System

Abstract

A system is provided.SOLUTION: The system includes a means for inputting a request such as a desired visiting place, a desired activity, and a desired food from a user, a means for transmitting request data to a server, a means for analyzing the request by using a generative artificial intelligence model and generating related sightseeing information, a means for translating the generated sightseeing information into a mother tongue of the user, a means for providing the translated information to the user, and a means for providing additional detailed information.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] When obtaining tourist information within Japan, inbound travelers often face challenges such as language barriers, fragmented information, and lack of local communication. This can make planning and executing a trip time-consuming and labor-intensive, making it difficult to achieve a satisfying trip. Furthermore, existing information provision systems are unable to fully respond to users' vague requests, resulting in a lack of accurate information. Furthermore, the presence of fake users in ratings and reviews reduces reliability. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems with a system that includes a means for users to input their desired destinations, activities, and cuisine preferences; a means for transmitting the desired data to a server; a means for analyzing the requests and generating relevant tourist information using a generative artificial intelligence model; a means for translating the generated tourist information into the user's native language; a means for providing the translated information to the user; and a means for providing additional details (such as fares, directions, and reservation methods). The system also has the function of transmitting and displaying the translated information on the user's device, receiving user-submitted ratings and reviews, storing them in a database, and using an artificial intelligence model to filter out fake users, ensuring the reliability of the information. In this way, travelers can quickly and accurately obtain travel information and smoothly communicate on-site, making trip planning and execution easier and more satisfying.

[0006] "User" refers to a traveler who uses the system to input requests such as desired places to visit, desired activities, and desired cuisine.

[0007] A "terminal" is a device that allows a user to input information and send it to a server, and specifically refers to electronic devices such as smartphones and PCs.

[0008] "Server" refers to a computer system that receives request data sent by users, analyzes it, and generates and provides related tourist information.

[0009] A "generative artificial intelligence model" refers to a machine learning model or algorithm that analyzes user requests and generates optimal tourist information based on those requests.

[0010] "Request data" refers to information entered by the user, such as desired places to visit, desired activities, and desired cuisine.

[0011] "Tourist information" refers to information about tourist spots, activities, restaurants, etc. generated based on the user's requests.

[0012] "Translation means" refers to the functionality of using a multilingual framework or API to translate the generated tourist information into the user's native language.

[0013] "Ratings and reviews" refer to ratings and comments posted by users about their experiences after traveling.

[0014] "Sakura users" refer to users who post inaccurate or falsified ratings and reviews.

[0015] The "reward system" refers to a function that awards reward points to users who post fair ratings or reviews. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] In order to put the present invention into practice, it is necessary to construct a system in which a plurality of means operate in cooperation with one another. A specific embodiment of the system will be described below.

[0038] overview

[0039] This system receives vague requests from users, analyzes them using a generative artificial intelligence model, and provides optimal tourist information. It also translates the information into the user's native language to support travel planning. It also increases reliability by introducing a reward system for posting ratings and reviews, and by incorporating a function to eliminate fake users.

[0040] Processing flow

[0041] 1. User Input

[0042] The user inputs their desired destinations, activities, cuisine, and other requests into an input form on the terminal.

[0043] For example, enter "I want to relax in a place rich in nature."

[0044] 2. Data Transmission

[0045] The terminal transmits the input request data to the server.

[0046] The transmission format is usually an API request (e.g., POST method), and the data format is JSON.

[0047] 3. Request analysis

[0048] The server parses and preprocesses the received request data, which includes tokenizing the text data and extracting features.

[0049] It uses a generative artificial intelligence model to analyze requests and generate a list of relevant attractions and activities.

[0050] 4. Recommendation information generation

[0051] The server uses the analysis results to generate tourist information that best suits the user's needs.

[0052] For example, in response to the above request, tourist spots around Mount Fuji are listed.

[0053] 5. Multilingual Translation

[0054] The server translates the generated tourist information into the user's native language.

[0055] A multilingual translation API (e.g., Google (registered trademark) translation API) is used for translation.

[0056] 6. Information provision

[0057] The server sends the translated recommendation information to the device,

[0058] The terminal displays it to the user.

[0059] For example, it might say, "We recommend the Mt. Fuji area. You can enjoy beautiful nature and relaxing hot springs."

[0060] 7. Providing additional information

[0061] If the user is interested in the recommended information, he or she sends a request to the server via the terminal requesting additional details (such as fare, directions, and reservation methods).

[0062] The server collects more detailed information and provides it to the user.

[0063] Posting and managing ratings and reviews

[0064] 1. Posting ratings and reviews

[0065] After traveling, users post ratings and reviews from their devices.

[0066] The device sends these posts to the server.

[0067] 2. Managing Posts

[0068] The server stores the received ratings and reviews in a database.

[0069] It uses a generative artificial intelligence model to analyze the content of posts and identify and filter out fake posts made by fake users.

[0070] 3. Reward System

[0071] The server awards reward points for posting legitimate ratings and reviews.

[0072] The terminal provides an interface where users can check their reward points and use them to plan their next trip.

[0073] This system configuration allows users to efficiently obtain tourist information and make travel plans. It also guarantees the reliability of ratings and reviews, allowing users to share travel experiences based on accurate information. This system will be a powerful support tool that allows each inbound traveler to enjoy Japan with peace of mind.

[0074] The processing flow will be explained below.

[0075] Step 1:

[0076] The user uses the terminal to enter their desired places to visit, desired activities, desired cuisine, etc. into a text input form. For example, they might enter "I want to relax in a place rich in nature."

[0077] Step 2:

[0078] The terminal sends the input request data to the server. The transmission format is usually an API request (e.g., POST method), and the data is sent in JSON format.

[0079] Step 3:

[0080] The server parses the received request data and performs pre-processing for input to the generative AI model, including tokenizing the text data and extracting features.

[0081] Step 4:

[0082] The server analyzes the request data using a generative AI model, which then generates a list of related tourist attractions and activities based on the request.

[0083] Step 5:

[0084] Based on the analysis results, the server generates recommendations that best fit the user's needs. For example, if a user requests "I want to relax in a place rich in nature," the server will suggest tourist spots around Mount Fuji.

[0085] Step 6:

[0086] The server translates the generated recommendation information into the user's native language using a multilingual translation API (e.g., Google Translate API).

[0087] Step 7:

[0088] The server sends the translated recommendation information to the terminal.

[0089] The device then displays the received information to the user. For example, it might say, "We recommend the Mt. Fuji area. You can enjoy beautiful nature and relaxing hot springs."

[0090] Step 8:

[0091] If the user is interested in the displayed recommendation information, the terminal sends a request to the server for more detailed information (such as fare, directions, and reservation methods).

[0092] Step 9:

[0093] The server receives the request for more information, gathers more specific information, and provides it to the user.

[0094] Step 10:

[0095] After a trip, users can post ratings and reviews via their devices, for example by entering rating scores and comments.

[0096] Step 11:

[0097] The device sends the posted ratings and reviews to the server.

[0098] Step 12:

[0099] The server stores the received ratings and reviews in a database, and also uses a generative artificial intelligence model to identify and filter out posts from fake users.

[0100] Step 13:

[0101] The server will award reward points for posting honest ratings and reviews.

[0102] Step 14:

[0103] The terminal provides an interface that allows the user to check the reward points that he or she has earned, and the user can use the reward points to plan his or her next trip.

[0104] Example 1

[0105] 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."

[0106] In modern travel planning, users gather a lot of information and spend a lot of time selecting appropriate tourist destinations. Furthermore, the information they obtain can be unreliable, which can significantly reduce their travel satisfaction. Furthermore, language barriers are a major obstacle to travel planning and information gathering on-site. To solve these problems, a system is needed that can accurately interpret users' needs, provide optimal tourist information, and utilize reliable reviews and ratings.

[0107] 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.

[0108] In this invention, the server comprises: a means for inputting requests from a user, such as desired places to visit, desired activities, desired dishes, etc.;

[0109] means for transmitting request data from the terminal to the server;

[0110] A means for analyzing requests and generating relevant tourist information using a generative artificial intelligence model;

[0111] A means for translating the generated tourist information into the user's native language;

[0112] a means for providing the translated information to a user;

[0113] A means to provide additional details (such as prices, directions, and booking instructions) if requested by the user;

[0114] A method to receive user ratings and reviews, analyze them, and filter out fake users.

[0115] A way to reward legitimate ratings and reviews,

[0116] a means for users to view reward points available for their next travel plan;

[0117] This will enable users to efficiently obtain reliable tourist information and plan their trips. It will also increase the reliability of ratings and reviews, allowing users to share travel experiences based on accurate information.

[0118] A "user" is an entity that uses this system to input requests such as desired places to visit, desired activities, and desired cuisine, and obtains information.

[0119] "Terminal" refers to a device that transmits user-entered requests to a server and displays information received from the server.

[0120] The "server" is a central processing device that analyzes requests received from users and generates and provides tourist information.

[0121] A "generative artificial intelligence model" is a type of algorithm or technology that analyzes user requests and generates relevant and appropriate tourist information.

[0122] "Request data" is data including information such as desired places to visit, desired activities, and desired dishes input by the user.

[0123] "Tourist information" refers to information about places and activities that a user would like to visit, including information about tourist spots, activities, places to eat, etc.

[0124] "Translation API" refers to a programming interface for converting information written in one language into another language.

[0125] "Ratings and reviews" refer to reviews and feedback posted by users after their trips, and are information that includes ratings.

[0126] "Sakura users" refer to fraudulent users who post falsified ratings and reviews.

[0127] "Reward points" are points awarded to users who post honest reviews and ratings, and can be used to plan their next trip, etc.

[0128] "Additional Details" refers to detailed information such as prices, directions, and reservation methods that is provided upon request by the user.

[0129] To implement this invention, it is necessary to build a system that links multiple means. This system receives vague requests from users, analyzes them using a generative artificial intelligence model, and provides optimal tourist information. It also has a function to translate information into the user's native language and support travel planning. It also increases reliability by introducing a reward system for posting ratings and reviews and by incorporating a function to eliminate fake users.

[0130] Hardware and software used

[0131] An implementation of this system includes the following hardware and software:

[0132] 1. Terminal: A device where a user inputs requests and displays information from a server. Examples include smartphones and personal computers.

[0133] 2. Server: The server analyzes the request data, generates tourist information, translates and manages the reward system.

[0134] 3. Generative AI model: An AI model (e.g., GPT-3 (registered trademark)) that analyzes user requests and generates relevant tourist information.

[0135] 4. Translation API: An API for translating tourist information into the user's native language (e.g., Google Translate API).

[0136] Program processing

[0137] Below is a detailed description of how the program works in this system:

[0138] 1. User Input

[0139] The user inputs into an input form on the terminal the desired places to visit, desired activities, desired cuisine, etc. As a specific example, the user inputs "I want to relax in a place rich in nature."

[0140] 2. Data Transmission

[0141] The terminal sends the input request data to the server. At this time, the data is usually sent using the POST method, and the format is JSON.

[0142] 3. Request analysis

[0143] The server analyzes the received request data, using natural language processing (NLP) techniques to tokenize the text data and extract key keywords, such as "nature," "relaxation," and "place."

[0144] 4. Information generation

[0145] The server analyzes the request using a generative artificial intelligence model (e.g., GPT-3) based on the extracted keywords and generates relevant tourist information. For example, tourist spots around Mount Fuji are selected.

[0146] 5. Multilingual Translation

[0147] To translate the generated tourist information into the user's native language, the server uses a multilingual translation API such as Google Translate API. For example, "We recommend the Mt. Fuji area. You can enjoy beautiful nature and relaxing hot springs."

[0148] 6. Information provision

[0149] The server sends the translated information to the device, which then displays it to the user. For example, it might say, "We recommend the Mount Fuji area. You can enjoy beautiful nature and relaxing hot springs."

[0150] 7. Providing additional information

[0151] If a user shows interest in the recommended information, they send a request for detailed information (such as fare, directions, and reservation methods) to the server via their device. The server collects the detailed information and provides it to the user.

[0152] 8. Posting and managing ratings and reviews

[0153] After their trip, users submit ratings and reviews from their devices. The server stores these posts in a database and uses a generative artificial intelligence model to identify fake users and filter out fake posts.

[0154] 9. Reward System

[0155] The server awards reward points for legitimate ratings and reviews, and the terminal provides an interface where users can check these points. Users can use these reward points to plan their next trip.

[0156] Examples of concrete examples and prompts

[0157] Example: If a user types, "I want to relax in a place rich in nature," the system will suggest tourist spots around Mount Fuji and display the message, "We recommend the Mount Fuji area. You can enjoy beautiful nature and relaxing hot springs."

[0158] Example prompt: "What are some relaxing tourist spots in a particular location?"

[0159] This system configuration allows users to efficiently obtain reliable tourist information and plan their trips. Furthermore, the reliability of ratings and reviews is guaranteed, allowing users to share travel experiences based on accurate information.

[0160] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0161] Step 1:

[0162] The user inputs their desired destinations, activities, and cuisine into the device. For example, they might input, "I want to relax in a place rich in nature." This input data is temporarily stored on the device. The input data is acquired through a text form.

[0163] Step 2:

[0164] The device sends the input request data to the server. At this time, the data is converted to JSON format and the POST method is used as the API request. The device organizes the request data as a JSON object and sends an HTTP request to the server.

[0165] Step 3:

[0166] The server analyzes the received request data and performs data preprocessing. This step includes tokenizing the text data and extracting features. For example, from the text "I want to relax in a place rich in nature," the keywords "nature," "relaxation," and "place" are extracted. Specifically, a text analysis algorithm is used to perform grammatical analysis and extract keywords.

[0167] Step 4:

[0168] The server inputs the extracted keywords into a generative AI model (e.g., GPT-3) to analyze the request. The generative AI model generates relevant tourist information based on these keywords. For example, the keywords "nature," "relaxation," and "place" generate tourist spots around Mount Fuji. The server temporarily stores the generated tourist information.

[0169] Step 5:

[0170] The server translates the generated tourist information into the user's native language. This translation uses a multilingual translation API (e.g., Google Translate API). The server sends the tourist information to the translation API and receives the translated results. For example, the translated text would read, "We recommend the Mt. Fuji area. You can enjoy beautiful nature and relaxing hot springs."

[0171] Step 6:

[0172] The server sends the translated information to the device. The data is converted back to JSON format and sent as an API response. The device analyzes the received data and displays it on the user interface. For example, it might say, "We recommend the Mt. Fuji area for you. You can enjoy beautiful nature and relaxing hot springs."

[0173] Step 7:

[0174] If a user is interested in a recommendation, they send a request to the server via their device for additional details (such as fare, directions, and reservation methods). Based on the input, the server collects the details from relevant databases and external services. This information is sent to the device, which then displays it to the user.

[0175] Step 8:

[0176] After a trip, users post ratings and reviews from their devices. The devices then send these posts to the server. The entered ratings and reviews are sent to the server in JSON format. A specific example of input includes text such as "The hot springs at Mt. Fuji were very relaxing."

[0177] Step 9:

[0178] The server stores the received ratings and reviews in a database and analyzes the posts using a generative AI model. This analysis includes identifying fake posts by fake users. Specifically, it detects large numbers of similar posts from the same IP address and filters out those that are in question.

[0179] Step 10:

[0180] The server will award reward points for posting valid ratings and reviews. These points will be reflected in the user's account, and the device will provide an interface where the reward points can be checked. The user can use these points to plan their next trip. For example, the server will notify the user that "10 points have been awarded."

[0181] (Application example 1)

[0182] 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."

[0183] Conventional tourism support systems have difficulty providing appropriate tourist information to users' vague requests, and their ability to display information in multiple languages ​​is limited. Furthermore, autonomous vehicles lack sufficient navigation functions to guide users to tourist spots based on their requests. This makes it difficult for users to visit tourist spots efficiently and safely.

[0184] 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.

[0185] In this invention, the server includes means for inputting requests from the user, such as desired places to visit, desired activities, and desired cuisine, means for transmitting the request data to the server, means for analyzing the requests using a generative artificial intelligence model and generating relevant tourist information, means for translating the generated tourist information into the user's native language, means for providing the translated information to the user, means for providing additional detailed information (fares, directions, reservation methods, etc.), and means for providing route guidance to the destination in cooperation with the autonomous driving system. This enables the suggestion of tourist information that meets the user's vague requests and efficient navigation to tourist spots in an autonomous driving vehicle.

[0186] "User" refers to an individual person or customer who uses the Service.

[0187] "Request data" refers to specific or vague request information such as desired places to visit, desired activities, and desired cuisine entered by the user.

[0188] "Server" means a centralized computer system for receiving, analyzing, and processing requested data and providing related information.

[0189] "Generative AI model" refers to advanced AI technology used to analyze user input data and generate relevant tourist information.

[0190] "Relevant tourist information" refers to information about tourist attractions, activities, food and drink provided based on user requests analyzed using a generative artificial intelligence model.

[0191] "Translated information" refers to information that has been converted from the generated tourist information into the user's native language that the user can understand.

[0192] "More Information" refers to additional information related to an attraction, such as prices, directions, and reservations.

[0193] An "autonomous driving system" refers to a system that autonomously controls vehicle operation and guides the vehicle to its destination based on user input.

[0194] "Route Guidance" refers to a function that works in conjunction with the autonomous driving system to provide the user with the optimal route to the tourist spots they wish to visit.

[0195] "In-vehicle display" refers to an information display device installed inside a vehicle, and refers to a display that displays tourist information and navigation information.

[0196] "Ratings and reviews" refer to opinions and impressions of the services provided and places visited by users after their trip.

[0197] "Database" refers to a structured information storage system for organizing and storing user ratings and reviews.

[0198] "Fake User" refers to a fake user hired to post inaccurate or false reviews.

[0199] A "voice recognition system" refers to a system that analyzes a user's voice input and converts it into text data.

[0200] To implement this invention, it is desirable to have the following system configuration: The system mainly receives user input, analyzes it, provides optimal tourist information, and also cooperates with the navigation of the autonomous vehicle.

[0201] Hardware and software used

[0202] In-vehicle display: An information display device installed inside a vehicle that displays tourist information and navigation information.

[0203] Speech recognition system: A system that analyzes the user's voice input and converts it into text data, for example, using the Google Speech-to-Text API.

[0204] Generative AI model: An AI technology used to analyze user input data and generate relevant tourist information. GPT-4 (registered trademark) provided by OpenAI (registered trademark) is used.

[0205] Translation API: An API for translating the generated information into the user's native language, for example, using the Google Translate API.

[0206] Navigation system: A system that controls the operation of an autonomous vehicle and guides it to a destination based on user input.

[0207] System Program Processing

[0208] 1. User input:

[0209] The user uses the voice recognition function on the in-car display or smartphone to input a vague request (e.g., "I want to relax in a place rich in nature"), which is then converted into text data.

[0210] 2. Sending requested data:

[0211] The text data converted by the speech recognition system is sent to the server via an API request (POST method, JSON format).

[0212] 3. Request analysis:

[0213] The server then sends the text data to a generative artificial intelligence model (GPT-4) to analyze the request, which then lists relevant tourist spots and activities.

[0214] 4. Tourism information generation:

[0215] The server then uses the analysis results to generate tourist information that best suits the user's needs, for example, recommending "naturally rich places around Mount Fuji."

[0216] 5. Translation:

[0217] The server uses the Google Translate API to translate the generated tourist information into the user's native language, thereby providing the information in a format that is easy for the user to understand.

[0218] 6. Information and navigation:

[0219] The translated information is displayed on the in-vehicle display and is also provided to the user as an audio guide. The server and the autonomous driving system also work together to provide route guidance to the suggested spots.

[0220] Examples of concrete examples and prompts

[0221] Specific examples

[0222] When a user speaks to the in-car display, "I want to relax in quiet nature," the speech is converted into text and sent to a server. A generative artificial intelligence model analyzes the speech and generates tourist information about the Mount Fuji area. This information is translated and displayed on the in-car display, saying, "We recommend the Mount Fuji area. Beautiful nature awaits you." The autonomous driving system then begins navigating to Mount Fuji.

[0223] Prompt Sentence Examples

[0224] "A user types 'I want to relax in a place rich in nature.' Suggest five related tourist spots."

[0225] This system will enable the proposal of tourist information that meets the user's vague requests and the efficient navigation to tourist spots in self-driving vehicles.

[0226] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0227] Step 1:

[0228] The user uses the in-car display or smartphone to input their wishes and requests by voice or text. For example, a request such as "I want to relax in a place rich in nature" can be input by voice into the in-car display. This input data is converted into text data by a voice recognition system (Google Speech-to-Text API). The voice input is output as text data.

[0229] Step 2:

[0230] The device sends the text data converted by the voice recognition system to the server via an API request (POST method, JSON format). This text data (request data) is input to the server.

[0231] Step 3:

[0232] The server tokenizes the received request data and preprocesses the text data. It then sends the data to a generative artificial intelligence model (GPT-4) to analyze the user's request. This process involves tokenizing the text, extracting features, and analyzing them to create a list of related tourist attractions and activities. For example, the output would be "Places rich in nature around Mt. Fuji."

[0233] Step 4:

[0234] The server generates tourist information that matches the user's needs based on the analysis results of the generative AI model. Information on recommended tourist spots and activities extracted from the analysis results is generated as text data. For example, information such as "Beautiful natural areas around Mt. Fuji" is generated.

[0235] Step 5:

[0236] The server sends the generated tourist information to a translation API (Google Translate API) to be translated into the user's native language. The translation API translates the received tourist information into the specified language and outputs the translated information.

[0237] Step 6:

[0238] The server sends the translated tourist information to the in-vehicle display. This information is used as part of the navigation display screen. It is also output as an audio guide and presented to the user. The translated information provided to the user is, for example, "We recommend the Mt. Fuji area. Beautiful nature awaits you."

[0239] Step 7:

[0240] The server works in conjunction with the autonomous driving system to send data to create a route guide to the suggested tourist spots. The autonomous driving system calculates the optimal navigation route based on this information and guides the vehicle to the destination. Specifically, a route planning algorithm is used to generate a route that takes into account traffic conditions and road information. The vehicle then begins autonomous driving according to this route.

[0241] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0242] In order to put this invention into practice, it is necessary to build a system that further combines an emotion engine that recognizes the user's emotions. A specific embodiment of the system is shown below.

[0243] overview

[0244] This system receives vague requests from users, analyzes them using a generative artificial intelligence model, and provides optimal tourist information. Furthermore, by combining it with an emotion engine that uses user emotional data to adjust recommendation information, more personalized information is provided. Furthermore, a translation function provides information in the user's native language, a reward system for posting ratings and reviews, and a function to eliminate fake users increase reliability.

[0245] Processing flow

[0246] 1. User Input

[0247] The user uses the terminal to input requests such as desired places to visit, desired activities, and desired cuisine into a text input form.

[0248] For example, enter "I want to relax in a place rich in nature."

[0249] 2. Data Transmission

[0250] The terminal sends the input request data to the server. The transmission format is usually an API request (e.g., POST method), and the data is sent in JSON format.

[0251] 3. Request analysis

[0252] The server parses and pre-processes the received request data, which includes tokenizing the text data and extracting features.

[0253] It uses a generative artificial intelligence model to analyze requests and generate a list of relevant attractions and activities.

[0254] 4. Recommendation information generation

[0255] The server uses the analysis results to generate recommendation information that best suits the user's needs.

[0256] For example, if a customer requests "I want to relax in a place rich in nature," we will suggest tourist spots around Mount Fuji.

[0257] 5. Multilingual Translation

[0258] The server translates the generated recommendation information into the user's native language using a multilingual translation API (e.g., Google Translate API).

[0259] 6. Emotion recognition

[0260] When providing information, the server uses an emotion engine to recognize the user's emotions. For example, it analyzes the user's facial expressions and tone of voice to quantify the user's emotions.

[0261] 7. Information provision

[0262] The server adjusts the recommendation information based on the user's emotional data and sends the most appropriate information to the terminal.

[0263] The device will then display the provided information to the user. For example, it might say, "We recommend the Mt. Fuji area. You can enjoy beautiful nature and relaxing hot springs."

[0264] 8. Providing additional information

[0265] If the user is interested in the recommended information, he or she sends a request to the server via the terminal requesting additional details (such as fare, directions, and reservation methods).

[0266] The server collects more detailed information and provides it to the user.

[0267] Posting and managing ratings and reviews

[0268] 1. Posting ratings and reviews

[0269] After a trip, users post ratings and reviews from their devices, for example, by entering rating scores and comments.

[0270] 2. Managing Posts

[0271] The terminal transmits the posted content to the server.

[0272] The server stores the received ratings and reviews in a database, uses an emotion engine to recognize the user's emotions at the time of posting, and uses this information to analyze the reliability of the review ratings. It also uses a generative artificial intelligence model to identify and exclude posts by fake users.

[0273] 3. Reward System

[0274] The server will award reward points for posting honest ratings and reviews.

[0275] The terminal provides an interface that allows the user to check the reward points that he or she has earned, and the user can use the reward points to plan his or her next trip.

[0276] This system configuration allows users to efficiently obtain tourist information and plan their trips. It also ensures that information provided takes user sentiment into consideration, and the reliability of ratings and reviews is guaranteed, allowing users to share their travel experiences based on accurate information. This system will be a powerful support tool for inbound travelers to enjoy Japan with peace of mind.

[0277] The processing flow will be explained below.

[0278] Step 1:

[0279] The user uses the terminal to enter their desired places to visit, desired activities, desired cuisine, etc. into a text input form. For example, they might enter "I want to relax in a place rich in nature."

[0280] Step 2:

[0281] The device sends the input request data to the server. The sending format is usually an API request (e.g., POST method), and the data is sent in JSON format.

[0282] Step 3:

[0283] The server parses the received request data and performs preprocessing to input it into the generative AI model. Preprocessing involves tokenizing the text data and extracting features.

[0284] Step 4:

[0285] The server uses a generative artificial intelligence model to analyze the request data, which then generates a list of relevant attractions and activities.

[0286] Step 5:

[0287] Based on the analysis results, the server generates recommendations that best fit the user's needs. For example, if a user requests "I want to relax in a place rich in nature," a list of tourist spots around Mount Fuji will be displayed.

[0288] Step 6:

[0289] The server translates the generated recommendation information into the user's native language using a multilingual translation API (e.g., Google Translate API).

[0290] Step 7:

[0291] The server uses an emotion engine to recognize the user's emotions by analyzing the user's input, facial expressions, or voice data.

[0292] Step 8:

[0293] The server adjusts the recommendation information based on the user's emotional data. For example, if the user is looking for "relaxation," it will prioritize information on quiet resorts.

[0294] Step 9:

[0295] The server sends the adjusted recommendation information to the terminal, which then displays the received information to the user.

[0296] For example, it might say, "We recommend the Mt. Fuji area. You can enjoy beautiful nature and relaxing hot springs."

[0297] Step 10:

[0298] If the user is interested in the displayed recommendation information, the terminal sends a request to the server for more detailed information (such as fare, directions, and reservation methods).

[0299] Step 11:

[0300] The server receives the request for more information, gathers more specific information, and provides it to the user.

[0301] For example, provide detailed information in the form of, "It takes about an hour by Shinkansen from Shinjuku Station to Mishima Station, and the fare is about 4,000 yen one way."

[0302] Step 12:

[0303] After a trip, users post ratings and reviews via their devices, inputting rating scores and comments, and sending the posted content from the device to the server.

[0304] Step 13:

[0305] The server stores the received ratings and reviews in a database. It uses an emotion engine to recognize the user's emotions at the time of posting and uses this information to analyze the reliability of the review ratings.

[0306] Step 14:

[0307] The server uses a generative artificial intelligence model to identify and filter out posts from fake users, ensuring the authenticity of ratings and reviews.

[0308] Step 15:

[0309] The server awards reward points for posting honest ratings and reviews. The terminal provides an interface where users can check the reward points they have earned, and users can use the reward points to plan their next trip.

[0310] Example 2

[0311] 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."

[0312] Conventional tourist information systems have limited ability to fully understand users' vague requests and diverse emotions and provide optimal information. Furthermore, the means to ensure the reliability and accuracy of information are insufficient, and there is no multilingual support or appropriate review management. As a result, users are unable to obtain satisfactory tourist information, and the quality of their tourist experience is reduced.

[0313] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting requests from the user, such as desired places to visit, desired activities, and desired cuisine, means for transmitting the request data to the server, means for analyzing the requests using a generative artificial intelligence model and generating relevant tourist information, means for translating the generated tourist information into the user's native language, means for recognizing the user's emotions using an emotion engine and adjusting recommended information, means for providing the translated information to the user, and means for providing additional detailed information (such as fares, directions, and reservation methods). This allows the user to obtain personalized, optimal tourist information based on their own vague requests, and enables a high-quality tourist experience through the provision of information according to emotions and reliable review management.

[0314] "User" refers to an individual who uses this system to obtain tourist information and post ratings and reviews.

[0315] "Request data" is data including text information such as desired places to visit, desired activities, and desired dishes input by the user.

[0316] "Generative AI model" refers to an AI technology that analyzes user input request data and generates related tourist information.

[0317] An "emotion engine" refers to technology that recognizes user emotions, quantifies and analyzes them, and adjusts recommendation information.

[0318] "Sakura users" refer to fake users who post false ratings and reviews for fraudulent purposes.

[0319] "Translation means" refers to the translation techniques and tools used to translate the generated tourist information into the user's native language.

[0320] "Recommended information" refers to information about tourist spots and activities provided based on the user's requests and emotional data.

[0321] "Review management means" refers to the means for receiving ratings and reviews posted by users, storing them in a database, and excluding posts by fake users.

[0322] A "reward system" refers to a mechanism that awards reward points for legitimate ratings and reviews posted by users, allowing users to use those points for travel planning.

[0323] This invention is a system for analyzing vague requests entered by users and providing optimal tourist information. The system utilizes user emotional data to optimize information and achieve multilingual support. It also ensures the reliability of ratings and reviews and introduces a reward system to improve the user experience. Specific embodiments of the system are described below.

[0324] overview

[0325] Users use a device (smartphone, PC, etc.) to input their desired places to visit, activities, cuisine, etc. The device sends the desired data to a server, which analyzes it and generates optimal tourist information. The hardware required is a device used by the user and a server. The software required includes a generative AI model, an emotion engine, a translation API, etc.

[0326] Hardware and software used

[0327] Terminal: A smartphone or PC used by a user to input and display data.

[0328] Server: A computer system for performing data analysis and information generation.

[0329] Generative AI model: AI technology for analyzing user requests and generating optimal tourist information (e.g., GPT-4).

[0330] Translation API: An API for translating the generated tourist information into the user's native language (e.g., Google Translate API).

[0331] Emotion engine: Technology for recognizing user emotions and adjusting recommendation information (e.g., Microsoft® Azure® Emotion API).

[0332] Examples of data analysis and information generation

[0333] 1. User Input:

[0334] The user can freely input the places and activities they want to visit into the input form on the device. For example, they can enter "I want to relax in a place rich in nature."

[0335] 2. Data transmission:

[0336] The terminal sends the input request data to the server in JSON format.

[0337] 3. Request analysis:

[0338] The server parses the received request data, tokenizes the text data, and extracts features. It then inputs a prompt to the generative AI model for analysis. An example of a prompt is as follows:

[0339] The user's request is "I want to relax in a place rich in nature." Please suggest the best tourist spots.

[0340] A generative artificial intelligence model generates a list of relevant attractions and activities.

[0341] 4. Information Translation:

[0342] The server translates the generated tourist information into the user's native language using a translation API, for example, "Relaxing natural spots near Mount Fuji" in English.

[0343] 5. Emotion recognition:

[0344] The emotion engine recognizes the user's emotions in real time, calculates an emotion score from the user's facial expressions and voice, and adjusts the recommendation information accordingly.

[0345] 6. Information provision:

[0346] The server sends the adjusted recommendation information to the device, which then displays it to the user. For example, it displays, "We recommend the Mt. Fuji area. You can enjoy beautiful nature and relaxing hot springs."

[0347] Providing additional information and managing reviews

[0348] If the user requests additional details, they send a request to the server via their device. The server collects the details (fare, directions, reservation methods, etc.) and provides them to the user. Furthermore, if the user submits a rating or review, the server stores it in a database and uses an artificial intelligence model to filter out fraudulent posts (fake users). Legitimate ratings and reviews are awarded reward points, which the user can use to plan their next trip.

[0349] As a result, users can obtain efficient and personalized tourist information and enjoy a high-quality travel experience.

[0350] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0351] Step 1:

[0352] The user uses the terminal to enter their desired places to visit, desired activities, desired cuisine, etc. into a text input form. For example, they might enter "I want to relax in a place rich in nature."

[0353] Input: User's request text

[0354] Output: The requested data is temporarily stored on the device.

[0355] Step 2:

[0356] The terminal sends the input request data to the server. Specifically, it uses Ajax of JavaScript (registered trademark) to send the data in JSON format.

[0357] Input: User's desired data

[0358] Output: The requested data is sent to the server in JSON format.

[0359] Step 3:

[0360] The server parses the received request data, tokenizes the text data, extracts features, and analyzes the text using the Python NLTK library.

[0361] Input: Request data in JSON format

[0362] Output: Tokenized and feature extracted data

[0363] Step 4:

[0364] The server inputs a prompt sentence into a generative AI model (e.g., GPT-4) for analysis. For example, the following prompt sentence is used:

[0365] Prompt: "The user wants to relax in a place surrounded by nature. Please suggest the best tourist spots."

[0366] Input: Tokenized and feature extracted data

[0367] Output: A list of related attractions and activities

[0368] Step 5:

[0369] The server translates the generated tourist information into the user's native language using a multilingual translation API (e.g., Google Translate API).

[0370] Input: List of tourist information

[0371] Output: Translated tourist information

[0372] Step 6:

[0373] The server uses an emotion engine (e.g., Microsoft Azure's Emotion API) to recognize emotions from user input or reactions. It analyzes the user's facial expressions and tone of voice and generates an emotion score.

[0374] Input: User input data, reaction data

[0375] Output: Sentiment score

[0376] Step 7:

[0377] The server adjusts the translated tourist information based on the emotion score to generate optimal recommendation information, which is then sent to the device.

[0378] Input: translated tourist information, sentiment score

[0379] Output: Adjusted recommendation information

[0380] Step 8:

[0381] The device will then display the provided recommendation information to the user. For example, it might say, "We recommend the Mt. Fuji area. You can enjoy beautiful nature and relaxing hot springs."

[0382] Input: Adjusted recommendation information

[0383] Output: Recommendation information that users can visually confirm

[0384] Step 9:

[0385] If the user is interested in the recommended information, he or she sends a request to the server via the terminal requesting additional details (such as fare, directions, and reservation methods).

[0386] Input: Request for additional information

[0387] Output: Request data is sent to the server

[0388] Step 10:

[0389] The server collects additional details and provides them to the user.

[0390] Input: Request data

[0391] Output: Additional details

[0392] Step 11:

[0393] After a trip, users post ratings and reviews from their devices, for example by entering rating scores and comments and attaching images.

[0394] Input: Ratings and review data

[0395] Output: Ratings and reviews data persisted on device

[0396] Step 12:

[0397] The terminal transmits the posted content to the server.

[0398] Input: Ratings and review data

[0399] Output: Posted data is sent to the server

[0400] Step 13:

[0401] The server stores the received ratings and reviews in a database, uses an emotion engine to recognize the user's emotions at the time of posting, and uses a generative artificial intelligence model to identify and filter out posts from fake users.

[0402] Input: Ratings and review data

[0403] Output: Data after reliability analysis, identification results of fake users

[0404] Step 14:

[0405] The server will award reward points for posting honest ratings and reviews.

[0406] Input: Data after reliability analysis

[0407] Output: User's reward points

[0408] Step 15:

[0409] The terminal provides an interface that allows the user to check the reward points that he or she has earned, and the user can use the reward points to plan his or her next trip.

[0410] Input: User's reward points

[0411] Output: Reward point information that can be visually confirmed by the user

[0412] (Application example 2)

[0413] 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."

[0414] In factory work, it is necessary to respond quickly and appropriately to vague requests from workers, and to create work plans that take into account the emotions and health of workers.In addition to tourist information, a system is needed that utilizes generative artificial intelligence models to provide optimal work plans for factory work.

[0415] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting requests from the user, such as desired places to visit, desired activities, and desired cuisine; means for transmitting the request data to the server; means for analyzing the requests using a generative artificial intelligence model and generating relevant tourist information; means for translating the generated tourist information into the user's native language; means for providing the translated information to the user; means for providing additional detailed information (such as fees, directions, and reservation methods); means for analyzing the user's emotional data using an emotion recognition engine; means for adjusting information based on the analyzed emotional data; and means for formulating an optimal work plan based on the adjusted information and automatically executing the work. This makes it possible to respond quickly and appropriately to worker requests and to create work plans that take into account the worker's emotions and health status.

[0416] "User emotion data" is data that quantifies the user's emotional state analyzed from the user's facial expression, tone of voice, behavior, etc.

[0417] A "generative artificial intelligence model" is an artificial intelligence algorithm that analyzes a user's vague requests and generates relevant information and work plans.

[0418] "Request data" refers to data including specific or vague requests such as desired places to visit, desired activities, and desired dishes entered by the user.

[0419] An "emotion recognition engine" is a software component that analyzes a user's voice and facial expression data to recognize their emotional state.

[0420] "Adjusting information" means optimizing the content of the information provided and work plans based on emotional data.

[0421] A "work plan" is a schedule or action plan for work that is planned and executed by a generative artificial intelligence model.

[0422] "Additional details" are specific details such as prices, directions, and reservation methods that accompany the information provided.

[0423] A "server" is a computer system that receives and analyzes user-requested data, and generates, adjusts, and provides related information.

[0424] This invention is a system in which a patrol / work robot in a factory analyzes vague requests from workers, creates an optimal work plan, and automatically executes it. This system takes into account the user's emotional data and can perform work more efficiently and safely.

[0425] System Configuration

[0426] 1. Emotion Recognition Engine

[0427] This engine is a software component that analyzes the voice and facial expression data of workers and outputs their emotional state as numerical data. It uses a camera and microphone to collect real-time emotional data of users.

[0428] 2. Generative AI Models

[0429] This is an algorithm that analyzes request data and generates a related work plan. This model has the ability to extract specific tasks from vague requests and convert them into actionable plans.

[0430] 3. Robot Controller

[0431] This is a component that controls robots in factories and automatically executes tasks based on the generated work plan. This control system manages the robot's operation and performs work efficiently.

[0432] Hardware and software used

[0433] Camera and microphone

[0434] It collects the worker's facial expressions and voice and uses them to provide data to the emotion recognition engine.

[0435] GPU Server

[0436] This is a server for high-speed processing of emotion recognition engines and generative AI models. Specifically, it is equipped with hardware capable of processing neural networks and deep learning.

[0437] Robots in the factory

[0438] These are creations that perform real tasks, such as a typical factory robot.

[0439] Example of operation

[0440] 1. The worker speaks to the patrol robot, saying, "This machine needs adjustment." At this time, the camera captures the worker's facial expression and the microphone collects the voice.

[0441] 2. The robot terminal uses an emotion recognition engine to analyze the worker's emotional state from their voice and facial expression data.

[0442] 3. The analyzed emotional data is combined with a generative artificial intelligence model to analyze the user's requests and generate an optimal work plan.

[0443] 4. The generated work plan is automatically executed by the robot controller, which adjusts the specified machine and notifies the work support system as necessary.

[0444] Example prompt sentence:

[0445] This machine needs adjustment.

[0446] In this way, it is possible to respond quickly and appropriately to the requests of workers and create optimal work plans that take emotional data into account, thereby improving work efficiency in the factory and reducing worker stress and fatigue.

[0447] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0448] Step 1:

[0449] The user inputs their request by voice or text. The device collects the voice data or text data using a microphone and a text input interface. Voice data or text data is obtained as input.

[0450] Step 2:

[0451] The device converts voice data into text. This conversion is done using speech recognition software. The input is voice data and the output is text data. Specifically, the speech recognition engine analyzes the voice waveform and converts it into the corresponding text.

[0452] Step 3:

[0453] The device uses a camera and microphone to collect the user's facial expressions and voice tone. The data is sent to an emotion recognition engine. The input is image data and voice tone data. The output is quantified emotion data. The emotion recognition engine extracts facial features from the image data and voice features from the voice data to quantify emotions.

[0454] Step 4:

[0455] The device sends the collected request data and emotion data to the server. The server receives this data. An API request (e.g., POST method) is used to send the data. The input is the request text data and emotion data. The output is a confirmation that the data has been transferred to the server.

[0456] Step 5:

[0457] The server uses a generative artificial intelligence model to analyze the user's request data. Specific tasks are extracted from the request. The request text data is used as input, and related tasks are extracted based on it. The output is an optimized task list. Specifically, the AI ​​model analyzes the text, extracts important keywords, and lists related tasks.

[0458] Step 6:

[0459] The server adjusts the generated task list based on the emotional data. The emotional data and the task list are used as inputs. The output is the adjusted task list. Specifically, if the emotional data indicates high stress, the server reevaluates the priority of the task list and starts with easier tasks.

[0460] Step 7:

[0461] The server creates an optimal work plan and sends it to the robot controller. The adjusted task list is used as input. The output is a confirmation that the work plan has been sent. The server converts the task list into a work plan and sends it to the robot controller.

[0462] Step 8:

[0463] The robot controller executes specific tasks based on the transmitted work plan. The input is the work plan data, and the output is the specific tasks to be performed. The robot moves to the specified location and performs adjustments or repairs as needed.

[0464] The above processing steps make it possible to respond quickly and appropriately to vague user requests, and to create and execute work plans that take into account the emotions and health status of workers.

[0465] 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.

[0466] 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.

[0467] 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.

[0468] [Second embodiment]

[0469] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0470] 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.

[0471] 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).

[0472] 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.

[0473] 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.

[0474] 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).

[0475] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0476] 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.

[0477] 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.

[0478] 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.

[0479] 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.

[0480] 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."

[0481] In order to put the present invention into practice, it is necessary to construct a system in which a plurality of means operate in cooperation with one another. A specific embodiment of the system will be described below.

[0482] overview

[0483] This system receives vague requests from users, analyzes them using a generative artificial intelligence model, and provides optimal tourist information. It also translates the information into the user's native language to support travel planning. It also increases reliability by introducing a reward system for posting ratings and reviews, and by incorporating a function to eliminate fake users.

[0484] Processing flow

[0485] 1. User Input

[0486] The user inputs their desired destinations, activities, cuisine, and other requests into an input form on the terminal.

[0487] For example, enter "I want to relax in a place rich in nature."

[0488] 2. Data Transmission

[0489] The terminal transmits the input request data to the server.

[0490] The transmission format is usually an API request (e.g., POST method), and the data format is JSON.

[0491] 3. Request analysis

[0492] The server parses and preprocesses the received request data, which includes tokenizing the text data and extracting features.

[0493] It uses a generative artificial intelligence model to analyze requests and generate a list of relevant attractions and activities.

[0494] 4. Recommendation information generation

[0495] The server uses the analysis results to generate tourist information that best suits the user's needs.

[0496] For example, in response to the above request, tourist spots around Mount Fuji are listed.

[0497] 5. Multilingual Translation

[0498] The server translates the generated tourist information into the user's native language.

[0499] Translation uses a multilingual translation API (e.g., Google Translate API).

[0500] 6. Information provision

[0501] The server sends the translated recommendation information to the device,

[0502] The terminal displays it to the user.

[0503] For example, it might say, "We recommend the Mt. Fuji area. You can enjoy beautiful nature and relaxing hot springs."

[0504] 7. Providing additional information

[0505] If the user is interested in the recommended information, he or she sends a request to the server via the terminal requesting additional details (such as fare, directions, and reservation methods).

[0506] The server collects more detailed information and provides it to the user.

[0507] Posting and managing ratings and reviews

[0508] 1. Posting ratings and reviews

[0509] After traveling, users post ratings and reviews from their devices.

[0510] The device sends these posts to the server.

[0511] 2. Managing Posts

[0512] The server stores the received ratings and reviews in a database.

[0513] It uses a generative artificial intelligence model to analyze the content of posts and identify and filter out fake posts made by fake users.

[0514] 3. Reward System

[0515] The server awards reward points for posting legitimate ratings and reviews.

[0516] The terminal provides an interface where users can check their reward points and use them to plan their next trip.

[0517] This system configuration allows users to efficiently obtain tourist information and make travel plans. It also guarantees the reliability of ratings and reviews, allowing users to share travel experiences based on accurate information. This system will be a powerful support tool that allows each inbound traveler to enjoy Japan with peace of mind.

[0518] The processing flow will be explained below.

[0519] Step 1:

[0520] The user uses the terminal to enter their desired places to visit, desired activities, desired cuisine, etc. into a text input form. For example, they might enter "I want to relax in a place rich in nature."

[0521] Step 2:

[0522] The terminal sends the input request data to the server. The transmission format is usually an API request (e.g., POST method), and the data is sent in JSON format.

[0523] Step 3:

[0524] The server parses the received request data and performs pre-processing for input to the generative AI model, including tokenizing the text data and extracting features.

[0525] Step 4:

[0526] The server analyzes the request data using a generative AI model, which then generates a list of related tourist attractions and activities based on the request.

[0527] Step 5:

[0528] Based on the analysis results, the server generates recommendations that best fit the user's needs. For example, if a user requests "I want to relax in a place rich in nature," the server will suggest tourist spots around Mount Fuji.

[0529] Step 6:

[0530] The server translates the generated recommendation information into the user's native language using a multilingual translation API (e.g., Google Translate API).

[0531] Step 7:

[0532] The server sends the translated recommendation information to the terminal.

[0533] The device then displays the received information to the user. For example, it might say, "We recommend the Mt. Fuji area. You can enjoy beautiful nature and relaxing hot springs."

[0534] Step 8:

[0535] If the user is interested in the displayed recommendation information, the terminal sends a request to the server for more detailed information (such as fare, directions, and reservation methods).

[0536] Step 9:

[0537] The server receives the request for more information, gathers more specific information, and provides it to the user.

[0538] Step 10:

[0539] After a trip, users can post ratings and reviews via their devices, for example by entering rating scores and comments.

[0540] Step 11:

[0541] The device sends the posted ratings and reviews to the server.

[0542] Step 12:

[0543] The server stores the received ratings and reviews in a database, and also uses a generative artificial intelligence model to identify and filter out posts from fake users.

[0544] Step 13:

[0545] The server will award reward points for posting honest ratings and reviews.

[0546] Step 14:

[0547] The terminal provides an interface that allows the user to check the reward points that he or she has earned, and the user can use the reward points to plan his or her next trip.

[0548] Example 1

[0549] 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."

[0550] In modern travel planning, users gather a lot of information and spend a lot of time selecting appropriate tourist destinations. Furthermore, the information they obtain can be unreliable, which can significantly reduce their travel satisfaction. Furthermore, language barriers are a major obstacle to travel planning and information gathering on-site. To solve these problems, a system is needed that can accurately interpret users' needs, provide optimal tourist information, and utilize reliable reviews and ratings.

[0551] 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.

[0552] In this invention, the server comprises: a means for inputting requests from a user, such as desired places to visit, desired activities, desired dishes, etc.;

[0553] means for transmitting request data from the terminal to the server;

[0554] A means for analyzing requests and generating relevant tourist information using a generative artificial intelligence model;

[0555] A means for translating the generated tourist information into the user's native language;

[0556] a means for providing the translated information to a user;

[0557] A means to provide additional details (such as prices, directions, and booking instructions) if requested by the user;

[0558] A method to receive user ratings and reviews, analyze them, and filter out fake users.

[0559] A way to reward legitimate ratings and reviews,

[0560] a means for users to view reward points available for their next travel plan;

[0561] This will enable users to efficiently obtain reliable tourist information and plan their trips. It will also increase the reliability of ratings and reviews, allowing users to share travel experiences based on accurate information.

[0562] A "user" is an entity that uses this system to input requests such as desired places to visit, desired activities, and desired cuisine, and obtains information.

[0563] "Terminal" refers to a device that transmits user-entered requests to a server and displays information received from the server.

[0564] The "server" is a central processing device that analyzes requests received from users and generates and provides tourist information.

[0565] A "generative artificial intelligence model" is a type of algorithm or technology that analyzes user requests and generates relevant and appropriate tourist information.

[0566] "Request data" is data including information such as desired places to visit, desired activities, and desired dishes input by the user.

[0567] "Tourist information" refers to information about places and activities that a user would like to visit, including information about tourist spots, activities, places to eat, etc.

[0568] "Translation API" refers to a programming interface for converting information written in one language into another language.

[0569] "Ratings and reviews" refer to reviews and feedback posted by users after their trips, and are information that includes ratings.

[0570] "Sakura users" refer to fraudulent users who post falsified ratings and reviews.

[0571] "Reward points" are points awarded to users who post honest reviews and ratings, and can be used to plan their next trip, etc.

[0572] "Additional Details" refers to detailed information such as prices, directions, and reservation methods that is provided upon request by the user.

[0573] To implement this invention, it is necessary to build a system that links multiple means. This system receives vague requests from users, analyzes them using a generative artificial intelligence model, and provides optimal tourist information. It also has a function to translate information into the user's native language and support travel planning. It also increases reliability by introducing a reward system for posting ratings and reviews and by incorporating a function to eliminate fake users.

[0574] Hardware and software used

[0575] An implementation of this system includes the following hardware and software:

[0576] 1. Terminal: A device where a user inputs requests and displays information from a server. Examples include smartphones and personal computers.

[0577] 2. Server: The server analyzes the request data, generates tourist information, translates and manages the reward system.

[0578] 3. Generative AI model: An AI model (e.g., GPT-3) that analyzes user requests and generates relevant tourist information.

[0579] 4. Translation API: An API for translating tourist information into the user's native language (e.g., Google Translate API).

[0580] Program processing

[0581] Below is a detailed description of how the program works in this system:

[0582] 1. User Input

[0583] The user inputs into an input form on the terminal the desired places to visit, desired activities, desired cuisine, etc. As a specific example, the user inputs "I want to relax in a place rich in nature."

[0584] 2. Data Transmission

[0585] The terminal sends the input request data to the server. At this time, the data is usually sent using the POST method, and the format is JSON.

[0586] 3. Request analysis

[0587] The server analyzes the received request data, using natural language processing (NLP) techniques to tokenize the text data and extract key keywords, such as "nature," "relaxation," and "place."

[0588] 4. Information generation

[0589] The server analyzes the request using a generative artificial intelligence model (e.g., GPT-3) based on the extracted keywords and generates relevant tourist information. For example, tourist spots around Mount Fuji are selected.

[0590] 5. Multilingual Translation

[0591] To translate the generated tourist information into the user's native language, the server uses a multilingual translation API such as Google Translate API. For example, "We recommend the Mt. Fuji area. You can enjoy beautiful nature and relaxing hot springs."

[0592] 6. Information provision

[0593] The server sends the translated information to the device, which then displays it to the user. For example, it might say, "We recommend the Mount Fuji area. You can enjoy beautiful nature and relaxing hot springs."

[0594] 7. Providing additional information

[0595] If a user shows interest in the recommended information, they send a request for detailed information (such as fare, directions, and reservation methods) to the server via their device. The server collects the detailed information and provides it to the user.

[0596] 8. Posting and managing ratings and reviews

[0597] After their trip, users submit ratings and reviews from their devices. The server stores these posts in a database and uses a generative artificial intelligence model to identify fake users and filter out fake posts.

[0598] 9. Reward System

[0599] The server awards reward points for legitimate ratings and reviews, and the terminal provides an interface where users can check these points. Users can use these reward points to plan their next trip.

[0600] Examples of concrete examples and prompts

[0601] Example: If a user types, "I want to relax in a place rich in nature," the system will suggest tourist spots around Mount Fuji and display the message, "We recommend the Mount Fuji area. You can enjoy beautiful nature and relaxing hot springs."

[0602] Example prompt: "What are some relaxing tourist spots in a particular location?"

[0603] This system configuration allows users to efficiently obtain reliable tourist information and plan their trips. Furthermore, the reliability of ratings and reviews is guaranteed, allowing users to share travel experiences based on accurate information.

[0604] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0605] Step 1:

[0606] The user inputs their desired destinations, activities, and cuisine into the device. For example, they might input, "I want to relax in a place rich in nature." This input data is temporarily stored on the device. The input data is acquired through a text form.

[0607] Step 2:

[0608] The device sends the input request data to the server. At this time, the data is converted to JSON format and the POST method is used as the API request. The device organizes the request data as a JSON object and sends an HTTP request to the server.

[0609] Step 3:

[0610] The server analyzes the received request data and performs data preprocessing. This step includes tokenizing the text data and extracting features. For example, from the text "I want to relax in a place rich in nature," the keywords "nature," "relaxation," and "place" are extracted. Specifically, a text analysis algorithm is used to perform grammatical analysis and extract keywords.

[0611] Step 4:

[0612] The server inputs the extracted keywords into a generative AI model (e.g., GPT-3) to analyze the request. The generative AI model generates relevant tourist information based on these keywords. For example, the keywords "nature," "relaxation," and "place" generate tourist spots around Mount Fuji. The server temporarily stores the generated tourist information.

[0613] Step 5:

[0614] The server translates the generated tourist information into the user's native language. This translation uses a multilingual translation API (e.g., Google Translate API). The server sends the tourist information to the translation API and receives the translated results. For example, the translated text would read, "We recommend the Mt. Fuji area. You can enjoy beautiful nature and relaxing hot springs."

[0615] Step 6:

[0616] The server sends the translated information to the device. The data is converted back to JSON format and sent as an API response. The device analyzes the received data and displays it on the user interface. For example, it might say, "We recommend the Mt. Fuji area for you. You can enjoy beautiful nature and relaxing hot springs."

[0617] Step 7:

[0618] If a user is interested in a recommendation, they send a request to the server via their device for additional details (such as fare, directions, and reservation methods). Based on the input, the server collects the details from relevant databases and external services. This information is sent to the device, which then displays it to the user.

[0619] Step 8:

[0620] After a trip, users post ratings and reviews from their devices. The devices then send these posts to the server. The entered ratings and reviews are sent to the server in JSON format. A specific example of input includes text such as "The hot springs at Mt. Fuji were very relaxing."

[0621] Step 9:

[0622] The server stores the received ratings and reviews in a database and analyzes the posts using a generative AI model. This analysis includes identifying fake posts by fake users. Specifically, it detects large numbers of similar posts from the same IP address and filters out those that are in question.

[0623] Step 10:

[0624] The server will award reward points for posting valid ratings and reviews. These points will be reflected in the user's account, and the device will provide an interface where the reward points can be checked. The user can use these points to plan their next trip. For example, the server will notify the user that "10 points have been awarded."

[0625] (Application example 1)

[0626] 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."

[0627] Conventional tourism support systems have difficulty providing appropriate tourist information to users' vague requests, and their ability to display information in multiple languages ​​is limited. Furthermore, autonomous vehicles lack sufficient navigation functions to guide users to tourist spots based on their requests. This makes it difficult for users to visit tourist spots efficiently and safely.

[0628] 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.

[0629] In this invention, the server includes means for inputting requests from the user, such as desired places to visit, desired activities, and desired cuisine, means for transmitting the request data to the server, means for analyzing the requests using a generative artificial intelligence model and generating relevant tourist information, means for translating the generated tourist information into the user's native language, means for providing the translated information to the user, means for providing additional detailed information (fares, directions, reservation methods, etc.), and means for providing route guidance to the destination in cooperation with the autonomous driving system. This enables the suggestion of tourist information that meets the user's vague requests and efficient navigation to tourist spots in an autonomous driving vehicle.

[0630] "User" refers to an individual person or customer who uses the Service.

[0631] "Request data" refers to specific or vague request information such as desired places to visit, desired activities, and desired cuisine entered by the user.

[0632] "Server" means a centralized computer system for receiving, analyzing, and processing requested data and providing related information.

[0633] "Generative AI model" refers to advanced AI technology used to analyze user input data and generate relevant tourist information.

[0634] "Relevant tourist information" refers to information about tourist attractions, activities, food and drink provided based on user requests analyzed using a generative artificial intelligence model.

[0635] "Translated information" refers to information that has been converted from the generated tourist information into the user's native language that the user can understand.

[0636] "More Information" refers to additional information related to an attraction, such as prices, directions, and reservations.

[0637] An "autonomous driving system" refers to a system that autonomously controls vehicle operation and guides the vehicle to its destination based on user input.

[0638] "Route Guidance" refers to a function that works in conjunction with the autonomous driving system to provide the user with the optimal route to the tourist spots they wish to visit.

[0639] "In-vehicle display" refers to an information display device installed inside a vehicle, and refers to a display that displays tourist information and navigation information.

[0640] "Ratings and reviews" refer to opinions and impressions of the services provided and places visited by users after their trip.

[0641] "Database" refers to a structured information storage system for organizing and storing user ratings and reviews.

[0642] "Fake User" refers to a fake user hired to post inaccurate or false reviews.

[0643] A "voice recognition system" refers to a system that analyzes a user's voice input and converts it into text data.

[0644] To implement this invention, it is desirable to have the following system configuration: The system mainly receives user input, analyzes it, provides optimal tourist information, and also cooperates with the navigation of the autonomous vehicle.

[0645] Hardware and software used

[0646] In-vehicle display: An information display device installed inside a vehicle that displays tourist information and navigation information.

[0647] Speech recognition system: A system that analyzes the user's voice input and converts it into text data, for example, using the Google Speech-to-Text API.

[0648] Generative AI model: This is an AI technology used to analyze user input data and generate relevant tourist information. It uses GPT-4 provided by OpenAI.

[0649] Translation API: An API for translating the generated information into the user's native language, for example, using the Google Translate API.

[0650] Navigation system: A system that controls the operation of an autonomous vehicle and guides it to a destination based on user input.

[0651] System Program Processing

[0652] 1. User input:

[0653] The user uses the voice recognition function on the in-car display or smartphone to input a vague request (e.g., "I want to relax in a place rich in nature"), which is then converted into text data.

[0654] 2. Sending requested data:

[0655] The text data converted by the speech recognition system is sent to the server via an API request (POST method, JSON format).

[0656] 3. Request analysis:

[0657] The server then sends the text data to a generative artificial intelligence model (GPT-4) to analyze the request, which then lists relevant tourist spots and activities.

[0658] 4. Tourism information generation:

[0659] The server then uses the analysis results to generate tourist information that best suits the user's needs, for example, recommending "naturally rich places around Mount Fuji."

[0660] 5. Translation:

[0661] The server uses the Google Translate API to translate the generated tourist information into the user's native language, thereby providing the information in a format that is easy for the user to understand.

[0662] 6. Information and navigation:

[0663] The translated information is displayed on the in-vehicle display and is also provided to the user as an audio guide. The server and the autonomous driving system also work together to provide route guidance to the suggested spots.

[0664] Examples of concrete examples and prompts

[0665] Specific examples

[0666] When a user speaks to the in-car display, "I want to relax in quiet nature," the speech is converted into text and sent to a server. A generative artificial intelligence model analyzes the speech and generates tourist information about the Mount Fuji area. This information is translated and displayed on the in-car display, saying, "We recommend the Mount Fuji area. Beautiful nature awaits you." The autonomous driving system then begins navigating to Mount Fuji.

[0667] Prompt Sentence Examples

[0668] "A user types 'I want to relax in a place rich in nature.' Suggest five related tourist spots."

[0669] This system will enable the proposal of tourist information that meets the user's vague requests and the efficient navigation to tourist spots in self-driving vehicles.

[0670] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0671] Step 1:

[0672] The user uses the in-car display or smartphone to input their wishes and requests by voice or text. For example, a request such as "I want to relax in a place rich in nature" can be input by voice into the in-car display. This input data is converted into text data by a voice recognition system (Google Speech-to-Text API). The voice input is output as text data.

[0673] Step 2:

[0674] The device sends the text data converted by the voice recognition system to the server via an API request (POST method, JSON format). This text data (request data) is input to the server.

[0675] Step 3:

[0676] The server tokenizes the received request data and preprocesses the text data. It then sends the data to a generative artificial intelligence model (GPT-4) to analyze the user's request. This process involves tokenizing the text, extracting features, and analyzing them to create a list of related tourist attractions and activities. For example, the output would be "Places rich in nature around Mt. Fuji."

[0677] Step 4:

[0678] The server generates tourist information that matches the user's needs based on the analysis results of the generative AI model. Information on recommended tourist spots and activities extracted from the analysis results is generated as text data. For example, information such as "Beautiful natural areas around Mt. Fuji" is generated.

[0679] Step 5:

[0680] The server sends the generated tourist information to a translation API (Google Translate API) to be translated into the user's native language. The translation API translates the received tourist information into the specified language and outputs the translated information.

[0681] Step 6:

[0682] The server sends the translated tourist information to the in-vehicle display. This information is used as part of the navigation display screen. It is also output as an audio guide and presented to the user. The translated information provided to the user is, for example, "We recommend the Mt. Fuji area. Beautiful nature awaits you."

[0683] Step 7:

[0684] The server works in conjunction with the autonomous driving system to send data to create a route guide to the suggested tourist spots. The autonomous driving system calculates the optimal navigation route based on this information and guides the vehicle to the destination. Specifically, a route planning algorithm is used to generate a route that takes into account traffic conditions and road information. The vehicle then begins autonomous driving according to this route.

[0685] 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.

[0686] In order to put this invention into practice, it is necessary to build a system that further combines an emotion engine that recognizes the user's emotions. A specific embodiment of the system is shown below.

[0687] overview

[0688] This system receives vague requests from users, analyzes them using a generative artificial intelligence model, and provides optimal tourist information. Furthermore, by combining it with an emotion engine that uses user emotional data to adjust recommendation information, more personalized information is provided. Furthermore, a translation function provides information in the user's native language, a reward system for posting ratings and reviews, and a function to eliminate fake users increase reliability.

[0689] Processing flow

[0690] 1. User Input

[0691] The user uses the terminal to input requests such as desired places to visit, desired activities, and desired cuisine into a text input form.

[0692] For example, enter "I want to relax in a place rich in nature."

[0693] 2. Data Transmission

[0694] The terminal sends the input request data to the server. The transmission format is usually an API request (e.g., POST method), and the data is sent in JSON format.

[0695] 3. Request analysis

[0696] The server parses and pre-processes the received request data, which includes tokenizing the text data and extracting features.

[0697] It uses a generative artificial intelligence model to analyze requests and generate a list of relevant attractions and activities.

[0698] 4. Recommendation information generation

[0699] The server uses the analysis results to generate recommendation information that best suits the user's needs.

[0700] For example, if a customer requests "I want to relax in a place rich in nature," we will suggest tourist spots around Mount Fuji.

[0701] 5. Multilingual Translation

[0702] The server translates the generated recommendation information into the user's native language using a multilingual translation API (e.g., Google Translate API).

[0703] 6. Emotion recognition

[0704] When providing information, the server uses an emotion engine to recognize the user's emotions. For example, it analyzes the user's facial expressions and tone of voice to quantify the user's emotions.

[0705] 7. Information provision

[0706] The server adjusts the recommendation information based on the user's emotional data and sends the most appropriate information to the terminal.

[0707] The device will then display the provided information to the user. For example, it might say, "We recommend the Mt. Fuji area. You can enjoy beautiful nature and relaxing hot springs."

[0708] 8. Providing additional information

[0709] If the user is interested in the recommended information, he or she sends a request to the server via the terminal requesting additional details (such as fare, directions, and reservation methods).

[0710] The server collects more detailed information and provides it to the user.

[0711] Posting and managing ratings and reviews

[0712] 1. Posting ratings and reviews

[0713] After a trip, users post ratings and reviews from their devices, for example, by entering rating scores and comments.

[0714] 2. Managing Posts

[0715] The terminal transmits the posted content to the server.

[0716] The server stores the received ratings and reviews in a database, uses an emotion engine to recognize the user's emotions at the time of posting, and uses this information to analyze the reliability of the review ratings. It also uses a generative artificial intelligence model to identify and exclude posts by fake users.

[0717] 3. Reward System

[0718] The server will award reward points for posting honest ratings and reviews.

[0719] The terminal provides an interface that allows the user to check the reward points that he or she has earned, and the user can use the reward points to plan his or her next trip.

[0720] This system configuration allows users to efficiently obtain tourist information and plan their trips. It also ensures that information provided takes user sentiment into consideration, and the reliability of ratings and reviews is guaranteed, allowing users to share their travel experiences based on accurate information. This system will be a powerful support tool for inbound travelers to enjoy Japan with peace of mind.

[0721] The processing flow will be explained below.

[0722] Step 1:

[0723] The user uses the terminal to enter their desired places to visit, desired activities, desired cuisine, etc. into a text input form. For example, they might enter "I want to relax in a place rich in nature."

[0724] Step 2:

[0725] The device sends the input request data to the server. The sending format is usually an API request (e.g., POST method), and the data is sent in JSON format.

[0726] Step 3:

[0727] The server parses the received request data and performs preprocessing to input it into the generative AI model. Preprocessing involves tokenizing the text data and extracting features.

[0728] Step 4:

[0729] The server uses a generative artificial intelligence model to analyze the request data, which then generates a list of relevant attractions and activities.

[0730] Step 5:

[0731] Based on the analysis results, the server generates recommendations that best fit the user's needs. For example, if a user requests "I want to relax in a place rich in nature," a list of tourist spots around Mount Fuji will be displayed.

[0732] Step 6:

[0733] The server translates the generated recommendation information into the user's native language using a multilingual translation API (e.g., Google Translate API).

[0734] Step 7:

[0735] The server uses an emotion engine to recognize the user's emotions by analyzing the user's input, facial expressions, or voice data.

[0736] Step 8:

[0737] The server adjusts the recommendation information based on the user's emotional data. For example, if the user is looking for "relaxation," it will prioritize information on quiet resorts.

[0738] Step 9:

[0739] The server sends the adjusted recommendation information to the terminal, which then displays the received information to the user.

[0740] For example, it might say, "We recommend the Mt. Fuji area. You can enjoy beautiful nature and relaxing hot springs."

[0741] Step 10:

[0742] If the user is interested in the displayed recommendation information, the terminal sends a request to the server for more detailed information (such as fare, directions, and reservation methods).

[0743] Step 11:

[0744] The server receives the request for more information, gathers more specific information, and provides it to the user.

[0745] For example, provide detailed information in the form of, "It takes about an hour by Shinkansen from Shinjuku Station to Mishima Station, and the fare is about 4,000 yen one way."

[0746] Step 12:

[0747] After a trip, users post ratings and reviews via their devices, inputting rating scores and comments, and sending the posted content from the device to the server.

[0748] Step 13:

[0749] The server stores the received ratings and reviews in a database. It uses an emotion engine to recognize the user's emotions at the time of posting and uses this information to analyze the reliability of the review ratings.

[0750] Step 14:

[0751] The server uses a generative artificial intelligence model to identify and filter out posts from fake users, ensuring the authenticity of ratings and reviews.

[0752] Step 15:

[0753] The server awards reward points for posting honest ratings and reviews. The terminal provides an interface where users can check the reward points they have earned, and users can use the reward points to plan their next trip.

[0754] Example 2

[0755] 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."

[0756] Conventional tourist information systems have limited ability to fully understand users' vague requests and diverse emotions and provide optimal information. Furthermore, the means to ensure the reliability and accuracy of information are insufficient, and there is no multilingual support or appropriate review management. As a result, users are unable to obtain satisfactory tourist information, and the quality of their tourist experience is reduced.

[0757] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting requests from the user, such as desired places to visit, desired activities, and desired cuisine, means for transmitting the request data to the server, means for analyzing the requests using a generative artificial intelligence model and generating relevant tourist information, means for translating the generated tourist information into the user's native language, means for recognizing the user's emotions using an emotion engine and adjusting recommended information, means for providing the translated information to the user, and means for providing additional detailed information (such as fares, directions, and reservation methods). This allows the user to obtain personalized, optimal tourist information based on their own vague requests, and enables a high-quality tourist experience through the provision of information according to emotions and reliable review management.

[0758] "User" refers to an individual who uses this system to obtain tourist information and post ratings and reviews.

[0759] "Request data" is data including text information such as desired places to visit, desired activities, and desired dishes input by the user.

[0760] "Generative AI model" refers to an AI technology that analyzes user input request data and generates related tourist information.

[0761] An "emotion engine" refers to technology that recognizes user emotions, quantifies and analyzes them, and adjusts recommendation information.

[0762] "Sakura users" refer to fake users who post false ratings and reviews for fraudulent purposes.

[0763] "Translation means" refers to the translation techniques and tools used to translate the generated tourist information into the user's native language.

[0764] "Recommended information" refers to information about tourist spots and activities provided based on the user's requests and emotional data.

[0765] "Review management means" refers to the means for receiving ratings and reviews posted by users, storing them in a database, and excluding posts by fake users.

[0766] A "reward system" refers to a mechanism that awards reward points for legitimate ratings and reviews posted by users, allowing users to use those points for travel planning.

[0767] This invention is a system for analyzing vague requests entered by users and providing optimal tourist information. The system utilizes user emotional data to optimize information and achieve multilingual support. It also ensures the reliability of ratings and reviews and introduces a reward system to improve the user experience. Specific embodiments of the system are described below.

[0768] overview

[0769] Users use a device (smartphone, PC, etc.) to input their desired places to visit, activities, cuisine, etc. The device sends the desired data to a server, which analyzes it and generates optimal tourist information. The hardware required is a device used by the user and a server. The software required includes a generative AI model, an emotion engine, a translation API, etc.

[0770] Hardware and software used

[0771] Terminal: A smartphone or PC used by a user to input and display data.

[0772] Server: A computer system for performing data analysis and information generation.

[0773] Generative AI model: AI technology for analyzing user requests and generating optimal tourist information (e.g., GPT-4).

[0774] Translation API: An API for translating the generated tourist information into the user's native language (e.g., Google Translate API).

[0775] Emotion engine: Technology for recognizing user emotions and adjusting recommendation information (e.g., Microsoft Azure's Emotion API).

[0776] Examples of data analysis and information generation

[0777] 1. User Input:

[0778] The user can freely input the places and activities they want to visit into the input form on the device. For example, they can enter "I want to relax in a place rich in nature."

[0779] 2. Data transmission:

[0780] The terminal sends the input request data to the server in JSON format.

[0781] 3. Request analysis:

[0782] The server parses the received request data, tokenizes the text data, and extracts features. It then inputs a prompt to the generative AI model for analysis. An example of a prompt is as follows:

[0783] The user's request is "I want to relax in a place rich in nature." Please suggest the best tourist spots.

[0784] A generative artificial intelligence model generates a list of relevant attractions and activities.

[0785] 4. Information Translation:

[0786] The server translates the generated tourist information into the user's native language using a translation API, for example, "Relaxing natural spots near Mount Fuji" in English.

[0787] 5. Emotion recognition:

[0788] The emotion engine recognizes the user's emotions in real time, calculates an emotion score from the user's facial expressions and voice, and adjusts the recommendation information accordingly.

[0789] 6. Information provision:

[0790] The server sends the adjusted recommendation information to the device, which then displays it to the user. For example, it displays, "We recommend the Mt. Fuji area. You can enjoy beautiful nature and relaxing hot springs."

[0791] Providing additional information and managing reviews

[0792] If the user requests additional details, they send a request to the server via their device. The server collects the details (fare, directions, reservation methods, etc.) and provides them to the user. Furthermore, if the user submits a rating or review, the server stores it in a database and uses an artificial intelligence model to filter out fraudulent posts (fake users). Legitimate ratings and reviews are awarded reward points, which the user can use to plan their next trip.

[0793] As a result, users can obtain efficient and personalized tourist information and enjoy a high-quality travel experience.

[0794] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0795] Step 1:

[0796] The user uses the terminal to enter their desired places to visit, desired activities, desired cuisine, etc. into a text input form. For example, they might enter "I want to relax in a place rich in nature."

[0797] Input: User's request text

[0798] Output: The requested data is temporarily stored on the device.

[0799] Step 2:

[0800] The device sends the input request data to the server. Specifically, it uses JavaScript Ajax to send the data in JSON format.

[0801] Input: User's desired data

[0802] Output: The requested data is sent to the server in JSON format.

[0803] Step 3:

[0804] The server parses the received request data, tokenizes the text data, extracts features, and analyzes the text using the Python NLTK library.

[0805] Input: Request data in JSON format

[0806] Output: Tokenized and feature extracted data

[0807] Step 4:

[0808] The server inputs a prompt sentence into a generative AI model (e.g., GPT-4) for analysis. For example, the following prompt sentence is used:

[0809] Prompt: "The user wants to relax in a place surrounded by nature. Please suggest the best tourist spots."

[0810] Input: Tokenized and feature extracted data

[0811] Output: A list of related attractions and activities

[0812] Step 5:

[0813] The server translates the generated tourist information into the user's native language using a multilingual translation API (e.g., Google Translate API).

[0814] Input: List of tourist information

[0815] Output: Translated tourist information

[0816] Step 6:

[0817] The server uses an emotion engine (e.g., Microsoft Azure's Emotion API) to recognize emotions from user input or reactions. It analyzes the user's facial expressions and tone of voice and generates an emotion score.

[0818] Input: User input data, reaction data

[0819] Output: Sentiment score

[0820] Step 7:

[0821] The server adjusts the translated tourist information based on the emotion score to generate optimal recommendation information, which is then sent to the device.

[0822] Input: translated tourist information, sentiment score

[0823] Output: Adjusted recommendation information

[0824] Step 8:

[0825] The device will then display the provided recommendation information to the user. For example, it might say, "We recommend the Mt. Fuji area. You can enjoy beautiful nature and relaxing hot springs."

[0826] Input: Adjusted recommendation information

[0827] Output: Recommendation information that users can visually confirm

[0828] Step 9:

[0829] If the user is interested in the recommended information, he or she sends a request to the server via the terminal requesting additional details (such as fare, directions, and reservation methods).

[0830] Input: Request for additional information

[0831] Output: Request data is sent to the server

[0832] Step 10:

[0833] The server collects additional details and provides them to the user.

[0834] Input: Request data

[0835] Output: Additional details

[0836] Step 11:

[0837] After a trip, users post ratings and reviews from their devices, for example by entering rating scores and comments and attaching images.

[0838] Input: Ratings and review data

[0839] Output: Ratings and reviews data persisted on device

[0840] Step 12:

[0841] The terminal transmits the posted content to the server.

[0842] Input: Ratings and review data

[0843] Output: Posted data is sent to the server

[0844] Step 13:

[0845] The server stores the received ratings and reviews in a database, uses an emotion engine to recognize the user's emotions at the time of posting, and uses a generative artificial intelligence model to identify and filter out posts from fake users.

[0846] Input: Ratings and review data

[0847] Output: Data after reliability analysis, identification results of fake users

[0848] Step 14:

[0849] The server will award reward points for posting honest ratings and reviews.

[0850] Input: Data after reliability analysis

[0851] Output: User's reward points

[0852] Step 15:

[0853] The terminal provides an interface that allows the user to check the reward points that he or she has earned, and the user can use the reward points to plan his or her next trip.

[0854] Input: User's reward points

[0855] Output: Reward point information that can be visually confirmed by the user

[0856] (Application example 2)

[0857] 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."

[0858] In factory work, it is necessary to respond quickly and appropriately to vague requests from workers, and to create work plans that take into account the emotions and health of workers.In addition to tourist information, a system is needed that utilizes generative artificial intelligence models to provide optimal work plans for factory work.

[0859] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting requests from the user, such as desired places to visit, desired activities, and desired cuisine; means for transmitting the request data to the server; means for analyzing the requests using a generative artificial intelligence model and generating relevant tourist information; means for translating the generated tourist information into the user's native language; means for providing the translated information to the user; means for providing additional detailed information (such as fees, directions, and reservation methods); means for analyzing the user's emotional data using an emotion recognition engine; means for adjusting information based on the analyzed emotional data; and means for formulating an optimal work plan based on the adjusted information and automatically executing the work. This makes it possible to respond quickly and appropriately to worker requests and to create work plans that take into account the worker's emotions and health status.

[0860] "User emotion data" is data that quantifies the user's emotional state analyzed from the user's facial expression, tone of voice, behavior, etc.

[0861] A "generative artificial intelligence model" is an artificial intelligence algorithm that analyzes a user's vague requests and generates relevant information and work plans.

[0862] "Request data" refers to data including specific or vague requests such as desired places to visit, desired activities, and desired dishes entered by the user.

[0863] An "emotion recognition engine" is a software component that analyzes a user's voice and facial expression data to recognize their emotional state.

[0864] "Adjusting information" means optimizing the content of the information provided and work plans based on emotional data.

[0865] A "work plan" is a schedule or action plan for work that is planned and executed by a generative artificial intelligence model.

[0866] "Additional details" are specific details such as prices, directions, and reservation methods that accompany the information provided.

[0867] A "server" is a computer system that receives and analyzes user-requested data, and generates, adjusts, and provides related information.

[0868] This invention is a system in which a patrol / work robot in a factory analyzes vague requests from workers, creates an optimal work plan, and automatically executes it. This system takes into account the user's emotional data and can perform work more efficiently and safely.

[0869] System Configuration

[0870] 1. Emotion Recognition Engine

[0871] This engine is a software component that analyzes the voice and facial expression data of workers and outputs their emotional state as numerical data. It uses a camera and microphone to collect real-time emotional data of users.

[0872] 2. Generative AI Models

[0873] This is an algorithm that analyzes request data and generates a related work plan. This model has the ability to extract specific tasks from vague requests and convert them into actionable plans.

[0874] 3. Robot Controller

[0875] This is a component that controls robots in factories and automatically executes tasks based on the generated work plan. This control system manages the robot's operation and performs work efficiently.

[0876] Hardware and software used

[0877] Camera and microphone

[0878] It collects the worker's facial expressions and voice and uses them to provide data to the emotion recognition engine.

[0879] GPU Server

[0880] This is a server for high-speed processing of emotion recognition engines and generative AI models. Specifically, it is equipped with hardware capable of processing neural networks and deep learning.

[0881] Robots in the factory

[0882] These are creations that perform real tasks, such as a typical factory robot.

[0883] Example of operation

[0884] 1. The worker speaks to the patrol robot, saying, "This machine needs adjustment." At this time, the camera captures the worker's facial expression and the microphone collects the voice.

[0885] 2. The robot terminal uses an emotion recognition engine to analyze the worker's emotional state from their voice and facial expression data.

[0886] 3. The analyzed emotional data is combined with a generative artificial intelligence model to analyze the user's requests and generate an optimal work plan.

[0887] 4. The generated work plan is automatically executed by the robot controller, which adjusts the specified machine and notifies the work support system as necessary.

[0888] Example prompt sentence:

[0889] This machine needs adjustment.

[0890] In this way, it is possible to respond quickly and appropriately to the requests of workers and create optimal work plans that take emotional data into account, thereby improving work efficiency in the factory and reducing worker stress and fatigue.

[0891] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0892] Step 1:

[0893] The user inputs their request by voice or text. The device collects the voice data or text data using a microphone and a text input interface. Voice data or text data is obtained as input.

[0894] Step 2:

[0895] The device converts voice data into text. This conversion is done using speech recognition software. The input is voice data and the output is text data. Specifically, the speech recognition engine analyzes the voice waveform and converts it into the corresponding text.

[0896] Step 3:

[0897] The device uses a camera and microphone to collect the user's facial expressions and voice tone. The data is sent to an emotion recognition engine. The input is image data and voice tone data. The output is quantified emotion data. The emotion recognition engine extracts facial features from the image data and voice features from the voice data to quantify emotions.

[0898] Step 4:

[0899] The device sends the collected request data and emotion data to the server. The server receives this data. An API request (e.g., POST method) is used to send the data. The input is the request text data and emotion data. The output is a confirmation that the data has been transferred to the server.

[0900] Step 5:

[0901] The server uses a generative artificial intelligence model to analyze the user's request data. Specific tasks are extracted from the request. The request text data is used as input, and related tasks are extracted based on it. The output is an optimized task list. Specifically, the AI ​​model analyzes the text, extracts important keywords, and lists related tasks.

[0902] Step 6:

[0903] The server adjusts the generated task list based on the emotional data. The emotional data and the task list are used as inputs. The output is the adjusted task list. Specifically, if the emotional data indicates high stress, the server reevaluates the priority of the task list and starts with easier tasks.

[0904] Step 7:

[0905] The server creates an optimal work plan and sends it to the robot controller. The adjusted task list is used as input. The output is a confirmation that the work plan has been sent. The server converts the task list into a work plan and sends it to the robot controller.

[0906] Step 8:

[0907] The robot controller executes specific tasks based on the transmitted work plan. The input is the work plan data, and the output is the specific tasks to be performed. The robot moves to the specified location and performs adjustments or repairs as needed.

[0908] The above processing steps make it possible to respond quickly and appropriately to vague user requests, and to create and execute work plans that take into account the emotions and health status of workers.

[0909] 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.

[0910] 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.

[0911] 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.

[0912] [Third embodiment]

[0913] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0914] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0915] 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).

[0916] 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.

[0917] 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.

[0918] 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).

[0919] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0920] 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.

[0921] 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.

[0922] 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.

[0923] 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.

[0924] 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."

[0925] In order to put the present invention into practice, it is necessary to construct a system in which a plurality of means operate in cooperation with one another. A specific embodiment of the system will be described below.

[0926] overview

[0927] This system receives vague requests from users, analyzes them using a generative artificial intelligence model, and provides optimal tourist information. It also translates the information into the user's native language to support travel planning. It also increases reliability by introducing a reward system for posting ratings and reviews, and by incorporating a function to eliminate fake users.

[0928] Processing flow

[0929] 1. User Input

[0930] The user inputs their desired destinations, activities, cuisine, and other requests into an input form on the terminal.

[0931] For example, enter "I want to relax in a place rich in nature."

[0932] 2. Data Transmission

[0933] The terminal transmits the input request data to the server.

[0934] The transmission format is usually an API request (e.g., POST method), and the data format is JSON.

[0935] 3. Request analysis

[0936] The server parses and preprocesses the received request data, which includes tokenizing the text data and extracting features.

[0937] It uses a generative artificial intelligence model to analyze requests and generate a list of relevant attractions and activities.

[0938] 4. Recommendation information generation

[0939] The server uses the analysis results to generate tourist information that best suits the user's needs.

[0940] For example, in response to the above request, tourist spots around Mount Fuji are listed.

[0941] 5. Multilingual Translation

[0942] The server translates the generated tourist information into the user's native language.

[0943] Translation uses a multilingual translation API (e.g., Google Translate API).

[0944] 6. Information provision

[0945] The server sends the translated recommendation information to the device,

[0946] The terminal displays it to the user.

[0947] For example, it might say, "We recommend the Mt. Fuji area. You can enjoy beautiful nature and relaxing hot springs."

[0948] 7. Providing additional information

[0949] If the user is interested in the recommended information, he or she sends a request to the server via the terminal requesting additional details (such as fare, directions, and reservation methods).

[0950] The server collects more detailed information and provides it to the user.

[0951] Posting and managing ratings and reviews

[0952] 1. Posting ratings and reviews

[0953] After traveling, users post ratings and reviews from their devices.

[0954] The device sends these posts to the server.

[0955] 2. Managing Posts

[0956] The server stores the received ratings and reviews in a database.

[0957] It uses a generative artificial intelligence model to analyze the content of posts and identify and filter out fake posts made by fake users.

[0958] 3. Reward System

[0959] The server awards reward points for posting legitimate ratings and reviews.

[0960] The terminal provides an interface where users can check their reward points and use them to plan their next trip.

[0961] This system configuration allows users to efficiently obtain tourist information and make travel plans. It also guarantees the reliability of ratings and reviews, allowing users to share travel experiences based on accurate information. This system will be a powerful support tool that allows each inbound traveler to enjoy Japan with peace of mind.

[0962] The processing flow will be explained below.

[0963] Step 1:

[0964] The user uses the terminal to enter their desired places to visit, desired activities, desired cuisine, etc. into a text input form. For example, they might enter "I want to relax in a place rich in nature."

[0965] Step 2:

[0966] The terminal sends the input request data to the server. The transmission format is usually an API request (e.g., POST method), and the data is sent in JSON format.

[0967] Step 3:

[0968] The server parses the received request data and performs pre-processing for input to the generative AI model, including tokenizing the text data and extracting features.

[0969] Step 4:

[0970] The server analyzes the request data using a generative AI model, which then generates a list of related tourist attractions and activities based on the request.

[0971] Step 5:

[0972] Based on the analysis results, the server generates recommendations that best fit the user's needs. For example, if a user requests "I want to relax in a place rich in nature," the server will suggest tourist spots around Mount Fuji.

[0973] Step 6:

[0974] The server translates the generated recommendation information into the user's native language using a multilingual translation API (e.g., Google Translate API).

[0975] Step 7:

[0976] The server sends the translated recommendation information to the terminal.

[0977] The device then displays the received information to the user. For example, it might say, "We recommend the Mt. Fuji area. You can enjoy beautiful nature and relaxing hot springs."

[0978] Step 8:

[0979] If the user is interested in the displayed recommendation information, the terminal sends a request to the server for more detailed information (such as fare, directions, and reservation methods).

[0980] Step 9:

[0981] The server receives the request for more information, gathers more specific information, and provides it to the user.

[0982] Step 10:

[0983] After a trip, users can post ratings and reviews via their devices, for example by entering rating scores and comments.

[0984] Step 11:

[0985] The device sends the posted ratings and reviews to the server.

[0986] Step 12:

[0987] The server stores the received ratings and reviews in a database, and also uses a generative artificial intelligence model to identify and filter out posts from fake users.

[0988] Step 13:

[0989] The server will award reward points for posting honest ratings and reviews.

[0990] Step 14:

[0991] The terminal provides an interface that allows the user to check the reward points that he or she has earned, and the user can use the reward points to plan his or her next trip.

[0992] Example 1

[0993] 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."

[0994] In modern travel planning, users gather a lot of information and spend a lot of time selecting appropriate tourist destinations. Furthermore, the information they obtain can be unreliable, which can significantly reduce their travel satisfaction. Furthermore, language barriers are a major obstacle to travel planning and information gathering on-site. To solve these problems, a system is needed that can accurately interpret users' needs, provide optimal tourist information, and utilize reliable reviews and ratings.

[0995] 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.

[0996] In this invention, the server comprises: a means for inputting requests from a user, such as desired places to visit, desired activities, desired dishes, etc.;

[0997] means for transmitting request data from the terminal to the server;

[0998] A means for analyzing requests and generating relevant tourist information using a generative artificial intelligence model;

[0999] A means for translating the generated tourist information into the user's native language;

[1000] a means for providing the translated information to a user;

[1001] A means to provide additional details (such as prices, directions, and booking instructions) if requested by the user;

[1002] A method to receive user ratings and reviews, analyze them, and filter out fake users.

[1003] A way to reward legitimate ratings and reviews,

[1004] a means for users to view reward points available for their next travel plan;

[1005] This will enable users to efficiently obtain reliable tourist information and plan their trips. It will also increase the reliability of ratings and reviews, allowing users to share travel experiences based on accurate information.

[1006] A "user" is an entity that uses this system to input requests such as desired places to visit, desired activities, and desired cuisine, and obtains information.

[1007] "Terminal" refers to a device that transmits user-entered requests to a server and displays information received from the server.

[1008] The "server" is a central processing device that analyzes requests received from users and generates and provides tourist information.

[1009] A "generative artificial intelligence model" is a type of algorithm or technology that analyzes user requests and generates relevant and appropriate tourist information.

[1010] "Request data" is data including information such as desired places to visit, desired activities, and desired dishes input by the user.

[1011] "Tourist information" refers to information about places and activities that a user would like to visit, including information about tourist spots, activities, places to eat, etc.

[1012] "Translation API" refers to a programming interface for converting information written in one language into another language.

[1013] "Ratings and reviews" refer to reviews and feedback posted by users after their trips, and are information that includes ratings.

[1014] "Sakura users" refer to fraudulent users who post falsified ratings and reviews.

[1015] "Reward points" are points awarded to users who post honest reviews and ratings, and can be used to plan their next trip, etc.

[1016] "Additional Details" refers to detailed information such as prices, directions, and reservation methods that is provided upon request by the user.

[1017] To implement this invention, it is necessary to build a system that links multiple means. This system receives vague requests from users, analyzes them using a generative artificial intelligence model, and provides optimal tourist information. It also has a function to translate information into the user's native language and support travel planning. It also increases reliability by introducing a reward system for posting ratings and reviews and by incorporating a function to eliminate fake users.

[1018] Hardware and software used

[1019] An implementation of this system includes the following hardware and software:

[1020] 1. Terminal: A device where a user inputs requests and displays information from a server. Examples include smartphones and personal computers.

[1021] 2. Server: The server analyzes the request data, generates tourist information, translates and manages the reward system.

[1022] 3. Generative AI model: An AI model (e.g., GPT-3) that analyzes user requests and generates relevant tourist information.

[1023] 4. Translation API: An API for translating tourist information into the user's native language (e.g., Google Translate API).

[1024] Program processing

[1025] Below is a detailed description of how the program works in this system:

[1026] 1. User Input

[1027] The user inputs into an input form on the terminal the desired places to visit, desired activities, desired cuisine, etc. As a specific example, the user inputs "I want to relax in a place rich in nature."

[1028] 2. Data Transmission

[1029] The terminal sends the input request data to the server. At this time, the data is usually sent using the POST method, and the format is JSON.

[1030] 3. Request analysis

[1031] The server analyzes the received request data, using natural language processing (NLP) techniques to tokenize the text data and extract key keywords, such as "nature," "relaxation," and "place."

[1032] 4. Information generation

[1033] The server analyzes the request using a generative artificial intelligence model (e.g., GPT-3) based on the extracted keywords and generates relevant tourist information. For example, tourist spots around Mount Fuji are selected.

[1034] 5. Multilingual Translation

[1035] To translate the generated tourist information into the user's native language, the server uses a multilingual translation API such as Google Translate API. For example, "We recommend the Mt. Fuji area. You can enjoy beautiful nature and relaxing hot springs."

[1036] 6. Information provision

[1037] The server sends the translated information to the device, which then displays it to the user. For example, it might say, "We recommend the Mount Fuji area. You can enjoy beautiful nature and relaxing hot springs."

[1038] 7. Providing additional information

[1039] If a user shows interest in the recommended information, they send a request for detailed information (such as fare, directions, and reservation methods) to the server via their device. The server collects the detailed information and provides it to the user.

[1040] 8. Posting and managing ratings and reviews

[1041] After their trip, users submit ratings and reviews from their devices. The server stores these posts in a database and uses a generative artificial intelligence model to identify fake users and filter out fake posts.

[1042] 9. Reward System

[1043] The server awards reward points for legitimate ratings and reviews, and the terminal provides an interface where users can check these points. Users can use these reward points to plan their next trip.

[1044] Examples of concrete examples and prompts

[1045] Example: If a user types, "I want to relax in a place rich in nature," the system will suggest tourist spots around Mount Fuji and display the message, "We recommend the Mount Fuji area. You can enjoy beautiful nature and relaxing hot springs."

[1046] Example prompt: "What are some relaxing tourist spots in a particular location?"

[1047] This system configuration allows users to efficiently obtain reliable tourist information and plan their trips. Furthermore, the reliability of ratings and reviews is guaranteed, allowing users to share travel experiences based on accurate information.

[1048] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1049] Step 1:

[1050] The user inputs their desired destinations, activities, and cuisine into the device. For example, they might input, "I want to relax in a place rich in nature." This input data is temporarily stored on the device. The input data is acquired through a text form.

[1051] Step 2:

[1052] The device sends the input request data to the server. At this time, the data is converted to JSON format and the POST method is used as the API request. The device organizes the request data as a JSON object and sends an HTTP request to the server.

[1053] Step 3:

[1054] The server analyzes the received request data and performs data preprocessing. This step includes tokenizing the text data and extracting features. For example, from the text "I want to relax in a place rich in nature," the keywords "nature," "relaxation," and "place" are extracted. Specifically, a text analysis algorithm is used to perform grammatical analysis and extract keywords.

[1055] Step 4:

[1056] The server inputs the extracted keywords into a generative AI model (e.g., GPT-3) to analyze the request. The generative AI model generates relevant tourist information based on these keywords. For example, the keywords "nature," "relaxation," and "place" generate tourist spots around Mount Fuji. The server temporarily stores the generated tourist information.

[1057] Step 5:

[1058] The server translates the generated tourist information into the user's native language. This translation uses a multilingual translation API (e.g., Google Translate API). The server sends the tourist information to the translation API and receives the translated results. For example, the translated text would read, "We recommend the Mt. Fuji area. You can enjoy beautiful nature and relaxing hot springs."

[1059] Step 6:

[1060] The server sends the translated information to the device. The data is converted back to JSON format and sent as an API response. The device analyzes the received data and displays it on the user interface. For example, it might say, "We recommend the Mt. Fuji area for you. You can enjoy beautiful nature and relaxing hot springs."

[1061] Step 7:

[1062] If a user is interested in a recommendation, they send a request to the server via their device for additional details (such as fare, directions, and reservation methods). Based on the input, the server collects the details from relevant databases and external services. This information is sent to the device, which then displays it to the user.

[1063] Step 8:

[1064] After a trip, users post ratings and reviews from their devices. The devices then send these posts to the server. The entered ratings and reviews are sent to the server in JSON format. A specific example of input includes text such as "The hot springs at Mt. Fuji were very relaxing."

[1065] Step 9:

[1066] The server stores the received ratings and reviews in a database and analyzes the posts using a generative AI model. This analysis includes identifying fake posts by fake users. Specifically, it detects large numbers of similar posts from the same IP address and filters out those that are in question.

[1067] Step 10:

[1068] The server will award reward points for posting valid ratings and reviews. These points will be reflected in the user's account, and the device will provide an interface where the reward points can be checked. The user can use these points to plan their next trip. For example, the server will notify the user that "10 points have been awarded."

[1069] (Application example 1)

[1070] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1071] Conventional tourism support systems have difficulty providing appropriate tourist information to users' vague requests, and their ability to display information in multiple languages ​​is limited. Furthermore, autonomous vehicles lack sufficient navigation functions to guide users to tourist spots based on their requests. This makes it difficult for users to visit tourist spots efficiently and safely.

[1072] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1073] In this invention, the server includes means for inputting requests from the user, such as desired places to visit, desired activities, and desired cuisine, means for transmitting the request data to the server, means for analyzing the requests using a generative artificial intelligence model and generating relevant tourist information, means for translating the generated tourist information into the user's native language, means for providing the translated information to the user, means for providing additional detailed information (fares, directions, reservation methods, etc.), and means for providing route guidance to the destination in cooperation with the autonomous driving system. This enables the suggestion of tourist information that meets the user's vague requests and efficient navigation to tourist spots in an autonomous driving vehicle.

[1074] "User" refers to an individual person or customer who uses the Service.

[1075] "Request data" refers to specific or vague request information such as desired places to visit, desired activities, and desired cuisine entered by the user.

[1076] "Server" means a centralized computer system for receiving, analyzing, and processing requested data and providing related information.

[1077] "Generative AI model" refers to advanced AI technology used to analyze user input data and generate relevant tourist information.

[1078] "Relevant tourist information" refers to information about tourist attractions, activities, food and drink provided based on user requests analyzed using a generative artificial intelligence model.

[1079] "Translated information" refers to information that has been converted from the generated tourist information into the user's native language that the user can understand.

[1080] "More Information" refers to additional information related to an attraction, such as prices, directions, and reservations.

[1081] An "autonomous driving system" refers to a system that autonomously controls vehicle operation and guides the vehicle to its destination based on user input.

[1082] "Route Guidance" refers to a function that works in conjunction with the autonomous driving system to provide the user with the optimal route to the tourist spots they wish to visit.

[1083] "In-vehicle display" refers to an information display device installed inside a vehicle, and refers to a display that displays tourist information and navigation information.

[1084] "Ratings and reviews" refer to opinions and impressions of the services provided and places visited by users after their trip.

[1085] "Database" refers to a structured information storage system for organizing and storing user ratings and reviews.

[1086] "Fake User" refers to a fake user hired to post inaccurate or false reviews.

[1087] A "voice recognition system" refers to a system that analyzes a user's voice input and converts it into text data.

[1088] To implement this invention, it is desirable to have the following system configuration: The system mainly receives user input, analyzes it, provides optimal tourist information, and also cooperates with the navigation of the autonomous vehicle.

[1089] Hardware and software used

[1090] In-vehicle display: An information display device installed inside a vehicle that displays tourist information and navigation information.

[1091] Speech recognition system: A system that analyzes the user's voice input and converts it into text data, for example, using the Google Speech-to-Text API.

[1092] Generative AI model: This is an AI technology used to analyze user input data and generate relevant tourist information. It uses GPT-4 provided by OpenAI.

[1093] Translation API: An API for translating the generated information into the user's native language, for example, using the Google Translate API.

[1094] Navigation system: A system that controls the operation of an autonomous vehicle and guides it to a destination based on user input.

[1095] System Program Processing

[1096] 1. User input:

[1097] The user uses the voice recognition function on the in-car display or smartphone to input a vague request (e.g., "I want to relax in a place rich in nature"), which is then converted into text data.

[1098] 2. Sending requested data:

[1099] The text data converted by the speech recognition system is sent to the server via an API request (POST method, JSON format).

[1100] 3. Request analysis:

[1101] The server then sends the text data to a generative artificial intelligence model (GPT-4) to analyze the request, which then lists relevant tourist spots and activities.

[1102] 4. Tourism information generation:

[1103] The server then uses the analysis results to generate tourist information that best suits the user's needs, for example, recommending "naturally rich places around Mount Fuji."

[1104] 5. Translation:

[1105] The server uses the Google Translate API to translate the generated tourist information into the user's native language, thereby providing the information in a format that is easy for the user to understand.

[1106] 6. Information and navigation:

[1107] The translated information is displayed on the in-vehicle display and is also provided to the user as an audio guide. The server and the autonomous driving system also work together to provide route guidance to the suggested spots.

[1108] Examples of concrete examples and prompts

[1109] Specific examples

[1110] When a user speaks to the in-car display, "I want to relax in quiet nature," the speech is converted into text and sent to a server. A generative artificial intelligence model analyzes the speech and generates tourist information about the Mount Fuji area. This information is translated and displayed on the in-car display, saying, "We recommend the Mount Fuji area. Beautiful nature awaits you." The autonomous driving system then begins navigating to Mount Fuji.

[1111] Prompt Sentence Examples

[1112] "A user types 'I want to relax in a place rich in nature.' Suggest five related tourist spots."

[1113] This system will enable the proposal of tourist information that meets the user's vague requests and the efficient navigation to tourist spots in self-driving vehicles.

[1114] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1115] Step 1:

[1116] The user uses the in-car display or smartphone to input their wishes and requests by voice or text. For example, a request such as "I want to relax in a place rich in nature" can be input by voice into the in-car display. This input data is converted into text data by a voice recognition system (Google Speech-to-Text API). The voice input is output as text data.

[1117] Step 2:

[1118] The device sends the text data converted by the voice recognition system to the server via an API request (POST method, JSON format). This text data (request data) is input to the server.

[1119] Step 3:

[1120] The server tokenizes the received request data and preprocesses the text data. It then sends the data to a generative artificial intelligence model (GPT-4) to analyze the user's request. This process involves tokenizing the text, extracting features, and analyzing them to create a list of related tourist attractions and activities. For example, the output would be "Places rich in nature around Mt. Fuji."

[1121] Step 4:

[1122] The server generates tourist information that matches the user's needs based on the analysis results of the generative AI model. Information on recommended tourist spots and activities extracted from the analysis results is generated as text data. For example, information such as "Beautiful natural areas around Mt. Fuji" is generated.

[1123] Step 5:

[1124] The server sends the generated tourist information to a translation API (Google Translate API) to be translated into the user's native language. The translation API translates the received tourist information into the specified language and outputs the translated information.

[1125] Step 6:

[1126] The server sends the translated tourist information to the in-vehicle display. This information is used as part of the navigation display screen. It is also output as an audio guide and presented to the user. The translated information provided to the user is, for example, "We recommend the Mt. Fuji area. Beautiful nature awaits you."

[1127] Step 7:

[1128] The server works in conjunction with the autonomous driving system to send data to create a route guide to the suggested tourist spots. The autonomous driving system calculates the optimal navigation route based on this information and guides the vehicle to the destination. Specifically, a route planning algorithm is used to generate a route that takes into account traffic conditions and road information. The vehicle then begins autonomous driving according to this route.

[1129] 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.

[1130] In order to put this invention into practice, it is necessary to build a system that further combines an emotion engine that recognizes the user's emotions. A specific embodiment of the system is shown below.

[1131] overview

[1132] This system receives vague requests from users, analyzes them using a generative artificial intelligence model, and provides optimal tourist information. Furthermore, by combining it with an emotion engine that uses user emotional data to adjust recommendation information, more personalized information is provided. Furthermore, a translation function provides information in the user's native language, a reward system for posting ratings and reviews, and a function to eliminate fake users increase reliability.

[1133] Processing flow

[1134] 1. User Input

[1135] The user uses the terminal to input requests such as desired places to visit, desired activities, and desired cuisine into a text input form.

[1136] For example, enter "I want to relax in a place rich in nature."

[1137] 2. Data Transmission

[1138] The terminal sends the input request data to the server. The transmission format is usually an API request (e.g., POST method), and the data is sent in JSON format.

[1139] 3. Request analysis

[1140] The server parses and pre-processes the received request data, which includes tokenizing the text data and extracting features.

[1141] It uses a generative artificial intelligence model to analyze requests and generate a list of relevant attractions and activities.

[1142] 4. Recommendation information generation

[1143] The server uses the analysis results to generate recommendation information that best suits the user's needs.

[1144] For example, if a customer requests "I want to relax in a place rich in nature," we will suggest tourist spots around Mount Fuji.

[1145] 5. Multilingual Translation

[1146] The server translates the generated recommendation information into the user's native language using a multilingual translation API (e.g., Google Translate API).

[1147] 6. Emotion recognition

[1148] When providing information, the server uses an emotion engine to recognize the user's emotions. For example, it analyzes the user's facial expressions and tone of voice to quantify the user's emotions.

[1149] 7. Information provision

[1150] The server adjusts the recommendation information based on the user's emotional data and sends the most appropriate information to the terminal.

[1151] The device will then display the provided information to the user. For example, it might say, "We recommend the Mt. Fuji area. You can enjoy beautiful nature and relaxing hot springs."

[1152] 8. Providing additional information

[1153] If the user is interested in the recommended information, he or she sends a request to the server via the terminal requesting additional details (such as fare, directions, and reservation methods).

[1154] The server collects more detailed information and provides it to the user.

[1155] Posting and managing ratings and reviews

[1156] 1. Posting ratings and reviews

[1157] After a trip, users post ratings and reviews from their devices, for example, by entering rating scores and comments.

[1158] 2. Managing Posts

[1159] The terminal transmits the posted content to the server.

[1160] The server stores the received ratings and reviews in a database, uses an emotion engine to recognize the user's emotions at the time of posting, and uses this information to analyze the reliability of the review ratings. It also uses a generative artificial intelligence model to identify and exclude posts by fake users.

[1161] 3. Reward System

[1162] The server will award reward points for posting honest ratings and reviews.

[1163] The terminal provides an interface that allows the user to check the reward points that he or she has earned, and the user can use the reward points to plan his or her next trip.

[1164] This system configuration allows users to efficiently obtain tourist information and plan their trips. It also ensures that information provided takes user sentiment into consideration, and the reliability of ratings and reviews is guaranteed, allowing users to share their travel experiences based on accurate information. This system will be a powerful support tool for inbound travelers to enjoy Japan with peace of mind.

[1165] The processing flow will be explained below.

[1166] Step 1:

[1167] The user uses the terminal to enter their desired places to visit, desired activities, desired cuisine, etc. into a text input form. For example, they might enter "I want to relax in a place rich in nature."

[1168] Step 2:

[1169] The device sends the input request data to the server. The sending format is usually an API request (e.g., POST method), and the data is sent in JSON format.

[1170] Step 3:

[1171] The server parses the received request data and performs preprocessing to input it into the generative AI model. Preprocessing involves tokenizing the text data and extracting features.

[1172] Step 4:

[1173] The server uses a generative artificial intelligence model to analyze the request data, which then generates a list of relevant attractions and activities.

[1174] Step 5:

[1175] Based on the analysis results, the server generates recommendations that best fit the user's needs. For example, if a user requests "I want to relax in a place rich in nature," a list of tourist spots around Mount Fuji will be displayed.

[1176] Step 6:

[1177] The server translates the generated recommendation information into the user's native language using a multilingual translation API (e.g., Google Translate API).

[1178] Step 7:

[1179] The server uses an emotion engine to recognize the user's emotions by analyzing the user's input, facial expressions, or voice data.

[1180] Step 8:

[1181] The server adjusts the recommendation information based on the user's emotional data. For example, if the user is looking for "relaxation," it will prioritize information on quiet resorts.

[1182] Step 9:

[1183] The server sends the adjusted recommendation information to the terminal, which then displays the received information to the user.

[1184] For example, it might say, "We recommend the Mt. Fuji area. You can enjoy beautiful nature and relaxing hot springs."

[1185] Step 10:

[1186] If the user is interested in the displayed recommendation information, the terminal sends a request to the server for more detailed information (such as fare, directions, and reservation methods).

[1187] Step 11:

[1188] The server receives the request for more information, gathers more specific information, and provides it to the user.

[1189] For example, provide detailed information in the form of, "It takes about an hour by Shinkansen from Shinjuku Station to Mishima Station, and the fare is about 4,000 yen one way."

[1190] Step 12:

[1191] After a trip, users post ratings and reviews via their devices, inputting rating scores and comments, and sending the posted content from the device to the server.

[1192] Step 13:

[1193] The server stores the received ratings and reviews in a database. It uses an emotion engine to recognize the user's emotions at the time of posting and uses this information to analyze the reliability of the review ratings.

[1194] Step 14:

[1195] The server uses a generative artificial intelligence model to identify and filter out posts from fake users, ensuring the authenticity of ratings and reviews.

[1196] Step 15:

[1197] The server awards reward points for posting honest ratings and reviews. The terminal provides an interface where users can check the reward points they have earned, and users can use the reward points to plan their next trip.

[1198] Example 2

[1199] 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."

[1200] Conventional tourist information systems have limited ability to fully understand users' vague requests and diverse emotions and provide optimal information. Furthermore, the means to ensure the reliability and accuracy of information are insufficient, and there is no multilingual support or appropriate review management. As a result, users are unable to obtain satisfactory tourist information, and the quality of their tourist experience is reduced.

[1201] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting requests from the user, such as desired places to visit, desired activities, and desired cuisine, means for transmitting the request data to the server, means for analyzing the requests using a generative artificial intelligence model and generating relevant tourist information, means for translating the generated tourist information into the user's native language, means for recognizing the user's emotions using an emotion engine and adjusting recommended information, means for providing the translated information to the user, and means for providing additional detailed information (such as fares, directions, and reservation methods). This allows the user to obtain personalized, optimal tourist information based on their own vague requests, and enables a high-quality tourist experience through the provision of information according to emotions and reliable review management.

[1202] "User" refers to an individual who uses this system to obtain tourist information and post ratings and reviews.

[1203] "Request data" is data including text information such as desired places to visit, desired activities, and desired dishes input by the user.

[1204] "Generative AI model" refers to an AI technology that analyzes user input request data and generates related tourist information.

[1205] An "emotion engine" refers to technology that recognizes user emotions, quantifies and analyzes them, and adjusts recommendation information.

[1206] "Sakura users" refer to fake users who post false ratings and reviews for fraudulent purposes.

[1207] "Translation means" refers to the translation techniques and tools used to translate the generated tourist information into the user's native language.

[1208] "Recommended information" refers to information about tourist spots and activities provided based on the user's requests and emotional data.

[1209] "Review management means" refers to the means for receiving ratings and reviews posted by users, storing them in a database, and excluding posts by fake users.

[1210] A "reward system" refers to a mechanism that awards reward points for legitimate ratings and reviews posted by users, allowing users to use those points for travel planning.

[1211] This invention is a system for analyzing vague requests entered by users and providing optimal tourist information. The system utilizes user emotional data to optimize information and achieve multilingual support. It also ensures the reliability of ratings and reviews and introduces a reward system to improve the user experience. Specific embodiments of the system are described below.

[1212] overview

[1213] Users use a device (smartphone, PC, etc.) to input their desired places to visit, activities, cuisine, etc. The device sends the desired data to a server, which analyzes it and generates optimal tourist information. The hardware required is a device used by the user and a server. The software required includes a generative AI model, an emotion engine, a translation API, etc.

[1214] Hardware and software used

[1215] Terminal: A smartphone or PC used by a user to input and display data.

[1216] Server: A computer system for performing data analysis and information generation.

[1217] Generative AI model: AI technology for analyzing user requests and generating optimal tourist information (e.g., GPT-4).

[1218] Translation API: An API for translating the generated tourist information into the user's native language (e.g., Google Translate API).

[1219] Emotion engine: Technology for recognizing user emotions and adjusting recommendation information (e.g., Microsoft Azure's Emotion API).

[1220] Examples of data analysis and information generation

[1221] 1. User Input:

[1222] The user can freely input the places and activities they want to visit into the input form on the device. For example, they can enter "I want to relax in a place rich in nature."

[1223] 2. Data transmission:

[1224] The terminal sends the input request data to the server in JSON format.

[1225] 3. Request analysis:

[1226] The server parses the received request data, tokenizes the text data, and extracts features. It then inputs a prompt to the generative AI model for analysis. An example of a prompt is as follows:

[1227] The user's request is "I want to relax in a place rich in nature." Please suggest the best tourist spots.

[1228] A generative artificial intelligence model generates a list of relevant attractions and activities.

[1229] 4. Information Translation:

[1230] The server translates the generated tourist information into the user's native language using a translation API, for example, "Relaxing natural spots near Mount Fuji" in English.

[1231] 5. Emotion recognition:

[1232] The emotion engine recognizes the user's emotions in real time, calculates an emotion score from the user's facial expressions and voice, and adjusts the recommendation information accordingly.

[1233] 6. Information provision:

[1234] The server sends the adjusted recommendation information to the device, which then displays it to the user. For example, it displays, "We recommend the Mt. Fuji area. You can enjoy beautiful nature and relaxing hot springs."

[1235] Providing additional information and managing reviews

[1236] If the user requests additional details, they send a request to the server via their device. The server collects the details (fare, directions, reservation methods, etc.) and provides them to the user. Furthermore, if the user submits a rating or review, the server stores it in a database and uses an artificial intelligence model to filter out fraudulent posts (fake users). Legitimate ratings and reviews are awarded reward points, which the user can use to plan their next trip.

[1237] As a result, users can obtain efficient and personalized tourist information and enjoy a high-quality travel experience.

[1238] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1239] Step 1:

[1240] The user uses the terminal to enter their desired places to visit, desired activities, desired cuisine, etc. into a text input form. For example, they might enter "I want to relax in a place rich in nature."

[1241] Input: User's request text

[1242] Output: The requested data is temporarily stored on the device.

[1243] Step 2:

[1244] The device sends the input request data to the server. Specifically, it uses JavaScript Ajax to send the data in JSON format.

[1245] Input: User's desired data

[1246] Output: The requested data is sent to the server in JSON format.

[1247] Step 3:

[1248] The server parses the received request data, tokenizes the text data, extracts features, and analyzes the text using the Python NLTK library.

[1249] Input: Request data in JSON format

[1250] Output: Tokenized and feature extracted data

[1251] Step 4:

[1252] The server inputs a prompt sentence into a generative AI model (e.g., GPT-4) for analysis. For example, the following prompt sentence is used:

[1253] Prompt: "The user wants to relax in a place surrounded by nature. Please suggest the best tourist spots."

[1254] Input: Tokenized and feature extracted data

[1255] Output: A list of related attractions and activities

[1256] Step 5:

[1257] The server translates the generated tourist information into the user's native language using a multilingual translation API (e.g., Google Translate API).

[1258] Input: List of tourist information

[1259] Output: Translated tourist information

[1260] Step 6:

[1261] The server uses an emotion engine (e.g., Microsoft Azure's Emotion API) to recognize emotions from user input or reactions. It analyzes the user's facial expressions and tone of voice and generates an emotion score.

[1262] Input: User input data, reaction data

[1263] Output: Sentiment score

[1264] Step 7:

[1265] The server adjusts the translated tourist information based on the emotion score to generate optimal recommendation information, which is then sent to the device.

[1266] Input: translated tourist information, sentiment score

[1267] Output: Adjusted recommendation information

[1268] Step 8:

[1269] The device will then display the provided recommendation information to the user. For example, it might say, "We recommend the Mt. Fuji area. You can enjoy beautiful nature and relaxing hot springs."

[1270] Input: Adjusted recommendation information

[1271] Output: Recommendation information that users can visually confirm

[1272] Step 9:

[1273] If the user is interested in the recommended information, he or she sends a request to the server via the terminal requesting additional details (such as fare, directions, and reservation methods).

[1274] Input: Request for additional information

[1275] Output: Request data is sent to the server

[1276] Step 10:

[1277] The server collects additional details and provides them to the user.

[1278] Input: Request data

[1279] Output: Additional details

[1280] Step 11:

[1281] After a trip, users post ratings and reviews from their devices, for example by entering rating scores and comments and attaching images.

[1282] Input: Ratings and review data

[1283] Output: Ratings and reviews data persisted on device

[1284] Step 12:

[1285] The terminal transmits the posted content to the server.

[1286] Input: Ratings and review data

[1287] Output: Posted data is sent to the server

[1288] Step 13:

[1289] The server stores the received ratings and reviews in a database, uses an emotion engine to recognize the user's emotions at the time of posting, and uses a generative artificial intelligence model to identify and filter out posts from fake users.

[1290] Input: Ratings and review data

[1291] Output: Data after reliability analysis, identification results of fake users

[1292] Step 14:

[1293] The server will award reward points for posting honest ratings and reviews.

[1294] Input: Data after reliability analysis

[1295] Output: User's reward points

[1296] Step 15:

[1297] The terminal provides an interface that allows the user to check the reward points that he or she has earned, and the user can use the reward points to plan his or her next trip.

[1298] Input: User's reward points

[1299] Output: Reward point information that can be visually confirmed by the user

[1300] (Application example 2)

[1301] 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."

[1302] In factory work, it is necessary to respond quickly and appropriately to vague requests from workers, and to create work plans that take into account the emotions and health of workers.In addition to tourist information, a system is needed that utilizes generative artificial intelligence models to provide optimal work plans for factory work.

[1303] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting requests from the user, such as desired places to visit, desired activities, and desired cuisine; means for transmitting the request data to the server; means for analyzing the requests using a generative artificial intelligence model and generating relevant tourist information; means for translating the generated tourist information into the user's native language; means for providing the translated information to the user; means for providing additional detailed information (such as fees, directions, and reservation methods); means for analyzing the user's emotional data using an emotion recognition engine; means for adjusting information based on the analyzed emotional data; and means for formulating an optimal work plan based on the adjusted information and automatically executing the work. This makes it possible to respond quickly and appropriately to worker requests and to create work plans that take into account the worker's emotions and health status.

[1304] "User emotion data" is data that quantifies the user's emotional state analyzed from the user's facial expression, tone of voice, behavior, etc.

[1305] A "generative artificial intelligence model" is an artificial intelligence algorithm that analyzes a user's vague requests and generates relevant information and work plans.

[1306] "Request data" refers to data including specific or vague requests such as desired places to visit, desired activities, and desired dishes entered by the user.

[1307] An "emotion recognition engine" is a software component that analyzes a user's voice and facial expression data to recognize their emotional state.

[1308] "Adjusting information" means optimizing the content of the information provided and work plans based on emotional data.

[1309] A "work plan" is a schedule or action plan for work that is planned and executed by a generative artificial intelligence model.

[1310] "Additional details" are specific details such as prices, directions, and reservation methods that accompany the information provided.

[1311] A "server" is a computer system that receives and analyzes user-requested data, and generates, adjusts, and provides related information.

[1312] This invention is a system in which a patrol / work robot in a factory analyzes vague requests from workers, creates an optimal work plan, and automatically executes it. This system takes into account the user's emotional data and can perform work more efficiently and safely.

[1313] System Configuration

[1314] 1. Emotion Recognition Engine

[1315] This engine is a software component that analyzes the voice and facial expression data of workers and outputs their emotional state as numerical data. It uses a camera and microphone to collect real-time emotional data of users.

[1316] 2. Generative AI Models

[1317] This is an algorithm that analyzes request data and generates a related work plan. This model has the ability to extract specific tasks from vague requests and convert them into actionable plans.

[1318] 3. Robot Controller

[1319] This is a component that controls robots in factories and automatically executes tasks based on the generated work plan. This control system manages the robot's operation and performs work efficiently.

[1320] Hardware and software used

[1321] Camera and microphone

[1322] It collects the worker's facial expressions and voice and uses them to provide data to the emotion recognition engine.

[1323] GPU Server

[1324] This is a server for high-speed processing of emotion recognition engines and generative AI models. Specifically, it is equipped with hardware capable of processing neural networks and deep learning.

[1325] Robots in the factory

[1326] These are creations that perform real tasks, such as a typical factory robot.

[1327] Example of operation

[1328] 1. The worker speaks to the patrol robot, saying, "This machine needs adjustment." At this time, the camera captures the worker's facial expression and the microphone collects the voice.

[1329] 2. The robot terminal uses an emotion recognition engine to analyze the worker's emotional state from their voice and facial expression data.

[1330] 3. The analyzed emotional data is combined with a generative artificial intelligence model to analyze the user's requests and generate an optimal work plan.

[1331] 4. The generated work plan is automatically executed by the robot controller, which adjusts the specified machine and notifies the work support system as necessary.

[1332] Example prompt sentence:

[1333] This machine needs adjustment.

[1334] In this way, it is possible to respond quickly and appropriately to the requests of workers and create optimal work plans that take emotional data into account, thereby improving work efficiency in the factory and reducing worker stress and fatigue.

[1335] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1336] Step 1:

[1337] The user inputs their request by voice or text. The device collects the voice data or text data using a microphone and a text input interface. Voice data or text data is obtained as input.

[1338] Step 2:

[1339] The device converts voice data into text. This conversion is done using speech recognition software. The input is voice data and the output is text data. Specifically, the speech recognition engine analyzes the voice waveform and converts it into the corresponding text.

[1340] Step 3:

[1341] The device uses a camera and microphone to collect the user's facial expressions and voice tone. The data is sent to an emotion recognition engine. The input is image data and voice tone data. The output is quantified emotion data. The emotion recognition engine extracts facial features from the image data and voice features from the voice data to quantify emotions.

[1342] Step 4:

[1343] The device sends the collected request data and emotion data to the server. The server receives this data. An API request (e.g., POST method) is used to send the data. The input is the request text data and emotion data. The output is a confirmation that the data has been transferred to the server.

[1344] Step 5:

[1345] The server uses a generative artificial intelligence model to analyze the user's request data. Specific tasks are extracted from the request. The request text data is used as input, and related tasks are extracted based on it. The output is an optimized task list. Specifically, the AI ​​model analyzes the text, extracts important keywords, and lists related tasks.

[1346] Step 6:

[1347] The server adjusts the generated task list based on the emotional data. The emotional data and the task list are used as inputs. The output is the adjusted task list. Specifically, if the emotional data indicates high stress, the server reevaluates the priority of the task list and starts with easier tasks.

[1348] Step 7:

[1349] The server creates an optimal work plan and sends it to the robot controller. The adjusted task list is used as input. The output is a confirmation that the work plan has been sent. The server converts the task list into a work plan and sends it to the robot controller.

[1350] Step 8:

[1351] The robot controller executes specific tasks based on the transmitted work plan. The input is the work plan data, and the output is the specific tasks to be performed. The robot moves to the specified location and performs adjustments or repairs as needed.

[1352] The above processing steps make it possible to respond quickly and appropriately to vague user requests, and to create and execute work plans that take into account the emotions and health status of workers.

[1353] 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.

[1354] 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.

[1355] 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.

[1356] [Fourth embodiment]

[1357] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1358] 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.

[1359] 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).

[1360] 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.

[1361] 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.

[1362] 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).

[1363] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1364] 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.

[1365] 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.

[1366] 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.

[1367] 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.

[1368] 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.

[1369] 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."

[1370] In order to put the present invention into practice, it is necessary to construct a system in which a plurality of means operate in cooperation with one another. A specific embodiment of the system will be described below.

[1371] overview

[1372] This system receives vague requests from users, analyzes them using a generative artificial intelligence model, and provides optimal tourist information. It also translates the information into the user's native language to support travel planning. It also increases reliability by introducing a reward system for posting ratings and reviews, and by incorporating a function to eliminate fake users.

[1373] Processing flow

[1374] 1. User Input

[1375] The user inputs their desired destinations, activities, cuisine, and other requests into an input form on the terminal.

[1376] For example, enter "I want to relax in a place rich in nature."

[1377] 2. Data Transmission

[1378] The terminal transmits the input request data to the server.

[1379] The transmission format is usually an API request (e.g., POST method), and the data format is JSON.

[1380] 3. Request analysis

[1381] The server parses and preprocesses the received request data, which includes tokenizing the text data and extracting features.

[1382] It uses a generative artificial intelligence model to analyze requests and generate a list of relevant attractions and activities.

[1383] 4. Recommendation information generation

[1384] The server uses the analysis results to generate tourist information that best suits the user's needs.

[1385] For example, in response to the above request, tourist spots around Mount Fuji are listed.

[1386] 5. Multilingual Translation

[1387] The server translates the generated tourist information into the user's native language.

[1388] Translation uses a multilingual translation API (e.g., Google Translate API).

[1389] 6. Information provision

[1390] The server sends the translated recommendation information to the device,

[1391] The terminal displays it to the user.

[1392] For example, it might say, "We recommend the Mt. Fuji area. You can enjoy beautiful nature and relaxing hot springs."

[1393] 7. Providing additional information

[1394] If the user is interested in the recommended information, he or she sends a request to the server via the terminal requesting additional details (such as fare, directions, and reservation methods).

[1395] The server collects more detailed information and provides it to the user.

[1396] Posting and managing ratings and reviews

[1397] 1. Posting ratings and reviews

[1398] After traveling, users post ratings and reviews from their devices.

[1399] The device sends these posts to the server.

[1400] 2. Managing Posts

[1401] The server stores the received ratings and reviews in a database.

[1402] It uses a generative artificial intelligence model to analyze the content of posts and identify and filter out fake posts made by fake users.

[1403] 3. Reward System

[1404] The server awards reward points for posting legitimate ratings and reviews.

[1405] The terminal provides an interface where users can check their reward points and use them to plan their next trip.

[1406] This system configuration allows users to efficiently obtain tourist information and make travel plans. It also guarantees the reliability of ratings and reviews, allowing users to share travel experiences based on accurate information. This system will be a powerful support tool that allows each inbound traveler to enjoy Japan with peace of mind.

[1407] The processing flow will be explained below.

[1408] Step 1:

[1409] The user uses the terminal to enter their desired places to visit, desired activities, desired cuisine, etc. into a text input form. For example, they might enter "I want to relax in a place rich in nature."

[1410] Step 2:

[1411] The terminal sends the input request data to the server. The transmission format is usually an API request (e.g., POST method), and the data is sent in JSON format.

[1412] Step 3:

[1413] The server parses the received request data and performs pre-processing for input to the generative AI model, including tokenizing the text data and extracting features.

[1414] Step 4:

[1415] The server analyzes the request data using a generative AI model, which then generates a list of related tourist attractions and activities based on the request.

[1416] Step 5:

[1417] Based on the analysis results, the server generates recommendations that best fit the user's needs. For example, if a user requests "I want to relax in a place rich in nature," the server will suggest tourist spots around Mount Fuji.

[1418] Step 6:

[1419] The server translates the generated recommendation information into the user's native language using a multilingual translation API (e.g., Google Translate API).

[1420] Step 7:

[1421] The server sends the translated recommendation information to the terminal.

[1422] The device then displays the received information to the user. For example, it might say, "We recommend the Mt. Fuji area. You can enjoy beautiful nature and relaxing hot springs."

[1423] Step 8:

[1424] If the user is interested in the displayed recommendation information, the terminal sends a request to the server for more detailed information (such as fare, directions, and reservation methods).

[1425] Step 9:

[1426] The server receives the request for more information, gathers more specific information, and provides it to the user.

[1427] Step 10:

[1428] After a trip, users can post ratings and reviews via their devices, for example by entering rating scores and comments.

[1429] Step 11:

[1430] The device sends the posted ratings and reviews to the server.

[1431] Step 12:

[1432] The server stores the received ratings and reviews in a database, and also uses a generative artificial intelligence model to identify and filter out posts from fake users.

[1433] Step 13:

[1434] The server will award reward points for posting honest ratings and reviews.

[1435] Step 14:

[1436] The terminal provides an interface that allows the user to check the reward points that he or she has earned, and the user can use the reward points to plan his or her next trip.

[1437] Example 1

[1438] 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."

[1439] In modern travel planning, users gather a lot of information and spend a lot of time selecting appropriate tourist destinations. Furthermore, the information they obtain can be unreliable, which can significantly reduce their travel satisfaction. Furthermore, language barriers are a major obstacle to travel planning and information gathering on-site. To solve these problems, a system is needed that can accurately interpret users' needs, provide optimal tourist information, and utilize reliable reviews and ratings.

[1440] 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.

[1441] In this invention, the server comprises: a means for inputting requests from a user, such as desired places to visit, desired activities, desired dishes, etc.;

[1442] means for transmitting request data from the terminal to the server;

[1443] A means for analyzing requests and generating relevant tourist information using a generative artificial intelligence model;

[1444] A means for translating the generated tourist information into the user's native language;

[1445] a means for providing the translated information to a user;

[1446] A means to provide additional details (such as prices, directions, and booking instructions) if requested by the user;

[1447] A method to receive user ratings and reviews, analyze them, and filter out fake users.

[1448] A way to reward legitimate ratings and reviews,

[1449] a means for users to view reward points available for their next travel plan;

[1450] This will enable users to efficiently obtain reliable tourist information and plan their trips. It will also increase the reliability of ratings and reviews, allowing users to share travel experiences based on accurate information.

[1451] A "user" is an entity that uses this system to input requests such as desired places to visit, desired activities, and desired cuisine, and obtains information.

[1452] "Terminal" refers to a device that transmits user-entered requests to a server and displays information received from the server.

[1453] The "server" is a central processing device that analyzes requests received from users and generates and provides tourist information.

[1454] A "generative artificial intelligence model" is a type of algorithm or technology that analyzes user requests and generates relevant and appropriate tourist information.

[1455] "Request data" is data including information such as desired places to visit, desired activities, and desired dishes input by the user.

[1456] "Tourist information" refers to information about places and activities that a user would like to visit, including information about tourist spots, activities, places to eat, etc.

[1457] "Translation API" refers to a programming interface for converting information written in one language into another language.

[1458] "Ratings and reviews" refer to reviews and feedback posted by users after their trips, and are information that includes ratings.

[1459] "Sakura users" refer to fraudulent users who post falsified ratings and reviews.

[1460] "Reward points" are points awarded to users who post honest reviews and ratings, and can be used to plan their next trip, etc.

[1461] "Additional Details" refers to detailed information such as prices, directions, and reservation methods that is provided upon request by the user.

[1462] To implement this invention, it is necessary to build a system that links multiple means. This system receives vague requests from users, analyzes them using a generative artificial intelligence model, and provides optimal tourist information. It also has a function to translate information into the user's native language and support travel planning. It also increases reliability by introducing a reward system for posting ratings and reviews and by incorporating a function to eliminate fake users.

[1463] Hardware and software used

[1464] An implementation of this system includes the following hardware and software:

[1465] 1. Terminal: A device where a user inputs requests and displays information from a server. Examples include smartphones and personal computers.

[1466] 2. Server: The server analyzes the request data, generates tourist information, translates and manages the reward system.

[1467] 3. Generative AI model: An AI model (e.g., GPT-3) that analyzes user requests and generates relevant tourist information.

[1468] 4. Translation API: An API for translating tourist information into the user's native language (e.g., Google Translate API).

[1469] Program processing

[1470] Below is a detailed description of how the program works in this system:

[1471] 1. User Input

[1472] The user inputs into an input form on the terminal the desired places to visit, desired activities, desired cuisine, etc. As a specific example, the user inputs "I want to relax in a place rich in nature."

[1473] 2. Data Transmission

[1474] The terminal sends the input request data to the server. At this time, the data is usually sent using the POST method, and the format is JSON.

[1475] 3. Request analysis

[1476] The server analyzes the received request data, using natural language processing (NLP) techniques to tokenize the text data and extract key keywords, such as "nature," "relaxation," and "place."

[1477] 4. Information generation

[1478] The server analyzes the request using a generative artificial intelligence model (e.g., GPT-3) based on the extracted keywords and generates relevant tourist information. For example, tourist spots around Mount Fuji are selected.

[1479] 5. Multilingual Translation

[1480] To translate the generated tourist information into the user's native language, the server uses a multilingual translation API such as Google Translate API. For example, "We recommend the Mt. Fuji area. You can enjoy beautiful nature and relaxing hot springs."

[1481] 6. Information provision

[1482] The server sends the translated information to the device, which then displays it to the user. For example, it might say, "We recommend the Mount Fuji area. You can enjoy beautiful nature and relaxing hot springs."

[1483] 7. Providing additional information

[1484] If a user shows interest in the recommended information, they send a request for detailed information (such as fare, directions, and reservation methods) to the server via their device. The server collects the detailed information and provides it to the user.

[1485] 8. Posting and managing ratings and reviews

[1486] After their trip, users submit ratings and reviews from their devices. The server stores these posts in a database and uses a generative artificial intelligence model to identify fake users and filter out fake posts.

[1487] 9. Reward System

[1488] The server awards reward points for legitimate ratings and reviews, and the terminal provides an interface where users can check these points. Users can use these reward points to plan their next trip.

[1489] Examples of concrete examples and prompts

[1490] Example: If a user types, "I want to relax in a place rich in nature," the system will suggest tourist spots around Mount Fuji and display the message, "We recommend the Mount Fuji area. You can enjoy beautiful nature and relaxing hot springs."

[1491] Example prompt: "What are some relaxing tourist spots in a particular location?"

[1492] This system configuration allows users to efficiently obtain reliable tourist information and plan their trips. Furthermore, the reliability of ratings and reviews is guaranteed, allowing users to share travel experiences based on accurate information.

[1493] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1494] Step 1:

[1495] The user inputs their desired destinations, activities, and cuisine into the device. For example, they might input, "I want to relax in a place rich in nature." This input data is temporarily stored on the device. The input data is acquired through a text form.

[1496] Step 2:

[1497] The device sends the input request data to the server. At this time, the data is converted to JSON format and the POST method is used as the API request. The device organizes the request data as a JSON object and sends an HTTP request to the server.

[1498] Step 3:

[1499] The server analyzes the received request data and performs data preprocessing. This step includes tokenizing the text data and extracting features. For example, from the text "I want to relax in a place rich in nature," the keywords "nature," "relaxation," and "place" are extracted. Specifically, a text analysis algorithm is used to perform grammatical analysis and extract keywords.

[1500] Step 4:

[1501] The server inputs the extracted keywords into a generative AI model (e.g., GPT-3) to analyze the request. The generative AI model generates relevant tourist information based on these keywords. For example, the keywords "nature," "relaxation," and "place" generate tourist spots around Mount Fuji. The server temporarily stores the generated tourist information.

[1502] Step 5:

[1503] The server translates the generated tourist information into the user's native language. This translation uses a multilingual translation API (e.g., Google Translate API). The server sends the tourist information to the translation API and receives the translated results. For example, the translated text would read, "We recommend the Mt. Fuji area. You can enjoy beautiful nature and relaxing hot springs."

[1504] Step 6:

[1505] The server sends the translated information to the device. The data is converted back to JSON format and sent as an API response. The device analyzes the received data and displays it on the user interface. For example, it might say, "We recommend the Mt. Fuji area for you. You can enjoy beautiful nature and relaxing hot springs."

[1506] Step 7:

[1507] If a user is interested in a recommendation, they send a request to the server via their device for additional details (such as fare, directions, and reservation methods). Based on the input, the server collects the details from relevant databases and external services. This information is sent to the device, which then displays it to the user.

[1508] Step 8:

[1509] After a trip, users post ratings and reviews from their devices. The devices then send these posts to the server. The entered ratings and reviews are sent to the server in JSON format. A specific example of input includes text such as "The hot springs at Mt. Fuji were very relaxing."

[1510] Step 9:

[1511] The server stores the received ratings and reviews in a database and analyzes the posts using a generative AI model. This analysis includes identifying fake posts by fake users. Specifically, it detects large numbers of similar posts from the same IP address and filters out those that are in question.

[1512] Step 10:

[1513] The server will award reward points for posting valid ratings and reviews. These points will be reflected in the user's account, and the device will provide an interface where the reward points can be checked. The user can use these points to plan their next trip. For example, the server will notify the user that "10 points have been awarded."

[1514] (Application example 1)

[1515] 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."

[1516] Conventional tourism support systems have difficulty providing appropriate tourist information to users' vague requests, and their ability to display information in multiple languages ​​is limited. Furthermore, autonomous vehicles lack sufficient navigation functions to guide users to tourist spots based on their requests. This makes it difficult for users to visit tourist spots efficiently and safely.

[1517] 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.

[1518] In this invention, the server includes means for inputting requests from the user, such as desired places to visit, desired activities, and desired cuisine, means for transmitting the request data to the server, means for analyzing the requests using a generative artificial intelligence model and generating relevant tourist information, means for translating the generated tourist information into the user's native language, means for providing the translated information to the user, means for providing additional detailed information (fares, directions, reservation methods, etc.), and means for providing route guidance to the destination in cooperation with the autonomous driving system. This enables the suggestion of tourist information that meets the user's vague requests and efficient navigation to tourist spots in an autonomous driving vehicle.

[1519] "User" refers to an individual person or customer who uses the Service.

[1520] "Request data" refers to specific or vague request information such as desired places to visit, desired activities, and desired cuisine entered by the user.

[1521] "Server" means a centralized computer system for receiving, analyzing, and processing requested data and providing related information.

[1522] "Generative AI model" refers to advanced AI technology used to analyze user input data and generate relevant tourist information.

[1523] "Relevant tourist information" refers to information about tourist attractions, activities, food and drink provided based on user requests analyzed using a generative artificial intelligence model.

[1524] "Translated information" refers to information that has been converted from the generated tourist information into the user's native language that the user can understand.

[1525] "More Information" refers to additional information related to an attraction, such as prices, directions, and reservations.

[1526] An "autonomous driving system" refers to a system that autonomously controls vehicle operation and guides the vehicle to its destination based on user input.

[1527] "Route Guidance" refers to a function that works in conjunction with the autonomous driving system to provide the user with the optimal route to the tourist spots they wish to visit.

[1528] "In-vehicle display" refers to an information display device installed inside a vehicle, and refers to a display that displays tourist information and navigation information.

[1529] "Ratings and reviews" refer to opinions and impressions of the services provided and places visited by users after their trip.

[1530] "Database" refers to a structured information storage system for organizing and storing user ratings and reviews.

[1531] "Fake User" refers to a fake user hired to post inaccurate or false reviews.

[1532] A "voice recognition system" refers to a system that analyzes a user's voice input and converts it into text data.

[1533] To implement this invention, it is desirable to have the following system configuration: The system mainly receives user input, analyzes it, provides optimal tourist information, and also cooperates with the navigation of the autonomous vehicle.

[1534] Hardware and software used

[1535] In-vehicle display: An information display device installed inside a vehicle that displays tourist information and navigation information.

[1536] Speech recognition system: A system that analyzes the user's voice input and converts it into text data, for example, using the Google Speech-to-Text API.

[1537] Generative AI model: This is an AI technology used to analyze user input data and generate relevant tourist information. It uses GPT-4 provided by OpenAI.

[1538] Translation API: An API for translating the generated information into the user's native language, for example, using the Google Translate API.

[1539] Navigation system: A system that controls the operation of an autonomous vehicle and guides it to a destination based on user input.

[1540] System Program Processing

[1541] 1. User input:

[1542] The user uses the voice recognition function on the in-car display or smartphone to input a vague request (e.g., "I want to relax in a place rich in nature"), which is then converted into text data.

[1543] 2. Sending requested data:

[1544] The text data converted by the speech recognition system is sent to the server via an API request (POST method, JSON format).

[1545] 3. Request analysis:

[1546] The server then sends the text data to a generative artificial intelligence model (GPT-4) to analyze the request, which then lists relevant tourist spots and activities.

[1547] 4. Tourism information generation:

[1548] The server then uses the analysis results to generate tourist information that best suits the user's needs, for example, recommending "naturally rich places around Mount Fuji."

[1549] 5. Translation:

[1550] The server uses the Google Translate API to translate the generated tourist information into the user's native language, thereby providing the information in a format that is easy for the user to understand.

[1551] 6. Information and navigation:

[1552] The translated information is displayed on the in-vehicle display and is also provided to the user as an audio guide. The server and the autonomous driving system also work together to provide route guidance to the suggested spots.

[1553] Examples of concrete examples and prompts

[1554] Specific examples

[1555] When a user speaks to the in-car display, "I want to relax in quiet nature," the speech is converted into text and sent to a server. A generative artificial intelligence model analyzes the speech and generates tourist information about the Mount Fuji area. This information is translated and displayed on the in-car display, saying, "We recommend the Mount Fuji area. Beautiful nature awaits you." The autonomous driving system then begins navigating to Mount Fuji.

[1556] Prompt Sentence Examples

[1557] "A user types 'I want to relax in a place rich in nature.' Suggest five related tourist spots."

[1558] This system will enable the proposal of tourist information that meets the user's vague requests and the efficient navigation to tourist spots in self-driving vehicles.

[1559] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1560] Step 1:

[1561] The user uses the in-car display or smartphone to input their wishes and requests by voice or text. For example, a request such as "I want to relax in a place rich in nature" can be input by voice into the in-car display. This input data is converted into text data by a voice recognition system (Google Speech-to-Text API). The voice input is output as text data.

[1562] Step 2:

[1563] The device sends the text data converted by the voice recognition system to the server via an API request (POST method, JSON format). This text data (request data) is input to the server.

[1564] Step 3:

[1565] The server tokenizes the received request data and preprocesses the text data. It then sends the data to a generative artificial intelligence model (GPT-4) to analyze the user's request. This process involves tokenizing the text, extracting features, and analyzing them to create a list of related tourist attractions and activities. For example, the output would be "Places rich in nature around Mt. Fuji."

[1566] Step 4:

[1567] The server generates tourist information that matches the user's needs based on the analysis results of the generative AI model. Information on recommended tourist spots and activities extracted from the analysis results is generated as text data. For example, information such as "Beautiful natural areas around Mt. Fuji" is generated.

[1568] Step 5:

[1569] The server sends the generated tourist information to a translation API (Google Translate API) to be translated into the user's native language. The translation API translates the received tourist information into the specified language and outputs the translated information.

[1570] Step 6:

[1571] The server sends the translated tourist information to the in-vehicle display. This information is used as part of the navigation display screen. It is also output as an audio guide and presented to the user. The translated information provided to the user is, for example, "We recommend the Mt. Fuji area. Beautiful nature awaits you."

[1572] Step 7:

[1573] The server works in conjunction with the autonomous driving system to send data to create a route guide to the suggested tourist spots. The autonomous driving system calculates the optimal navigation route based on this information and guides the vehicle to the destination. Specifically, a route planning algorithm is used to generate a route that takes into account traffic conditions and road information. The vehicle then begins autonomous driving according to this route.

[1574] 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.

[1575] In order to put this invention into practice, it is necessary to build a system that further combines an emotion engine that recognizes the user's emotions. A specific embodiment of the system is shown below.

[1576] overview

[1577] This system receives vague requests from users, analyzes them using a generative artificial intelligence model, and provides optimal tourist information. Furthermore, by combining it with an emotion engine that uses user emotional data to adjust recommendation information, more personalized information is provided. Furthermore, a translation function provides information in the user's native language, a reward system for posting ratings and reviews, and a function to eliminate fake users increase reliability.

[1578] Processing flow

[1579] 1. User Input

[1580] The user uses the terminal to input requests such as desired places to visit, desired activities, and desired cuisine into a text input form.

[1581] For example, enter "I want to relax in a place rich in nature."

[1582] 2. Data Transmission

[1583] The terminal sends the input request data to the server. The transmission format is usually an API request (e.g., POST method), and the data is sent in JSON format.

[1584] 3. Request analysis

[1585] The server parses and pre-processes the received request data, which includes tokenizing the text data and extracting features.

[1586] It uses a generative artificial intelligence model to analyze requests and generate a list of relevant attractions and activities.

[1587] 4. Recommendation information generation

[1588] The server uses the analysis results to generate recommendation information that best suits the user's needs.

[1589] For example, if a customer requests "I want to relax in a place rich in nature," we will suggest tourist spots around Mount Fuji.

[1590] 5. Multilingual Translation

[1591] The server translates the generated recommendation information into the user's native language using a multilingual translation API (e.g., Google Translate API).

[1592] 6. Emotion recognition

[1593] When providing information, the server uses an emotion engine to recognize the user's emotions. For example, it analyzes the user's facial expressions and tone of voice to quantify the user's emotions.

[1594] 7. Information provision

[1595] The server adjusts the recommendation information based on the user's emotional data and sends the most appropriate information to the terminal.

[1596] The device will then display the provided information to the user. For example, it might say, "We recommend the Mt. Fuji area. You can enjoy beautiful nature and relaxing hot springs."

[1597] 8. Providing additional information

[1598] If the user is interested in the recommended information, he or she sends a request to the server via the terminal requesting additional details (such as fare, directions, and reservation methods).

[1599] The server collects more detailed information and provides it to the user.

[1600] Posting and managing ratings and reviews

[1601] 1. Posting ratings and reviews

[1602] After a trip, users post ratings and reviews from their devices, for example, by entering rating scores and comments.

[1603] 2. Managing Posts

[1604] The terminal transmits the posted content to the server.

[1605] The server stores the received ratings and reviews in a database, uses an emotion engine to recognize the user's emotions at the time of posting, and uses this information to analyze the reliability of the review ratings. It also uses a generative artificial intelligence model to identify and exclude posts by fake users.

[1606] 3. Reward System

[1607] The server will award reward points for posting honest ratings and reviews.

[1608] The terminal provides an interface that allows the user to check the reward points that he or she has earned, and the user can use the reward points to plan his or her next trip.

[1609] This system configuration allows users to efficiently obtain tourist information and plan their trips. It also ensures that information provided takes user sentiment into consideration, and the reliability of ratings and reviews is guaranteed, allowing users to share their travel experiences based on accurate information. This system will be a powerful support tool for inbound travelers to enjoy Japan with peace of mind.

[1610] The processing flow will be explained below.

[1611] Step 1:

[1612] The user uses the terminal to enter their desired places to visit, desired activities, desired cuisine, etc. into a text input form. For example, they might enter "I want to relax in a place rich in nature."

[1613] Step 2:

[1614] The device sends the input request data to the server. The sending format is usually an API request (e.g., POST method), and the data is sent in JSON format.

[1615] Step 3:

[1616] The server parses the received request data and performs preprocessing to input it into the generative AI model. Preprocessing involves tokenizing the text data and extracting features.

[1617] Step 4:

[1618] The server uses a generative artificial intelligence model to analyze the request data, which then generates a list of relevant attractions and activities.

[1619] Step 5:

[1620] Based on the analysis results, the server generates recommendations that best fit the user's needs. For example, if a user requests "I want to relax in a place rich in nature," a list of tourist spots around Mount Fuji will be displayed.

[1621] Step 6:

[1622] The server translates the generated recommendation information into the user's native language using a multilingual translation API (e.g., Google Translate API).

[1623] Step 7:

[1624] The server uses an emotion engine to recognize the user's emotions by analyzing the user's input, facial expressions, or voice data.

[1625] Step 8:

[1626] The server adjusts the recommendation information based on the user's emotional data. For example, if the user is looking for "relaxation," it will prioritize information on quiet resorts.

[1627] Step 9:

[1628] The server sends the adjusted recommendation information to the terminal, which then displays the received information to the user.

[1629] For example, it might say, "We recommend the Mt. Fuji area. You can enjoy beautiful nature and relaxing hot springs."

[1630] Step 10:

[1631] If the user is interested in the displayed recommendation information, the terminal sends a request to the server for more detailed information (such as fare, directions, and reservation methods).

[1632] Step 11:

[1633] The server receives the request for more information, gathers more specific information, and provides it to the user.

[1634] For example, provide detailed information in the form of, "It takes about an hour by Shinkansen from Shinjuku Station to Mishima Station, and the fare is about 4,000 yen one way."

[1635] Step 12:

[1636] After a trip, users post ratings and reviews via their devices, inputting rating scores and comments, and sending the posted content from the device to the server.

[1637] Step 13:

[1638] The server stores the received ratings and reviews in a database. It uses an emotion engine to recognize the user's emotions at the time of posting and uses this information to analyze the reliability of the review ratings.

[1639] Step 14:

[1640] The server uses a generative artificial intelligence model to identify and filter out posts from fake users, ensuring the authenticity of ratings and reviews.

[1641] Step 15:

[1642] The server awards reward points for posting honest ratings and reviews. The terminal provides an interface where users can check the reward points they have earned, and users can use the reward points to plan their next trip.

[1643] Example 2

[1644] 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."

[1645] Conventional tourist information systems have limited ability to fully understand users' vague requests and diverse emotions and provide optimal information. Furthermore, the means to ensure the reliability and accuracy of information are insufficient, and there is no multilingual support or appropriate review management. As a result, users are unable to obtain satisfactory tourist information, and the quality of their tourist experience is reduced.

[1646] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting requests from the user, such as desired places to visit, desired activities, and desired cuisine, means for transmitting the request data to the server, means for analyzing the requests using a generative artificial intelligence model and generating relevant tourist information, means for translating the generated tourist information into the user's native language, means for recognizing the user's emotions using an emotion engine and adjusting recommended information, means for providing the translated information to the user, and means for providing additional detailed information (such as fares, directions, and reservation methods). This allows the user to obtain personalized, optimal tourist information based on their own vague requests, and enables a high-quality tourist experience through the provision of information according to emotions and reliable review management.

[1647] "User" refers to an individual who uses this system to obtain tourist information and post ratings and reviews.

[1648] "Request data" is data including text information such as desired places to visit, desired activities, and desired dishes input by the user.

[1649] "Generative AI model" refers to an AI technology that analyzes user input request data and generates related tourist information.

[1650] An "emotion engine" refers to technology that recognizes user emotions, quantifies and analyzes them, and adjusts recommendation information.

[1651] "Sakura users" refer to fake users who post false ratings and reviews for fraudulent purposes.

[1652] "Translation means" refers to the translation techniques and tools used to translate the generated tourist information into the user's native language.

[1653] "Recommended information" refers to information about tourist spots and activities provided based on the user's requests and emotional data.

[1654] "Review management means" refers to the means for receiving ratings and reviews posted by users, storing them in a database, and excluding posts by fake users.

[1655] A "reward system" refers to a mechanism that awards reward points for legitimate ratings and reviews posted by users, allowing users to use those points for travel planning.

[1656] This invention is a system for analyzing vague requests entered by users and providing optimal tourist information. The system utilizes user emotional data to optimize information and achieve multilingual support. It also ensures the reliability of ratings and reviews and introduces a reward system to improve the user experience. Specific embodiments of the system are described below.

[1657] overview

[1658] Users use a device (smartphone, PC, etc.) to input their desired places to visit, activities, cuisine, etc. The device sends the desired data to a server, which analyzes it and generates optimal tourist information. The hardware required is a device used by the user and a server. The software required includes a generative AI model, an emotion engine, a translation API, etc.

[1659] Hardware and software used

[1660] Terminal: A smartphone or PC used by a user to input and display data.

[1661] Server: A computer system for performing data analysis and information generation.

[1662] Generative AI model: AI technology for analyzing user requests and generating optimal tourist information (e.g., GPT-4).

[1663] Translation API: An API for translating the generated tourist information into the user's native language (e.g., Google Translate API).

[1664] Emotion engine: Technology for recognizing user emotions and adjusting recommendation information (e.g., Microsoft Azure's Emotion API).

[1665] Examples of data analysis and information generation

[1666] 1. User Input:

[1667] The user can freely input the places and activities they want to visit into the input form on the device. For example, they can enter "I want to relax in a place rich in nature."

[1668] 2. Data transmission:

[1669] The terminal sends the input request data to the server in JSON format.

[1670] 3. Request analysis:

[1671] The server parses the received request data, tokenizes the text data, and extracts features. It then inputs a prompt to the generative AI model for analysis. An example of a prompt is as follows:

[1672] The user's request is "I want to relax in a place rich in nature." Please suggest the best tourist spots.

[1673] A generative artificial intelligence model generates a list of relevant attractions and activities.

[1674] 4. Information Translation:

[1675] The server translates the generated tourist information into the user's native language using a translation API, for example, "Relaxing natural spots near Mount Fuji" in English.

[1676] 5. Emotion recognition:

[1677] The emotion engine recognizes the user's emotions in real time, calculates an emotion score from the user's facial expressions and voice, and adjusts the recommendation information accordingly.

[1678] 6. Information provision:

[1679] The server sends the adjusted recommendation information to the device, which then displays it to the user. For example, it displays, "We recommend the Mt. Fuji area. You can enjoy beautiful nature and relaxing hot springs."

[1680] Providing additional information and managing reviews

[1681] If the user requests additional details, they send a request to the server via their device. The server collects the details (fare, directions, reservation methods, etc.) and provides them to the user. Furthermore, if the user submits a rating or review, the server stores it in a database and uses an artificial intelligence model to filter out fraudulent posts (fake users). Legitimate ratings and reviews are awarded reward points, which the user can use to plan their next trip.

[1682] As a result, users can obtain efficient and personalized tourist information and enjoy a high-quality travel experience.

[1683] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1684] Step 1:

[1685] The user uses the terminal to enter their desired places to visit, desired activities, desired cuisine, etc. into a text input form. For example, they might enter "I want to relax in a place rich in nature."

[1686] Input: User's request text

[1687] Output: The requested data is temporarily stored on the device.

[1688] Step 2:

[1689] The device sends the input request data to the server. Specifically, it uses JavaScript Ajax to send the data in JSON format.

[1690] Input: User's desired data

[1691] Output: The requested data is sent to the server in JSON format.

[1692] Step 3:

[1693] The server parses the received request data, tokenizes the text data, extracts features, and analyzes the text using the Python NLTK library.

[1694] Input: Request data in JSON format

[1695] Output: Tokenized and feature extracted data

[1696] Step 4:

[1697] The server inputs a prompt sentence into a generative AI model (e.g., GPT-4) for analysis. For example, the following prompt sentence is used:

[1698] Prompt: "The user wants to relax in a place surrounded by nature. Please suggest the best tourist spots."

[1699] Input: Tokenized and feature extracted data

[1700] Output: A list of related attractions and activities

[1701] Step 5:

[1702] The server translates the generated tourist information into the user's native language using a multilingual translation API (e.g., Google Translate API).

[1703] Input: List of tourist information

[1704] Output: Translated tourist information

[1705] Step 6:

[1706] The server uses an emotion engine (e.g., Microsoft Azure's Emotion API) to recognize emotions from user input or reactions. It analyzes the user's facial expressions and tone of voice and generates an emotion score.

[1707] Input: User input data, reaction data

[1708] Output: Sentiment score

[1709] Step 7:

[1710] The server adjusts the translated tourist information based on the emotion score to generate optimal recommendation information, which is then sent to the device.

[1711] Input: translated tourist information, sentiment score

[1712] Output: Adjusted recommendation information

[1713] Step 8:

[1714] The device will then display the provided recommendation information to the user. For example, it might say, "We recommend the Mt. Fuji area. You can enjoy beautiful nature and relaxing hot springs."

[1715] Input: Adjusted recommendation information

[1716] Output: Recommendation information that users can visually confirm

[1717] Step 9:

[1718] If the user is interested in the recommended information, he or she sends a request to the server via the terminal requesting additional details (such as fare, directions, and reservation methods).

[1719] Input: Request for additional information

[1720] Output: Request data is sent to the server

[1721] Step 10:

[1722] The server collects additional details and provides them to the user.

[1723] Input: Request data

[1724] Output: Additional details

[1725] Step 11:

[1726] After a trip, users post ratings and reviews from their devices, for example by entering rating scores and comments and attaching images.

[1727] Input: Ratings and review data

[1728] Output: Ratings and reviews data persisted on device

[1729] Step 12:

[1730] The terminal transmits the posted content to the server.

[1731] Input: Ratings and review data

[1732] Output: Posted data is sent to the server

[1733] Step 13:

[1734] The server stores the received ratings and reviews in a database, uses an emotion engine to recognize the user's emotions at the time of posting, and uses a generative artificial intelligence model to identify and filter out posts from fake users.

[1735] Input: Ratings and review data

[1736] Output: Data after reliability analysis, identification results of fake users

[1737] Step 14:

[1738] The server will award reward points for posting honest ratings and reviews.

[1739] Input: Data after reliability analysis

[1740] Output: User's reward points

[1741] Step 15:

[1742] The terminal provides an interface that allows the user to check the reward points that he or she has earned, and the user can use the reward points to plan his or her next trip.

[1743] Input: User's reward points

[1744] Output: Reward point information that can be visually confirmed by the user

[1745] (Application example 2)

[1746] 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."

[1747] In factory work, it is necessary to respond quickly and appropriately to vague requests from workers, and to create work plans that take into account the emotions and health of workers.In addition to tourist information, a system is needed that utilizes generative artificial intelligence models to provide optimal work plans for factory work.

[1748] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting requests from the user, such as desired places to visit, desired activities, and desired cuisine; means for transmitting the request data to the server; means for analyzing the requests using a generative artificial intelligence model and generating relevant tourist information; means for translating the generated tourist information into the user's native language; means for providing the translated information to the user; means for providing additional detailed information (such as fees, directions, and reservation methods); means for analyzing the user's emotional data using an emotion recognition engine; means for adjusting information based on the analyzed emotional data; and means for formulating an optimal work plan based on the adjusted information and automatically executing the work. This makes it possible to respond quickly and appropriately to worker requests and to create work plans that take into account the worker's emotions and health status.

[1749] "User emotion data" is data that quantifies the user's emotional state analyzed from the user's facial expression, tone of voice, behavior, etc.

[1750] A "generative artificial intelligence model" is an artificial intelligence algorithm that analyzes a user's vague requests and generates relevant information and work plans.

[1751] "Request data" refers to data including specific or vague requests such as desired places to visit, desired activities, and desired dishes entered by the user.

[1752] An "emotion recognition engine" is a software component that analyzes a user's voice and facial expression data to recognize their emotional state.

[1753] "Adjusting information" means optimizing the content of the information provided and work plans based on emotional data.

[1754] A "work plan" is a schedule or action plan for work that is planned and executed by a generative artificial intelligence model.

[1755] "Additional details" are specific details such as prices, directions, and reservation methods that accompany the information provided.

[1756] A "server" is a computer system that receives and analyzes user-requested data, and generates, adjusts, and provides related information.

[1757] This invention is a system in which a patrol / work robot in a factory analyzes vague requests from workers, creates an optimal work plan, and automatically executes it. This system takes into account the user's emotional data and can perform work more efficiently and safely.

[1758] System Configuration

[1759] 1. Emotion Recognition Engine

[1760] This engine is a software component that analyzes the voice and facial expression data of workers and outputs their emotional state as numerical data. It uses a camera and microphone to collect real-time emotional data of users.

[1761] 2. Generative AI Models

[1762] This is an algorithm that analyzes request data and generates a related work plan. This model has the ability to extract specific tasks from vague requests and convert them into actionable plans.

[1763] 3. Robot Controller

[1764] This is a component that controls robots in factories and automatically executes tasks based on the generated work plan. This control system manages the robot's operation and performs work efficiently.

[1765] Hardware and software used

[1766] Camera and microphone

[1767] It collects the worker's facial expressions and voice and uses them to provide data to the emotion recognition engine.

[1768] GPU Server

[1769] This is a server for high-speed processing of emotion recognition engines and generative AI models. Specifically, it is equipped with hardware capable of processing neural networks and deep learning.

[1770] Robots in the factory

[1771] These are creations that perform real tasks, such as a typical factory robot.

[1772] Example of operation

[1773] 1. The worker speaks to the patrol robot, saying, "This machine needs adjustment." At this time, the camera captures the worker's facial expression and the microphone collects the voice.

[1774] 2. The robot terminal uses an emotion recognition engine to analyze the worker's emotional state from their voice and facial expression data.

[1775] 3. The analyzed emotional data is combined with a generative artificial intelligence model to analyze the user's requests and generate an optimal work plan.

[1776] 4. The generated work plan is automatically executed by the robot controller, which adjusts the specified machine and notifies the work support system as necessary.

[1777] Example prompt sentence:

[1778] This machine needs adjustment.

[1779] In this way, it is possible to respond quickly and appropriately to the requests of workers and create optimal work plans that take emotional data into account, thereby improving work efficiency in the factory and reducing worker stress and fatigue.

[1780] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1781] Step 1:

[1782] The user inputs their request by voice or text. The device collects the voice data or text data using a microphone and a text input interface. Voice data or text data is obtained as input.

[1783] Step 2:

[1784] The device converts voice data into text. This conversion is done using speech recognition software. The input is voice data and the output is text data. Specifically, the speech recognition engine analyzes the voice waveform and converts it into the corresponding text.

[1785] Step 3:

[1786] The device uses a camera and microphone to collect the user's facial expressions and voice tone. The data is sent to an emotion recognition engine. The input is image data and voice tone data. The output is quantified emotion data. The emotion recognition engine extracts facial features from the image data and voice features from the voice data to quantify emotions.

[1787] Step 4:

[1788] The device sends the collected request data and emotion data to the server. The server receives this data. An API request (e.g., POST method) is used to send the data. The input is the request text data and emotion data. The output is a confirmation that the data has been transferred to the server.

[1789] Step 5:

[1790] The server uses a generative artificial intelligence model to analyze the user's request data. Specific tasks are extracted from the request. The request text data is used as input, and related tasks are extracted based on it. The output is an optimized task list. Specifically, the AI ​​model analyzes the text, extracts important keywords, and lists related tasks.

[1791] Step 6:

[1792] The server adjusts the generated task list based on the emotional data. The emotional data and the task list are used as inputs. The output is the adjusted task list. Specifically, if the emotional data indicates high stress, the server reevaluates the priority of the task list and starts with easier tasks.

[1793] Step 7:

[1794] The server creates an optimal work plan and sends it to the robot controller. The adjusted task list is used as input. The output is a confirmation that the work plan has been sent. The server converts the task list into a work plan and sends it to the robot controller.

[1795] Step 8:

[1796] The robot controller executes specific tasks based on the transmitted work plan. The input is the work plan data, and the output is the specific tasks to be performed. The robot moves to the specified location and performs adjustments or repairs as needed.

[1797] The above processing steps make it possible to respond quickly and appropriately to vague user requests, and to create and execute work plans that take into account the emotions and health status of workers.

[1798] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1799] 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.

[1800] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1801] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1802] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1803] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1804] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1805] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1806] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1807] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1808] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1809] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1810] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1811] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1812] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1813] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1814] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1815] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1816] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1817] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1818] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1819] The following is further disclosed regarding the above embodiment.

[1820] (Claim 1)

[1821] A means for inputting requests from the user, such as desired places to visit, desired activities, desired cuisine, etc.;

[1822] means for transmitting request data to a server;

[1823] A means for analyzing requests and generating relevant tourist information using a generative artificial intelligence model;

[1824] A means for translating the generated tourist information into the user's native language;

[1825] a means for providing the translated information to a user;

[1826] A system that includes a means to provide additional details (such as prices, directions, and reservations).

[1827] (Claim 2)

[1828] 10. The system of claim 1, wherein the translated information is sent to the user's terminal and displayed.

[1829] (Claim 3)

[1830] 10. The system of claim 1, further comprising means for receiving user-submitted ratings and reviews, storing them in a database, and using an artificial intelligence model to filter out fake users.

[1831] "Example 1"

[1832] (Claim 1)

[1833] A means for inputting requests from the user, such as desired places to visit, desired activities, desired cuisine, etc.;

[1834] means for transmitting request data from the terminal to the server;

[1835] A means for analyzing requests and generating relevant tourist information using a generative artificial intelligence model;

[1836] A means for translating the generated tourist information into the user's native language;

[1837] a means for providing the translated information to a user;

[1838] A means to provide additional details (such as prices, directions, and booking instructions) if requested by the user;

[1839] A method to receive user ratings and reviews, analyze them, and filter out fake users.

[1840] A way to reward legitimate ratings and reviews,

[1841] a means for users to view reward points available for their next travel plan;

[1842] A system including:

[1843] (Claim 2)

[1844] 10. The system of claim 1, wherein the translated information is sent to the user's terminal and displayed.

[1845] (Claim 3)

[1846] 2. The system of claim 1, further comprising means for storing the ratings and reviews analyzed using the generative artificial intelligence model in a database.

[1847] "Application Example 1"

[1848] (Claim 1)

[1849] A means for inputting requests from the user, such as desired places to visit, desired activities, desired cuisine, etc.;

[1850] means for transmitting request data to a server;

[1851] A means for analyzing requests and generating relevant tourist information using a generative artificial intelligence model;

[1852] A means for translating the generated tourist information into the user's native language;

[1853] a means for providing the translated information to a user;

[1854] A means to provide additional details (such as prices, directions, and reservations) and

[1855] A system that includes a means for providing route guidance to a destination in cooperation with an automated driving system.

[1856] (Claim 2)

[1857] 10. The system of claim 1, wherein the translated information is transmitted to a user's terminal or an in-vehicle display for display.

[1858] (Claim 3)

[1859] 10. The system of claim 1, further comprising means for receiving user-submitted ratings and reviews, storing them in a database, and using an artificial intelligence model to filter out fake users.

[1860] "Example 2: Combining Emotion Engines"

[1861] (Claim 1)

[1862] A means for inputting requests from the user, such as desired places to visit, desired activities, desired cuisine, etc.;

[1863] means for transmitting request data to a server;

[1864] A means for analyzing requests and generating relevant tourist information using a generative artificial intelligence model;

[1865] A means for translating the generated tourist information into the user's native language;

[1866] a means for recognizing user emotions using an emotion engine and adjusting recommendation information;

[1867] a means for providing the translated information to a user;

[1868] A system that includes a means to provide additional details (such as prices, directions, and reservations).

[1869] (Claim 2)

[1870] 10. The system of claim 1, wherein the translated information is sent to the user's terminal and displayed.

[1871] (Claim 3)

[1872] 10. The system of claim 1, further comprising means for receiving user-submitted ratings and reviews, storing them in a database, and using an artificial intelligence model to filter out fake users.

[1873] "Application example 2 when combining emotion engines"

[1874] (Claim 1)

[1875] A means for inputting requests from the user, such as desired places to visit, desired activities, desired cuisine, etc.;

[1876] means for transmitting request data to a server;

[1877] A means for analyzing requests and generating relevant tourist information using a generative artificial intelligence model;

[1878] A means for translating the generated tourist information into the user's native language;

[1879] a means for providing the translated information to a user;

[1880] A means to provide additional details (such as prices, directions, and reservations) and

[1881] means for analyzing user emotion data using an emotion recognition engine;

[1882] a means for adjusting information based on the analyzed emotion data;

[1883] A system that includes a means to create an optimal work plan based on the adjusted information and automatically execute the work.

[1884] (Claim 2)

[1885] 10. The system of claim 1, wherein the translated information is sent to the user's terminal and displayed.

[1886] (Claim 3)

[1887] 10. The system of claim 1, further comprising means for receiving user-submitted ratings and reviews, storing them in a database, and using an artificial intelligence model to filter out fake users. [Explanation of symbols]

[1888] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for inputting requests from the user, such as desired places to visit, desired activities, desired cuisine, etc.; means for transmitting request data to a server; A means for analyzing requests and generating relevant tourist information using a generative artificial intelligence model; A means for translating the generated tourist information into the user's native language; a means for providing the translated information to a user; A system that includes a means for providing additional details.

2. 2. The system of claim 1, wherein the translated information is transmitted to a user's terminal and displayed.

3. 10. The system of claim 1, further comprising means for receiving user-submitted ratings and reviews, storing them in a database, and using an artificial intelligence model to filter out fake users.

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