get user information

[0176] Terminal: First, the GPS module is used to obtain the user's current location. The input is the location data from the GPS, and the output is the current location information stored in the location information database.

[0177] Terminal: After the user enters their travel plan (start point, destination, and itinerary) using a dedicated input form, the travel plan is saved in local storage. The input is the travel plan information from the user, and the output is the travel plan data stored in local storage.

[0178] Terminal: Converts the acquired current location and travel plan information into packets and sends them to a central server using a security protocol (e.g., SSL / TLS). The input is location information and travel plan information, and the output is the data transmitted in a secure format.

[0179] Step 2: Predicting extreme weather

[0180] Server: Obtains weather forecast data from an external weather database. The input is an API request, and the output is the weather forecast data stored in the weather database on the server.

[0181] Server: Analyzes the acquired weather forecast data and uses a machine learning model (e.g., LSTM model) to calculate the probability of occurrence of extreme weather along the user's travel route. The input is weather data, and the output is the predicted results of extreme weather. The analysis uses a method that compares the data with past weather data.

[0182] Step 3: Find the best accommodation and transportation

[0183] Server: Searches for accommodations and transportation options near the user's current location and destination based on the results of extreme weather forecasts. The input is the results of the extreme weather forecast and location information, and the output is a list of candidate accommodations and transportation options.

[0184] Server: Accommodation information is obtained from an external API (e.g., accommodation information API), and transportation information is obtained from the corresponding API (e.g., transportation information API). The input is the API request, and the output is information such as room and seat availability, price, rating, and timetable.

[0185] Server: Considers the acquired information and selects the optimal accommodation and transportation method. The input is information on accommodation and transportation methods, and the output is the optimal selection result.

[0186] Step 4: Propose and notify users

[0187] Server: Creates a list of recommendations for optimal accommodations and transportation in HTML format and generates a notification message. The input is the optimal selection result, and the output is the notification message.

[0188] Server: Sends the generated proposal list to the user device and performs push notification (e.g., using Firebase Cloud Messaging). The input is the notification message, and the output is the delivery to the user device.

[0189] Terminal: Displays received notifications to the user and provides an interface where the user can view the details. The input is the notification message and the output is the displayed notification content.

[0190] Step 5: Reservation support

[0191] User: Review the notification and select from suggested accommodations and transportation options. The input is the list of suggestions, and the output is the user's choice.

[0192] Terminal: Sends user selections in a secure format to a central server. The input is the user selection and the output is the data sent in a secure format.

[0193] Server: Automates the booking process for the user's selected accommodation and transportation and sends confirmation to the user. Inputs are user selections and API requests, and outputs are booking confirmation and payment information. Payment information is processed using a secure payment gateway (e.g., payment API).

[0194] (Application example 1)

[0195] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0196] In recent years, extreme weather events have become more frequent, increasing the risk of encountering unexpected weather conditions while traveling. Accordingly, there is a demand for systems that can quickly suggest appropriate accommodations and transportation options to ensure travelers can travel safely and comfortably. Furthermore, with the spread of autonomous vehicles, there is a need for technologies that enable these vehicles to respond to extreme weather and suggest appropriate evacuation routes and commercial facilities where they can stop. However, conventional systems cannot adequately meet these demands, resulting in issues such as a lack of safety and efficiency for travelers and autonomous vehicle users.

[0197] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0198] In this invention, the server includes means for acquiring location information and travel plan information from the user terminal, means for acquiring and analyzing weather data to predict abnormal weather, means for searching for facilities and means of transportation in the user's current location and surrounding areas and presenting optimal options, means for notifying the user terminal of suggestions regarding recommended facilities and means of transportation, means for automatically reserving facilities and means of transportation based on the user's selection, and means for suggesting evacuation routes and commercial facilities where the autonomous vehicle can stop based on the current location and weather forecast, thereby enabling users of autonomous vehicles to safely and quickly find evacuation routes and commercial facilities when abnormal weather occurs.

[0199] "User terminals" are information terminal devices used by travelers and drivers, including smartphones, tablets, and in-vehicle displays.

[0200] "Location information" means data obtained using GPS or other location information systems that indicates the current location of a user device or autonomous vehicle.

[0201] "Travel plan information" refers to information about a traveler's itinerary, such as the departure point, destination, route, and itinerary.

[0202] "Weather data" includes weather forecasts and information on abnormal weather conditions obtained from weather providers, and is data used to make weather predictions.

[0203] "Facilities" refers to commercial facilities available to users, such as accommodations, restaurants, and gas stations.

[0204] "Means of transportation" refers to the means of transportation that users can use to get around, such as cars, trains, bullet trains, and buses.

[0205] "Evacuation route" refers to the recommended route for users to safely evacuate in the event of an emergency such as extreme weather.

[0206] "Suggestion" refers to the optimal options or courses of action that the system presents to the user based on the analyzed data.

[0207] "Commercial facilities" refers to hotels, restaurants, gas stations, and other facilities where users can stop and use their vehicles during extreme weather.

[0208] "Reservation process" means that the system automatically completes the process to secure accommodation, transportation, etc. based on the user's selections.

[0209] This invention is a system that, when a user encounters unexpected abnormal weather while traveling, suggests optimal evacuation routes and commercial facilities (such as accommodations, restaurants, and gas stations) based on the user's current location, and can make reservations as needed. The system consists of a user terminal, a central server, and an external weather database.

[0210] System configuration

[0211] 1. User Device

[0212] The user device, which can be a smartphone, tablet, or an on-board display in an autonomous vehicle, transmits location and travel plan information to a central server and also receives notifications from the system.

[0213] 2. Central Server

[0214] The central server consists of several components:

[0215] Location information acquisition function: Acquires location information from the user's device using GPS, etc.

[0216] Travel plan information management function: Receives travel plans entered by users and saves them in a database.

[0217] Weather data analysis function: Obtains weather data from an external weather database (e.g., Weather API), analyzes the data, and predicts abnormal weather.

[0218] Optimal facility and evacuation route recommendation function: Implements an algorithm that suggests appropriate commercial facilities and evacuation routes based on the user's current location and weather data.

[0219] Reservation management function: Automatically make reservations at commercial facilities selected by the user and manage confirmation information.

[0220] 3. External Weather Database

[0221] The latest weather forecast information is obtained from an external weather database, mainly using the Weather API, and the data is analyzed on the server to predict abnormal weather.

[0222] Example

[0223] Let's explain what happens when a user is traveling from Tokyo to Osaka and a typhoon is suddenly predicted to form near Nagoya.

[0224] The system obtains from GPS that the user's current location is Nagoya and sends this information to a central server.

[0225] The central server obtains the latest weather forecast information from the Weather API and predicts the occurrence of a large typhoon in Nagoya.

[0226] The server checks availability at accommodations, restaurants, gas stations, etc. around Nagoya and notifies the user of the best options. For example, it can provide information to users about available rooms at a hotel in front of Nagoya Station and available seats on an early morning Shinkansen train.

[0227] The user selects the proposed commercial facility from the terminal and transmits the selection information to the central server.

[0228] The server automatically completes the reservation procedure for the selected accommodation and sends reservation confirmation information to the user's terminal.

[0229] Prompt Sentence Examples

[0230] Here are some examples of prompts for generative AI models:

[0231] "If extreme weather is predicted to occur while an autonomous vehicle is en route to its destination, please suggest the best evacuation route and commercial facilities (hotels, restaurants, gas stations) based on the current location and weather forecast. Please provide specific locations and suggestions in detail."

[0232] In this way, the system of the present invention ensures that traveling users can safely and efficiently respond to extreme weather events.

[0233] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0234] Step 1:

[0235] The user device acquires location information.

[0236] Input: Current location data from GPS

[0237] Output: User's current location

[0238] Specific operation: The user device uses the GPS module to obtain the user's current location in real time. This location information is expressed as latitude and longitude data.

[0239] Step 2:

[0240] The user terminal inputs travel plan information.

[0241] Input: The origin, destination, and dates of a trip manually entered by the user

[0242] Output: Travel plan information

[0243] Specific operation: The user inputs the departure point, destination, and travel dates via a smartphone or in-car display. This data is sent to the server as travel plan information.

[0244] Step 3:

[0245] The server retrieves abnormal weather information from a weather database.

[0246] Input: Weather data provider API request

[0247] Output: Weather forecast data

[0248] What it does: The server retrieves the latest weather forecast data from external weather data providers via API requests. This data is used to predict extreme weather events.

[0249] Step 4:

[0250] The server analyzes weather data and predicts abnormal weather.

[0251] Input: Retrieved weather forecast data

[0252] Output: Extreme weather forecast results

[0253] Specific operation: The server analyzes the acquired weather forecast data and determines whether it matches certain conditions (e.g., the path of a typhoon, a forecast of heavy snowfall). Based on the results of this analysis, it predicts whether abnormal weather will occur.

[0254] Step 5:

[0255] The server searches for facilities and transportation options near the user's current location.

[0256] Input: User's location information, travel plan information, extreme weather forecast results

[0257] Output: A list of recommended facilities and transportation options

[0258] Specific operation: The server searches for nearby accommodations, restaurants, gas stations, and transportation options based on the user's location information and weather forecast results, utilizing APIs from hotel booking sites and information on public transport timetables.

[0259] Step 6:

[0260] The server sends a list of recommended facilities and transportation options to the user's terminal.

[0261] Input: List of recommended facilities and transportation options

[0262] Output: User notification

[0263] Specific operation: The server selects the most suitable facilities and transportation methods from the search results and sends the list of suggestions to the user's device. The user's device displays this list on its screen and informs the user of countermeasures against abnormal weather.

[0264] Step 7:

[0265] The user selects from suggested facilities and transportation options.

[0266] Input: Suggestion list

[0267] Output: User selection information

[0268] Specific operation: The user selects the desired facility or means of transportation from the suggested list displayed on the terminal and sends the selected information to the server.

[0269] Step 8:

[0270] The server automatically processes reservations for the selected facilities and transportation.

[0271] Input: User selection information

[0272] Output: Reservation confirmation information

[0273] Specific operation: The server automatically completes the online reservation process for the facility and transportation selected by the user and sends the confirmation information to the user's device. This is done using the API of a hotel reservation site or the online transportation reservation system.

[0274] As a result, this system can help users respond appropriately and quickly even if abnormal weather occurs during their trip.

[0275] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0276] The embodiment of the present invention will be described as an autonomous AI platform for a travel support system that combines an emotion engine that recognizes user emotions. Below, the program processing of this system will be explained in natural language.

[0277] Overall system overview

[0278] This system suggests optimal accommodations and transportation options based on the user's current location in the event of unexpected extreme weather during travel, and further adjusts the suggestions based on the user's emotions, allowing for flexible and emotionally sensitive changes to the itinerary. The system mainly consists of the following components: user terminal, central server, external weather forecast database, and emotion engine.

[0279] Program processing

[0280] 1. Obtaining user information

[0281] Device: Before the user starts their trip, they launch the application on their device, use GPS to obtain their current location, and then input their travel plan information, such as their departure point, destination, and travel dates.

[0282] Terminal: Transmits the acquired current location and travel plan information to the central server.

[0283] 2. Predicting extreme weather

[0284] Server: Accesses an external weather forecast database (e.g., a weather forecast API) to retrieve weather forecast data for the user's travel route and destination.

[0285] Server: Analyzes the acquired weather forecast data and evaluates the possibility of abnormal weather (e.g., typhoons, heavy rain, heavy snow, etc.) occurring.

[0286] 3. Acquiring emotional information

[0287] Terminal: Acquires the user's voice data and facial expression data.

[0288] Terminal: Sends acquired emotion data to the emotion engine.

[0289] Emotion engine: Analyzes voice and facial expression data to determine the user's current emotional state.

[0290] 4. Find the best accommodation and transportation

[0291] Server: Based on the results of the abnormal weather forecast, searches for accommodation and transportation options near the user's current location and destination.

[0292] Server: Selects the best accommodation based on availability, price, property ratings, and location.

[0293] Server: Checks the availability and timetables of transportation options such as bullet trains, buses, taxis, and rental cars, and selects the most suitable means of transportation.

[0294] 5. Adjusting suggestions based on emotions

[0295] Server: Adjusts the content and format of suggestions based on the emotional information provided by the emotion engine.

[0296] Server: If the user is "tired," the server will prioritize hotels with good relaxation facilities, and make suggestions that take into consideration the user's emotions.

[0297] 6. User Suggestions and Notifications

[0298] Server: Creates a list of recommendations for optimal accommodations and transportation options and sends them to the user's device.

[0299] On device: Show a notification to the user so they can review the suggestion.

[0300] 7. Reservation procedure support

[0301] User: Decide on a choice from suggested accommodations and transportation options.

[0302] Terminal: Sends user selections to a central server.

[0303] Server: Automatically initiates the booking process for the accommodation and transportation selected by the user.

[0304] Server: Processes the necessary payment information and generates the reservation confirmation.

[0305] 8. Sending confirmation information

[0306] Server: Sends reservation confirmation information to the user's terminal.

[0307] Terminal: Display reservation confirmation information to the user.

[0308] Specific examples

[0309] For example, let's consider a case where a user is traveling from Tokyo to Osaka and suddenly a typhoon is predicted to form near Nagoya.

[0310] 1. Obtaining user information

[0311] Device: The user's current location is determined to be Nagoya via GPS.

[0312] Terminal: The travel plan, which includes the fact that the user is on the way from Tokyo to Osaka, and the itinerary, is transmitted to the server.

[0313] 2. Predicting extreme weather

[0314] Server: Obtains forecasts of approaching typhoons from a weather forecast database.

[0315] Server: Predicts that the typhoon will have a major impact on Nagoya.

[0316] 3. Acquiring emotional information

[0317] Device: Obtain emotional data such as "tired" from the user's voice.

[0318] Terminal: Sends this emotion data to the server.

[0319] Emotion engine: Analyzes voice data to determine if the user is tired.

[0320] 4. Find the best accommodation and transportation

[0321] Server: Search affiliated accommodations and transportation options around Nagoya.

[0322] Server: Check that there is a room available at the hotel in front of Nagoya Station and select that the price is 10,000 yen.

[0323] Server: Check whether there are seats available on the early morning Shinkansen train.

[0324] 5. Adjusting suggestions based on emotions

[0325] Server: Based on the user's feeling of "tired," prioritize hotels with excellent relaxation facilities.

[0326] 6. User Suggestions and Notifications

[0327] Server: Notify the user that "There are vacancies at Hotel X in front of Nagoya Station (10,000 yen / night). The hotel has ample relaxation facilities. You can also take the early morning Shinkansen."

[0328] On your device: Show a notification to the user so they can review the suggestion.

[0329] 7. Reservation procedure support

[0330] User: Select a hotel and an early morning Shinkansen flight.

[0331] Terminal: Sends the user's selection to the server.

[0332] Server: Automates the booking process for the selected accommodation and transportation and sends confirmation to the user.

[0333] In this way, the system of the present invention can respond quickly and effectively to abnormal weather conditions, and can increase travel satisfaction by making suggestions that take into consideration the user's emotions.

[0334] The processing flow will be explained below.

[0335] Step 1:

[0336] Device: Before the user starts their trip, they launch the application on their device, use GPS to obtain their current location, and then input their travel plan information, such as their departure point, destination, and travel dates.

[0337] Terminal: Transmits the acquired current location and travel plan information to the central server.

[0338] Step 2:

[0339] Server: Accesses an external weather forecast database (e.g., a weather forecast API) to retrieve weather forecast data for the user's travel route and destination.

[0340] Server: Analyzes the acquired weather forecast data and evaluates the possibility of abnormal weather (e.g., typhoons, heavy rain, heavy snow, etc.) occurring.

[0341] Step 3:

[0342] Terminal: Acquires emotion data based on the voice data and camera footage provided by the user.

[0343] Terminal: Sends emotion data to the emotion engine for analysis.

[0344] Step 4:

[0345] Emotion engine: Analyzes voice and facial expression data to determine the user's current emotional state (e.g., tired, stressed, etc.).

[0346] Emotion engine: Sends the judgment results to the central server.

[0347] Step 5:

[0348] Server: Based on the results of the abnormal weather forecast, searches for accommodation and transportation options near the user's current location and destination.

[0349] Server: Selects the best accommodation based on availability, price, property ratings, and location.

[0350] Server: Checks the availability and timetables of transportation options such as bullet trains, buses, taxis, and rental cars, and selects the most suitable means of transportation.

[0351] Step 6:

[0352] Server: Adjusts the content and format of suggestions based on the emotional information provided by the emotion engine.

[0353] Server: Depending on the user's emotional state, the server adjusts the suggestions to suit the user's current condition. For example, if the user feels "tired," the server prioritizes hotels with relaxation facilities.

[0354] Step 7:

[0355] Server: Aggregates and ranks the best accommodation and transportation options.

[0356] Server: Creates a proposal list based on this information and sends it to the user's device.

[0357] Step 8:

[0358] Device: Receives the suggestion list and displays a notification to the user.

[0359] Terminal: A message such as "The typhoon may cause travel disruptions. There are vacancies at Hotel X in front of Nagoya Station (10,000 yen / night). It also has excellent relaxation facilities. You can also take an early morning Shinkansen flight." will be displayed.

[0360] User: Decide on a choice from suggested accommodations and transportation options.

[0361] Step 9:

[0362] Terminal: Sends the user's selections to the server.

[0363] Server: Automates the booking process for the selected accommodation and transportation.

[0364] Server: Processes the necessary payment information and generates the reservation confirmation.

[0365] Step 10:

[0366] Server: Sends reservation confirmation information to the user's terminal.

[0367] Terminal: Display reservation confirmation information to the user.

[0368] Through these steps, the system can respond quickly and effectively to unexpected extreme weather events that occur during travel, and can also improve users' travel experience by making suggestions that take users' emotions into consideration.

[0369] Example 2

[0370] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0371] Conventional systems often fail to respond quickly and effectively to extreme weather events that occur during travel. They also fail to flexibly adjust travel plans based on the user's emotional state, which can lead to lower travel satisfaction. Furthermore, suggestions for accommodations and transportation options do not take into account the user's current state of mind, making it difficult to provide optimal recommendations tailored to the user's needs.

[0372] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0373] In this invention, the server includes means for acquiring location information and travel plan information from the user terminal, means for acquiring and analyzing weather forecast data to predict abnormal weather, means for acquiring emotion data and analyzing it with an emotion engine, means for searching for accommodations and transportation options in the user's current location and surrounding area and presenting optimal options, means for adjusting the suggestions based on the emotion information, means for notifying the user terminal of the suggestions regarding recommended accommodations and transportation options, means for automatically reserving accommodations and transportation options based on the user's selections, and means for notifying the user of reservation confirmation information. This enables quick and effective response to abnormal weather and flexible adjustment of travel plans based on the user's emotional state.

[0374] A "user terminal" is an electronic device operated by a user, such as a smartphone, tablet, or personal computer.

[0375] "Location Information" means your current geographic location determined using GPS or other positioning technology.

[0376] "Travel plan information" refers to information entered by a user regarding a trip, and includes data such as the departure point, destination, and itinerary.

[0377] "Extreme weather" refers to meteorological phenomena that deviate significantly from normal weather patterns, such as typhoons, heavy rain, and heavy snow.

[0378] "Weather forecast data" refers to forecast information about future weather provided by meteorological agencies and data provision services.

[0379] "Emotional data" refers to data that represents the user's emotional state, obtained from the user's voice and facial expressions.

[0380] An "emotion engine" refers to a system that analyzes acquired voice and facial expression data to determine the user's emotional state.

[0381] "Accommodation" refers to hotels, inns, and other accommodation facilities where users can stay.

[0382] "Transportation" refers to the means used by users to travel, such as bullet trains, buses, taxis, and rental cars.

[0383] "Reservation confirmation information" is information indicating that a reservation for accommodation or transportation has been confirmed, and includes a QR code (registered trademark) and reservation number.

[0384] The present invention will be described as an autonomous AI platform for a travel support system that combines an emotion engine that recognizes user emotions. This system enables rapid response to abnormal weather and flexible adjustment of travel plans based on user emotions.

[0385] Required Hardware and Software

[0386] User devices: Smartphones, tablets, personal computers, etc. These devices must be equipped with a GPS module, camera, and microphone. The application uses this hardware to obtain user information and collect emotion data.

[0387] Central Server: A central server for data processing and analysis, retrieving and analyzing weather forecast data, managing user location and travel plan information, running the sentiment engine, and searching for optimal accommodation and transportation options.

[0388] Weather Forecast API: An API for connecting to an external weather forecast database. Use this API to obtain forecast data for extreme weather.

[0389] Emotion Engine: Software that analyzes voice and facial expression data to determine the user's emotional state. It uses machine learning models to classify emotions.

[0390] System Overview

[0391] 1. Obtaining User Information:

[0392] Before starting a trip, the user launches the application on their device and uses GPS to determine their current location. The user then enters information such as the departure point, destination, and travel dates, which is then sent to a central server.

[0393] 2. Predicting extreme weather:

[0394] The server accesses the weather forecast API to obtain weather forecast data for the user's travel route and destination, analyzes the obtained data, and evaluates the possibility of extreme weather occurring.

[0395] 3. Acquiring emotional information:

[0396] The user device captures voice and facial expression data and sends them to the emotion engine, which analyzes the data and determines the user's current emotional state.

[0397] 4. Find the best accommodation and transportation:

[0398] The server searches for accommodations and transportation options near the user's current location and destination based on the results of the extreme weather forecast, taking into account the availability, price, ratings, and location of the accommodations, and also searches for and selects transportation options.

[0399] 5. Adjusting suggestions based on emotions:

[0400] The server adjusts the suggestions based on the emotional information provided by the emotion engine. For example, if the user is "tired," it will prioritize hotels with excellent relaxation facilities.

[0401] 6. User Suggestions and Notices:

[0402] The server creates a list of recommendations for optimal accommodations and transportation options and sends it to the user's device, which then notifies the user of the recommendations and allows them to review them.

[0403] 7. Booking assistance:

[0404] Once the user makes a selection from the suggested accommodations and transportation options, the selection is sent to a central server, which automatically initiates the booking process, processes payment information, and generates a booking confirmation.

[0405] 8. Sending confirmation information:

[0406] Finally, the server sends the reservation confirmation information to the user terminal, which the user can confirm.

[0407] Specific examples

[0408] For example, consider a situation where a user is traveling from Tokyo to Osaka and a typhoon is predicted to occur near Nagoya. Nagoya is acquired as the user's current location, and the server uses a weather forecast API to collect and analyze information about the approaching typhoon. If the user is determined to be "tired," the server will prioritize suggesting a hotel in front of Nagoya Station with ample relaxation facilities and notify the user that seats are available on an early morning Shinkansen train.

[0409] Example prompt: "We have availability at Hotel X in front of Nagoya Station (10,000 yen / night). It has excellent relaxation facilities and you can catch an early morning Shinkansen."

[0410] In this way, the system of the present invention can respond quickly and effectively to abnormal weather conditions, and can increase travel satisfaction by making suggestions that take into consideration the user's emotions.

[0411] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0412] Program processing flow

[0413] Step 1: Get user information

[0414] Step 2: Predicting extreme weather

[0415] Step 3: Acquiring emotional information

[0416] Step 4: Find the best accommodation and transportation

[0417] Step 5: Adjust your suggestions based on emotion

[0418] Step 6: Propose and notify users

[0419] Step 7: Reservation support

[0420] Step 8: Submit confirmation information

[0421] Specific explanation of each processing step

[0422] Step 1: Get user information

[0423] Input: User's location and travel plan information

[0424] Output: Current location and travel plan information sent to a central server

[0425] User: Launches the application on their smartphone or tablet before starting their trip.

[0426] Specific action: Tap the icon to open the application.

[0427] Device: When the application launches, it uses GPS to obtain the user's current location.

[0428] Specific operation: The GPS module collects the current latitude and longitude information.

[0429] User: Enters travel plan information into the application, including origin, destination, and travel dates.

[0430] Specific actions: Enter information using text boxes and calendar input.

[0431] Terminal: Transmits the acquired current location and travel plan information to the central server.

[0432] Specific operation: Sends data using network communication in the background.

[0433] Step 2: Predicting extreme weather

[0434] Input: Weather forecast data for the user's travel route and destination

[0435] Output: Probability of occurrence of analyzed extreme weather events

[0436] Server: Accesses the weather forecast API to connect to an external weather forecast database.

[0437] Specific operation: Send an API request and retrieve weather forecast data.

[0438] Server: Analyzes the acquired weather forecast data and evaluates the possibility of abnormal weather occurring.

[0439] Specific operations: Analyze weather data using pattern matching and statistical models.

[0440] Step 3: Acquiring emotional information

[0441] Input: User's voice and facial expression data

[0442] Output: Emotion information analyzed by the emotion engine

[0443] Device: The user's voice data and facial expression data are acquired using a camera and microphone.

[0444] Specific operation: Collects audio with a microphone and captures facial expression data with a camera.

[0445] Terminal: Sends acquired emotion data to the emotion engine.

[0446] Specific behavior: Send data to the emotion engine in real time.

[0447] Emotion engine: Analyzes voice and facial expression data to determine the user's current emotional state.

[0448] Specific behavior: Classifying emotions using machine learning models.

[0449] Step 4: Find the best accommodation and transportation

[0450] Input: User's current location, accommodation information near the destination, and transportation information

[0451] Output: Selection of optimal accommodation and transportation options

[0452] Server: Searches for accommodations and transportation options near the user's current location and destination.

[0453] Specific operation: Query the database and extract facilities that meet the criteria.

[0454] Server: Selects the best accommodation based on availability, price, ratings, and location.

[0455] Specific operation: Apply filtering conditions to select the most suitable facility.

[0456] Server: Checks the availability and timetables of transportation options (Shinkansen, buses, taxis, rental cars, etc.) and selects the most suitable transportation option.

[0457] Specific operation: Obtain data from external API and select the best option.

[0458] Step 5: Adjust your suggestions based on emotion

[0459] Input: Emotion information provided by the emotion engine

[0460] Output: Suggestions that take user emotions into consideration

[0461] Server: Adjusts the proposal content based on the emotional information provided by the emotion engine.

[0462] Specific action: Reevaluate the proposal based on the emotional data.

[0463] Server: If the user is "tired," prioritize hotels with relaxation facilities.

[0464] Specific actions: Create a list of suggestions and focus on relaxation facilities.

[0465] Step 6: Propose and notify users

[0466] Input: A list of suggestions generated from the server

[0467] Output: Proposal sent to user's device

[0468] Server: Generates a list of suggestions for the best accommodations and transportation options.

[0469] Specific actions: List the suggestions and format them in a way that is easy for the user to understand.

[0470] Server: Sends the proposal list to the user device.

[0471] Specific operation: Send data using a communication protocol.

[0472] On your device: Show a notification to the user so they can review the suggestion.

[0473] Specific behavior: Display a push notification or alert box.

[0474] Step 7: Reservation support

[0475] Input: User's chosen accommodation and transportation options

[0476] Output: Booking completion and confirmation information

[0477] User: Decide on a choice from suggested accommodations and transportation options.

[0478] Action: Click an option from the list.

[0479] Terminal: Sends user selections to a central server.

[0480] Specific operation: The selection information is sent as a packet.

[0481] Server: Automatically initiates the booking process for the selected accommodation and transportation.

[0482] Specific actions: Access the reservation system and submit the required information.

[0483] Server: Processes the necessary payment information and generates the reservation confirmation.

[0484] Specific operation: Make a payment through a payment gateway.

[0485] Step 8: Submit confirmation information

[0486] Input: Generated booking confirmation information

[0487] Output: Reservation confirmation displayed on the user's device

[0488] Server: Generates reservation confirmation information.

[0489] What it does: Create a confirmation with a QR code, reservation number, and details.

[0490] Server: Sends reservation confirmation information to the user's terminal.

[0491] Specific operation: Encodes data and sends it to the terminal.

[0492] Terminal: Display reservation confirmation information to the user.

[0493] Specific action: Notify in-app or via email.

[0494] The above is the specific processing flow of the system and the detailed operation at each step.

[0495] (Application example 2)

[0496] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0497] Conventionally, when abnormal weather occurs during a trip, there has been a lack of a means to respond flexibly to the user's emotional state. This has caused stress and made it difficult for users to find appropriate accommodations and transportation. Furthermore, the lack of appropriate countermeasures in the event of abnormal weather has led to problems such as a decrease in travel safety and satisfaction. The present invention aims to provide a system that provides a quick and effective response to abnormal weather while taking into account the user's emotional state.

[0498] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0499] In this invention, the server includes means for acquiring location information and travel plan information from the user terminal, means for acquiring and analyzing weather forecast data to predict abnormal weather, means for analyzing the user's voice data and facial expression data to determine the user's emotional state, means for searching for accommodations and transportation options in the user's current location and surrounding areas and presenting optimal options, means for adjusting the suggestions based on the user's emotional state, means for notifying the user terminal of suggestions regarding recommended accommodations and transportation options, and means for automatically reserving accommodations and transportation options based on the user's selection. This enables flexible suggestions that take the user's emotional state into consideration, thereby increasing the safety and satisfaction of travel even during abnormal weather.

[0500] Below are definitions of key terms found in the claims.

[0501] "User terminal" means a device operated by a user for inputting and obtaining location information and travel planning information.

[0502] "Location information" is data that indicates a specific location using global positioning system technology.

[0503] "Travel planning information" refers to information about the itinerary, departure point, destination, etc. of a trip that the user is planning.

[0504] "Extreme weather" refers to weather phenomena that deviate significantly from normal weather conditions and may affect travel.

[0505] "Weather forecast data" refers to weather-related information obtained from a weather forecast application programming interface.

[0506] "Analysis" is the process of analyzing acquired data to derive meaningful information.

[0507] "Voice data" means data that is a digital recording of what a user says.

[0508] "Facial expression data" is data used to analyze the user's facial expressions and determine their emotions.

[0509] "Emotional state" is information that indicates the user's current emotion, and includes, for example, "tired" or "happy."

[0510] "Accommodation" means a facility (such as a hotel or inn) provided for temporary stay by users.

[0511] "Transportation" refers to the means of travelling to a destination (such as bullet train, bus, taxi, rental car, etc.).

[0512] The "best option" refers to the accommodation or transportation that best suits the user's emotional state and current situation.

[0513] "Proposal content" is information about accommodation and transportation that the server presents to the user.

[0514] "Reservation" is the process of securing accommodation or transportation in advance.

[0515] The present invention will be described as a food delivery support system incorporating an emotion engine that recognizes user emotions. The system uses voice and facial expression data to determine the user's emotions and suggests food delivery options suitable for the user, even in extreme weather. The main components of the system are a user terminal, a central server, an external weather forecast database, and the emotion engine.

[0516] Overall system overview

[0517] The central server receives location information and food delivery request information from the user's device, and then refers to an external weather forecast database to predict extreme weather.The emotion engine then analyzes the voice and facial expression data to determine the user's emotional state, and provides the food delivery options that best suit the user's emotional state and current situation.

[0518] Program processing

[0519] The program in this system works as follows:

[0520] 1. Obtaining User Information:

[0521] Device: The user launches the application, obtains their current location using GPS, and inputs their desired meal type, budget, and other information.

[0522] Terminal: Sends the acquired current location and request information to the central server.

[0523] 2. Predicting extreme weather:

[0524] Server: Accesses an external weather forecast database (such as a weather forecast API) and obtains weather forecast data for the user's current location.

[0525] Server: Analyzes the acquired weather forecast data and evaluates the possibility of abnormal weather (e.g., typhoons, heavy rain, heavy snow, etc.) occurring.

[0526] 3. Acquiring emotional information:

[0527] Terminal: Acquires the user's voice data and facial expression data.

[0528] Terminal: Sends acquired emotion data to the emotion engine.

[0529] Emotion engine: Analyzes voice and facial expression data to determine the user's current emotional state.

[0530] 4. Find the best food delivery options:

[0531] Server: Based on the results of extreme weather forecasts, search for food delivery options based on the user's current location and request information.

[0532] Server: Based on your emotional state, prioritize meals that reduce stress and fatigue, such as relaxation dishes and comfortable delivery options.

[0533] 5. User Suggestions and Notices:

[0534] Server: Creates a list of recommendations for optimal food delivery options and sends them to the user's device.

[0535] On device: Show a notification to the user so they can review the suggestion.

[0536] Specific examples

[0537] For example, let's say a user is at home and a typhoon is predicted to be approaching. The emotion engine determines from the user's voice data that they are feeling stressed. In this case, the system will suggest relaxing meals and options that can be delivered safely and quickly.

[0538] Example prompt sentence:

[0539] If you recognize that the user is stressed, suggest the best food delivery options for when a typhoon is approaching, including relaxation foods and safe delivery methods.

[0540] The present invention makes it possible to propose food delivery that takes into account the user's emotional state, allowing them to enjoy meals with peace of mind even during extreme weather conditions.

[0541] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0542] Program processing flow

[0543] Step 1:

[0544] Retrieving User Information

[0545] Device: The user launches the application, obtains their current location using GPS, and enters their desired meal type, budget, and other required information.

[0546] Input: GPS data (location information), user input data (type of meal, budget)

[0547] Output: location information, request information

[0548] How it works: The application uses the smartphone's GPS to obtain the user's current location and latitude and longitude, and then prompts the user to enter their desired meal type, budget, and other information through the user interface.

[0549] Step 2:

[0550] Predicting extreme weather

[0551] Server: Accesses an external weather forecast database (weather forecast API) and obtains weather forecast data for the user's current location.

[0552] Input: location information, weather forecast data

[0553] Output: Extreme weather forecast data

[0554] How it works: The server calls the weather forecast API and receives weather forecast data for the current location. This data is then analyzed to assess the likelihood of extreme weather occurring.

[0555] Step 3:

[0556] Acquiring emotional information

[0557] Terminal: Acquires the user's voice data and facial expression data and sends them to the emotion engine.

[0558] Input: Voice data, facial expression data

[0559] Output: Emotional state data

[0560] How it works: The application uses the smartphone's microphone and camera to record voice and facial expressions. The recorded data is then sent to the emotion engine for analysis, which determines the user's emotional state (e.g., stress, fatigue, etc.).

[0561] Step 4:

[0562] Finding the best food delivery options

[0563] Server: Searches for the most appropriate food delivery options based on the user's location and request information, based on extreme weather forecasts and emotional state.

[0564] Input: Extreme weather forecast data, emotional state data, location information, request information

[0565] Output: List of food delivery options

[0566] What it does: The server uses the food delivery service's API to search for available options near the user's current location, and prioritizes, for example, stress-reducing meals and safe delivery options based on the user's emotional state.

[0567] Step 5:

[0568] User suggestions and notifications

[0569] Server: Creates a list of recommendations for optimal food delivery options and sends them to the user's device.

[0570] Input: List of food delivery options

[0571] Output: Proposal notification

[0572] What it does: The server generates a list of suggestions and sends them to the user's device as a push notification or in-app notification, allowing the user to review and select.

[0573] Step 6:

[0574] Supporting user selection and booking procedures

[0575] User: Choose from suggested food delivery options.

[0576] Input: Suggestion notification, user selection

[0577] Output: Selected data

[0578] How it works: A user uses a smartphone app to select the food delivery options they want from the options provided. This selection is then sent to the server.

[0579] Step 7:

[0580] Completing the reservation process

[0581] Server: Automatically initiates the reservation and ordering process for the food delivery option selected by the user.

[0582] Input:Selection data

[0583] Output: Reservation confirmation information

[0584] Specific operation: The server calls the delivery service's API based on the selected food delivery option, confirms the order, generates a reservation confirmation, and sends it to the user's device.

[0585] Each step involves specific data inputs and outputs resulting from processing based on that data, with each step leading to the next step.The system aims to increase user safety and satisfaction by taking into account the user's emotional state and providing optimal food delivery options, even during extreme weather.

[0586] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0587] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0588] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0589] [Second embodiment]

[0590] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0591] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0592] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0593] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0594] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0595] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0596] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0597] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0598] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0599] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0600] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0601] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0602] The embodiment of the present invention will be described as an autonomous AI platform that proposes optimal accommodations and transportation methods based on the user's current location and flexibly changes the itinerary in response to unexpected abnormal weather conditions during travel. The program processing of this system will be explained below in natural language.

[0603] Overall system overview

[0604] This system automatically performs a series of processes: collecting information from user devices, predicting extreme weather, suggesting optimal accommodations and transportation options, notifying users, and completing reservations. The system mainly consists of the following components: user devices, a central server, and an external weather forecast database.

[0605] Program processing

[0606] 1. Obtaining user information

[0607] Device: When a user travels, the device uses GPS to obtain the user's current location and inputs the user's travel plan (start point, destination, and itinerary) into the device.

[0608] Terminal: Sends the acquired current location and travel plan information to a central server.

[0609] 2. Predicting extreme weather

[0610] Server: Obtains weather forecast data from an external weather database.

[0611] Server: Analyzes the acquired data and predicts unexpected abnormal weather (e.g., typhoons, heavy rain, heavy snow, etc.) along the user's travel route.

[0612] 3. Find the best accommodation and transportation

[0613] Server: Based on the results of the abnormal weather forecast, searches for accommodation and transportation options near the user's current location and destination.

[0614] Server: Selects the best accommodation based on availability, price, property ratings, and location.

[0615] Server: Checks the availability and timetables of transportation options such as bullet trains, buses, taxis, and rental cars, and selects the most suitable means of transportation.

[0616] 4. User Suggestions and Notifications

[0617] Server: Generates a list of suggestions for the best accommodations and transportation options.

[0618] Server: Sends this proposal list to the user's terminal and notifies them.

[0619] On device: Show a notification to the user so they can review the suggestion.

[0620] 5. Reservation procedure support

[0621] User: Choose from suggested accommodations and transportation options.

[0622] Terminal: Sends user selections to a central server.

[0623] Server: Automatically initiates the booking process for the accommodation and transportation selected by the user.

[0624] Server: Processes the booking confirmation and payment information and sends the booking confirmation to the user.

[0625] Specific examples

[0626] Let's take the example of a user traveling from Tokyo to Osaka when a typhoon is suddenly predicted to form near Nagoya.

[0627] 1. Obtaining user information

[0628] Device: The user's current location is determined to be Nagoya via GPS.

[0629] Terminal: The travel plan, which includes the fact that the user is on the way from Tokyo to Osaka, and the itinerary, is transmitted to the server.

[0630] 2. Predicting extreme weather

[0631] Server: Obtains forecasts of approaching typhoons from a weather forecast database.

[0632] Server: Predicts that the typhoon will have a major impact on Nagoya.

[0633] 3. Find the best accommodation and transportation

[0634] Server: Search affiliated accommodations and transportation options around Nagoya.

[0635] Server: Check that there is a room available at the hotel in front of Nagoya Station and select that the price is 10,000 yen.

[0636] Server: Check whether there are seats available on the early morning Shinkansen train.

[0637] 4. User Suggestions and Notifications

[0638] Server: Creates an optimal list of suggestions including hotels in front of Nagoya Station and early morning Shinkansen trains, and notifies the device.

[0639] Device: Display a notification to the user advising them to stay overnight in Nagoya and take an early flight due to the typhoon.

[0640] 5. Reservation procedure support

[0641] User: Select a hotel and an early morning Shinkansen flight.

[0642] Terminal: Sends the user's selection to the server.

[0643] Server: Automates the booking process for the selected accommodation and transportation and sends confirmation to the user.

[0644] In this way, the system of the present invention can support the user's trip smoothly even if unexpected abnormal weather occurs during the trip.

[0645] The processing flow will be explained below.

[0646] Step 1:

[0647] Device: Before the user starts their journey, they launch an application on their device and use GPS to obtain the user's current location.

[0648] Terminal: where the user enters travel plan information such as origin, destination, and travel dates.

[0649] Terminal: Sends the acquired current location and travel plan information to the server.

[0650] Step 2:

[0651] Server: Accesses an external weather forecast database (e.g., a weather forecast API) to retrieve weather forecast data for the user's travel route and destination.

[0652] Server: Analyzes the acquired weather forecast data and evaluates the possibility of abnormal weather (e.g., typhoons, heavy rain, heavy snow, etc.) occurring.

[0653] Step 3:

[0654] Server: Based on the analysis results, determine whether unexpected extreme weather will affect the user's travel route or destination.

[0655] Server: If an impact is anticipated, determine the scope and timeframe of the impact.

[0656] Step 4:

[0657] Server: Finds the best accommodation and transportation options, taking into account the user's current location and the expected impact of extreme weather.

[0658] Server: Check availability and pricing information of partner hotels to select the best accommodation.

[0659] Server: Checks the timetables and availability of available transportation options (such as bullet trains, buses, taxis, and rental cars) and selects the most suitable mode of transportation.

[0660] Step 5:

[0661] Server: Aggregates and ranks the best accommodation and transportation options.

[0662] Server: Creates a proposal list based on this information and sends it to the user's device.

[0663] Step 6:

[0664] Device: Receives the suggestion list and displays a notification to the user.

[0665] Terminal: A message such as "The typhoon may cause travel disruptions. There are vacancies at the XX Hotel in front of Nagoya Station (10,000 yen / night). You can also take the early morning Shinkansen." will be displayed.

[0666] User: Decide on a choice from suggested accommodations and transportation options.

[0667] Step 7:

[0668] Terminal: Sends the user's selections to the server.

[0669] Server: Automates the booking process for the selected accommodation and transportation.

[0670] Server: Processes the necessary payment information and generates the reservation confirmation.

[0671] Step 8:

[0672] Server: Sends reservation confirmation information to the user's terminal.

[0673] Terminal: Display reservation confirmation information to the user.

[0674] Through these steps, the system can respond quickly and effectively to unexpected extreme weather events that occur during travel, allowing users to flexibly change their itinerary.

[0675] Example 1

[0676] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0677] When unexpected extreme weather occurs during a trip, it can be difficult for users to quickly find the best accommodation and transportation. A platform is needed to solve this problem and make travel schedule changes smoothly.

[0678] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0679] In this invention, the server includes means for acquiring location information and travel plan information from the user terminal, means for acquiring and analyzing weather data to predict abnormal weather, and means for searching for accommodations and transportation options near the user's current location and destination and presenting optimal options. This allows the user to quickly find optimal accommodations and transportation options and smoothly change their travel schedule even if unexpected abnormal weather occurs during their trip.

[0680] "User device" refers to an electronic device that can be operated by a user, including a smartphone, tablet, laptop, etc.

[0681] "Location information" refers to geographic coordinate data that indicates a user's current location and is obtained using technologies such as GPS.

[0682] "Travel Plan Information" means data that includes the origin, destination, dates, and other related information for a trip that a user is planning.

[0683] "Abnormal weather" refers to weather phenomena that deviate significantly from normal weather conditions, including typhoons, heavy rain, and heavy snow.

[0684] "Weather Data" means atmospheric data, including weather forecasts and historical weather records.

[0685] "Accommodation" means a facility that provides a place for users to stay, including hotels, motels, inns, etc.

[0686] "Transportation" refers to the means used by users to travel, including trains, buses, taxis, rental cars, etc.

[0687] "Searching" means finding information in a database or on the Internet based on specific criteria.

[0688] "Notification" means sending information to a user terminal to notify the user.

[0689] "Reservation" means the process of reserving a particular service or product in advance, and may include payment information.

[0690] "Security protocols" are the standards for communication methods used to ensure data security, including SSL / TLS.

[0691] "Satellite positioning technology" is a technology that uses artificial satellites to measure specific locations on Earth.

[0692] "Weather Information API" refers to an application program interface for obtaining weather data, and is used to obtain information from external weather information services.

[0693] This invention will be described as an autonomous AI platform that suggests optimal accommodation and transportation options based on the user's current location and flexibly changes the itinerary in response to unexpected abnormal weather conditions while traveling.

[0694] The system consists of a user terminal, a central server, and an external weather database. Its main processes are obtaining user information, forecasting extreme weather, searching for optimal accommodation and transportation options, notifying users, and completing reservation procedures.

[0695] Hardware and software used

[0696] User device: Electronic devices such as smartphones, tablets, and laptops that use GPS modules to obtain location information.

[0697] Central server: Web server, database server. Analyzes weather data using the LSTM model as a machine learning model.

[0698] External weather database: An API that provides weather information (e.g., Weather API).

[0699] Data processing and calculation

[0700] 1. Obtaining user information

[0701] Device: The user's current location is acquired using GPS. The user inputs their travel plan (start point, destination, and itinerary).

[0702] Device: Sends location and travel plan information to a central server. Data is transmitted using a secure protocol (e.g., SSL / TLS).

[0703] 2. Predicting extreme weather

[0704] Server: Obtains weather forecast data from an external weather database and stores it in the weather database.

[0705] Server: Uses LSTM model to calculate the probability of occurrence of extreme weather events. Performs data analysis to identify the probability along the user's travel route.

[0706] 3. Find the best accommodation and transportation

[0707] Server: Searches for accommodation and transportation based on the results of abnormal weather forecasts. Accommodation information is obtained from an external API (e.g., accommodation information API), and transportation information is obtained from the API in charge (e.g., transportation information API).

[0708] Server: Selects the best accommodation based on availability, price, and ratings of the acquired accommodations, and also selects the transportation method.

[0709] 4. User Suggestions and Notifications

[0710] Server: Creates a list of best accommodation and transportation suggestions in HTML format.

[0711] Server: Generates a notification message and pushes it to the user's device (e.g., using Firebase Cloud Messaging).

[0712] On your device: Show the notification you received to the user so they can review the suggestion.

[0713] 5. Reservation procedure support

[0714] User: Choose from suggested accommodations and transportation options.

[0715] Terminal: Sends user selections to a central server.

[0716] Server: Automates the booking process for the selected accommodation and transportation and sends confirmation to the user. Processes payment details using a secure payment gateway (e.g. payment API).

[0717] Specific examples

[0718] Let's take the example of a user traveling from Tokyo to Osaka when a typhoon is suddenly predicted to form near Nagoya.

[0719] 1. Obtaining user information

[0720] Device: The user's current location is determined to be Nagoya via GPS.

[0721] Terminal: The travel plan, which includes the fact that the user is on the way from Tokyo to Osaka, and the itinerary, is transmitted to the server.

[0722] 2. Predicting extreme weather

[0723] Server: Obtains forecasts of approaching typhoons from a weather forecast database.

[0724] Server: Predicts that the typhoon will have a major impact on Nagoya.

[0725] 3. Find the best accommodation and transportation

[0726] Server: Search affiliated accommodations and transportation options around Nagoya.

[0727] Server: Check that there is a room available at the hotel in front of Nagoya Station and select that the price is 10,000 yen.

[0728] Server: Check whether there are seats available on the early morning Shinkansen train.

[0729] 4. User Suggestions and Notifications

[0730] Server: Creates an optimal list of suggestions including hotels in front of Nagoya Station and early morning Shinkansen trains, and notifies the device.

[0731] Device: Display a notification to the user advising them to stay overnight in Nagoya and take an early flight due to the typhoon.

[0732] 5. Reservation procedure support

[0733] User: Select a hotel and an early morning Shinkansen flight.

[0734] Terminal: Sends the user's selection to the server.

[0735] Server: Automates the booking process for the selected accommodation and transportation and sends confirmation to the user.

[0736] Example prompts to input to the generative AI model

[0737] Example prompt:

[0738] "If a user traveling from Tokyo to Osaka suddenly arrives in Nagoya and a typhoon is predicted to approach, please explain in detail each processing step of the system that will suggest appropriate accommodation and transportation options to the user and automatically complete the reservation process."

[0739] The above is a specific embodiment for carrying out the present invention.

[0740] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0741] Step 1: Get user information

[0742] Terminal: First, the GPS module is used to obtain the user's current location. The input is the location data from the GPS, and the output is the current location information stored in the location information database.

[0743] Terminal: After the user enters their travel plan (start point, destination, and itinerary) using a dedicated input form, the travel plan is saved in local storage. The input is the travel plan information from the user, and the output is the travel plan data stored in local storage.

[0744] Terminal: Converts the acquired current location and travel plan information into packets and sends them to a central server using a security protocol (e.g., SSL / TLS). The input is location information and travel plan information, and the output is the data transmitted in a secure format.

[0745] Step 2: Predicting extreme weather

[0746] Server: Obtains weather forecast data from an external weather database. The input is an API request, and the output is the weather forecast data stored in the weather database on the server.

[0747] Server: Analyzes the acquired weather forecast data and uses a machine learning model (e.g., LSTM model) to calculate the probability of occurrence of extreme weather along the user's travel route. The input is weather data, and the output is the predicted results of extreme weather. The analysis uses a method that compares the data with past weather data.

[0748] Step 3: Find the best accommodation and transportation

[0749] Server: Searches for accommodations and transportation options near the user's current location and destination based on the results of extreme weather forecasts. The input is the results of the extreme weather forecast and location information, and the output is a list of candidate accommodations and transportation options.

[0750] Server: Accommodation information is obtained from an external API (e.g., accommodation information API), and transportation information is obtained from the corresponding API (e.g., transportation information API). The input is the API request, and the output is information such as room and seat availability, price, rating, and timetable.

[0751] Server: Considers the acquired information and selects the optimal accommodation and transportation method. The input is information on accommodation and transportation methods, and the output is the optimal selection result.

[0752] Step 4: Propose and notify users

[0753] Server: Creates a list of recommendations for optimal accommodations and transportation in HTML format and generates a notification message. The input is the optimal selection result, and the output is the notification message.

[0754] Server: Sends the generated proposal list to the user device and performs push notification (e.g., using Firebase Cloud Messaging). The input is the notification message, and the output is the delivery to the user device.

[0755] Terminal: Displays received notifications to the user and provides an interface where the user can view the details. The input is the notification message and the output is the displayed notification content.

[0756] Step 5: Reservation support

[0757] User: Review the notification and select from suggested accommodations and transportation options. The input is the list of suggestions, and the output is the user's choice.

[0758] Terminal: Sends user selections in a secure format to a central server. The input is the user selection and the output is the data sent in a secure format.

[0759] Server: Automates the booking process for the user's selected accommodation and transportation and sends confirmation to the user. Inputs are user selections and API requests, and outputs are booking confirmation and payment information. Payment information is processed using a secure payment gateway (e.g., payment API).

[0760] (Application example 1)

[0761] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0762] In recent years, extreme weather events have become more frequent, increasing the risk of encountering unexpected weather conditions while traveling. Accordingly, there is a demand for systems that can quickly suggest appropriate accommodations and transportation options to ensure travelers can travel safely and comfortably. Furthermore, with the spread of autonomous vehicles, there is a need for technologies that enable these vehicles to respond to extreme weather and suggest appropriate evacuation routes and commercial facilities where they can stop. However, conventional systems cannot adequately meet these demands, resulting in issues such as a lack of safety and efficiency for travelers and autonomous vehicle users.

[0763] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0764] In this invention, the server includes means for acquiring location information and travel plan information from the user terminal, means for acquiring and analyzing weather data to predict abnormal weather, means for searching for facilities and means of transportation in the user's current location and surrounding areas and presenting optimal options, means for notifying the user terminal of suggestions regarding recommended facilities and means of transportation, means for automatically reserving facilities and means of transportation based on the user's selection, and means for suggesting evacuation routes and commercial facilities where the autonomous vehicle can stop based on the current location and weather forecast, thereby enabling users of autonomous vehicles to safely and quickly find evacuation routes and commercial facilities when abnormal weather occurs.

[0765] "User terminals" are information terminal devices used by travelers and drivers, including smartphones, tablets, and in-vehicle displays.

[0766] "Location information" means data obtained using GPS or other location information systems that indicates the current location of a user device or autonomous vehicle.

[0767] "Travel plan information" refers to information about a traveler's itinerary, such as the departure point, destination, route, and itinerary.

[0768] "Weather data" includes weather forecasts and information on abnormal weather conditions obtained from weather providers, and is data used to make weather predictions.

[0769] "Facilities" refers to commercial facilities available to users, such as accommodations, restaurants, and gas stations.

[0770] "Means of transportation" refers to the means of transportation that users can use to get around, such as cars, trains, bullet trains, and buses.

[0771] "Evacuation route" refers to the recommended route for users to safely evacuate in the event of an emergency such as extreme weather.

[0772] "Suggestion" refers to the optimal options or courses of action that the system presents to the user based on the analyzed data.

[0773] "Commercial facilities" refers to hotels, restaurants, gas stations, and other facilities where users can stop and use their vehicles during extreme weather.

[0774] "Reservation process" means that the system automatically completes the process to secure accommodation, transportation, etc. based on the user's selections.

[0775] This invention is a system that, when a user encounters unexpected abnormal weather while traveling, suggests optimal evacuation routes and commercial facilities (such as accommodations, restaurants, and gas stations) based on the user's current location, and can make reservations as needed. The system consists of a user terminal, a central server, and an external weather database.

[0776] System configuration

[0777] 1. User Device

[0778] The user device, which can be a smartphone, tablet, or an on-board display in an autonomous vehicle, transmits location and travel plan information to a central server and also receives notifications from the system.

[0779] 2. Central Server

[0780] The central server consists of several components:

[0781] Location information acquisition function: Acquires location information from the user's device using GPS, etc.

[0782] Travel plan information management function: Receives travel plans entered by users and saves them in a database.

[0783] Weather data analysis function: Obtains weather data from an external weather database (e.g., Weather API), analyzes the data, and predicts abnormal weather.

[0784] Optimal facility and evacuation route recommendation function: Implements an algorithm that suggests appropriate commercial facilities and evacuation routes based on the user's current location and weather data.

[0785] Reservation management function: Automatically make reservations at commercial facilities selected by the user and manage confirmation information.

[0786] 3. External Weather Database

[0787] The latest weather forecast information is obtained from an external weather database, mainly using the Weather API, and the data is analyzed on the server to predict abnormal weather.

[0788] Example

[0789] Let's explain what happens when a user is traveling from Tokyo to Osaka and a typhoon is suddenly predicted to form near Nagoya.

[0790] The system obtains from GPS that the user's current location is Nagoya and sends this information to a central server.

[0791] The central server obtains the latest weather forecast information from the Weather API and predicts the occurrence of a large typhoon in Nagoya.

[0792] The server checks availability at accommodations, restaurants, gas stations, etc. around Nagoya and notifies the user of the best options. For example, it can provide information to users about available rooms at a hotel in front of Nagoya Station and available seats on an early morning Shinkansen train.

[0793] The user selects the proposed commercial facility from the terminal and transmits the selection information to the central server.

[0794] The server automatically completes the reservation procedure for the selected accommodation and sends reservation confirmation information to the user's terminal.

[0795] Prompt Sentence Examples

[0796] Here are some examples of prompts for generative AI models:

[0797] "If extreme weather is predicted to occur while an autonomous vehicle is en route to its destination, please suggest the best evacuation route and commercial facilities (hotels, restaurants, gas stations) based on the current location and weather forecast. Please provide specific locations and suggestions in detail."

[0798] In this way, the system of the present invention ensures that traveling users can safely and efficiently respond to extreme weather events.

[0799] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0800] Step 1:

[0801] The user device acquires location information.

[0802] Input: Current location data from GPS

[0803] Output: User's current location

[0804] Specific operation: The user device uses the GPS module to obtain the user's current location in real time. This location information is expressed as latitude and longitude data.

[0805] Step 2:

[0806] The user terminal inputs travel plan information.

[0807] Input: The origin, destination, and dates of a trip manually entered by the user

[0808] Output: Travel plan information

[0809] Specific operation: The user inputs the departure point, destination, and travel dates via a smartphone or in-car display. This data is sent to the server as travel plan information.

[0810] Step 3:

[0811] The server retrieves abnormal weather information from a weather database.

[0812] Input: Weather data provider API request

[0813] Output: Weather forecast data

[0814] What it does: The server retrieves the latest weather forecast data from external weather data providers via API requests. This data is used to predict extreme weather events.

[0815] Step 4:

[0816] The server analyzes weather data and predicts abnormal weather.

[0817] Input: Retrieved weather forecast data

[0818] Output: Extreme weather forecast results

[0819] Specific operation: The server analyzes the acquired weather forecast data and determines whether it matches certain conditions (e.g., the path of a typhoon, a forecast of heavy snowfall). Based on the results of this analysis, it predicts whether abnormal weather will occur.

[0820] Step 5:

[0821] The server searches for facilities and transportation options near the user's current location.

[0822] Input: User's location information, travel plan information, extreme weather forecast results

[0823] Output: A list of recommended facilities and transportation options

[0824] Specific operation: The server searches for nearby accommodations, restaurants, gas stations, and transportation options based on the user's location information and weather forecast results, utilizing APIs from hotel booking sites and information on public transport timetables.

[0825] Step 6:

[0826] The server sends a list of recommended facilities and transportation options to the user's terminal.

[0827] Input: List of recommended facilities and transportation options

[0828] Output: User notification

[0829] Specific operation: The server selects the most suitable facilities and transportation methods from the search results and sends the list of suggestions to the user's device. The user's device displays this list on its screen and informs the user of countermeasures against abnormal weather.

[0830] Step 7:

[0831] The user selects from suggested facilities and transportation options.

[0832] Input: Suggestion list

[0833] Output: User selection information

[0834] Specific operation: The user selects the desired facility or means of transportation from the suggested list displayed on the terminal and sends the selected information to the server.

[0835] Step 8:

[0836] The server automatically processes reservations for the selected facilities and transportation.

[0837] Input: User selection information

[0838] Output: Reservation confirmation information

[0839] Specific operation: The server automatically completes the online reservation process for the facility and transportation selected by the user and sends the confirmation information to the user's device. This is done using the API of a hotel reservation site or the online transportation reservation system.

[0840] As a result, this system can help users respond appropriately and quickly even if abnormal weather occurs during their trip.

[0841] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0842] The embodiment of the present invention will be described as an autonomous AI platform for a travel support system that combines an emotion engine that recognizes user emotions. Below, the program processing of this system will be explained in natural language.

[0843] Overall system overview

[0844] This system suggests optimal accommodations and transportation options based on the user's current location in the event of unexpected extreme weather during travel, and further adjusts the suggestions based on the user's emotions, allowing for flexible and emotionally sensitive changes to the itinerary. The system mainly consists of the following components: user terminal, central server, external weather forecast database, and emotion engine.

[0845] Program processing

[0846] 1. Obtaining user information

[0847] Device: Before the user starts their trip, they launch the application on their device, use GPS to obtain their current location, and then input their travel plan information, such as their departure point, destination, and travel dates.

[0848] Terminal: Transmits the acquired current location and travel plan information to the central server.

[0849] 2. Predicting extreme weather

[0850] Server: Accesses an external weather forecast database (e.g., a weather forecast API) to retrieve weather forecast data for the user's travel route and destination.

[0851] Server: Analyzes the acquired weather forecast data and evaluates the possibility of abnormal weather (e.g., typhoons, heavy rain, heavy snow, etc.) occurring.

[0852] 3. Acquiring emotional information

[0853] Terminal: Acquires the user's voice data and facial expression data.

[0854] Terminal: Sends acquired emotion data to the emotion engine.

[0855] Emotion engine: Analyzes voice and facial expression data to determine the user's current emotional state.

[0856] 4. Find the best accommodation and transportation

[0857] Server: Based on the results of the abnormal weather forecast, searches for accommodation and transportation options near the user's current location and destination.

[0858] Server: Selects the best accommodation based on availability, price, property ratings, and location.

[0859] Server: Checks the availability and timetables of transportation options such as bullet trains, buses, taxis, and rental cars, and selects the most suitable means of transportation.

[0860] 5. Adjusting suggestions based on emotions

[0861] Server: Adjusts the content and format of suggestions based on the emotional information provided by the emotion engine.

[0862] Server: If the user is "tired," the server will prioritize hotels with good relaxation facilities, and make suggestions that take into consideration the user's emotions.

[0863] 6. User Suggestions and Notifications

[0864] Server: Creates a list of recommendations for optimal accommodations and transportation options and sends them to the user's device.

[0865] On device: Show a notification to the user so they can review the suggestion.

[0866] 7. Reservation procedure support

[0867] User: Decide on a choice from suggested accommodations and transportation options.

[0868] Terminal: Sends user selections to a central server.

[0869] Server: Automatically initiates the booking process for the accommodation and transportation selected by the user.

[0870] Server: Processes the necessary payment information and generates the reservation confirmation.

[0871] 8. Sending confirmation information

[0872] Server: Sends reservation confirmation information to the user's terminal.

[0873] Terminal: Display reservation confirmation information to the user.

[0874] Specific examples

[0875] For example, let's consider a case where a user is traveling from Tokyo to Osaka and suddenly a typhoon is predicted to form near Nagoya.

[0876] 1. Obtaining user information

[0877] Device: The user's current location is determined to be Nagoya via GPS.

[0878] Terminal: The travel plan, which includes the fact that the user is on the way from Tokyo to Osaka, and the itinerary, is transmitted to the server.

[0879] 2. Predicting extreme weather

[0880] Server: Obtains forecasts of approaching typhoons from a weather forecast database.

[0881] Server: Predicts that the typhoon will have a major impact on Nagoya.

[0882] 3. Acquiring emotional information

[0883] Device: Obtain emotional data such as "tired" from the user's voice.

[0884] Terminal: Sends this emotion data to the server.

[0885] Emotion engine: Analyzes voice data to determine if the user is tired.

[0886] 4. Find the best accommodation and transportation

[0887] Server: Search affiliated accommodations and transportation options around Nagoya.

[0888] Server: Check that there is a room available at the hotel in front of Nagoya Station and select that the price is 10,000 yen.

[0889] Server: Check whether there are seats available on the early morning Shinkansen train.

[0890] 5. Adjusting suggestions based on emotions

[0891] Server: Based on the user's feeling of "tired," prioritize hotels with excellent relaxation facilities.

[0892] 6. User Suggestions and Notifications

[0893] Server: Notify the user that "There are vacancies at Hotel X in front of Nagoya Station (10,000 yen / night). The hotel has ample relaxation facilities. You can also take the early morning Shinkansen."

[0894] On your device: Show a notification to the user so they can review the suggestion.

[0895] 7. Reservation procedure support

[0896] User: Select a hotel and an early morning Shinkansen flight.

[0897] Terminal: Sends the user's selection to the server.

[0898] Server: Automates the booking process for the selected accommodation and transportation and sends confirmation to the user.

[0899] In this way, the system of the present invention can respond quickly and effectively to abnormal weather conditions, and can increase travel satisfaction by making suggestions that take into consideration the user's emotions.

[0900] The processing flow will be explained below.

[0901] Step 1:

[0902] Device: Before the user starts their trip, they launch the application on their device, use GPS to obtain their current location, and then input their travel plan information, such as their departure point, destination, and travel dates.

[0903] Terminal: Transmits the acquired current location and travel plan information to the central server.

[0904] Step 2:

[0905] Server: Accesses an external weather forecast database (e.g., a weather forecast API) to retrieve weather forecast data for the user's travel route and destination.

[0906] Server: Analyzes the acquired weather forecast data and evaluates the possibility of abnormal weather (e.g., typhoons, heavy rain, heavy snow, etc.) occurring.

[0907] Step 3:

[0908] Terminal: Acquires emotion data based on the voice data and camera footage provided by the user.

[0909] Terminal: Sends emotion data to the emotion engine for analysis.

[0910] Step 4:

[0911] Emotion engine: Analyzes voice and facial expression data to determine the user's current emotional state (e.g., tired, stressed, etc.).

[0912] Emotion engine: Sends the judgment results to the central server.

[0913] Step 5:

[0914] Server: Based on the results of the abnormal weather forecast, searches for accommodation and transportation options near the user's current location and destination.

[0915] Server: Selects the best accommodation based on availability, price, property ratings, and location.

[0916] Server: Checks the availability and timetables of transportation options such as bullet trains, buses, taxis, and rental cars, and selects the most suitable means of transportation.

[0917] Step 6:

[0918] Server: Adjusts the content and format of suggestions based on the emotional information provided by the emotion engine.

[0919] Server: Depending on the user's emotional state, the server adjusts the suggestions to suit the user's current condition. For example, if the user feels "tired," the server prioritizes hotels with relaxation facilities.

[0920] Step 7:

[0921] Server: Aggregates and ranks the best accommodation and transportation options.

[0922] Server: Creates a proposal list based on this information and sends it to the user's device.

[0923] Step 8:

[0924] Device: Receives the suggestion list and displays a notification to the user.

[0925] Terminal: A message such as "The typhoon may cause travel disruptions. There are vacancies at Hotel X in front of Nagoya Station (10,000 yen / night). It also has excellent relaxation facilities. You can also take an early morning Shinkansen flight." will be displayed.

[0926] User: Decide on a choice from suggested accommodations and transportation options.

[0927] Step 9:

[0928] Terminal: Sends the user's selections to the server.

[0929] Server: Automates the booking process for the selected accommodation and transportation.

[0930] Server: Processes the necessary payment information and generates the reservation confirmation.

[0931] Step 10:

[0932] Server: Sends reservation confirmation information to the user's terminal.

[0933] Terminal: Display reservation confirmation information to the user.

[0934] Through these steps, the system can respond quickly and effectively to unexpected extreme weather events that occur during travel, and can also improve users' travel experience by making suggestions that take users' emotions into consideration.

[0935] Example 2

[0936] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0937] Conventional systems often fail to respond quickly and effectively to extreme weather events that occur during travel. They also fail to flexibly adjust travel plans based on the user's emotional state, which can lead to lower travel satisfaction. Furthermore, suggestions for accommodations and transportation options do not take into account the user's current state of mind, making it difficult to provide optimal recommendations tailored to the user's needs.

[0938] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0939] In this invention, the server includes means for acquiring location information and travel plan information from the user terminal, means for acquiring and analyzing weather forecast data to predict abnormal weather, means for acquiring emotion data and analyzing it with an emotion engine, means for searching for accommodations and transportation options in the user's current location and surrounding area and presenting optimal options, means for adjusting the suggestions based on the emotion information, means for notifying the user terminal of the suggestions regarding recommended accommodations and transportation options, means for automatically reserving accommodations and transportation options based on the user's selections, and means for notifying the user of reservation confirmation information. This enables quick and effective response to abnormal weather and flexible adjustment of travel plans based on the user's emotional state.

[0940] A "user terminal" is an electronic device operated by a user, such as a smartphone, tablet, or personal computer.

[0941] "Location Information" means your current geographic location determined using GPS or other positioning technology.

[0942] "Travel plan information" refers to information entered by a user regarding a trip, and includes data such as the departure point, destination, and itinerary.

[0943] "Extreme weather" refers to meteorological phenomena that deviate significantly from normal weather patterns, such as typhoons, heavy rain, and heavy snow.

[0944] "Weather forecast data" refers to forecast information about future weather provided by meteorological agencies and data provision services.

[0945] "Emotional data" refers to data that represents the user's emotional state, obtained from the user's voice and facial expressions.

[0946] An "emotion engine" refers to a system that analyzes acquired voice and facial expression data to determine the user's emotional state.

[0947] "Accommodation" refers to hotels, inns, and other accommodation facilities where users can stay.

[0948] "Transportation" refers to the means used by users to travel, such as bullet trains, buses, taxis, and rental cars.

[0949] "Reservation confirmation information" is information indicating that a reservation for accommodation or transportation has been confirmed, and includes a QR code, reservation number, etc.

[0950] The present invention will be described as an autonomous AI platform for a travel support system that combines an emotion engine that recognizes user emotions. This system enables rapid response to abnormal weather and flexible adjustment of travel plans based on user emotions.

[0951] Required Hardware and Software

[0952] User devices: Smartphones, tablets, personal computers, etc. These devices must be equipped with a GPS module, camera, and microphone. The application uses this hardware to obtain user information and collect emotion data.

[0953] Central Server: A central server for data processing and analysis, retrieving and analyzing weather forecast data, managing user location and travel plan information, running the sentiment engine, and searching for optimal accommodation and transportation options.

[0954] Weather Forecast API: An API for connecting to an external weather forecast database. Use this API to obtain forecast data for extreme weather.

[0955] Emotion Engine: Software that analyzes voice and facial expression data to determine the user's emotional state. It uses machine learning models to classify emotions.

[0956] System Overview

[0957] 1. Obtaining User Information:

[0958] Before starting a trip, the user launches the application on their device and uses GPS to determine their current location. The user then enters information such as the departure point, destination, and travel dates, which is then sent to a central server.

[0959] 2. Predicting extreme weather:

[0960] The server accesses the weather forecast API to obtain weather forecast data for the user's travel route and destination, analyzes the obtained data, and evaluates the possibility of extreme weather occurring.

[0961] 3. Acquiring emotional information:

[0962] The user device captures voice and facial expression data and sends them to the emotion engine, which analyzes the data and determines the user's current emotional state.

[0963] 4. Find the best accommodation and transportation:

[0964] The server searches for accommodations and transportation options near the user's current location and destination based on the results of the extreme weather forecast, taking into account the availability, price, ratings, and location of the accommodations, and also searches for and selects transportation options.

[0965] 5. Adjusting suggestions based on emotions:

[0966] The server adjusts the suggestions based on the emotional information provided by the emotion engine. For example, if the user is "tired," it will prioritize hotels with excellent relaxation facilities.

[0967] 6. User Suggestions and Notices:

[0968] The server creates a list of recommendations for optimal accommodations and transportation options and sends it to the user's device, which then notifies the user of the recommendations and allows them to review them.

[0969] 7. Booking assistance:

[0970] Once the user makes a selection from the suggested accommodations and transportation options, the selection is sent to a central server, which automatically initiates the booking process, processes payment information, and generates a booking confirmation.

[0971] 8. Sending confirmation information:

[0972] Finally, the server sends the reservation confirmation information to the user terminal, which the user can confirm.

[0973] Specific examples

[0974] For example, consider a situation where a user is traveling from Tokyo to Osaka and a typhoon is predicted to occur near Nagoya. Nagoya is acquired as the user's current location, and the server uses a weather forecast API to collect and analyze information about the approaching typhoon. If the user is determined to be "tired," the server will prioritize suggesting a hotel in front of Nagoya Station with ample relaxation facilities and notify the user that seats are available on an early morning Shinkansen train.

[0975] Example prompt: "We have availability at Hotel X in front of Nagoya Station (10,000 yen / night). It has excellent relaxation facilities and you can catch an early morning Shinkansen."

[0976] In this way, the system of the present invention can respond quickly and effectively to abnormal weather conditions, and can increase travel satisfaction by making suggestions that take into consideration the user's emotions.

[0977] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0978] Program processing flow

[0979] Step 1: Get user information

[0980] Step 2: Predicting extreme weather

[0981] Step 3: Acquiring emotional information

[0982] Step 4: Find the best accommodation and transportation

[0983] Step 5: Adjust your suggestions based on emotion

[0984] Step 6: Propose and notify users

[0985] Step 7: Reservation support

[0986] Step 8: Submit confirmation information

[0987] Specific explanation of each processing step

[0988] Step 1: Get user information

[0989] Input: User's location and travel plan information

[0990] Output: Current location and travel plan information sent to a central server

[0991] User: Launches the application on their smartphone or tablet before starting their trip.

[0992] Specific action: Tap the icon to open the application.

[0993] Device: When the application launches, it uses GPS to obtain the user's current location.

[0994] Specific operation: The GPS module collects the current latitude and longitude information.

[0995] User: Enters travel plan information into the application, including origin, destination, and travel dates.

[0996] Specific actions: Enter information using text boxes and calendar input.

[0997] Terminal: Transmits the acquired current location and travel plan information to the central server.

[0998] Specific operation: Sends data using network communication in the background.

[0999] Step 2: Predicting extreme weather

[1000] Input: Weather forecast data for the user's travel route and destination

[1001] Output: Probability of occurrence of analyzed extreme weather events

[1002] Server: Accesses the weather forecast API to connect to an external weather forecast database.

[1003] Specific operation: Send an API request and retrieve weather forecast data.

[1004] Server: Analyzes the acquired weather forecast data and evaluates the possibility of abnormal weather occurring.

[1005] Specific operations: Analyze weather data using pattern matching and statistical models.

[1006] Step 3: Acquiring emotional information

[1007] Input: User's voice and facial expression data

[1008] Output: Emotion information analyzed by the emotion engine

[1009] Device: The user's voice data and facial expression data are acquired using a camera and microphone.

[1010] Specific operation: Collects audio with a microphone and captures facial expression data with a camera.

[1011] Terminal: Sends acquired emotion data to the emotion engine.

[1012] Specific behavior: Send data to the emotion engine in real time.

[1013] Emotion engine: Analyzes voice and facial expression data to determine the user's current emotional state.

[1014] Specific behavior: Classifying emotions using machine learning models.

[1015] Step 4: Find the best accommodation and transportation

[1016] Input: User's current location, accommodation information near the destination, and transportation information

[1017] Output: Selection of optimal accommodation and transportation options

[1018] Server: Searches for accommodations and transportation options near the user's current location and destination.

[1019] Specific operation: Query the database and extract facilities that meet the criteria.

[1020] Server: Selects the best accommodation based on availability, price, ratings, and location.

[1021] Specific operation: Apply filtering conditions to select the most suitable facility.

[1022] Server: Checks the availability and timetables of transportation options (Shinkansen, buses, taxis, rental cars, etc.) and selects the most suitable transportation option.

[1023] Specific operation: Obtain data from external API and select the best option.

[1024] Step 5: Adjust your suggestions based on emotion

[1025] Input: Emotion information provided by the emotion engine

[1026] Output: Suggestions that take user emotions into consideration

[1027] Server: Adjusts the proposal content based on the emotional information provided by the emotion engine.

[1028] Specific action: Reevaluate the proposal based on the emotional data.

[1029] Server: If the user is "tired," prioritize hotels with relaxation facilities.

[1030] Specific actions: Create a list of suggestions and focus on relaxation facilities.

[1031] Step 6: Propose and notify users

[1032] Input: A list of suggestions generated from the server

[1033] Output: Proposal sent to user's device

[1034] Server: Generates a list of suggestions for the best accommodations and transportation options.

[1035] Specific actions: List the suggestions and format them in a way that is easy for the user to understand.

[1036] Server: Sends the proposal list to the user device.

[1037] Specific operation: Send data using a communication protocol.

[1038] On your device: Show a notification to the user so they can review the suggestion.

[1039] Specific behavior: Display a push notification or alert box.

[1040] Step 7: Reservation support

[1041] Input: User's chosen accommodation and transportation options

[1042] Output: Booking completion and confirmation information

[1043] User: Decide on a choice from suggested accommodations and transportation options.

[1044] Action: Click an option from the list.

[1045] Terminal: Sends user selections to a central server.

[1046] Specific operation: The selection information is sent as a packet.

[1047] Server: Automatically initiates the booking process for the selected accommodation and transportation.

[1048] Specific actions: Access the reservation system and submit the required information.

[1049] Server: Processes the necessary payment information and generates the reservation confirmation.

[1050] Specific operation: Make a payment through a payment gateway.

[1051] Step 8: Submit confirmation information

[1052] Input: Generated booking confirmation information

[1053] Output: Reservation confirmation displayed on the user's device

[1054] Server: Generates reservation confirmation information.

[1055] What it does: Create a confirmation with a QR code, reservation number, and details.

[1056] Server: Sends reservation confirmation information to the user's terminal.

[1057] Specific operation: Encodes data and sends it to the terminal.

[1058] Terminal: Display reservation confirmation information to the user.

[1059] Specific action: Notify in-app or via email.

[1060] The above is the specific processing flow of the system and the detailed operation at each step.

[1061] (Application example 2)

[1062] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1063] Conventionally, when abnormal weather occurs during a trip, there has been a lack of a means to respond flexibly to the user's emotional state. This has caused stress and made it difficult for users to find appropriate accommodations and transportation. Furthermore, the lack of appropriate countermeasures in the event of abnormal weather has led to problems such as a decrease in travel safety and satisfaction. The present invention aims to provide a system that provides a quick and effective response to abnormal weather while taking into account the user's emotional state.

[1064] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1065] In this invention, the server includes means for acquiring location information and travel plan information from the user terminal, means for acquiring and analyzing weather forecast data to predict abnormal weather, means for analyzing the user's voice data and facial expression data to determine the user's emotional state, means for searching for accommodations and transportation options in the user's current location and surrounding areas and presenting optimal options, means for adjusting the suggestions based on the user's emotional state, means for notifying the user terminal of suggestions regarding recommended accommodations and transportation options, and means for automatically reserving accommodations and transportation options based on the user's selection. This enables flexible suggestions that take the user's emotional state into consideration, thereby increasing the safety and satisfaction of travel even during abnormal weather.

[1066] Below are definitions of key terms found in the claims.

[1067] "User terminal" means a device operated by a user for inputting and obtaining location information and travel planning information.

[1068] "Location information" is data that indicates a specific location using global positioning system technology.

[1069] "Travel planning information" refers to information about the itinerary, departure point, destination, etc. of a trip that the user is planning.

[1070] "Extreme weather" refers to weather phenomena that deviate significantly from normal weather conditions and may affect travel.

[1071] "Weather forecast data" refers to weather-related information obtained from a weather forecast application programming interface.

[1072] "Analysis" is the process of analyzing acquired data to derive meaningful information.

[1073] "Voice data" means data that is a digital recording of what a user says.

[1074] "Facial expression data" is data used to analyze the user's facial expressions and determine their emotions.

[1075] "Emotional state" is information that indicates the user's current emotion, and includes, for example, "tired" or "happy."

[1076] "Accommodation" means a facility (such as a hotel or inn) provided for temporary stay by users.

[1077] "Transportation" refers to the means of travelling to a destination (such as bullet train, bus, taxi, rental car, etc.).

[1078] The "best option" refers to the accommodation or transportation that best suits the user's emotional state and current situation.

[1079] "Proposal content" is information about accommodation and transportation that the server presents to the user.

[1080] "Reservation" is the process of securing accommodation or transportation in advance.

[1081] The present invention will be described as a food delivery support system incorporating an emotion engine that recognizes user emotions. The system uses voice and facial expression data to determine the user's emotions and suggests food delivery options suitable for the user, even in extreme weather. The main components of the system are a user terminal, a central server, an external weather forecast database, and the emotion engine.

[1082] Overall system overview

[1083] The central server receives location information and food delivery request information from the user's device, and then refers to an external weather forecast database to predict extreme weather.The emotion engine then analyzes the voice and facial expression data to determine the user's emotional state, and provides the food delivery options that best suit the user's emotional state and current situation.

[1084] Program processing

[1085] The program in this system works as follows:

[1086] 1. Obtaining User Information:

[1087] Device: The user launches the application, obtains their current location using GPS, and inputs their desired meal type, budget, and other information.

[1088] Terminal: Sends the acquired current location and request information to the central server.

[1089] 2. Predicting extreme weather:

[1090] Server: Accesses an external weather forecast database (such as a weather forecast API) and obtains weather forecast data for the user's current location.

[1091] Server: Analyzes the acquired weather forecast data and evaluates the possibility of abnormal weather (e.g., typhoons, heavy rain, heavy snow, etc.) occurring.

[1092] 3. Acquiring emotional information:

[1093] Terminal: Acquires the user's voice data and facial expression data.

[1094] Terminal: Sends acquired emotion data to the emotion engine.

[1095] Emotion engine: Analyzes voice and facial expression data to determine the user's current emotional state.

[1096] 4. Find the best food delivery options:

[1097] Server: Based on the results of extreme weather forecasts, search for food delivery options based on the user's current location and request information.

[1098] Server: Based on your emotional state, prioritize meals that reduce stress and fatigue, such as relaxation dishes and comfortable delivery options.

[1099] 5. User Suggestions and Notices:

[1100] Server: Creates a list of recommendations for optimal food delivery options and sends them to the user's device.

[1101] On device: Show a notification to the user so they can review the suggestion.

[1102] Specific examples

[1103] For example, let's say a user is at home and a typhoon is predicted to be approaching. The emotion engine determines from the user's voice data that they are feeling stressed. In this case, the system will suggest relaxing meals and options that can be delivered safely and quickly.

[1104] Example prompt sentence:

[1105] If you recognize that the user is stressed, suggest the best food delivery options for when a typhoon is approaching, including relaxation foods and safe delivery methods.

[1106] The present invention makes it possible to propose food delivery that takes into account the user's emotional state, allowing them to enjoy meals with peace of mind even during extreme weather conditions.

[1107] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1108] Program processing flow

[1109] Step 1:

[1110] Retrieving User Information

[1111] Device: The user launches the application, obtains their current location using GPS, and enters their desired meal type, budget, and other required information.

[1112] Input: GPS data (location information), user input data (type of meal, budget)

[1113] Output: location information, request information

[1114] How it works: The application uses the smartphone's GPS to obtain the user's current location and latitude and longitude, and then prompts the user to enter their desired meal type, budget, and other information through the user interface.

[1115] Step 2:

[1116] Predicting extreme weather

[1117] Server: Accesses an external weather forecast database (weather forecast API) and obtains weather forecast data for the user's current location.

[1118] Input: location information, weather forecast data

[1119] Output: Extreme weather forecast data

[1120] How it works: The server calls the weather forecast API and receives weather forecast data for the current location. This data is then analyzed to assess the likelihood of extreme weather occurring.

[1121] Step 3:

[1122] Acquiring emotional information

[1123] Terminal: Acquires the user's voice data and facial expression data and sends them to the emotion engine.

[1124] Input: Voice data, facial expression data

[1125] Output: Emotional state data

[1126] How it works: The application uses the smartphone's microphone and camera to record voice and facial expressions. The recorded data is then sent to the emotion engine for analysis, which determines the user's emotional state (e.g., stress, fatigue, etc.).

[1127] Step 4:

[1128] Finding the best food delivery options

[1129] Server: Searches for the most appropriate food delivery options based on the user's location and request information, based on extreme weather forecasts and emotional state.

[1130] Input: Extreme weather forecast data, emotional state data, location information, request information

[1131] Output: List of food delivery options

[1132] What it does: The server uses the food delivery service's API to search for available options near the user's current location, and prioritizes, for example, stress-reducing meals and safe delivery options based on the user's emotional state.

[1133] Step 5:

[1134] User suggestions and notifications

[1135] Server: Creates a list of recommendations for optimal food delivery options and sends them to the user's device.

[1136] Input: List of food delivery options

[1137] Output: Proposal notification

[1138] What it does: The server generates a list of suggestions and sends them to the user's device as a push notification or in-app notification, allowing the user to review and select.

[1139] Step 6:

[1140] Supporting user selection and booking procedures

[1141] User: Choose from suggested food delivery options.

[1142] Input: Suggestion notification, user selection

[1143] Output: Selected data

[1144] How it works: A user uses a smartphone app to select the food delivery options they want from the options provided. This selection is then sent to the server.

[1145] Step 7:

[1146] Completing the reservation process

[1147] Server: Automatically initiates the reservation and ordering process for the food delivery option selected by the user.

[1148] Input:Selection data

[1149] Output: Reservation confirmation information

[1150] Specific operation: The server calls the delivery service's API based on the selected food delivery option, confirms the order, generates a reservation confirmation, and sends it to the user's device.

[1151] Each step involves specific data inputs and outputs resulting from processing based on that data, with each step leading to the next step.The system aims to increase user safety and satisfaction by taking into account the user's emotional state and providing optimal food delivery options, even during extreme weather.

[1152] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1153] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1154] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1155] [Third embodiment]

[1156] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1157] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1158] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1159] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1160] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1162] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1163] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1164] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1166] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1167] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1168] The embodiment of the present invention will be described as an autonomous AI platform that proposes optimal accommodations and transportation methods based on the user's current location and flexibly changes the itinerary in response to unexpected abnormal weather conditions during travel. The program processing of this system will be explained below in natural language.

[1169] Overall system overview

[1170] This system automatically performs a series of processes: collecting information from user devices, predicting extreme weather, suggesting optimal accommodations and transportation options, notifying users, and completing reservations. The system mainly consists of the following components: user devices, a central server, and an external weather forecast database.

[1171] Program processing

[1172] 1. Obtaining user information

[1173] Device: When a user travels, the device uses GPS to obtain the user's current location and inputs the user's travel plan (start point, destination, and itinerary) into the device.

[1174] Terminal: Sends the acquired current location and travel plan information to a central server.

[1175] 2. Predicting extreme weather

[1176] Server: Obtains weather forecast data from an external weather database.

[1177] Server: Analyzes the acquired data and predicts unexpected abnormal weather (e.g., typhoons, heavy rain, heavy snow, etc.) along the user's travel route.

[1178] 3. Find the best accommodation and transportation

[1179] Server: Based on the results of the abnormal weather forecast, searches for accommodation and transportation options near the user's current location and destination.

[1180] Server: Selects the best accommodation based on availability, price, property ratings, and location.

[1181] Server: Checks the availability and timetables of transportation options such as bullet trains, buses, taxis, and rental cars, and selects the most suitable means of transportation.

[1182] 4. User Suggestions and Notifications

[1183] Server: Generates a list of suggestions for the best accommodations and transportation options.

[1184] Server: Sends this proposal list to the user's terminal and notifies them.

[1185] On device: Show a notification to the user so they can review the suggestion.

[1186] 5. Reservation procedure support

[1187] User: Choose from suggested accommodations and transportation options.

[1188] Terminal: Sends user selections to a central server.

[1189] Server: Automatically initiates the booking process for the accommodation and transportation selected by the user.

[1190] Server: Processes the booking confirmation and payment information and sends the booking confirmation to the user.

[1191] Specific examples

[1192] Let's take the example of a user traveling from Tokyo to Osaka when a typhoon is suddenly predicted to form near Nagoya.

[1193] 1. Obtaining user information

[1194] Device: The user's current location is determined to be Nagoya via GPS.

[1195] Terminal: The travel plan, which includes the fact that the user is on the way from Tokyo to Osaka, and the itinerary, is transmitted to the server.

[1196] 2. Predicting extreme weather

[1197] Server: Obtains forecasts of approaching typhoons from a weather forecast database.

[1198] Server: Predicts that the typhoon will have a major impact on Nagoya.

[1199] 3. Find the best accommodation and transportation

[1200] Server: Search affiliated accommodations and transportation options around Nagoya.

[1201] Server: Check that there is a room available at the hotel in front of Nagoya Station and select that the price is 10,000 yen.

[1202] Server: Check whether there are seats available on the early morning Shinkansen train.

[1203] 4. User Suggestions and Notifications

[1204] Server: Creates an optimal list of suggestions including hotels in front of Nagoya Station and early morning Shinkansen trains, and notifies the device.

[1205] Device: Display a notification to the user advising them to stay overnight in Nagoya and take an early flight due to the typhoon.

[1206] 5. Reservation procedure support

[1207] User: Select a hotel and an early morning Shinkansen flight.

[1208] Terminal: Sends the user's selection to the server.

[1209] Server: Automates the booking process for the selected accommodation and transportation and sends confirmation to the user.

[1210] In this way, the system of the present invention can support the user's trip smoothly even if unexpected abnormal weather occurs during the trip.

[1211] The processing flow will be explained below.

[1212] Step 1:

[1213] Device: Before the user starts their journey, they launch an application on their device and use GPS to obtain the user's current location.

[1214] Terminal: where the user enters travel plan information such as origin, destination, and travel dates.

[1215] Terminal: Sends the acquired current location and travel plan information to the server.

[1216] Step 2:

[1217] Server: Accesses an external weather forecast database (e.g., a weather forecast API) to retrieve weather forecast data for the user's travel route and destination.

[1218] Server: Analyzes the acquired weather forecast data and evaluates the possibility of abnormal weather (e.g., typhoons, heavy rain, heavy snow, etc.) occurring.

[1219] Step 3:

[1220] Server: Based on the analysis results, determine whether unexpected extreme weather will affect the user's travel route or destination.

[1221] Server: If an impact is anticipated, determine the scope and timeframe of the impact.

[1222] Step 4:

[1223] Server: Finds the best accommodation and transportation options, taking into account the user's current location and the expected impact of extreme weather.

[1224] Server: Check availability and pricing information of partner hotels to select the best accommodation.

[1225] Server: Checks the timetables and availability of available transportation options (such as bullet trains, buses, taxis, and rental cars) and selects the most suitable mode of transportation.

[1226] Step 5:

[1227] Server: Aggregates and ranks the best accommodation and transportation options.

[1228] Server: Creates a proposal list based on this information and sends it to the user's device.

[1229] Step 6:

[1230] Device: Receives the suggestion list and displays a notification to the user.

[1231] Terminal: A message such as "The typhoon may cause travel disruptions. There are vacancies at the XX Hotel in front of Nagoya Station (10,000 yen / night). You can also take the early morning Shinkansen." will be displayed.

[1232] User: Decide on a choice from suggested accommodations and transportation options.

[1233] Step 7:

[1234] Terminal: Sends the user's selections to the server.

[1235] Server: Automates the booking process for the selected accommodation and transportation.

[1236] Server: Processes the necessary payment information and generates the reservation confirmation.

[1237] Step 8:

[1238] Server: Sends reservation confirmation information to the user's terminal.

[1239] Terminal: Display reservation confirmation information to the user.

[1240] Through these steps, the system can respond quickly and effectively to unexpected extreme weather events that occur during travel, allowing users to flexibly change their itinerary.

[1241] Example 1

[1242] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1243] When unexpected extreme weather occurs during a trip, it can be difficult for users to quickly find the best accommodation and transportation. A platform is needed to solve this problem and make travel schedule changes smoothly.

[1244] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1245] In this invention, the server includes means for acquiring location information and travel plan information from the user terminal, means for acquiring and analyzing weather data to predict abnormal weather, and means for searching for accommodations and transportation options near the user's current location and destination and presenting optimal options. This allows the user to quickly find optimal accommodations and transportation options and smoothly change their travel schedule even if unexpected abnormal weather occurs during their trip.

[1246] "User device" refers to an electronic device that can be operated by a user, including a smartphone, tablet, laptop, etc.

[1247] "Location information" refers to geographic coordinate data that indicates a user's current location and is obtained using technologies such as GPS.

[1248] "Travel Plan Information" means data that includes the origin, destination, dates, and other related information for a trip that a user is planning.

[1249] "Abnormal weather" refers to weather phenomena that deviate significantly from normal weather conditions, including typhoons, heavy rain, and heavy snow.

[1250] "Weather Data" means atmospheric data, including weather forecasts and historical weather records.

[1251] "Accommodation" means a facility that provides a place for users to stay, including hotels, motels, inns, etc.

[1252] "Transportation" refers to the means used by users to travel, including trains, buses, taxis, rental cars, etc.

[1253] "Searching" means finding information in a database or on the Internet based on specific criteria.

[1254] "Notification" means sending information to a user terminal to notify the user.

[1255] "Reservation" means the process of reserving a particular service or product in advance, and may include payment information.

[1256] "Security protocols" are the standards for communication methods used to ensure data security, including SSL / TLS.

[1257] "Satellite positioning technology" is a technology that uses artificial satellites to measure specific locations on Earth.

[1258] "Weather Information API" refers to an application program interface for obtaining weather data, and is used to obtain information from external weather information services.

[1259] This invention will be described as an autonomous AI platform that suggests optimal accommodation and transportation options based on the user's current location and flexibly changes the itinerary in response to unexpected abnormal weather conditions while traveling.

[1260] The system consists of a user terminal, a central server, and an external weather database. Its main processes are obtaining user information, forecasting extreme weather, searching for optimal accommodation and transportation options, notifying users, and completing reservation procedures.

[1261] Hardware and software used

[1262] User device: Electronic devices such as smartphones, tablets, and laptops that use GPS modules to obtain location information.

[1263] Central server: Web server, database server. Analyzes weather data using the LSTM model as a machine learning model.

[1264] External weather database: An API that provides weather information (e.g., Weather API).

[1265] Data processing and calculation

[1266] 1. Obtaining user information

[1267] Device: The user's current location is acquired using GPS. The user inputs their travel plan (start point, destination, and itinerary).

[1268] Device: Sends location and travel plan information to a central server. Data is transmitted using a secure protocol (e.g., SSL / TLS).

[1269] 2. Predicting extreme weather

[1270] Server: Obtains weather forecast data from an external weather database and stores it in the weather database.

[1271] Server: Uses LSTM model to calculate the probability of occurrence of extreme weather events. Performs data analysis to identify the probability along the user's travel route.

[1272] 3. Find the best accommodation and transportation

[1273] Server: Searches for accommodation and transportation based on the results of abnormal weather forecasts. Accommodation information is obtained from an external API (e.g., accommodation information API), and transportation information is obtained from the API in charge (e.g., transportation information API).

[1274] Server: Selects the best accommodation based on availability, price, and ratings of the acquired accommodations, and also selects the transportation method.

[1275] 4. User Suggestions and Notifications

[1276] Server: Creates a list of best accommodation and transportation suggestions in HTML format.

[1277] Server: Generates a notification message and pushes it to the user's device (e.g., using Firebase Cloud Messaging).

[1278] On your device: Show the notification you received to the user so they can review the suggestion.

[1279] 5. Reservation procedure support

[1280] User: Choose from suggested accommodations and transportation options.

[1281] Terminal: Sends user selections to a central server.

[1282] Server: Automates the booking process for the selected accommodation and transportation and sends confirmation to the user. Processes payment details using a secure payment gateway (e.g. payment API).

[1283] Specific examples

[1284] Let's take the example of a user traveling from Tokyo to Osaka when a typhoon is suddenly predicted to form near Nagoya.

[1285] 1. Obtaining user information

[1286] Device: The user's current location is determined to be Nagoya via GPS.

[1287] Terminal: The travel plan, which includes the fact that the user is on the way from Tokyo to Osaka, and the itinerary, is transmitted to the server.

[1288] 2. Predicting extreme weather

[1289] Server: Obtains forecasts of approaching typhoons from a weather forecast database.

[1290] Server: Predicts that the typhoon will have a major impact on Nagoya.

[1291] 3. Find the best accommodation and transportation

[1292] Server: Search affiliated accommodations and transportation options around Nagoya.

[1293] Server: Check that there is a room available at the hotel in front of Nagoya Station and select that the price is 10,000 yen.

[1294] Server: Check whether there are seats available on the early morning Shinkansen train.

[1295] 4. User Suggestions and Notifications

[1296] Server: Creates an optimal list of suggestions including hotels in front of Nagoya Station and early morning Shinkansen trains, and notifies the device.

[1297] Device: Display a notification to the user advising them to stay overnight in Nagoya and take an early flight due to the typhoon.

[1298] 5. Reservation procedure support

[1299] User: Select a hotel and an early morning Shinkansen flight.

[1300] Terminal: Sends the user's selection to the server.

[1301] Server: Automates the booking process for the selected accommodation and transportation and sends confirmation to the user.

[1302] Example prompts to input to the generative AI model

[1303] Example prompt:

[1304] "If a user traveling from Tokyo to Osaka suddenly arrives in Nagoya and a typhoon is predicted to approach, please explain in detail each processing step of the system that will suggest appropriate accommodation and transportation options to the user and automatically complete the reservation process."

[1305] The above is a specific embodiment for carrying out the present invention.

[1306] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1307] Step 1: Get user information

[1308] Terminal: First, the GPS module is used to obtain the user's current location. The input is the location data from the GPS, and the output is the current location information stored in the location information database.

[1309] Terminal: After the user enters their travel plan (start point, destination, and itinerary) using a dedicated input form, the travel plan is saved in local storage. The input is the travel plan information from the user, and the output is the travel plan data stored in local storage.

[1310] Terminal: Converts the acquired current location and travel plan information into packets and sends them to a central server using a security protocol (e.g., SSL / TLS). The input is location information and travel plan information, and the output is the data transmitted in a secure format.

[1311] Step 2: Predicting extreme weather

[1312] Server: Obtains weather forecast data from an external weather database. The input is an API request, and the output is the weather forecast data stored in the weather database on the server.

[1313] Server: Analyzes the acquired weather forecast data and uses a machine learning model (e.g., LSTM model) to calculate the probability of occurrence of extreme weather along the user's travel route. The input is weather data, and the output is the predicted results of extreme weather. The analysis uses a method that compares the data with past weather data.

[1314] Step 3: Find the best accommodation and transportation

[1315] Server: Searches for accommodations and transportation options near the user's current location and destination based on the results of extreme weather forecasts. The input is the results of the extreme weather forecast and location information, and the output is a list of candidate accommodations and transportation options.

[1316] Server: Accommodation information is obtained from an external API (e.g., accommodation information API), and transportation information is obtained from the corresponding API (e.g., transportation information API). The input is the API request, and the output is information such as room and seat availability, price, rating, and timetable.

[1317] Server: Considers the acquired information and selects the optimal accommodation and transportation method. The input is information on accommodation and transportation methods, and the output is the optimal selection result.

[1318] Step 4: Propose and notify users

[1319] Server: Creates a list of recommendations for optimal accommodations and transportation in HTML format and generates a notification message. The input is the optimal selection result, and the output is the notification message.

[1320] Server: Sends the generated proposal list to the user device and performs push notification (e.g., using Firebase Cloud Messaging). The input is the notification message, and the output is the delivery to the user device.

[1321] Terminal: Displays received notifications to the user and provides an interface where the user can view the details. The input is the notification message and the output is the displayed notification content.

[1322] Step 5: Reservation support

[1323] User: Review the notification and select from suggested accommodations and transportation options. The input is the list of suggestions, and the output is the user's choice.

[1324] Terminal: Sends user selections in a secure format to a central server. The input is the user selection and the output is the data sent in a secure format.

[1325] Server: Automates the booking process for the user's selected accommodation and transportation and sends confirmation to the user. Inputs are user selections and API requests, and outputs are booking confirmation and payment information. Payment information is processed using a secure payment gateway (e.g., payment API).

[1326] (Application example 1)

[1327] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1328] In recent years, extreme weather events have become more frequent, increasing the risk of encountering unexpected weather conditions while traveling. Accordingly, there is a demand for systems that can quickly suggest appropriate accommodations and transportation options to ensure travelers can travel safely and comfortably. Furthermore, with the spread of autonomous vehicles, there is a need for technologies that enable these vehicles to respond to extreme weather and suggest appropriate evacuation routes and commercial facilities where they can stop. However, conventional systems cannot adequately meet these demands, resulting in issues such as a lack of safety and efficiency for travelers and autonomous vehicle users.

[1329] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1330] In this invention, the server includes means for acquiring location information and travel plan information from the user terminal, means for acquiring and analyzing weather data to predict abnormal weather, means for searching for facilities and means of transportation in the user's current location and surrounding areas and presenting optimal options, means for notifying the user terminal of suggestions regarding recommended facilities and means of transportation, means for automatically reserving facilities and means of transportation based on the user's selection, and means for suggesting evacuation routes and commercial facilities where the autonomous vehicle can stop based on the current location and weather forecast, thereby enabling users of autonomous vehicles to safely and quickly find evacuation routes and commercial facilities when abnormal weather occurs.

[1331] "User terminals" are information terminal devices used by travelers and drivers, including smartphones, tablets, and in-vehicle displays.

[1332] "Location information" means data obtained using GPS or other location information systems that indicates the current location of a user device or autonomous vehicle.

[1333] "Travel plan information" refers to information about a traveler's itinerary, such as the departure point, destination, route, and itinerary.

[1334] "Weather data" includes weather forecasts and information on abnormal weather conditions obtained from weather providers, and is data used to make weather predictions.

[1335] "Facilities" refers to commercial facilities available to users, such as accommodations, restaurants, and gas stations.

[1336] "Means of transportation" refers to the means of transportation that users can use to get around, such as cars, trains, bullet trains, and buses.

[1337] "Evacuation route" refers to the recommended route for users to safely evacuate in the event of an emergency such as extreme weather.

[1338] "Suggestion" refers to the optimal options or courses of action that the system presents to the user based on the analyzed data.

[1339] "Commercial facilities" refers to hotels, restaurants, gas stations, and other facilities where users can stop and use their vehicles during extreme weather.

[1340] "Reservation process" means that the system automatically completes the process to secure accommodation, transportation, etc. based on the user's selections.

[1341] This invention is a system that, when a user encounters unexpected abnormal weather while traveling, suggests optimal evacuation routes and commercial facilities (such as accommodations, restaurants, and gas stations) based on the user's current location, and can make reservations as needed. The system consists of a user terminal, a central server, and an external weather database.

[1342] System configuration

[1343] 1. User Device

[1344] The user device, which can be a smartphone, tablet, or an on-board display in an autonomous vehicle, transmits location and travel plan information to a central server and also receives notifications from the system.

[1345] 2. Central Server

[1346] The central server consists of several components:

[1347] Location information acquisition function: Acquires location information from the user's device using GPS, etc.

[1348] Travel plan information management function: Receives travel plans entered by users and saves them in a database.

[1349] Weather data analysis function: Obtains weather data from an external weather database (e.g., Weather API), analyzes the data, and predicts abnormal weather.

[1350] Optimal facility and evacuation route recommendation function: Implements an algorithm that suggests appropriate commercial facilities and evacuation routes based on the user's current location and weather data.

[1351] Reservation management function: Automatically make reservations at commercial facilities selected by the user and manage confirmation information.

[1352] 3. External Weather Database

[1353] The latest weather forecast information is obtained from an external weather database, mainly using the Weather API, and the data is analyzed on the server to predict abnormal weather.

[1354] Example

[1355] Let's explain what happens when a user is traveling from Tokyo to Osaka and a typhoon is suddenly predicted to form near Nagoya.

[1356] The system obtains from GPS that the user's current location is Nagoya and sends this information to a central server.

[1357] The central server obtains the latest weather forecast information from the Weather API and predicts the occurrence of a large typhoon in Nagoya.

[1358] The server checks availability at accommodations, restaurants, gas stations, etc. around Nagoya and notifies the user of the best options. For example, it can provide information to users about available rooms at a hotel in front of Nagoya Station and available seats on an early morning Shinkansen train.

[1359] The user selects the proposed commercial facility from the terminal and transmits the selection information to the central server.

[1360] The server automatically completes the reservation procedure for the selected accommodation and sends reservation confirmation information to the user's terminal.

[1361] Prompt Sentence Examples

[1362] Here are some examples of prompts for generative AI models:

[1363] "If extreme weather is predicted to occur while an autonomous vehicle is en route to its destination, please suggest the best evacuation route and commercial facilities (hotels, restaurants, gas stations) based on the current location and weather forecast. Please provide specific locations and suggestions in detail."

[1364] In this way, the system of the present invention ensures that traveling users can safely and efficiently respond to extreme weather events.

[1365] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1366] Step 1:

[1367] The user device acquires location information.

[1368] Input: Current location data from GPS

[1369] Output: User's current location

[1370] Specific operation: The user device uses the GPS module to obtain the user's current location in real time. This location information is expressed as latitude and longitude data.

[1371] Step 2:

[1372] The user terminal inputs travel plan information.

[1373] Input: The origin, destination, and dates of a trip manually entered by the user

[1374] Output: Travel plan information

[1375] Specific operation: The user inputs the departure point, destination, and travel dates via a smartphone or in-car display. This data is sent to the server as travel plan information.

[1376] Step 3:

[1377] The server retrieves abnormal weather information from a weather database.

[1378] Input: Weather data provider API request

[1379] Output: Weather forecast data

[1380] What it does: The server retrieves the latest weather forecast data from external weather data providers via API requests. This data is used to predict extreme weather events.

[1381] Step 4:

[1382] The server analyzes weather data and predicts abnormal weather.

[1383] Input: Retrieved weather forecast data

[1384] Output: Extreme weather forecast results

[1385] Specific operation: The server analyzes the acquired weather forecast data and determines whether it matches certain conditions (e.g., the path of a typhoon, a forecast of heavy snowfall). Based on the results of this analysis, it predicts whether abnormal weather will occur.

[1386] Step 5:

[1387] The server searches for facilities and transportation options near the user's current location.

[1388] Input: User's location information, travel plan information, extreme weather forecast results

[1389] Output: A list of recommended facilities and transportation options

[1390] Specific operation: The server searches for nearby accommodations, restaurants, gas stations, and transportation options based on the user's location information and weather forecast results, utilizing APIs from hotel booking sites and information on public transport timetables.

[1391] Step 6:

[1392] The server sends a list of recommended facilities and transportation options to the user's terminal.

[1393] Input: List of recommended facilities and transportation options

[1394] Output: User notification

[1395] Specific operation: The server selects the most suitable facilities and transportation methods from the search results and sends the list of suggestions to the user's device. The user's device displays this list on its screen and informs the user of countermeasures against abnormal weather.

[1396] Step 7:

[1397] The user selects from suggested facilities and transportation options.

[1398] Input: Suggestion list

[1399] Output: User selection information

[1400] Specific operation: The user selects the desired facility or means of transportation from the suggested list displayed on the terminal and sends the selected information to the server.

[1401] Step 8:

[1402] The server automatically processes reservations for the selected facilities and transportation.

[1403] Input: User selection information

[1404] Output: Reservation confirmation information

[1405] Specific operation: The server automatically completes the online reservation process for the facility and transportation selected by the user and sends the confirmation information to the user's device. This is done using the API of a hotel reservation site or the online transportation reservation system.

[1406] As a result, this system can help users respond appropriately and quickly even if abnormal weather occurs during their trip.

[1407] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1408] The embodiment of the present invention will be described as an autonomous AI platform for a travel support system that combines an emotion engine that recognizes user emotions. Below, the program processing of this system will be explained in natural language.

[1409] Overall system overview

[1410] This system suggests optimal accommodations and transportation options based on the user's current location in the event of unexpected extreme weather during travel, and further adjusts the suggestions based on the user's emotions, allowing for flexible and emotionally sensitive changes to the itinerary. The system mainly consists of the following components: user terminal, central server, external weather forecast database, and emotion engine.

[1411] Program processing

[1412] 1. Obtaining user information

[1413] Device: Before the user starts their trip, they launch the application on their device, use GPS to obtain their current location, and then input their travel plan information, such as their departure point, destination, and travel dates.

[1414] Terminal: Transmits the acquired current location and travel plan information to the central server.

[1415] 2. Predicting extreme weather

[1416] Server: Accesses an external weather forecast database (e.g., a weather forecast API) to retrieve weather forecast data for the user's travel route and destination.

[1417] Server: Analyzes the acquired weather forecast data and evaluates the possibility of abnormal weather (e.g., typhoons, heavy rain, heavy snow, etc.) occurring.

[1418] 3. Acquiring emotional information

[1419] Terminal: Acquires the user's voice data and facial expression data.

[1420] Terminal: Sends acquired emotion data to the emotion engine.

[1421] Emotion engine: Analyzes voice and facial expression data to determine the user's current emotional state.

[1422] 4. Find the best accommodation and transportation

[1423] Server: Based on the results of the abnormal weather forecast, searches for accommodation and transportation options near the user's current location and destination.

[1424] Server: Selects the best accommodation based on availability, price, property ratings, and location.

[1425] Server: Checks the availability and timetables of transportation options such as bullet trains, buses, taxis, and rental cars, and selects the most suitable means of transportation.

[1426] 5. Adjusting suggestions based on emotions

[1427] Server: Adjusts the content and format of suggestions based on the emotional information provided by the emotion engine.

[1428] Server: If the user is "tired," the server will prioritize hotels with good relaxation facilities, and make suggestions that take into consideration the user's emotions.

[1429] 6. User Suggestions and Notifications

[1430] Server: Creates a list of recommendations for optimal accommodations and transportation options and sends them to the user's device.

[1431] On device: Show a notification to the user so they can review the suggestion.

[1432] 7. Reservation procedure support

[1433] User: Decide on a choice from suggested accommodations and transportation options.

[1434] Terminal: Sends user selections to a central server.

[1435] Server: Automatically initiates the booking process for the accommodation and transportation selected by the user.

[1436] Server: Processes the necessary payment information and generates the reservation confirmation.

[1437] 8. Sending confirmation information

[1438] Server: Sends reservation confirmation information to the user's terminal.

[1439] Terminal: Display reservation confirmation information to the user.

[1440] Specific examples

[1441] For example, let's consider a case where a user is traveling from Tokyo to Osaka and suddenly a typhoon is predicted to form near Nagoya.

[1442] 1. Obtaining user information

[1443] Device: The user's current location is determined to be Nagoya via GPS.

[1444] Terminal: The travel plan, which includes the fact that the user is on the way from Tokyo to Osaka, and the itinerary, is transmitted to the server.

[1445] 2. Predicting extreme weather

[1446] Server: Obtains forecasts of approaching typhoons from a weather forecast database.

[1447] Server: Predicts that the typhoon will have a major impact on Nagoya.

[1448] 3. Acquiring emotional information

[1449] Device: Obtain emotional data such as "tired" from the user's voice.

[1450] Terminal: Sends this emotion data to the server.

[1451] Emotion engine: Analyzes voice data to determine if the user is tired.

[1452] 4. Find the best accommodation and transportation

[1453] Server: Search affiliated accommodations and transportation options around Nagoya.

[1454] Server: Check that there is a room available at the hotel in front of Nagoya Station and select that the price is 10,000 yen.

[1455] Server: Check whether there are seats available on the early morning Shinkansen train.

[1456] 5. Adjusting suggestions based on emotions

[1457] Server: Based on the user's feeling of "tired," prioritize hotels with excellent relaxation facilities.

[1458] 6. User Suggestions and Notifications

[1459] Server: Notify the user that "There are vacancies at Hotel X in front of Nagoya Station (10,000 yen / night). The hotel has ample relaxation facilities. You can also take the early morning Shinkansen."

[1460] On your device: Show a notification to the user so they can review the suggestion.

[1461] 7. Reservation procedure support

[1462] User: Select a hotel and an early morning Shinkansen flight.

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

[1464] Server: Automates the booking process for the selected accommodation and transportation and sends confirmation to the user.

[1465] In this way, the system of the present invention can respond quickly and effectively to abnormal weather conditions, and can increase travel satisfaction by making suggestions that take into consideration the user's emotions.

[1466] The processing flow will be explained below.

[1467] Step 1:

[1468] Device: Before the user starts their trip, they launch the application on their device, use GPS to obtain their current location, and then input their travel plan information, such as their departure point, destination, and travel dates.

[1469] Terminal: Transmits the acquired current location and travel plan information to the central server.

[1470] Step 2:

[1471] Server: Accesses an external weather forecast database (e.g., a weather forecast API) to retrieve weather forecast data for the user's travel route and destination.

[1472] Server: Analyzes the acquired weather forecast data and evaluates the possibility of abnormal weather (e.g., typhoons, heavy rain, heavy snow, etc.) occurring.

[1473] Step 3:

[1474] Terminal: Acquires emotion data based on the voice data and camera footage provided by the user.

[1475] Terminal: Sends emotion data to the emotion engine for analysis.

[1476] Step 4:

[1477] Emotion engine: Analyzes voice and facial expression data to determine the user's current emotional state (e.g., tired, stressed, etc.).

[1478] Emotion engine: Sends the judgment results to the central server.

[1479] Step 5:

[1480] Server: Based on the results of the abnormal weather forecast, searches for accommodation and transportation options near the user's current location and destination.

[1481] Server: Selects the best accommodation based on availability, price, property ratings, and location.

[1482] Server: Checks the availability and timetables of transportation options such as bullet trains, buses, taxis, and rental cars, and selects the most suitable means of transportation.

[1483] Step 6:

[1484] Server: Adjusts the content and format of suggestions based on the emotional information provided by the emotion engine.

[1485] Server: Depending on the user's emotional state, the server adjusts the suggestions to suit the user's current condition. For example, if the user feels "tired," the server prioritizes hotels with relaxation facilities.

[1486] Step 7:

[1487] Server: Aggregates and ranks the best accommodation and transportation options.

[1488] Server: Creates a proposal list based on this information and sends it to the user's device.

[1489] Step 8:

[1490] Device: Receives the suggestion list and displays a notification to the user.

[1491] Terminal: A message such as "The typhoon may cause travel disruptions. There are vacancies at Hotel X in front of Nagoya Station (10,000 yen / night). It also has excellent relaxation facilities. You can also take an early morning Shinkansen flight." will be displayed.

[1492] User: Decide on a choice from suggested accommodations and transportation options.

[1493] Step 9:

[1494] Terminal: Sends the user's selections to the server.

[1495] Server: Automates the booking process for the selected accommodation and transportation.

[1496] Server: Processes the necessary payment information and generates the reservation confirmation.

[1497] Step 10:

[1498] Server: Sends reservation confirmation information to the user's terminal.

[1499] Terminal: Display reservation confirmation information to the user.

[1500] Through these steps, the system can respond quickly and effectively to unexpected extreme weather events that occur during travel, and can also improve users' travel experience by making suggestions that take users' emotions into consideration.

[1501] Example 2

[1502] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1503] Conventional systems often fail to respond quickly and effectively to extreme weather events that occur during travel. They also fail to flexibly adjust travel plans based on the user's emotional state, which can lead to lower travel satisfaction. Furthermore, suggestions for accommodations and transportation options do not take into account the user's current state of mind, making it difficult to provide optimal recommendations tailored to the user's needs.

[1504] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1505] In this invention, the server includes means for acquiring location information and travel plan information from the user terminal, means for acquiring and analyzing weather forecast data to predict abnormal weather, means for acquiring emotion data and analyzing it with an emotion engine, means for searching for accommodations and transportation options in the user's current location and surrounding area and presenting optimal options, means for adjusting the suggestions based on the emotion information, means for notifying the user terminal of the suggestions regarding recommended accommodations and transportation options, means for automatically reserving accommodations and transportation options based on the user's selections, and means for notifying the user of reservation confirmation information. This enables quick and effective response to abnormal weather and flexible adjustment of travel plans based on the user's emotional state.

[1506] A "user terminal" is an electronic device operated by a user, such as a smartphone, tablet, or personal computer.

[1507] "Location Information" means your current geographic location determined using GPS or other positioning technology.

[1508] "Travel plan information" refers to information entered by a user regarding a trip, and includes data such as the departure point, destination, and itinerary.

[1509] "Extreme weather" refers to meteorological phenomena that deviate significantly from normal weather patterns, such as typhoons, heavy rain, and heavy snow.

[1510] "Weather forecast data" refers to forecast information about future weather provided by meteorological agencies and data provision services.

[1511] "Emotional data" refers to data that represents the user's emotional state, obtained from the user's voice and facial expressions.

[1512] An "emotion engine" refers to a system that analyzes acquired voice and facial expression data to determine the user's emotional state.

[1513] "Accommodation" refers to hotels, inns, and other accommodation facilities where users can stay.

[1514] "Transportation" refers to the means used by users to travel, such as bullet trains, buses, taxis, and rental cars.

[1515] "Reservation confirmation information" is information indicating that a reservation for accommodation or transportation has been confirmed, and includes a QR code, reservation number, etc.

[1516] The present invention will be described as an autonomous AI platform for a travel support system that combines an emotion engine that recognizes user emotions. This system enables rapid response to abnormal weather and flexible adjustment of travel plans based on user emotions.

[1517] Required Hardware and Software

[1518] User devices: Smartphones, tablets, personal computers, etc. These devices must be equipped with a GPS module, camera, and microphone. The application uses this hardware to obtain user information and collect emotion data.

[1519] Central Server: A central server for data processing and analysis, retrieving and analyzing weather forecast data, managing user location and travel plan information, running the sentiment engine, and searching for optimal accommodation and transportation options.

[1520] Weather Forecast API: An API for connecting to an external weather forecast database. Use this API to obtain forecast data for extreme weather.

[1521] Emotion Engine: Software that analyzes voice and facial expression data to determine the user's emotional state. It uses machine learning models to classify emotions.

[1522] System Overview

[1523] 1. Obtaining User Information:

[1524] Before starting a trip, the user launches the application on their device and uses GPS to determine their current location. The user then enters information such as the departure point, destination, and travel dates, which is then sent to a central server.

[1525] 2. Predicting extreme weather:

[1526] The server accesses the weather forecast API to obtain weather forecast data for the user's travel route and destination, analyzes the obtained data, and evaluates the possibility of extreme weather occurring.

[1527] 3. Acquiring emotional information:

[1528] The user device captures voice and facial expression data and sends them to the emotion engine, which analyzes the data and determines the user's current emotional state.

[1529] 4. Find the best accommodation and transportation:

[1530] The server searches for accommodations and transportation options near the user's current location and destination based on the results of the extreme weather forecast, taking into account the availability, price, ratings, and location of the accommodations, and also searches for and selects transportation options.

[1531] 5. Adjusting suggestions based on emotions:

[1532] The server adjusts the suggestions based on the emotional information provided by the emotion engine. For example, if the user is "tired," it will prioritize hotels with excellent relaxation facilities.

[1533] 6. User Suggestions and Notices:

[1534] The server creates a list of recommendations for optimal accommodations and transportation options and sends it to the user's device, which then notifies the user of the recommendations and allows them to review them.

[1535] 7. Booking assistance:

[1536] Once the user makes a selection from the suggested accommodations and transportation options, the selection is sent to a central server, which automatically initiates the booking process, processes payment information, and generates a booking confirmation.

[1537] 8. Sending confirmation information:

[1538] Finally, the server sends the reservation confirmation information to the user terminal, which the user can confirm.

[1539] Specific examples

[1540] For example, consider a situation where a user is traveling from Tokyo to Osaka and a typhoon is predicted to occur near Nagoya. Nagoya is acquired as the user's current location, and the server uses a weather forecast API to collect and analyze information about the approaching typhoon. If the user is determined to be "tired," the server will prioritize suggesting a hotel in front of Nagoya Station with ample relaxation facilities and notify the user that seats are available on an early morning Shinkansen train.

[1541] Example prompt: "We have availability at Hotel X in front of Nagoya Station (10,000 yen / night). It has excellent relaxation facilities and you can catch an early morning Shinkansen."

[1542] In this way, the system of the present invention can respond quickly and effectively to abnormal weather conditions, and can increase travel satisfaction by making suggestions that take into consideration the user's emotions.

[1543] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1544] Program processing flow

[1545] Step 1: Get user information

[1546] Step 2: Predicting extreme weather

[1547] Step 3: Acquiring emotional information

[1548] Step 4: Find the best accommodation and transportation

[1549] Step 5: Adjust your suggestions based on emotion

[1550] Step 6: Propose and notify users

[1551] Step 7: Reservation support

[1552] Step 8: Submit confirmation information

[1553] Specific explanation of each processing step

[1554] Step 1: Get user information

[1555] Input: User's location and travel plan information

[1556] Output: Current location and travel plan information sent to a central server

[1557] User: Launches the application on their smartphone or tablet before starting their trip.

[1558] Specific action: Tap the icon to open the application.

[1559] Device: When the application launches, it uses GPS to obtain the user's current location.

[1560] Specific operation: The GPS module collects the current latitude and longitude information.

[1561] User: Enters travel plan information into the application, including origin, destination, and travel dates.

[1562] Specific actions: Enter information using text boxes and calendar input.

[1563] Terminal: Transmits the acquired current location and travel plan information to the central server.

[1564] Specific operation: Sends data using network communication in the background.

[1565] Step 2: Predicting extreme weather

[1566] Input: Weather forecast data for the user's travel route and destination

[1567] Output: Probability of occurrence of analyzed extreme weather events

[1568] Server: Accesses the weather forecast API to connect to an external weather forecast database.

[1569] Specific operation: Send an API request and retrieve weather forecast data.

[1570] Server: Analyzes the acquired weather forecast data and evaluates the possibility of abnormal weather occurring.

[1571] Specific operations: Analyze weather data using pattern matching and statistical models.

[1572] Step 3: Acquiring emotional information

[1573] Input: User's voice and facial expression data

[1574] Output: Emotion information analyzed by the emotion engine

[1575] Device: The user's voice data and facial expression data are acquired using a camera and microphone.

[1576] Specific operation: Collects audio with a microphone and captures facial expression data with a camera.

[1577] Terminal: Sends acquired emotion data to the emotion engine.

[1578] Specific behavior: Send data to the emotion engine in real time.

[1579] Emotion engine: Analyzes voice and facial expression data to determine the user's current emotional state.

[1580] Specific behavior: Classifying emotions using machine learning models.

[1581] Step 4: Find the best accommodation and transportation

[1582] Input: User's current location, accommodation information near the destination, and transportation information

[1583] Output: Selection of optimal accommodation and transportation options

[1584] Server: Searches for accommodations and transportation options near the user's current location and destination.

[1585] Specific operation: Query the database and extract facilities that meet the criteria.

[1586] Server: Selects the best accommodation based on availability, price, ratings, and location.

[1587] Specific operation: Apply filtering conditions to select the most suitable facility.

[1588] Server: Checks the availability and timetables of transportation options (Shinkansen, buses, taxis, rental cars, etc.) and selects the most suitable transportation option.

[1589] Specific operation: Obtain data from external API and select the best option.

[1590] Step 5: Adjust your suggestions based on emotion

[1591] Input: Emotion information provided by the emotion engine

[1592] Output: Suggestions that take user emotions into consideration

[1593] Server: Adjusts the proposal content based on the emotional information provided by the emotion engine.

[1594] Specific action: Reevaluate the proposal based on the emotional data.

[1595] Server: If the user is "tired," prioritize hotels with relaxation facilities.

[1596] Specific actions: Create a list of suggestions and focus on relaxation facilities.

[1597] Step 6: Propose and notify users

[1598] Input: A list of suggestions generated from the server

[1599] Output: Proposal sent to user's device

[1600] Server: Generates a list of suggestions for the best accommodations and transportation options.

[1601] Specific actions: List the suggestions and format them in a way that is easy for the user to understand.

[1602] Server: Sends the proposal list to the user device.

[1603] Specific operation: Send data using a communication protocol.

[1604] On your device: Show a notification to the user so they can review the suggestion.

[1605] Specific behavior: Display a push notification or alert box.

[1606] Step 7: Reservation support

[1607] Input: User's chosen accommodation and transportation options

[1608] Output: Booking completion and confirmation information

[1609] User: Decide on a choice from suggested accommodations and transportation options.

[1610] Action: Click an option from the list.

[1611] Terminal: Sends user selections to a central server.

[1612] Specific operation: The selection information is sent as a packet.

[1613] Server: Automatically initiates the booking process for the selected accommodation and transportation.

[1614] Specific actions: Access the reservation system and submit the required information.

[1615] Server: Processes the necessary payment information and generates the reservation confirmation.

[1616] Specific operation: Make a payment through a payment gateway.

[1617] Step 8: Submit confirmation information

[1618] Input: Generated booking confirmation information

[1619] Output: Reservation confirmation displayed on the user's device

[1620] Server: Generates reservation confirmation information.

[1621] What it does: Create a confirmation with a QR code, reservation number, and details.

[1622] Server: Sends reservation confirmation information to the user's terminal.

[1623] Specific operation: Encodes data and sends it to the terminal.

[1624] Terminal: Display reservation confirmation information to the user.

[1625] Specific action: Notify in-app or via email.

[1626] The above is the specific processing flow of the system and the detailed operation at each step.

[1627] (Application example 2)

[1628] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1629] Conventionally, when abnormal weather occurs during a trip, there has been a lack of a means to respond flexibly to the user's emotional state. This has caused stress and made it difficult for users to find appropriate accommodations and transportation. Furthermore, the lack of appropriate countermeasures in the event of abnormal weather has led to problems such as a decrease in travel safety and satisfaction. The present invention aims to provide a system that provides a quick and effective response to abnormal weather while taking into account the user's emotional state.

[1630] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1631] In this invention, the server includes means for acquiring location information and travel plan information from the user terminal, means for acquiring and analyzing weather forecast data to predict abnormal weather, means for analyzing the user's voice data and facial expression data to determine the user's emotional state, means for searching for accommodations and transportation options in the user's current location and surrounding areas and presenting optimal options, means for adjusting the suggestions based on the user's emotional state, means for notifying the user terminal of suggestions regarding recommended accommodations and transportation options, and means for automatically reserving accommodations and transportation options based on the user's selection. This enables flexible suggestions that take the user's emotional state into consideration, thereby increasing the safety and satisfaction of travel even during abnormal weather.

[1632] Below are definitions of key terms found in the claims.

[1633] "User terminal" means a device operated by a user for inputting and obtaining location information and travel planning information.

[1634] "Location information" is data that indicates a specific location using global positioning system technology.

[1635] "Travel planning information" refers to information about the itinerary, departure point, destination, etc. of a trip that the user is planning.

[1636] "Extreme weather" refers to weather phenomena that deviate significantly from normal weather conditions and may affect travel.

[1637] "Weather forecast data" refers to weather-related information obtained from a weather forecast application programming interface.

[1638] "Analysis" is the process of analyzing acquired data to derive meaningful information.

[1639] "Voice data" means data that is a digital recording of what a user says.

[1640] "Facial expression data" is data used to analyze the user's facial expressions and determine their emotions.

[1641] "Emotional state" is information that indicates the user's current emotion, and includes, for example, "tired" or "happy."

[1642] "Accommodation" means a facility (such as a hotel or inn) provided for temporary stay by users.

[1643] "Transportation" refers to the means of travelling to a destination (such as bullet train, bus, taxi, rental car, etc.).

[1644] The "best option" refers to the accommodation or transportation that best suits the user's emotional state and current situation.

[1645] "Proposal content" is information about accommodation and transportation that the server presents to the user.

[1646] "Reservation" is the process of securing accommodation or transportation in advance.

[1647] The present invention will be described as a food delivery support system incorporating an emotion engine that recognizes user emotions. The system uses voice and facial expression data to determine the user's emotions and suggests food delivery options suitable for the user, even in extreme weather. The main components of the system are a user terminal, a central server, an external weather forecast database, and the emotion engine.

[1648] Overall system overview

[1649] The central server receives location information and food delivery request information from the user's device, and then refers to an external weather forecast database to predict extreme weather.The emotion engine then analyzes the voice and facial expression data to determine the user's emotional state, and provides the food delivery options that best suit the user's emotional state and current situation.

[1650] Program processing

[1651] The program in this system works as follows:

[1652] 1. Obtaining User Information:

[1653] Device: The user launches the application, obtains their current location using GPS, and inputs their desired meal type, budget, and other information.

[1654] Terminal: Sends the acquired current location and request information to the central server.

[1655] 2. Predicting extreme weather:

[1656] Server: Accesses an external weather forecast database (such as a weather forecast API) and obtains weather forecast data for the user's current location.

[1657] Server: Analyzes the acquired weather forecast data and evaluates the possibility of abnormal weather (e.g., typhoons, heavy rain, heavy snow, etc.) occurring.

[1658] 3. Acquiring emotional information:

[1659] Terminal: Acquires the user's voice data and facial expression data.

[1660] Terminal: Sends acquired emotion data to the emotion engine.

[1661] Emotion engine: Analyzes voice and facial expression data to determine the user's current emotional state.

[1662] 4. Find the best food delivery options:

[1663] Server: Based on the results of extreme weather forecasts, search for food delivery options based on the user's current location and request information.

[1664] Server: Based on your emotional state, prioritize meals that reduce stress and fatigue, such as relaxation dishes and comfortable delivery options.

[1665] 5. User Suggestions and Notices:

[1666] Server: Creates a list of recommendations for optimal food delivery options and sends them to the user's device.

[1667] On device: Show a notification to the user so they can review the suggestion.

[1668] Specific examples

[1669] For example, let's say a user is at home and a typhoon is predicted to be approaching. The emotion engine determines from the user's voice data that they are feeling stressed. In this case, the system will suggest relaxing meals and options that can be delivered safely and quickly.

[1670] Example prompt sentence:

[1671] If you recognize that the user is stressed, suggest the best food delivery options for when a typhoon is approaching, including relaxation foods and safe delivery methods.

[1672] The present invention makes it possible to propose food delivery that takes into account the user's emotional state, allowing them to enjoy meals with peace of mind even during extreme weather conditions.

[1673] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1674] Program processing flow

[1675] Step 1:

[1676] Retrieving User Information

[1677] Device: The user launches the application, obtains their current location using GPS, and enters their desired meal type, budget, and other required information.

[1678] Input: GPS data (location information), user input data (type of meal, budget)

[1679] Output: location information, request information

[1680] How it works: The application uses the smartphone's GPS to obtain the user's current location and latitude and longitude, and then prompts the user to enter their desired meal type, budget, and other information through the user interface.

[1681] Step 2:

[1682] Predicting extreme weather

[1683] Server: Accesses an external weather forecast database (weather forecast API) and obtains weather forecast data for the user's current location.

[1684] Input: location information, weather forecast data

[1685] Output: Extreme weather forecast data

[1686] How it works: The server calls the weather forecast API and receives weather forecast data for the current location. This data is then analyzed to assess the likelihood of extreme weather occurring.

[1687] Step 3:

[1688] Acquiring emotional information

[1689] Terminal: Acquires the user's voice data and facial expression data and sends them to the emotion engine.

[1690] Input: Voice data, facial expression data

[1691] Output: Emotional state data

[1692] How it works: The application uses the smartphone's microphone and camera to record voice and facial expressions. The recorded data is then sent to the emotion engine for analysis, which determines the user's emotional state (e.g., stress, fatigue, etc.).

[1693] Step 4:

[1694] Finding the best food delivery options

[1695] Server: Searches for the most appropriate food delivery options based on the user's location and request information, based on extreme weather forecasts and emotional state.

[1696] Input: Extreme weather forecast data, emotional state data, location information, request information

[1697] Output: List of food delivery options

[1698] What it does: The server uses the food delivery service's API to search for available options near the user's current location, and prioritizes, for example, stress-reducing meals and safe delivery options based on the user's emotional state.

[1699] Step 5:

[1700] User suggestions and notifications

[1701] Server: Creates a list of recommendations for optimal food delivery options and sends them to the user's device.

[1702] Input: List of food delivery options

[1703] Output: Proposal notification

[1704] What it does: The server generates a list of suggestions and sends them to the user's device as a push notification or in-app notification, allowing the user to review and select.

[1705] Step 6:

[1706] Supporting user selection and booking procedures

[1707] User: Choose from suggested food delivery options.

[1708] Input: Suggestion notification, user selection

[1709] Output: Selected data

[1710] How it works: A user uses a smartphone app to select the food delivery options they want from the options provided. This selection is then sent to the server.

[1711] Step 7:

[1712] Completing the reservation process

[1713] Server: Automatically initiates the reservation and ordering process for the food delivery option selected by the user.

[1714] Input:Selection data

[1715] Output: Reservation confirmation information

[1716] Specific operation: The server calls the delivery service's API based on the selected food delivery option, confirms the order, generates a reservation confirmation, and sends it to the user's device.

[1717] Each step involves specific data inputs and outputs resulting from processing based on that data, with each step leading to the next step.The system aims to increase user safety and satisfaction by taking into account the user's emotional state and providing optimal food delivery options, even during extreme weather.

[1718] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1719] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1720] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1721] [Fourth embodiment]

[1722] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1723] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1724] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1725] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1726] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1727] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1728] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1729] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1730] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1731] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1732] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1733] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1734] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1735] The embodiment of the present invention will be described as an autonomous AI platform that proposes optimal accommodations and transportation methods based on the user's current location and flexibly changes the itinerary in response to unexpected abnormal weather conditions during travel. The program processing of this system will be explained below in natural language.

[1736] Overall system overview

[1737] This system automatically performs a series of processes: collecting information from user devices, predicting extreme weather, suggesting optimal accommodations and transportation options, notifying users, and completing reservations. The system mainly consists of the following components: user devices, a central server, and an external weather forecast database.

[1738] Program processing

[1739] 1. Obtaining user information

[1740] Device: When a user travels, the device uses GPS to obtain the user's current location and inputs the user's travel plan (start point, destination, and itinerary) into the device.

[1741] Terminal: Sends the acquired current location and travel plan information to a central server.

[1742] 2. Predicting extreme weather

[1743] Server: Obtains weather forecast data from an external weather database.

[1744] Server: Analyzes the acquired data and predicts unexpected abnormal weather (e.g., typhoons, heavy rain, heavy snow, etc.) along the user's travel route.

[1745] 3. Find the best accommodation and transportation

[1746] Server: Based on the results of the abnormal weather forecast, searches for accommodation and transportation options near the user's current location and destination.

[1747] Server: Selects the best accommodation based on availability, price, property ratings, and location.

[1748] Server: Checks the availability and timetables of transportation options such as bullet trains, buses, taxis, and rental cars, and selects the most suitable means of transportation.

[1749] 4. User Suggestions and Notifications

[1750] Server: Generates a list of suggestions for the best accommodations and transportation options.

[1751] Server: Sends this proposal list to the user's terminal and notifies them.

[1752] On device: Show a notification to the user so they can review the suggestion.

[1753] 5. Reservation procedure support

[1754] User: Choose from suggested accommodations and transportation options.

[1755] Terminal: Sends user selections to a central server.

[1756] Server: Automatically initiates the booking process for the accommodation and transportation selected by the user.

[1757] Server: Processes the booking confirmation and payment information and sends the booking confirmation to the user.

[1758] Specific examples

[1759] Let's take the example of a user traveling from Tokyo to Osaka when a typhoon is suddenly predicted to form near Nagoya.

[1760] 1. Obtaining user information

[1761] Device: The user's current location is determined to be Nagoya via GPS.

[1762] Terminal: The travel plan, which includes the fact that the user is on the way from Tokyo to Osaka, and the itinerary, is transmitted to the server.

[1763] 2. Predicting extreme weather

[1764] Server: Obtains forecasts of approaching typhoons from a weather forecast database.

[1765] Server: Predicts that the typhoon will have a major impact on Nagoya.

[1766] 3. Find the best accommodation and transportation

[1767] Server: Search affiliated accommodations and transportation options around Nagoya.

[1768] Server: Check that there is a room available at the hotel in front of Nagoya Station and select that the price is 10,000 yen.

[1769] Server: Check whether there are seats available on the early morning Shinkansen train.

[1770] 4. User Suggestions and Notifications

[1771] Server: Creates an optimal list of suggestions including hotels in front of Nagoya Station and early morning Shinkansen trains, and notifies the device.

[1772] Device: Display a notification to the user advising them to stay overnight in Nagoya and take an early flight due to the typhoon.

[1773] 5. Reservation procedure support

[1774] User: Select a hotel and an early morning Shinkansen flight.

[1775] Terminal: Sends the user's selection to the server.

[1776] Server: Automates the booking process for the selected accommodation and transportation and sends confirmation to the user.

[1777] In this way, the system of the present invention can support the user's trip smoothly even if unexpected abnormal weather occurs during the trip.

[1778] The processing flow will be explained below.

[1779] Step 1:

[1780] Device: Before the user starts their journey, they launch an application on their device and use GPS to obtain the user's current location.

[1781] Terminal: where the user enters travel plan information such as origin, destination, and travel dates.

[1782] Terminal: Sends the acquired current location and travel plan information to the server.

[1783] Step 2:

[1784] Server: Accesses an external weather forecast database (e.g., a weather forecast API) to retrieve weather forecast data for the user's travel route and destination.

[1785] Server: Analyzes the acquired weather forecast data and evaluates the possibility of abnormal weather (e.g., typhoons, heavy rain, heavy snow, etc.) occurring.

[1786] Step 3:

[1787] Server: Based on the analysis results, determine whether unexpected extreme weather will affect the user's travel route or destination.

[1788] Server: If an impact is anticipated, determine the scope and timeframe of the impact.

[1789] Step 4:

[1790] Server: Finds the best accommodation and transportation options, taking into account the user's current location and the expected impact of extreme weather.

[1791] Server: Check availability and pricing information of partner hotels to select the best accommodation.

[1792] Server: Checks the timetables and availability of available transportation options (such as bullet trains, buses, taxis, and rental cars) and selects the most suitable mode of transportation.

[1793] Step 5:

[1794] Server: Aggregates and ranks the best accommodation and transportation options.

[1795] Server: Creates a proposal list based on this information and sends it to the user's device.

[1796] Step 6:

[1797] Device: Receives the suggestion list and displays a notification to the user.

[1798] Terminal: A message such as "The typhoon may cause travel disruptions. There are vacancies at the XX Hotel in front of Nagoya Station (10,000 yen / night). You can also take the early morning Shinkansen." will be displayed.

[1799] User: Decide on a choice from suggested accommodations and transportation options.

[1800] Step 7:

[1801] Terminal: Sends the user's selections to the server.

[1802] Server: Automates the booking process for the selected accommodation and transportation.

[1803] Server: Processes the necessary payment information and generates the reservation confirmation.

[1804] Step 8:

[1805] Server: Sends reservation confirmation information to the user's terminal.

[1806] Terminal: Display reservation confirmation information to the user.

[1807] Through these steps, the system can respond quickly and effectively to unexpected extreme weather events that occur during travel, allowing users to flexibly change their itinerary.

[1808] Example 1

[1809] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1810] When unexpected extreme weather occurs during a trip, it can be difficult for users to quickly find the best accommodation and transportation. A platform is needed to solve this problem and make travel schedule changes smoothly.

[1811] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1812] In this invention, the server includes means for acquiring location information and travel plan information from the user terminal, means for acquiring and analyzing weather data to predict abnormal weather, and means for searching for accommodations and transportation options near the user's current location and destination and presenting optimal options. This allows the user to quickly find optimal accommodations and transportation options and smoothly change their travel schedule even if unexpected abnormal weather occurs during their trip.

[1813] "User device" refers to an electronic device that can be operated by a user, including a smartphone, tablet, laptop, etc.

[1814] "Location information" refers to geographic coordinate data that indicates a user's current location and is obtained using technologies such as GPS.

[1815] "Travel Plan Information" means data that includes the origin, destination, dates, and other related information for a trip that a user is planning.

[1816] "Abnormal weather" refers to weather phenomena that deviate significantly from normal weather conditions, including typhoons, heavy rain, and heavy snow.

[1817] "Weather Data" means atmospheric data, including weather forecasts and historical weather records.

[1818] "Accommodation" means a facility that provides a place for users to stay, including hotels, motels, inns, etc.

[1819] "Transportation" refers to the means used by users to travel, including trains, buses, taxis, rental cars, etc.

[1820] "Searching" means finding information in a database or on the Internet based on specific criteria.

[1821] "Notification" means sending information to a user terminal to notify the user.

[1822] "Reservation" means the process of reserving a particular service or product in advance, and may include payment information.

[1823] "Security protocols" are the standards for communication methods used to ensure data security, including SSL / TLS.

[1824] "Satellite positioning technology" is a technology that uses artificial satellites to measure specific locations on Earth.

[1825] "Weather Information API" refers to an application program interface for obtaining weather data, and is used to obtain information from external weather information services.

[1826] This invention will be described as an autonomous AI platform that suggests optimal accommodation and transportation options based on the user's current location and flexibly changes the itinerary in response to unexpected abnormal weather conditions while traveling.

[1827] The system consists of a user terminal, a central server, and an external weather database. Its main processes are obtaining user information, forecasting extreme weather, searching for optimal accommodation and transportation options, notifying users, and completing reservation procedures.

[1828] Hardware and software used

[1829] User device: Electronic devices such as smartphones, tablets, and laptops that use GPS modules to obtain location information.

[1830] Central server: Web server, database server. Analyzes weather data using the LSTM model as a machine learning model.

[1831] External weather database: An API that provides weather information (e.g., Weather API).

[1832] Data processing and calculation

[1833] 1. Obtaining user information

[1834] Device: The user's current location is acquired using GPS. The user inputs their travel plan (start point, destination, and itinerary).

[1835] Device: Sends location and travel plan information to a central server. Data is transmitted using a secure protocol (e.g., SSL / TLS).

[1836] 2. Predicting extreme weather

[1837] Server: Obtains weather forecast data from an external weather database and stores it in the weather database.

[1838] Server: Uses LSTM model to calculate the probability of occurrence of extreme weather events. Performs data analysis to identify the probability along the user's travel route.

[1839] 3. Find the best accommodation and transportation

[1840] Server: Searches for accommodation and transportation based on the results of abnormal weather forecasts. Accommodation information is obtained from an external API (e.g., accommodation information API), and transportation information is obtained from the API in charge (e.g., transportation information API).

[1841] Server: Selects the best accommodation based on availability, price, and ratings of the acquired accommodations, and also selects the transportation method.

[1842] 4. User Suggestions and Notifications

[1843] Server: Creates a list of best accommodation and transportation suggestions in HTML format.

[1844] Server: Generates a notification message and pushes it to the user's device (e.g., using Firebase Cloud Messaging).

[1845] On your device: Show the notification you received to the user so they can review the suggestion.

[1846] 5. Reservation procedure support

[1847] User: Choose from suggested accommodations and transportation options.

[1848] Terminal: Sends user selections to a central server.

[1849] Server: Automates the booking process for the selected accommodation and transportation and sends confirmation to the user. Processes payment details using a secure payment gateway (e.g. payment API).

[1850] Specific examples

[1851] Let's take the example of a user traveling from Tokyo to Osaka when a typhoon is suddenly predicted to form near Nagoya.

[1852] 1. Obtaining user information

[1853] Device: The user's current location is determined to be Nagoya via GPS.

[1854] Terminal: The travel plan, which includes the fact that the user is on the way from Tokyo to Osaka, and the itinerary, is transmitted to the server.

[1855] 2. Predicting extreme weather

[1856] Server: Obtains forecasts of approaching typhoons from a weather forecast database.

[1857] Server: Predicts that the typhoon will have a major impact on Nagoya.

[1858] 3. Find the best accommodation and transportation

[1859] Server: Search affiliated accommodations and transportation options around Nagoya.

[1860] Server: Check that there is a room available at the hotel in front of Nagoya Station and select that the price is 10,000 yen.

[1861] Server: Check whether there are seats available on the early morning Shinkansen train.

[1862] 4. User Suggestions and Notifications

[1863] Server: Creates an optimal list of suggestions including hotels in front of Nagoya Station and early morning Shinkansen trains, and notifies the device.

[1864] Device: Display a notification to the user advising them to stay overnight in Nagoya and take an early flight due to the typhoon.

[1865] 5. Reservation procedure support

[1866] User: Select a hotel and an early morning Shinkansen flight.

[1867] Terminal: Sends the user's selection to the server.

[1868] Server: Automates the booking process for the selected accommodation and transportation and sends confirmation to the user.

[1869] Example prompts to input to the generative AI model

[1870] Example prompt:

[1871] "If a user traveling from Tokyo to Osaka suddenly arrives in Nagoya and a typhoon is predicted to approach, please explain in detail each processing step of the system that will suggest appropriate accommodation and transportation options to the user and automatically complete the reservation process."

[1872] The above is a specific embodiment for carrying out the present invention.

[1873] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1874] Step 1: Get user information

[1875] Terminal: First, the GPS module is used to obtain the user's current location. The input is the location data from the GPS, and the output is the current location information stored in the location information database.

[1876] Terminal: After the user enters their travel plan (start point, destination, and itinerary) using a dedicated input form, the travel plan is saved in local storage. The input is the travel plan information from the user, and the output is the travel plan data stored in local storage.

[1877] Terminal: Converts the acquired current location and travel plan information into packets and sends them to a central server using a security protocol (e.g., SSL / TLS). The input is location information and travel plan information, and the output is the data transmitted in a secure format.

[1878] Step 2: Predicting extreme weather

[1879] Server: Obtains weather forecast data from an external weather database. The input is an API request, and the output is the weather forecast data stored in the weather database on the server.

[1880] Server: Analyzes the acquired weather forecast data and uses a machine learning model (e.g., LSTM model) to calculate the probability of occurrence of extreme weather along the user's travel route. The input is weather data, and the output is the predicted results of extreme weather. The analysis uses a method that compares the data with past weather data.

[1881] Step 3: Find the best accommodation and transportation

[1882] Server: Searches for accommodations and transportation options near the user's current location and destination based on the results of extreme weather forecasts. The input is the results of the extreme weather forecast and location information, and the output is a list of candidate accommodations and transportation options.

[1883] Server: Accommodation information is obtained from an external API (e.g., accommodation information API), and transportation information is obtained from the corresponding API (e.g., transportation information API). The input is the API request, and the output is information such as room and seat availability, price, rating, and timetable.

[1884] Server: Considers the acquired information and selects the optimal accommodation and transportation method. The input is information on accommodation and transportation methods, and the output is the optimal selection result.

[1885] Step 4: Propose and notify users

[1886] Server: Creates a list of recommendations for optimal accommodations and transportation in HTML format and generates a notification message. The input is the optimal selection result, and the output is the notification message.

[1887] Server: Sends the generated proposal list to the user device and performs push notification (e.g., using Firebase Cloud Messaging). The input is the notification message, and the output is the delivery to the user device.

[1888] Terminal: Displays received notifications to the user and provides an interface where the user can view the details. The input is the notification message and the output is the displayed notification content.

[1889] Step 5: Reservation support

[1890] User: Review the notification and select from suggested accommodations and transportation options. The input is the list of suggestions, and the output is the user's choice.

[1891] Terminal: Sends user selections in a secure format to a central server. The input is the user selection and the output is the data sent in a secure format.

[1892] Server: Automates the booking process for the user's selected accommodation and transportation and sends confirmation to the user. Inputs are user selections and API requests, and outputs are booking confirmation and payment information. Payment information is processed using a secure payment gateway (e.g., payment API).

[1893] (Application example 1)

[1894] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1895] In recent years, extreme weather events have become more frequent, increasing the risk of encountering unexpected weather conditions while traveling. Accordingly, there is a demand for systems that can quickly suggest appropriate accommodations and transportation options to ensure travelers can travel safely and comfortably. Furthermore, with the spread of autonomous vehicles, there is a need for technologies that enable these vehicles to respond to extreme weather and suggest appropriate evacuation routes and commercial facilities where they can stop. However, conventional systems cannot adequately meet these demands, resulting in issues such as a lack of safety and efficiency for travelers and autonomous vehicle users.

[1896] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1897] In this invention, the server includes means for acquiring location information and travel plan information from the user terminal, means for acquiring and analyzing weather data to predict abnormal weather, means for searching for facilities and means of transportation in the user's current location and surrounding areas and presenting optimal options, means for notifying the user terminal of suggestions regarding recommended facilities and means of transportation, means for automatically reserving facilities and means of transportation based on the user's selection, and means for suggesting evacuation routes and commercial facilities where the autonomous vehicle can stop based on the current location and weather forecast, thereby enabling users of autonomous vehicles to safely and quickly find evacuation routes and commercial facilities when abnormal weather occurs.

[1898] "User terminals" are information terminal devices used by travelers and drivers, including smartphones, tablets, and in-vehicle displays.

[1899] "Location information" means data obtained using GPS or other location information systems that indicates the current location of a user device or autonomous vehicle.

[1900] "Travel plan information" refers to information about a traveler's itinerary, such as the departure point, destination, route, and itinerary.

[1901] "Weather data" includes weather forecasts and information on abnormal weather conditions obtained from weather providers, and is data used to make weather predictions.

[1902] "Facilities" refers to commercial facilities available to users, such as accommodations, restaurants, and gas stations.

[1903] "Means of transportation" refers to the means of transportation that users can use to get around, such as cars, trains, bullet trains, and buses.

[1904] "Evacuation route" refers to the recommended route for users to safely evacuate in the event of an emergency such as extreme weather.

[1905] "Suggestion" refers to the optimal options or courses of action that the system presents to the user based on the analyzed data.

[1906] "Commercial facilities" refers to hotels, restaurants, gas stations, and other facilities where users can stop and use their vehicles during extreme weather.

[1907] "Reservation process" means that the system automatically completes the process to secure accommodation, transportation, etc. based on the user's selections.

[1908] This invention is a system that, when a user encounters unexpected abnormal weather while traveling, suggests optimal evacuation routes and commercial facilities (such as accommodations, restaurants, and gas stations) based on the user's current location, and can make reservations as needed. The system consists of a user terminal, a central server, and an external weather database.

[1909] System configuration

[1910] 1. User Device

[1911] The user device, which can be a smartphone, tablet, or an on-board display in an autonomous vehicle, transmits location and travel plan information to a central server and also receives notifications from the system.

[1912] 2. Central Server

[1913] The central server consists of several components:

[1914] Location information acquisition function: Acquires location information from the user's device using GPS, etc.

[1915] Travel plan information management function: Receives travel plans entered by users and saves them in a database.

[1916] Weather data analysis function: Obtains weather data from an external weather database (e.g., Weather API), analyzes the data, and predicts abnormal weather.

[1917] Optimal facility and evacuation route recommendation function: Implements an algorithm that suggests appropriate commercial facilities and evacuation routes based on the user's current location and weather data.

[1918] Reservation management function: Automatically make reservations at commercial facilities selected by the user and manage confirmation information.

[1919] 3. External Weather Database

[1920] The latest weather forecast information is obtained from an external weather database, mainly using the Weather API, and the data is analyzed on the server to predict abnormal weather.

[1921] Example

[1922] Let's explain what happens when a user is traveling from Tokyo to Osaka and a typhoon is suddenly predicted to form near Nagoya.

[1923] The system obtains from GPS that the user's current location is Nagoya and sends this information to a central server.

[1924] The central server obtains the latest weather forecast information from the Weather API and predicts the occurrence of a large typhoon in Nagoya.

[1925] The server checks availability at accommodations, restaurants, gas stations, etc. around Nagoya and notifies the user of the best options. For example, it can provide information to users about available rooms at a hotel in front of Nagoya Station and available seats on an early morning Shinkansen train.

[1926] The user selects the proposed commercial facility from the terminal and transmits the selection information to the central server.

[1927] The server automatically completes the reservation procedure for the selected accommodation and sends reservation confirmation information to the user's terminal.

[1928] Prompt Sentence Examples

[1929] Here are some examples of prompts for generative AI models:

[1930] "If extreme weather is predicted to occur while an autonomous vehicle is en route to its destination, please suggest the best evacuation route and commercial facilities (hotels, restaurants, gas stations) based on the current location and weather forecast. Please provide specific locations and suggestions in detail."

[1931] In this way, the system of the present invention ensures that traveling users can safely and efficiently respond to extreme weather events.

[1932] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1933] Step 1:

[1934] The user device acquires location information.

[1935] Input: Current location data from GPS

[1936] Output: User's current location

[1937] Specific operation: The user device uses the GPS module to obtain the user's current location in real time. This location information is expressed as latitude and longitude data.

[1938] Step 2:

[1939] The user terminal inputs travel plan information.

[1940] Input: The origin, destination, and dates of a trip manually entered by the user

[1941] Output: Travel plan information

[1942] Specific operation: The user inputs the departure point, destination, and travel dates via a smartphone or in-car display. This data is sent to the server as travel plan information.

[1943] Step 3:

[1944] The server retrieves abnormal weather information from a weather database.

[1945] Input: Weather data provider API request

[1946] Output: Weather forecast data

[1947] What it does: The server retrieves the latest weather forecast data from external weather data providers via API requests. This data is used to predict extreme weather events.

[1948] Step 4:

[1949] The server analyzes weather data and predicts abnormal weather.

[1950] Input: Retrieved weather forecast data

[1951] Output: Extreme weather forecast results

[1952] Specific operation: The server analyzes the acquired weather forecast data and determines whether it matches certain conditions (e.g., the path of a typhoon, a forecast of heavy snowfall). Based on the results of this analysis, it predicts whether abnormal weather will occur.

[1953] Step 5:

[1954] The server searches for facilities and transportation options near the user's current location.

[1955] Input: User's location information, travel plan information, extreme weather forecast results

[1956] Output: A list of recommended facilities and transportation options

[1957] Specific operation: The server searches for nearby accommodations, restaurants, gas stations, and transportation options based on the user's location information and weather forecast results, utilizing APIs from hotel booking sites and information on public transport timetables.

[1958] Step 6:

[1959] The server sends a list of recommended facilities and transportation options to the user's terminal.

[1960] Input: List of recommended facilities and transportation options

[1961] Output: User notification

[1962] Specific operation: The server selects the most suitable facilities and transportation methods from the search results and sends the list of suggestions to the user's device. The user's device displays this list on its screen and informs the user of countermeasures against abnormal weather.

[1963] Step 7:

[1964] The user selects from suggested facilities and transportation options.

[1965] Input: Suggestion list

[1966] Output: User selection information

[1967] Specific operation: The user selects the desired facility or means of transportation from the suggested list displayed on the terminal and sends the selected information to the server.

[1968] Step 8:

[1969] The server automatically processes reservations for the selected facilities and transportation.

[1970] Input: User selection information

[1971] Output: Reservation confirmation information

[1972] Specific operation: The server automatically completes the online reservation process for the facility and transportation selected by the user and sends the confirmation information to the user's device. This is done using the API of a hotel reservation site or the online transportation reservation system.

[1973] As a result, this system can help users respond appropriately and quickly even if abnormal weather occurs during their trip.

[1974] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1975] The embodiment of the present invention will be described as an autonomous AI platform for a travel support system that combines an emotion engine that recognizes user emotions. Below, the program processing of this system will be explained in natural language.

[1976] Overall system overview

[1977] This system suggests optimal accommodations and transportation options based on the user's current location in the event of unexpected extreme weather during travel, and further adjusts the suggestions based on the user's emotions, allowing for flexible and emotionally sensitive changes to the itinerary. The system mainly consists of the following components: user terminal, central server, external weather forecast database, and emotion engine.

[1978] Program processing

[1979] 1. Obtaining user information

[1980] Device: Before the user starts their trip, they launch the application on their device, use GPS to obtain their current location, and then input their travel plan information, such as their departure point, destination, and travel dates.

[1981] Terminal: Transmits the acquired current location and travel plan information to the central server.

[1982] 2. Predicting extreme weather

[1983] Server: Accesses an external weather forecast database (e.g., a weather forecast API) to retrieve weather forecast data for the user's travel route and destination.

[1984] Server: Analyzes the acquired weather forecast data and evaluates the possibility of abnormal weather (e.g., typhoons, heavy rain, heavy snow, etc.) occurring.

[1985] 3. Acquiring emotional information

[1986] Terminal: Acquires the user's voice data and facial expression data.

[1987] Terminal: Sends acquired emotion data to the emotion engine.

[1988] Emotion engine: Analyzes voice and facial expression data to determine the user's current emotional state.

[1989] 4. Find the best accommodation and transportation

[1990] Server: Based on the results of the abnormal weather forecast, searches for accommodation and transportation options near the user's current location and destination.

[1991] Server: Selects the best accommodation based on availability, price, property ratings, and location.

[1992] Server: Checks the availability and timetables of transportation options such as bullet trains, buses, taxis, and rental cars, and selects the most suitable means of transportation.

[1993] 5. Adjusting suggestions based on emotions

[1994] Server: Adjusts the content and format of suggestions based on the emotional information provided by the emotion engine.

[1995] Server: If the user is "tired," the server will prioritize hotels with good relaxation facilities, and make suggestions that take into consideration the user's emotions.

[1996] 6. User Suggestions and Notifications

[1997] Server: Creates a list of recommendations for optimal accommodations and transportation options and sends them to the user's device.

[1998] On device: Show a notification to the user so they can review the suggestion.

[1999] 7. Reservation procedure support

[2000] User: Decide on a choice from suggested accommodations and transportation options.

[2001] Terminal: Sends user selections to a central server.

[2002] Server: Automatically initiates the booking process for the accommodation and transportation selected by the user.

[2003] Server: Processes the necessary payment information and generates the reservation confirmation.

[2004] 8. Sending confirmation information

[2005] Server: Sends reservation confirmation information to the user's terminal.

[2006] Terminal: Display reservation confirmation information to the user.

[2007] Specific examples

[2008] For example, let's consider a case where a user is traveling from Tokyo to Osaka and suddenly a typhoon is predicted to form near Nagoya.

[2009] 1. Obtaining user information

[2010] Device: The user's current location is determined to be Nagoya via GPS.

[2011] Terminal: The travel plan, which includes the fact that the user is on the way from Tokyo to Osaka, and the itinerary, is transmitted to the server.

[2012] 2. Predicting extreme weather

[2013] Server: Obtains forecasts of approaching typhoons from a weather forecast database.

[2014] Server: Predicts that the typhoon will have a major impact on Nagoya.

[2015] 3. Acquiring emotional information

[2016] Device: Obtain emotional data such as "tired" from the user's voice.

[2017] Terminal: Sends this emotion data to the server.

[2018] Emotion engine: Analyzes voice data to determine if the user is tired.

[2019] 4. Find the best accommodation and transportation

[2020] Server: Search affiliated accommodations and transportation options around Nagoya.

[2021] Server: Check that there is a room available at the hotel in front of Nagoya Station and select that the price is 10,000 yen.

[2022] Server: Check whether there are seats available on the early morning Shinkansen train.

[2023] 5. Adjusting suggestions based on emotions

[2024] Server: Based on the user's feeling of "tired," prioritize hotels with excellent relaxation facilities.

[2025] 6. User Suggestions and Notifications

[2026] Server: Notify the user that "There are vacancies at Hotel X in front of Nagoya Station (10,000 yen / night). The hotel has ample relaxation facilities. You can also take the early morning Shinkansen."

[2027] On your device: Show a notification to the user so they can review the suggestion.

[2028] 7. Reservation procedure support

[2029] User: Select a hotel and an early morning Shinkansen flight.

[2030] Terminal: Sends the user's selection to the server.

[2031] Server: Automates the booking process for the selected accommodation and transportation and sends confirmation to the user.

[2032] In this way, the system of the present invention can respond quickly and effectively to abnormal weather conditions, and can increase travel satisfaction by making suggestions that take into consideration the user's emotions.

[2033] The processing flow will be explained below.

[2034] Step 1:

[2035] Device: Before the user starts their trip, they launch the application on their device, use GPS to obtain their current location, and then input their travel plan information, such as their departure point, destination, and travel dates.

[2036] Terminal: Transmits the acquired current location and travel plan information to the central server.

[2037] Step 2:

[2038] Server: Accesses an external weather forecast database (e.g., a weather forecast API) to retrieve weather forecast data for the user's travel route and destination.

[2039] Server: Analyzes the acquired weather forecast data and evaluates the possibility of abnormal weather (e.g., typhoons, heavy rain, heavy snow, etc.) occurring.

[2040] Step 3:

[2041] Terminal: Acquires emotion data based on the voice data and camera footage provided by the user.

[2042] Terminal: Sends emotion data to the emotion engine for analysis.

[2043] Step 4:

[2044] Emotion engine: Analyzes voice and facial expression data to determine the user's current emotional state (e.g., tired, stressed, etc.).

[2045] Emotion engine: Sends the judgment results to the central server.

[2046] Step 5:

[2047] Server: Based on the results of the abnormal weather forecast, searches for accommodation and transportation options near the user's current location and destination.

[2048] Server: Selects the best accommodation based on availability, price, property ratings, and location.

[2049] Server: Checks the availability and timetables of transportation options such as bullet trains, buses, taxis, and rental cars, and selects the most suitable means of transportation.

[2050] Step 6:

[2051] Server: Adjusts the content and format of suggestions based on the emotional information provided by the emotion engine.

[2052] Server: Depending on the user's emotional state, the server adjusts the suggestions to suit the user's current condition. For example, if the user feels "tired," the server prioritizes hotels with relaxation facilities.

[2053] Step 7:

[2054] Server: Aggregates and ranks the best accommodation and transportation options.

[2055] Server: Creates a proposal list based on this information and sends it to the user's device.

[2056] Step 8:

[2057] Device: Receives the suggestion list and displays a notification to the user.

[2058] Terminal: A message such as "The typhoon may cause travel disruptions. There are vacancies at Hotel X in front of Nagoya Station (10,000 yen / night). It also has excellent relaxation facilities. You can also take an early morning Shinkansen flight." will be displayed.

[2059] User: Decide on a choice from suggested accommodations and transportation options.

[2060] Step 9:

[2061] Terminal: Sends the user's selections to the server.

[2062] Server: Automates the booking process for the selected accommodation and transportation.

[2063] Server: Processes the necessary payment information and generates the reservation confirmation.

[2064] Step 10:

[2065] Server: Sends reservation confirmation information to the user's terminal.

[2066] Terminal: Display reservation confirmation information to the user.

[2067] Through these steps, the system can respond quickly and effectively to unexpected extreme weather events that occur during travel, and can also improve users' travel experience by making suggestions that take users' emotions into consideration.

[2068] Example 2

[2069] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2070] Conventional systems often fail to respond quickly and effectively to extreme weather events that occur during travel. They also fail to flexibly adjust travel plans based on the user's emotional state, which can lead to lower travel satisfaction. Furthermore, suggestions for accommodations and transportation options do not take into account the user's current state of mind, making it difficult to provide optimal recommendations tailored to the user's needs.

[2071] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2072] In this invention, the server includes means for acquiring location information and travel plan information from the user terminal, means for acquiring and analyzing weather forecast data to predict abnormal weather, means for acquiring emotion data and analyzing it with an emotion engine, means for searching for accommodations and transportation options in the user's current location and surrounding area and presenting optimal options, means for adjusting the suggestions based on the emotion information, means for notifying the user terminal of the suggestions regarding recommended accommodations and transportation options, means for automatically reserving accommodations and transportation options based on the user's selections, and means for notifying the user of reservation confirmation information. This enables quick and effective response to abnormal weather and flexible adjustment of travel plans based on the user's emotional state.

[2073] A "user terminal" is an electronic device operated by a user, such as a smartphone, tablet, or personal computer.

[2074] "Location Information" means your current geographic location determined using GPS or other positioning technology.

[2075] "Travel plan information" refers to information entered by a user regarding a trip, and includes data such as the departure point, destination, and itinerary.

[2076] "Extreme weather" refers to meteorological phenomena that deviate significantly from normal weather patterns, such as typhoons, heavy rain, and heavy snow.

[2077] "Weather forecast data" refers to forecast information about future weather provided by meteorological agencies and data provision services.

[2078] "Emotional data" refers to data that represents the user's emotional state, obtained from the user's voice and facial expressions.

[2079] An "emotion engine" refers to a system that analyzes acquired voice and facial expression data to determine the user's emotional state.

[2080] "Accommodation" refers to hotels, inns, and other accommodation facilities where users can stay.

[2081] "Transportation" refers to the means used by users to travel, such as bullet trains, buses, taxis, and rental cars.

[2082] "Reservation confirmation information" is information indicating that a reservation for accommodation or transportation has been confirmed, and includes a QR code, reservation number, etc.

[2083] The present invention will be described as an autonomous AI platform for a travel support system that combines an emotion engine that recognizes user emotions. This system enables rapid response to abnormal weather and flexible adjustment of travel plans based on user emotions.

[2084] Required Hardware and Software

[2085] User devices: Smartphones, tablets, personal computers, etc. These devices must be equipped with a GPS module, camera, and microphone. The application uses this hardware to obtain user information and collect emotion data.

[2086] Central Server: A central server for data processing and analysis, retrieving and analyzing weather forecast data, managing user location and travel plan information, running the sentiment engine, and searching for optimal accommodation and transportation options.

[2087] Weather Forecast API: An API for connecting to an external weather forecast database. Use this API to obtain forecast data for extreme weather.

[2088] Emotion Engine: Software that analyzes voice and facial expression data to determine the user's emotional state. It uses machine learning models to classify emotions.

[2089] System Overview

[2090] 1. Obtaining User Information:

[2091] Before starting a trip, the user launches the application on their device and uses GPS to determine their current location. The user then enters information such as the departure point, destination, and travel dates, which is then sent to a central server.

[2092] 2. Predicting extreme weather:

[2093] The server accesses the weather forecast API to obtain weather forecast data for the user's travel route and destination, analyzes the obtained data, and evaluates the possibility of extreme weather occurring.

[2094] 3. Acquiring emotional information:

[2095] The user device captures voice and facial expression data and sends them to the emotion engine, which analyzes the data and determines the user's current emotional state.

[2096] 4. Find the best accommodation and transportation:

[2097] The server searches for accommodations and transportation options near the user's current location and destination based on the results of the extreme weather forecast, taking into account the availability, price, ratings, and location of the accommodations, and also searches for and selects transportation options.

[2098] 5. Adjusting suggestions based on emotions:

[2099] The server adjusts the suggestions based on the emotional information provided by the emotion engine. For example, if the user is "tired," it will prioritize hotels with excellent relaxation facilities.

[2100] 6. User Suggestions and Notices:

[2101] The server creates a list of recommendations for optimal accommodations and transportation options and sends it to the user's device, which then notifies the user of the recommendations and allows them to review them.

[2102] 7. Booking assistance:

[2103] Once the user makes a selection from the suggested accommodations and transportation options, the selection is sent to a central server, which automatically initiates the booking process, processes payment information, and generates a booking confirmation.

[2104] 8. Sending confirmation information:

[2105] Finally, the server sends the reservation confirmation information to the user terminal, which the user can confirm.

[2106] Specific examples

[2107] For example, consider a situation where a user is traveling from Tokyo to Osaka and a typhoon is predicted to occur near Nagoya. Nagoya is acquired as the user's current location, and the server uses a weather forecast API to collect and analyze information about the approaching typhoon. If the user is determined to be "tired," the server will prioritize suggesting a hotel in front of Nagoya Station with ample relaxation facilities and notify the user that seats are available on an early morning Shinkansen train.

[2108] Example prompt: "We have availability at Hotel X in front of Nagoya Station (10,000 yen / night). It has excellent relaxation facilities and you can catch an early morning Shinkansen."

[2109] In this way, the system of the present invention can respond quickly and effectively to abnormal weather conditions, and can increase travel satisfaction by making suggestions that take into consideration the user's emotions.

[2110] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2111] Program processing flow

[2112] Step 1: Get user information

[2113] Step 2: Predicting extreme weather

[2114] Step 3: Acquiring emotional information

[2115] Step 4: Find the best accommodation and transportation

[2116] Step 5: Adjust your suggestions based on emotion

[2117] Step 6: Propose and notify users

[2118] Step 7: Reservation support

[2119] Step 8: Submit confirmation information

[2120] Specific explanation of each processing step

[2121] Step 1: Get user information

[2122] Input: User's location and travel plan information

[2123] Output: Current location and travel plan information sent to a central server

[2124] User: Launches the application on their smartphone or tablet before starting their trip.

[2125] Specific action: Tap the icon to open the application.

[2126] Device: When the application launches, it uses GPS to obtain the user's current location.

[2127] Specific operation: The GPS module collects the current latitude and longitude information.

[2128] User: Enters travel plan information into the application, including origin, destination, and travel dates.

[2129] Specific actions: Enter information using text boxes and calendar input.

[2130] Terminal: Transmits the acquired current location and travel plan information to the central server.

[2131] Specific operation: Sends data using network communication in the background.

[2132] Step 2: Predicting extreme weather

[2133] Input: Weather forecast data for the user's travel route and destination

[2134] Output: Probability of occurrence of analyzed extreme weather events

[2135] Server: Accesses the weather forecast API to connect to an external weather forecast database.

[2136] Specific operation: Send an API request and retrieve weather forecast data.

[2137] Server: Analyzes the acquired weather forecast data and evaluates the possibility of abnormal weather occurring.

[2138] Specific operations: Analyze weather data using pattern matching and statistical models.

[2139] Step 3: Acquiring emotional information

[2140] Input: User's voice and facial expression data

[2141] Output: Emotion information analyzed by the emotion engine

[2142] Device: The user's voice data and facial expression data are acquired using a camera and microphone.

[2143] Specific operation: Collects audio with a microphone and captures facial expression data with a camera.

[2144] Terminal: Sends acquired emotion data to the emotion engine.

[2145] Specific behavior: Send data to the emotion engine in real time.

[2146] Emotion engine: Analyzes voice and facial expression data to determine the user's current emotional state.

[2147] Specific behavior: Classifying emotions using machine learning models.

[2148] Step 4: Find the best accommodation and transportation

[2149] Input: User's current location, accommodation information near the destination, and transportation information

[2150] Output: Selection of optimal accommodation and transportation options

[2151] Server: Searches for accommodations and transportation options near the user's current location and destination.

[2152] Specific operation: Query the database and extract facilities that meet the criteria.

[2153] Server: Selects the best accommodation based on availability, price, ratings, and location.

[2154] Specific operation: Apply filtering conditions to select the most suitable facility.

[2155] Server: Checks the availability and timetables of transportation options (Shinkansen, buses, taxis, rental cars, etc.) and selects the most suitable transportation option.

[2156] Specific operation: Obtain data from external API and select the best option.

[2157] Step 5: Adjust your suggestions based on emotion

[2158] Input: Emotion information provided by the emotion engine

[2159] Output: Suggestions that take user emotions into consideration

[2160] Server: Adjusts the proposal content based on the emotional information provided by the emotion engine.

[2161] Specific action: Reevaluate the proposal based on the emotional data.

[2162] Server: If the user is "tired," prioritize hotels with relaxation facilities.

[2163] Specific actions: Create a list of suggestions and focus on relaxation facilities.

[2164] Step 6: Propose and notify users

[2165] Input: A list of suggestions generated from the server

[2166] Output: Proposal sent to user's device

[2167] Server: Generates a list of suggestions for the best accommodations and transportation options.

[2168] Specific actions: List the suggestions and format them in a way that is easy for the user to understand.

[2169] Server: Sends the proposal list to the user device.

[2170] Specific operation: Send data using a communication protocol.

[2171] On your device: Show a notification to the user so they can review the suggestion.

[2172] Specific behavior: Display a push notification or alert box.

[2173] Step 7: Reservation support

[2174] Input: User's chosen accommodation and transportation options

[2175] Output: Booking completion and confirmation information

[2176] User: Decide on a choice from suggested accommodations and transportation options.

[2177] Action: Click an option from the list.

[2178] Terminal: Sends user selections to a central server.

[2179] Specific operation: The selection information is sent as a packet.

[2180] Server: Automatically initiates the booking process for the selected accommodation and transportation.

[2181] Specific actions: Access the reservation system and submit the required information.

[2182] Server: Processes the necessary payment information and generates the reservation confirmation.

[2183] Specific operation: Make a payment through a payment gateway.

[2184] Step 8: Submit confirmation information

[2185] Input: Generated booking confirmation information

[2186] Output: Reservation confirmation displayed on the user's device

[2187] Server: Generates reservation confirmation information.

[2188] What it does: Create a confirmation with a QR code, reservation number, and details.

[2189] Server: Sends reservation confirmation information to the user's terminal.

[2190] Specific operation: Encodes data and sends it to the terminal.

[2191] Terminal: Display reservation confirmation information to the user.

[2192] Specific action: Notify in-app or via email.

[2193] The above is the specific processing flow of the system and the detailed operation at each step.

[2194] (Application example 2)

[2195] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2196] Conventionally, when abnormal weather occurs during a trip, there has been a lack of a means to respond flexibly to the user's emotional state. This has caused stress and made it difficult for users to find appropriate accommodations and transportation. Furthermore, the lack of appropriate countermeasures in the event of abnormal weather has led to problems such as a decrease in travel safety and satisfaction. The present invention aims to provide a system that provides a quick and effective response to abnormal weather while taking into account the user's emotional state.

[2197] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2198] In this invention, the server includes means for acquiring location information and travel plan information from the user terminal, means for acquiring and analyzing weather forecast data to predict abnormal weather, means for analyzing the user's voice data and facial expression data to determine the user's emotional state, means for searching for accommodations and transportation options in the user's current location and surrounding areas and presenting optimal options, means for adjusting the suggestions based on the user's emotional state, means for notifying the user terminal of suggestions regarding recommended accommodations and transportation options, and means for automatically reserving accommodations and transportation options based on the user's selection. This enables flexible suggestions that take the user's emotional state into consideration, thereby increasing the safety and satisfaction of travel even during abnormal weather.

[2199] Below are definitions of key terms found in the claims.

[2200] "User terminal" means a device operated by a user for inputting and obtaining location information and travel planning information.

[2201] "Location information" is data that indicates a specific location using global positioning system technology.

[2202] "Travel planning information" refers to information about the itinerary, departure point, destination, etc. of a trip that the user is planning.

[2203] "Extreme weather" refers to weather phenomena that deviate significantly from normal weather conditions and may affect travel.

[2204] "Weather forecast data" refers to weather-related information obtained from a weather forecast application programming interface.

[2205] "Analysis" is the process of analyzing acquired data to derive meaningful information.

[2206] "Voice data" means data that is a digital recording of what a user says.

[2207] "Facial expression data" is data used to analyze the user's facial expressions and determine their emotions.

[2208] "Emotional state" is information that indicates the user's current emotion, and includes, for example, "tired" or "happy."

[2209] "Accommodation" means a facility (such as a hotel or inn) provided for temporary stay by users.

[2210] "Transportation" refers to the means of travelling to a destination (such as bullet train, bus, taxi, rental car, etc.).

[2211] The "best option" refers to the accommodation or transportation that best suits the user's emotional state and current situation.

[2212] "Proposal content" is information about accommodation and transportation that the server presents to the user.

[2213] "Reservation" is the process of securing accommodation or transportation in advance.

[2214] The present invention will be described as a food delivery support system incorporating an emotion engine that recognizes user emotions. The system uses voice and facial expression data to determine the user's emotions and suggests food delivery options suitable for the user, even in extreme weather. The main components of the system are a user terminal, a central server, an external weather forecast database, and the emotion engine.

[2215] Overall system overview

[2216] The central server receives location information and food delivery request information from the user's device, and then refers to an external weather forecast database to predict extreme weather.The emotion engine then analyzes the voice and facial expression data to determine the user's emotional state, and provides the food delivery options that best suit the user's emotional state and current situation.

[2217] Program processing

[2218] The program in this system works as follows:

[2219] 1. Obtaining User Information:

[2220] Device: The user launches the application, obtains their current location using GPS, and inputs their desired meal type, budget, and other information.

[2221] Terminal: Sends the acquired current location and request information to the central server.

[2222] 2. Predicting extreme weather:

[2223] Server: Accesses an external weather forecast database (such as a weather forecast API) and obtains weather forecast data for the user's current location.

[2224] Server: Analyzes the acquired weather forecast data and evaluates the possibility of abnormal weather (e.g., typhoons, heavy rain, heavy snow, etc.) occurring.

[2225] 3. Acquiring emotional information:

[2226] Terminal: Acquires the user's voice data and facial expression data.

[2227] Terminal: Sends acquired emotion data to the emotion engine.

[2228] Emotion engine: Analyzes voice and facial expression data to determine the user's current emotional state.

[2229] 4. Find the best food delivery options:

[2230] Server: Based on the results of extreme weather forecasts, search for food delivery options based on the user's current location and request information.

[2231] Server: Based on your emotional state, prioritize meals that reduce stress and fatigue, such as relaxation dishes and comfortable delivery options.

[2232] 5. User Suggestions and Notices:

[2233] Server: Creates a list of recommendations for optimal food delivery options and sends them to the user's device.

[2234] On device: Show a notification to the user so they can review the suggestion.

[2235] Specific examples

[2236] For example, let's say a user is at home and a typhoon is predicted to be approach...

Claims

1. means for obtaining location information and travel plan information from the user device; a means for acquiring and analyzing weather forecast data to predict extreme weather events; A way to search for accommodation and transportation options in and around your current location and present the best options; means for providing recommended accommodation and transportation suggestions to the user's device; means for automatically booking accommodation and transportation based on user selections; A system including:

2. 2. The system of claim 1, wherein the means for obtaining location information from the user terminal uses GPS technology.

3. 2. The system of claim 1, wherein the means for acquiring weather forecast data uses a weather forecast API.

4. 2. The system according to claim 1, wherein the means for presenting the most suitable accommodations and transportation means is a system for ranking the accommodations and transportation means taking into consideration geographical factors, travel time, availability, price, and user preferences.

5. The system according to claim 1 , wherein the means for notifying the user terminal uses push notification.

6. 2. The system of claim 1, wherein the means for automatically making a reservation processes necessary payment information and generates reservation confirmation information.

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