System

Abstract

A system is provided.SOLUTION: A system comprising: means for a user to input a request; means for a server to receive the request and collect relevant information in multiple languages; means for a generation AI to analyze the relevant information in multiple languages and generate a detailed experience report; and means for the server to provide the generated experience report to the user.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] In today's busy lifestyles, it is extremely difficult to effectively utilize limited time and resources to plan and select trips or high-value purchases. In particular, when a single visit or purchase involves a significant amount of money, such as an overseas trip, a luxury restaurant, a luxury hotel, or buying a car or property, the risk of making a mistake in the selection is high. Making the best choice in such situations requires detailed and reliable information, but currently, gathering such information is extremely cumbersome. In particular, the limited means of providing integrated multilingual information makes it difficult for users to make appropriate decisions. Therefore, the present invention aims to solve these problems and provide a means for users to efficiently obtain detailed information and make optimal choices. [Means for solving the problem]

[0005] The present invention provides a system that collects and analyzes related information in multiple languages ​​and generates and provides detailed experience reports in response to user requests. Specifically, the user inputs a request from a terminal, a server receives the request, and based on the request, collects related information in multiple languages. Next, a generation AI analyzes this information and generates detailed experience reports of tourist spots and high-value items. This report is sent from the server to the terminal and provided to the user. This allows users to obtain detailed and reliable information in advance, enabling them to make optimal travel plans and purchase choices.

[0006] "User" means an individual or entity that uses the System to enter requests and view and use the information provided.

[0007] A "request" is an input that specifically indicates the information or experience that a user wants to obtain.

[0008] "Terminal" means a device through which a User inputs requests and receives and displays information provided by a Server. Examples include smartphones, tablets, and computers.

[0009] A "server" is a computer system whose role is to receive user requests, collect relevant information, and pass it on to the generation AI.

[0010] "Multilingual related information" refers to data about tourist destinations or high-value items in multiple languages, such as English, Japanese, and French.

[0011] "Generative AI" is an artificial intelligence technology that analyzes relevant information input in multiple languages ​​and automatically generates detailed experience reports.

[0012] An "experience report" is a report that describes in text and photographs a detailed experience of a specific tourist spot or high-priced product based on information analyzed by the generation AI.

[0013] "Providing" refers to transmitting the generated experience report to a terminal in a format accessible to the user and displaying it. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] This invention is a system that collects and analyzes related information in multiple languages ​​in response to requests entered by users, and generates and provides detailed experience reports. The program processing required to realize the operation of this system is explained below in natural language.

[0036] overview

[0037] When a user requests detailed information about a specific tourist spot, luxury restaurant, luxury hotel, car, or property, the server receives the request and collects related information in multiple languages ​​from the Internet. The generative AI then analyzes the collected information and creates a detailed experience report. This report is then sent to the user's device via the server and provided to the user.

[0038] Program processing overview

[0039] Receiving request input

[0040] Users input requests for specific tourist attractions or luxury items through a dedicated app or website. For example, a user might request, "I want more information about the Eiffel Tower in Paris."

[0041] Information collection and generation AI analysis

[0042] The server receives requests sent from devices and collects multilingual information from relevant websites and databases. For example, it collects information related to the "Eiffel Tower" in English, Japanese, and French from the internet. Next, the generative AI analyzes this multilingual information and generates a detailed experience report of tourist attractions and high-ticket items. The report includes recent events, featured photos, visitor reviews, and more.

[0043] Report Generation

[0044] The server organizes the experience reports created by the generative AI and creates an easy-to-read report containing text and images.

[0045] Providing reports

[0046] The server then sends the generated report to the user's device for display. The user can then view the report and use it to help plan their trip or make a purchase. For example, a user can use a detailed report about the Eiffel Tower to plan their visit and sightseeing route.

[0047] Specific examples

[0048] Example 1: Pre-visiting a tourist spot

[0049] 1. A user is planning a trip to Paris, France and wants to know more about the Eiffel Tower.

[0050] 2. The user fills in the app's input form with "More information about the Eiffel Tower in Paris" and submits the request.

[0051] 3. The server receives this request and collects information related to the Eiffel Tower (e.g., latest events, directions, visitor reviews) from across the Internet in multiple languages.

[0052] 4. Generative AI analyzes the collected information and creates a detailed experience report, including high-resolution photos, tips for visiting, and highlights.

[0053] 5. The server sends the generated report to the user's terminal for display.

[0054] 6. The user browses the detailed information provided and makes efficient and satisfying travel plans.

[0055] Example 2: Previewing a luxury restaurant

[0056] 1. A user wants to make a reservation at a highly rated restaurant in Rome, Italy, but wants more information about the restaurant.

[0057] 2. The user enters the name of a specific restaurant into the app and requests more information.

[0058] 3. Based on the request, the server collects information about the restaurant in multiple languages ​​from the Internet and analyzes the latest menu, reviews, and the atmosphere of the restaurant.

[0059] 4. Generative AI creates a detailed report based on the collected information, including menu items, chef characteristics, and visitor experiences.

[0060] 5. The server sends the generated report to the user's terminal for display.

[0061] 6. Users can avoid mistakes by referring to the provided report and checking the restaurant's atmosphere and menu in detail before making a reservation.

[0062] In this way, the present invention provides powerful support for users to efficiently obtain detailed information and make optimal travel plans and purchasing choices.

[0063] The processing flow will be explained below.

[0064] Step 1:

[0065] A user enters a request for a specific tourist attraction or luxury item through a dedicated app or website, for example, "I'd like more information about the Eiffel Tower in Paris."

[0066] Step 2:

[0067] The device takes the user's input and formats it into an appropriate form, for example, "Sightseeing spot: Eiffel Tower."

[0068] Step 3:

[0069] The terminal sends the formatted request over the network to the server.

[0070] Step 4:

[0071] The server analyzes the request received from the device and identifies the target information. For example, it sets a task to "collect information related to the Eiffel Tower."

[0072] Step 5:

[0073] The server generates queries to gather relevant information from multilingual sources on the Net (e.g., English, Japanese, and French websites).

[0074] Step 6:

[0075] The server queries and gathers multilingual information from relevant websites and databases, such as "latest event information and visitor reviews about the Eiffel Tower."

[0076] Step 7:

[0077] The server preprocesses the collected raw multilingual data and converts it into a format that can be analyzed by the generative AI.

[0078] Step 8:

[0079] Generative AI analyzes the pre-processed data and generates detailed experience reports for tourist attractions and high-value items, such as the Eiffel Tower, including tourist information, highlights, and things to watch out for.

[0080] Step 9:

[0081] Generative AI organizes the generated report, arranging text and high-resolution images in an easy-to-read layout.

[0082] Step 10:

[0083] The server transmits the generated experience report to the terminal for providing to the user.

[0084] Step 11:

[0085] The terminal receives the report sent from the server.

[0086] Step 12:

[0087] The device then displays the received report to the user, for example providing detailed information about the Eiffel Tower in an app or browser.

[0088] Step 13:

[0089] Users can view the detailed reports provided to help them plan their trips and make purchasing decisions, for example, by planning their visit to the Eiffel Tower.

[0090] Example 1

[0091] 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."

[0092] Today's users want to quickly obtain detailed information when planning a trip or purchasing luxury goods. However, traditional information gathering methods lacked multilingual support and detailed analysis, making it difficult for users to obtain the precise, multifaceted information they needed. Furthermore, there was a lack of a way to efficiently analyze the information requested by users and provide it in a visually easy-to-understand report format. This left users unable to make informed decisions and making efficient plans.

[0093] 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.

[0094] In this invention, the server includes a means for a user to input a request, a means for collecting related information in multiple languages, a means for a generative AI model to analyze the collected related information in multiple languages ​​and generate a detailed experience report, and a means for transmitting the report to the user's device via encrypted communication. This makes it possible to quickly and multifacetedly collect and analyze the information requested by the user and provide it in a visually easy-to-understand report format.

[0095] The "means for a user to input a request" refers to an interface that allows a user to request specific information using an input device, such as an input form on a dedicated application or website.

[0096] A "server" is a computer system that receives user requests and processes related information. It communicates with user terminals via a network.

[0097] "Means for collecting relevant information in multiple languages" refers to a system that uses web crawling tools and APIs to collect necessary data from multiple language sources on the Internet.

[0098] A "generative AI model" is an algorithm that analyzes collected information and generates a detailed report to provide to users. Examples include generative models such as GPT-3 and BERT.

[0099] The "means of analysis" refers to the process by which the generative AI model categorizes, extracts, and summarizes the information collected. Natural language processing algorithms are used.

[0100] "Means for generating experience reports" refers to the process of creating a report in a visually understandable format based on the information analyzed by the generative AI model, including output in HTML and PDF formats.

[0101] The "means for providing to the user" refers to a communication means for providing the generated experience report to the user, including sending the report to the user's terminal using an encrypted communication protocol (HTTPS).

[0102] "Terminal" refers to a device on which a user inputs requests and receives and views reports sent from the server. For example, this applies to a smartphone or a PC.

[0103] "Encrypted communication" refers to a protocol used to securely transmit and receive data. Data is encrypted before transmission to protect user privacy.

[0104] A "dedicated application" is a specific piece of software that allows users to input requests. It is installed on a smartphone or tablet and is specialized for a specific function.

[0105] A "website" is an online interface through which users enter requests over the Internet, accessed via a browser.

[0106] "Natural language processing algorithms" are programs that analyze text data and perform tasks such as semantic analysis, summarization, and classification. They are included in generative AI models.

[0107] "HTML" is a markup language for building web pages. It is used to present the experience report in a visually understandable format.

[0108] "PDF" is a file format for viewing documents on various devices. It is used to save the generated experience report.

[0109] In this invention, a user uses a dedicated application or website to input a request. The dedicated application is installed on a smartphone or tablet and has an interactive UI. The user inputs a prompt, for example, "I want more information about the Eiffel Tower in Paris." The website allows the user to input the request via a browser over the Internet.

[0110] When a user inputs a request, the device receives the request and sends it to the server. The server analyzes the request and starts a process to collect relevant information. Specifically, the server uses web crawling tools such as Google's search API, BeautifulSoup, and Selenium to gather the necessary data from multilingual sources.

[0111] The collected data is analyzed by a generative AI model, which uses natural language processing algorithms such as GPT-3 and BERT. The generative AI model classifies the collected information in multiple languages, extracts important information, summarizes it, and generates a detailed experience report to provide to users. This report includes related photos, event information, directions, and evaluation reviews.

[0112] The generated report is generated by the server in HTML or PDF format, allowing users to receive information in a visually easy-to-understand format. The report is sent to the user's device via encrypted communication (HTTPS), ensuring security and privacy.

[0113] Users can view the reports sent to their devices and make travel plans or purchasing decisions based on the detailed information. For example, a user can view a detailed report on the Eiffel Tower to plan the time to visit and the sightseeing route, or make a reservation decision based on information on high-end restaurants in Rome.

[0114] Specific examples

[0115] Example 1: Pre-visiting a tourist spot

[0116] 1. The user opens the dedicated application and enters a request: "Tell me about the Eiffel Tower in Paris."

[0117] 2. The server receives this request and collects information from relevant multilingual websites.

[0118] 3. A generative AI model analyzes the collected information and generates a detailed experience report, including up-to-date event information, directions, visitor reviews, and more.

[0119] 4. The server sends the generated report to the user's device using encrypted communication.

[0120] 5. The user views the provided report and uses it to plan their trip.

[0121] Example 2: Previewing a luxury restaurant

[0122] 1. A user visits a website and types, "Please give me the latest information on fine dining restaurants in Rome."

[0123] 2. The server collects information about the restaurant from relevant multilingual websites.

[0124] 3. A generative AI model analyzes the collected information and generates a detailed report, including menu items, chef highlights, and visitor experiences.

[0125] 4. The server sends the generated report to the user's device.

[0126] 5. The user reviews the report and makes a booking decision.

[0127] This invention allows users to efficiently gather detailed information they need and receive it in a visually easy-to-understand report format, which helps them make better decisions when it comes to travel and luxury purchases.

[0128] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0129] Step 1:

[0130] The user accesses a dedicated application or website and enters a request.

[0131] Specifically, the user enters a prompt such as "Tell me about the Eiffel Tower in Paris" into the app's text input field and presses the send button.

[0132] Input: User text input (prompt)

[0133] Output: The request data is sent from the terminal to the server.

[0134] Step 2:

[0135] The server receives the request sent from the terminal.

[0136] The server analyzes the request data and generates a search query to gather the required information.

[0137] Specifically, a search query is generated to collect "information about the Eiffel Tower" based on the request data.

[0138] Input: Request data

[0139] Output: Search query

[0140] Step 3:

[0141] A server uses the generated search query to gather relevant information on the Internet in multiple languages.

[0142] The server uses Google's search API and web crawling tools such as BeautifulSoup and Selenium to gather relevant information in multiple languages.

[0143] Specifically, it scrapes the latest information, visitor reviews, photos, etc. about the Eiffel Tower from English, Japanese, and French websites.

[0144] Input: Search query

[0145] Output: Collected multilingual web data

[0146] Step 4:

[0147] The server passes the collected multilingual information to the generative AI model.

[0148] A generative AI model analyzes the collected information and generates a detailed experience report.

[0149] Specifically, it uses natural language processing algorithms such as GPT-3 and BERT to classify, extract, and summarize important information to create an experience report.

[0150] Input: Collected multilingual web data

[0151] Output: Parsed experience report

[0152] Step 5:

[0153] The server compiles the generated experience reports and generates a user-friendly report in HTML or PDF format.

[0154] Specifically, the system creates a visually easy-to-understand report based on the collected information, including high-resolution photos and charts.

[0155] Input: Parsed experience report

[0156] Output: Report in HTML or PDF format

[0157] Step 6:

[0158] The server sends the generated report to the user's terminal.

[0159] In this case, encrypted communication (HTTPS) is used to ensure data privacy and security.

[0160] Input: Report in HTML or PDF format

[0161] Output: Report sent to user's terminal

[0162] Step 7:

[0163] The user views the provided report on the device.

[0164] Specifically, users can open the report on their smartphone or computer and use it to help plan their trip or make purchasing decisions.

[0165] Input: Report submitted

[0166] Output: Viewed details

[0167] (Application example 1)

[0168] 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."

[0169] Conventional tourist information systems have the problem that it is time-consuming for users to quickly obtain specific information. Furthermore, they are not able to adequately respond to real-time information requests from inside a vehicle. As a result, it is difficult to efficiently gather information while traveling, resulting in an unsatisfactory experience. Therefore, there is a need for a system that allows users to obtain detailed multilingual tourist reports in real time while on the move.

[0170] 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.

[0171] In this invention, the server includes means for a user to input a request, means for the server to receive the request and collect related information in multiple languages, means for a generation AI to analyze the related information in multiple languages ​​and generate a detailed experience report, means for the server to provide the generated experience report to the user, means for the server to receive the request via an interface in the vehicle and for an on-board computer to collect and analyze related information in multiple languages, and means for displaying the generated experience report on an on-board display. This allows users to obtain detailed information in real time even while on the move and to efficiently plan their sightseeing trips.

[0172] "User" refers to any individual or entity using the Service.

[0173] The "means for inputting a request" refers to an interface that a user uses to request information, and includes a touch panel, a voice input system, and the like.

[0174] "Server" refers to a computer system that receives requests from users and collects, processes, and provides relevant information in multiple languages ​​in response to those requests.

[0175] "Means of collecting relevant information in multiple languages" refers to means of collecting necessary information written in multiple languages ​​via the Internet or databases.

[0176] "Generative AI" refers to an artificial intelligence model that analyzes collected multilingual information and generates detailed experience reports based on it.

[0177] An "Experience Report" is a report containing detailed information about a specific subject requested by a user, including photos, reviews, event information, etc.

[0178] "Interfaces inside the vehicle" refers to input and display devices installed inside the vehicle, including touch panels and displays.

[0179] "On-board computer" refers to a computer installed inside a vehicle, and is hardware used to collect, analyze, and display information.

[0180] An "in-vehicle display" is a screen installed inside a vehicle and refers to a device for displaying information to the user.

[0181] The present invention is a system for providing detailed tourist information in real time within a vehicle. The system starts when a user inputs a request through an in-vehicle interface. Specific embodiments for realizing this system are described below.

[0182] 1. User request input

[0183] The user requests detailed information about tourist attractions, restaurants, etc. through an in-car interface. The interface can be a touch panel or a voice input system. For example, the user might say, "I'd like more information about a famous tower in a specific city."

[0184] 2. Server receives requests and collects information

[0185] The server receives user requests and collects relevant information in multiple languages ​​from the internet using web scraping tools (such as Scrapy) and database APIs (such as Google Places API). The server organizes this information and sends it to the generation AI for analysis.

[0186] 3. Information analysis and report generation using generative AI

[0187] The generative AI (e.g., OpenAI's GPT-4) analyzes relevant information in multiple languages ​​and generates a detailed experience report, including the history of the target tourist destination, the latest events, visitor reviews, photos, etc. The generative AI uses prompt sentences such as:

[0188] "Gather up-to-date information about famous towers in the designated cities and prepare a detailed tourist guide report. Please include event information and visitor reviews, among other things."

[0189] "Generate a report containing the history and highlights of famous towers in selected cities, as well as the latest visitor experiences."

[0190] 4. Serving and Displaying Reports by the Server

[0191] The generated experience report is sent to the user's in-vehicle display via the server and displayed. The user can view the report on the dashboard display and use it as a reference for planning their trip. For example, the user can use the provided report to plan their visit schedule and sightseeing route in detail.

[0192] Specific example explanation

[0193] When a user types "I want to know about famous riverside towers" into the car's touch panel, the on-board computer sends this request to the server. The server collects multilingual information from the internet and sends it to the generation AI. The generation AI generates a detailed experience report based on the prompt, "Collect the latest information about famous riverside towers and create a report as a detailed tourist guide. Please include event information and visitor reviews in particular." The generated report is displayed on the car's display, allowing the user to obtain detailed tourist information in real time.

[0194] This system allows users to obtain detailed tourist information in real time while on the move, enabling them to efficiently plan their sightseeing trips.

[0195] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0196] Step 1:

[0197] The user inputs a request via the car's interface (touchscreen or voice input system), for example, "I want more information about a famous tower in a specific city." Once this request is input, it is sent to the car's computer.

[0198] Input: "Detailed information about famous towers in a given city"

[0199] Output: Request data sent to the on-board computer

[0200] Step 2:

[0201] The server receives requests sent from the vehicle's onboard computer and generates queries based on the received requests to collect relevant information in multiple languages ​​from the internet and databases. This information is collected using web scraping tools (such as Scrapy) and database APIs (such as Google Places API).

[0202] Input: Request data sent from the on-board computer

[0203] Output: Collected multilingual related information (HTML data, API response)

[0204] Step 3:

[0205] The server organizes the collected multilingual related information and sends it to a generation AI (e.g., OpenAI's GPT-4). At this time, a prompt for analysis is also generated. The prompt is, "Collect the latest information about famous towers in the specified city and create a report as a detailed tourist guide. Please include event information and visitor reviews in particular."

[0206] Input: Collected multilingual related information

[0207] Output: Prompts and related information sent to the generation AI for analysis.

[0208] Step 4:

[0209] Based on the prompts sent and the collected information, the generative AI analyzes the data and generates a detailed experience report, including the history of the target tourist destination, the latest events, visitor reviews, photos, and more.

[0210] Input: Prompt text, related information in multiple languages

[0211] Output: A detailed experience report (text data, images)

[0212] Step 5:

[0213] The server organizes the generated experience reports and formats them for display on the vehicle's in-vehicle display, using HTML / CSS formatting to create an easy-to-read report.

[0214] Input: Generated experience report (text data, images)

[0215] Output: The report in a viewable format

[0216] Step 6:

[0217] The server then sends the formatted report to the in-vehicle display for display, allowing users to view detailed tourist information on the in-vehicle display while traveling.

[0218] Input: Formatted report

[0219] Output: Detailed experience report displayed on the in-car display

[0220] 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.

[0221] This invention combines a system that collects and analyzes related information in multiple languages ​​in response to requests entered by users, and generates and provides detailed experience reports, with an emotion engine that recognizes the user's emotions. The program processing required to achieve this specific operation is explained below in natural language.

[0222] overview

[0223] When a user requests detailed information about a specific tourist spot, luxury restaurant, luxury hotel, car, or property, the server receives the request and collects related information in multiple languages ​​from the Internet. The emotion engine recognizes the user's emotion and dynamically changes the scope and content of the information collected based on that emotion. The generation AI analyzes the collected information and generates a detailed experience report. This report is sent to the user's device via the server and provided to the user.

[0224] Program processing overview

[0225] Receiving request input and emotion recognition

[0226] A user inputs a request for a specific tourist spot or luxury item through a dedicated app or website. At this time, the emotion engine recognizes the user's emotions from their facial expressions and voice input. For example, if a user inputs "I want to know more about the Eiffel Tower in Paris," it will recognize emotions such as excitement and anticipation.

[0227] Information collection and generation AI analysis

[0228] The server receives requests sent from the device and collects multilingual information from related websites and databases. During this collection process, the emotion engine dynamically changes the scope and content of the information collection based on the user's emotions. For example, if the user is excited, it will prioritize collecting information about particularly inspiring attractions and events. Next, the generation AI analyzes this multilingual information and generates detailed experience reports of tourist spots and high-value items. The reports include the latest events, distinctive photos, visitor reviews, and more.

[0229] Report Generation

[0230] The server organizes the experience reports created by the generative AI and creates an easy-to-read report containing both text and images. Based on the recognition results of the emotion engine, the report is optimized to include expressions and content appropriate to the user's emotions.

[0231] Providing reports

[0232] The server sends the generated report to the user's device and displays it. The user can then view the report and use it to plan their trip or make a purchase. For example, a user can use the detailed report on the Eiffel Tower to plan their visit and sightseeing route.

[0233] Specific examples

[0234] Example 1: Pre-visiting a tourist spot

[0235] 1. A user is planning a trip to Paris, France and wants to know more about the Eiffel Tower.

[0236] 2. The user enters "More information about the Eiffel Tower in Paris" into the app's input form and submits a request. At this time, the emotion engine recognizes the user's expectations.

[0237] 3. The server receives the request and collects information related to the Eiffel Tower in multiple languages ​​from the Internet. The emotion engine prioritizes collecting particularly impressive sights and reviews based on the perceived expectations.

[0238] 4. Generative AI analyzes this information and creates a detailed experience report, including high-resolution photos, tips for visiting, and highlights.

[0239] 5. The server sends the generated report to the user's device and displays it. Based on the results of the emotion engine, it includes expressions that heighten the sense of anticipation.

[0240] 6. The user browses the detailed information provided and makes efficient and satisfying travel plans.

[0241] Example 2: Previewing a luxury restaurant

[0242] 1. A user wants to make a reservation at a highly rated restaurant in Rome, Italy, but wants more information about the restaurant.

[0243] 2. The user enters the name of a specific restaurant into the app to request more information, and the emotion engine recognizes the user's expectations.

[0244] 3. Based on the request, the server collects information about the restaurant in multiple languages ​​from the Internet, analyzing the latest menu, reviews, and the restaurant's atmosphere. The emotion engine prioritizes information that is particularly interesting to the user based on perceived expectations.

[0245] 4. Generative AI uses the collected information to create a detailed report, including menu items, chef highlights, and visitor experiences.

[0246] 5. The server sends the generated report to the user's device and displays it. Depending on the results of the emotion engine, interesting details are highlighted.

[0247] 6. The user can use the provided report to check the restaurant's atmosphere and menu in detail before making a reservation, making the best choice.

[0248] In this way, by combining emotion engines, it becomes possible to provide optimal information tailored to the user's emotions, supporting decision-making when planning trips or purchasing high-value items.

[0249] The processing flow will be explained below.

[0250] Step 1:

[0251] Users input requests for specific tourist attractions or luxury items through a dedicated app or website. For example, a user might input a request such as, "I want more information about the Eiffel Tower in Paris."

[0252] Step 2:

[0253] The emotion engine uses a facial recognition camera and microphone to analyze the user's emotions as they input. For example, it can detect "expectation" or "excitement" from the user's facial expressions and tone of voice.

[0254] Step 3:

[0255] The device sends the request along with the emotion data analyzed by the emotion engine to the server. For example, the request is formatted as "Request content: Eiffel Tower, Emotion: Anticipation."

[0256] Step 4:

[0257] The server analyzes the request and emotion data received from the device and identifies information about the target tourist spot. For example, set "collect information about the Eiffel Tower."

[0258] Step 5:

[0259] The server dynamically changes the priority and scope of information collection based on the results of the emotion engine, and collects related information in multiple languages ​​from the Internet. For example, it may prioritize collecting "impressive sights" and "latest event information" related to the Eiffel Tower.

[0260] Step 6:

[0261] The server preprocesses the collected raw multilingual data and converts it into a format that can be analyzed by the generative AI.

[0262] Step 7:

[0263] Generative AI analyzes the pre-processed data and generates detailed experience reports for tourist attractions and high-ticket items, such as a detailed report including "Eiffel Tower tourist information," "latest events," and "visitor reviews."

[0264] Step 8:

[0265] Based on the results of the emotion engine, the generative AI optimizes the report's expression and content to suit the user's emotions. For example, it highlights recommended attractions for users with "expectations."

[0266] Step 9:

[0267] The server organizes and formats the generated experience reports for presentation to the user, for example, in an easy-to-read layout including text and high-resolution photos.

[0268] Step 10:

[0269] The server sends the generated detailed report to the user's terminal.

[0270] Step 11:

[0271] The device receives the report sent from the server and displays it to the user, for example, providing detailed information about the Eiffel Tower in an app or browser.

[0272] Step 12:

[0273] Users can view the detailed reports provided to help inform their travel planning and purchasing decisions, for example, to plan their visit to the Eiffel Tower.

[0274] Example 2

[0275] 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."

[0276] When collecting information about tourist spots or luxury goods, the information provided may not be suited to the user's emotions, resulting in a poor user experience. Furthermore, conventional information collection systems often collect uniform information in response to user requests, making it difficult to provide information optimized for individual users.

[0277] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for the user to input a request, a means for the terminal to recognize the user's emotion, a means for the server to receive the request and dynamically collect related information in multiple languages ​​based on the emotion information, a means for the generative AI model to analyze the related information in multiple languages ​​and generate a detailed experience report that matches the emotion, and a means for the server to provide the generated experience report to the user. This makes it possible to provide optimal information based on the user's emotion.

[0278] "User" refers to the end user who requests information from the System and views and uses the information provided.

[0279] "Terminal" refers to an electronic device that allows a user to input information and communicate with a server through an application or website.

[0280] "Means for recognizing emotions" refers to the function that enables the device to analyze the user's facial expressions and voice and identify their emotional state.

[0281] "Server" refers to the computer system that receives requests from users, collects necessary information, and creates and provides reports using generative AI models.

[0282] "Means for dynamically collecting related information in multiple languages" refers to a function that allows a server to collect related information provided in different languages ​​from the Internet and adjust the scope and content according to the user's feelings.

[0283] "Generative AI model" refers to an artificial intelligence model that analyzes related multilingual information collected by the server and automatically generates a detailed experience report that matches the user's emotions.

[0284] An "experience report" is a detailed report created by a generative AI model based on collected information, and includes a wide range of information such as tourist destinations and luxury items.

[0285] This invention combines a system that collects and analyzes related information in multiple languages ​​in response to requests entered by users, and generates and provides detailed experience reports with an emotion engine that recognizes the user's emotions.

[0286] System configuration

[0287] Hardware

[0288] Device: An electronic device, such as a smartphone, tablet, or computer, through which a user inputs requests and performs emotion recognition.

[0289] Server: A high-performance computer system that collects and analyzes data, runs generative AI models, and generates reports.

[0290] software

[0291] Dedicated application or website: Software that allows users to enter requests and view related information and reports.

[0292] Emotion engine: Software containing algorithms for recognizing emotions by analyzing a user's facial expressions and voice.

[0293] Generative AI model: An artificial intelligence model for generating detailed experience reports based on collected multilingual related information, for example, using an advanced natural language processing model such as GPT-4.

[0294] Program processing

[0295] A user requests detailed information about a specific tourist spot or luxury item through a dedicated app or website. The device passes the user's facial expressions and voice input to the emotion engine to recognize the user's emotions. For example, if the user requests "I want more information about the Eiffel Tower in Paris," the emotion engine can identify the user's anticipation and excitement.

[0296] The server receives the user's request and emotion information, and collects relevant multilingual information from related websites and databases (e.g., Wikipedia, travel review sites, etc.). Based on the results of the emotion engine, it prioritizes the collection of particularly moving or interesting information.

[0297] The generative AI model analyzes the collected information and generates a detailed experience report that matches the user's emotions. An example prompt might be, "Please tell me the latest information about attractions and events at the Eiffel Tower."

[0298] The server organizes the generated report and sends it to the user's device in an easy-to-read format. The report includes special expressions based on the user's emotions, and the user makes travel plans and purchase decisions based on these.

[0299] Specific examples

[0300] Example 1: Pre-visiting a tourist spot

[0301] A user opens the app, types in "More information about the Eiffel Tower in Paris," and submits the request.

[0302] The device records the user's facial expressions and voice in real time, and an emotion engine identifies "expectations."

[0303] The server receives the request and emotion information and collects information in multiple languages, prioritizing particularly inspiring sights and the latest event information.

[0304] A generative AI model analyzes the collected information and generates a detailed report.

[0305] The server sends the report to the user's terminal, and the user views it to make a visiting plan.

[0306] Example 2: Previewing a luxury restaurant

[0307] A user types the name of a specific restaurant into the app and requests more information.

[0308] The device recognizes the user's emotions, and the emotion engine identifies "excitement."

[0309] The server collects multilingual information based on requests and sentiment information, prioritizing menus and reviews that are particularly interesting.

[0310] The generative AI model generates a detailed report, which the server sends to the user's device.

[0311] Users can view the report and check restaurant details before booking.

[0312] In this way, the system of the present invention can dynamically collect relevant information based on the user's emotions and generate and provide a detailed and personalized experience report to support the user's decision-making.

[0313] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0314] Specific processing steps of the system

[0315] Step 1: Enter your request

[0316] Users use a dedicated app or website to enter information about specific tourist destinations or luxury items.

[0317] Input: User types "I want to know more about the Eiffel Tower."

[0318] Output: The input request text is sent to the system.

[0319] Step 2: Recognize emotions

[0320] The device uses a camera and microphone to record facial expressions and voice to recognize the user's emotions.

[0321] Input: User's facial expression data, voice data.

[0322] Data processing: Emotion recognition algorithms analyze facial and audio data to identify emotional states (e.g., anticipation, excitement).

[0323] Output: The recognized emotion data is sent to the system.

[0324] Step 3: Receiving requests and emotions

[0325] The server receives the user's request text and emotion data.

[0326] Input: User request text, emotion data.

[0327] Data operation: Log a combination of request text and emotion data.

[0328] Output: The combined data is passed on to the next process.

[0329] Step 4: Gather information

[0330] The server collects relevant information from the internet in multiple languages, and dynamically adjusts the scope and content of the information collected based on emotional data during the collection process.

[0331] Input: Request text, emotion data.

[0332] Data processing: Scrip multilingual information from relevant websites and databases to prioritize sentiment-driven search terms.

[0333] Output: Collected multilingual information data.

[0334] Step 5: Analyze the information

[0335] The server uses a generative AI model to analyze the collected information and generate a detailed experience report.

[0336] Input: Collected multilingual information data.

[0337] Prompt generation: The generative AI model generates prompts such as, "Please tell me the latest information about attractions and events at the Eiffel Tower."

[0338] Data calculation: A generative AI model analyzes based on prompts and generates a detailed experience report.

[0339] Output: The generated experience report.

[0340] Step 6: Organize your reports

[0341] The server organizes the generated reports, formats them into an easy-to-read format, and adds the most appropriate expressions based on the results of the emotion engine.

[0342] Input: Generated experience reports, sentiment data.

[0343] Data manipulation: Formatting reports containing text and images. Adding sentiment-based emphasis.

[0344] Output: A nicely formatted report.

[0345] Step 7: Submit the report

[0346] The server sends the generated report to the user's terminal.

[0347] Input: The formatted report.

[0348] Data calculation: Sends data to the user's terminal according to the report transmission protocol.

[0349] Output: Report data ready for viewing on the user's device.

[0350] Step 8: View the report

[0351] The user views the provided report on the device.

[0352] Input: Submitted report data.

[0353] Data calculation: The rendering process for displaying the report.

[0354] Output: The report is presented in a user-readable format.

[0355] These steps will result in a system that collects and analyzes optimal information based on the user's emotions, and generates and provides a detailed experience report.

[0356] (Application example 2)

[0357] 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."

[0358] Conventional information gathering systems are limited to providing simple information based on user input requests, and do not provide customized information based on the user's emotions or interests. As a result, it is difficult for users to obtain the specific information they need that is relevant to their emotions, and they are unable to support highly satisfying decision-making. It is also difficult to effectively collect and analyze information in multiple languages.

[0359] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0360] In this invention, the server includes a means for a user to input a request, a means for the server to receive the request and collect related information in multiple languages, a means for an emotion recognition engine to recognize the user's emotion and dynamically change the range and content of the information collection based on the emotion, a means for a generation AI to analyze the related information in multiple languages ​​and generate a detailed experience report, and a means for the server to provide the generated experience report to the user. This makes it possible to provide detailed information customized according to the user's emotion, thereby supporting decision-making with a high level of satisfaction.

[0361] A "user" is an end-user who utilizes the system of the present invention to request information.

[0362] A "request" is a request or inquiry made by a user for specific information.

[0363] "Server" means a computer system that receives requests from users, collects and analyzes relevant information based on those requests, and provides generated reports.

[0364] "Multilingual" means including multiple different languages, such as collecting and analyzing information in English, Japanese, and French.

[0365] "Related information" is data related to tourist spots, luxury restaurants, luxury hotels, products, etc., collected based on user requests.

[0366] An "emotion recognition engine" is a component that has the function of detecting and analyzing emotions from the user's facial expressions and voice input.

[0367] "Emotion" refers to the psychological state expressed when a user wants to know specific information, and includes, for example, a sense of expectation, excitement, etc.

[0368] "Generative AI" refers to an artificial intelligence model that analyzes collected multilingual related information and generates detailed experience reports to provide to users.

[0369] An "Experience Report" is a detailed report generated based on relevant information, including up-to-date event information, visitor reviews, and high-resolution photos.

[0370] A "terminal" is a device used by a user to input a request, such as a smartphone, tablet, or computer.

[0371] "Dynamic change" means that the scope and content of information to be collected can be flexibly adjusted according to the user's emotions and real-time situations.

[0372] This invention relates to a system that collects and analyzes related information in multiple languages ​​based on user requests, and generates and provides detailed experience reports. This system is combined with an emotion recognition engine that recognizes the user's emotions, enabling the provision of information according to the user's emotions.

[0373] Overall system configuration

[0374] The system consists of a terminal that receives user input requests, a server that receives the requests and collects related information, an emotion recognition engine that recognizes the user's emotions, a generation AI that analyzes the collected information and generates a detailed experience report, and a means for providing the report to the terminal.

[0375] Program processing overview

[0376] User request input

[0377] Users use devices such as smartphones or tablets to input requests about specific tourist spots or luxury items. At this time, the emotion recognition engine analyzes the user's facial expressions and voice input to recognize their emotions. For example, a user can input a request such as, "I'd like to know product reviews of the latest smartphones."

[0378] Server information collection

[0379] The request sent from the device is received by the server, which automatically collects multilingual information from related websites and databases. During this collection process, the emotion recognition engine dynamically adjusts the scope and content of the information collection based on the user's emotions.

[0380] Generative AI analysis and report generation

[0381] The collected multilingual related information is analyzed by the Generative AI, which then generates a detailed experience report of tourist attractions and luxury products. Based on the results of the emotion recognition engine, the report is optimized to include expressions and content that match the user's emotions. The report includes the latest event information, high-resolution photos, visitor reviews, and more.

[0382] Providing reports

[0383] The generated experience report is sent from the server to the user's device and displayed. The user can view detailed information and use it as a reference for travel planning and purchasing decisions.

[0384] Specific examples

[0385] For example, if a user requests, "I'm traveling to Paris, France, and I'd like detailed information about the Eiffel Tower," the emotion recognition engine will recognize the user's expectations. The server will collect multilingual information about the Eiffel Tower, and the generation AI will analyze it to generate a detailed experience report, including high-resolution photos, highlights, and the latest event information.

[0386] Prompt Sentence Examples

[0387] "I'd like to learn more about the latest smartphones. Product reviews, photos, feature recommendations, etc."

[0388] In this way, the system of the present invention can provide information customized according to the user's emotions, thereby supporting decision-making with a high level of satisfaction.

[0389] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0390] Step 1:

[0391] A user inputs a request using a device such as a smartphone or tablet. The input request is for specific information, and the user inputs a prompt such as, "I am traveling to Paris, France, and would like detailed information about the Eiffel Tower." This becomes the input data on the device.

[0392] Step 2:

[0393] The emotion recognition engine recognizes the user's emotions based on the user's facial expressions and voice input collected by the device. The input data is the user's facial expression data and voice data, and the output data is the user's emotional state (for example, anticipation or excitement). The emotion recognition engine analyzes this data to identify the user's current emotion.

[0394] Step 3:

[0395] The request and recognized emotional information are sent from the device to the server. The input data at this time is the user's input request and emotional information, which the server receives. Based on the request and emotional information, the server collects multilingual information from related websites and databases.

[0396] Step 4:

[0397] The server dynamically adjusts the scope and content of the information it collects based on the results of the emotion recognition engine. Specifically, if the user expresses anticipation, it prioritizes collecting information about particularly inspiring attractions and events. The input data for this process is information from the websites and databases to be collected, and the output data is related information corresponding to the emotion.

[0398] Step 5:

[0399] The collected multilingual information is analyzed by a generation AI, which uses this input data to generate detailed experience reports of tourist spots and luxury products. The output data is a customized, detailed experience report based on related information.

[0400] Step 6:

[0401] The server receives the generated experience report and optimizes it to include expressions and content appropriate to the user's emotions. This optimization process takes into account the results of the emotion recognition engine. The optimized experience report is the output data.

[0402] Step 7:

[0403] The server finally sends the generated and optimized experience report to the user's device, which receives it and displays it to the user. The user can then view the detailed experience report provided and use it as a reference for travel planning and purchasing decisions, thereby enabling the user to make a satisfactory decision.

[0404] 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.

[0405] 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.

[0406] 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.

[0407] [Second embodiment]

[0408] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0409] 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.

[0410] 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).

[0411] 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.

[0412] 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.

[0413] 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).

[0414] 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.

[0415] 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.

[0416] 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.

[0417] 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.

[0418] 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.

[0419] 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."

[0420] This invention is a system that collects and analyzes related information in multiple languages ​​in response to requests entered by users, and generates and provides detailed experience reports. The program processing required to realize the operation of this system is explained below in natural language.

[0421] overview

[0422] When a user requests detailed information about a specific tourist spot, luxury restaurant, luxury hotel, car, or property, the server receives the request and collects related information in multiple languages ​​from the Internet. The generative AI then analyzes the collected information and creates a detailed experience report. This report is then sent to the user's device via the server and provided to the user.

[0423] Program processing overview

[0424] Receiving request input

[0425] Users input requests for specific tourist attractions or luxury items through a dedicated app or website. For example, a user might request, "I want more information about the Eiffel Tower in Paris."

[0426] Information collection and generation AI analysis

[0427] The server receives requests sent from devices and collects multilingual information from relevant websites and databases. For example, it collects information related to the "Eiffel Tower" in English, Japanese, and French from the internet. Next, the generative AI analyzes this multilingual information and generates a detailed experience report of tourist attractions and high-ticket items. The report includes recent events, featured photos, visitor reviews, and more.

[0428] Report Generation

[0429] The server organizes the experience reports created by the generative AI and creates an easy-to-read report containing text and images.

[0430] Providing reports

[0431] The server then sends the generated report to the user's device for display. The user can then view the report and use it to help plan their trip or make a purchase. For example, a user can use a detailed report about the Eiffel Tower to plan their visit and sightseeing route.

[0432] Specific examples

[0433] Example 1: Pre-visiting a tourist spot

[0434] 1. A user is planning a trip to Paris, France and wants to know more about the Eiffel Tower.

[0435] 2. The user fills in the app's input form with "More information about the Eiffel Tower in Paris" and submits the request.

[0436] 3. The server receives this request and collects information related to the Eiffel Tower (e.g., latest events, directions, visitor reviews) from across the Internet in multiple languages.

[0437] 4. Generative AI analyzes the collected information and creates a detailed experience report, including high-resolution photos, tips for visiting, and highlights.

[0438] 5. The server sends the generated report to the user's terminal for display.

[0439] 6. The user browses the detailed information provided and makes efficient and satisfying travel plans.

[0440] Example 2: Previewing a luxury restaurant

[0441] 1. A user wants to make a reservation at a highly rated restaurant in Rome, Italy, but wants more information about the restaurant.

[0442] 2. The user enters the name of a specific restaurant into the app and requests more information.

[0443] 3. Based on the request, the server collects information about the restaurant in multiple languages ​​from the Internet and analyzes the latest menu, reviews, and the atmosphere of the restaurant.

[0444] 4. Generative AI creates a detailed report based on the collected information, including menu items, chef characteristics, and visitor experiences.

[0445] 5. The server sends the generated report to the user's terminal for display.

[0446] 6. Users can avoid mistakes by referring to the provided report and checking the restaurant's atmosphere and menu in detail before making a reservation.

[0447] In this way, the present invention provides powerful support for users to efficiently obtain detailed information and make optimal travel plans and purchasing choices.

[0448] The processing flow will be explained below.

[0449] Step 1:

[0450] A user enters a request for a specific tourist attraction or luxury item through a dedicated app or website, for example, "I'd like more information about the Eiffel Tower in Paris."

[0451] Step 2:

[0452] The device takes the user's input and formats it into an appropriate form, for example, "Sightseeing spot: Eiffel Tower."

[0453] Step 3:

[0454] The terminal sends the formatted request over the network to the server.

[0455] Step 4:

[0456] The server analyzes the request received from the device and identifies the target information. For example, it sets a task to "collect information related to the Eiffel Tower."

[0457] Step 5:

[0458] The server generates queries to gather relevant information from multilingual sources on the Net (e.g., English, Japanese, and French websites).

[0459] Step 6:

[0460] The server queries and gathers multilingual information from relevant websites and databases, such as "latest event information and visitor reviews about the Eiffel Tower."

[0461] Step 7:

[0462] The server preprocesses the collected raw multilingual data and converts it into a format that can be analyzed by the generative AI.

[0463] Step 8:

[0464] Generative AI analyzes the pre-processed data and generates detailed experience reports for tourist attractions and high-value items, such as the Eiffel Tower, including tourist information, highlights, and things to watch out for.

[0465] Step 9:

[0466] Generative AI organizes the generated report, arranging text and high-resolution images in an easy-to-read layout.

[0467] Step 10:

[0468] The server transmits the generated experience report to the terminal for providing to the user.

[0469] Step 11:

[0470] The terminal receives the report sent from the server.

[0471] Step 12:

[0472] The device then displays the received report to the user, for example providing detailed information about the Eiffel Tower in an app or browser.

[0473] Step 13:

[0474] Users can view the detailed reports provided to help them plan their trips and make purchasing decisions, for example, by planning their visit to the Eiffel Tower.

[0475] Example 1

[0476] 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."

[0477] Today's users want to quickly obtain detailed information when planning a trip or purchasing luxury goods. However, traditional information gathering methods lacked multilingual support and detailed analysis, making it difficult for users to obtain the precise, multifaceted information they needed. Furthermore, there was a lack of a way to efficiently analyze the information requested by users and provide it in a visually easy-to-understand report format. This left users unable to make informed decisions and making efficient plans.

[0478] 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.

[0479] In this invention, the server includes a means for a user to input a request, a means for collecting related information in multiple languages, a means for a generative AI model to analyze the collected related information in multiple languages ​​and generate a detailed experience report, and a means for transmitting the report to the user's device via encrypted communication. This makes it possible to quickly and multifacetedly collect and analyze the information requested by the user and provide it in a visually easy-to-understand report format.

[0480] The "means for a user to input a request" refers to an interface that allows a user to request specific information using an input device, such as an input form on a dedicated application or website.

[0481] A "server" is a computer system that receives user requests and processes related information. It communicates with user terminals via a network.

[0482] "Means for collecting relevant information in multiple languages" refers to a system that uses web crawling tools and APIs to collect necessary data from multiple language sources on the Internet.

[0483] A "generative AI model" is an algorithm that analyzes collected information and generates a detailed report to provide to users. Examples include generative models such as GPT-3 and BERT.

[0484] The "means of analysis" refers to the process by which the generative AI model categorizes, extracts, and summarizes the information collected. Natural language processing algorithms are used.

[0485] "Means for generating experience reports" refers to the process of creating a report in a visually understandable format based on the information analyzed by the generative AI model, including output in HTML and PDF formats.

[0486] The "means for providing to the user" refers to a communication means for providing the generated experience report to the user, including sending the report to the user's terminal using an encrypted communication protocol (HTTPS).

[0487] "Terminal" refers to a device on which a user inputs requests and receives and views reports sent from the server. For example, this applies to a smartphone or a PC.

[0488] "Encrypted communication" refers to a protocol used to securely transmit and receive data. Data is encrypted before transmission to protect user privacy.

[0489] A "dedicated application" is a specific piece of software that allows users to input requests. It is installed on a smartphone or tablet and is specialized for a specific function.

[0490] A "website" is an online interface through which users enter requests over the Internet, accessed via a browser.

[0491] "Natural language processing algorithms" are programs that analyze text data and perform tasks such as semantic analysis, summarization, and classification. They are included in generative AI models.

[0492] "HTML" is a markup language for building web pages. It is used to present the experience report in a visually understandable format.

[0493] "PDF" is a file format for viewing documents on various devices. It is used to save the generated experience report.

[0494] In this invention, a user uses a dedicated application or website to input a request. The dedicated application is installed on a smartphone or tablet and has an interactive UI. The user inputs a prompt, for example, "I want more information about the Eiffel Tower in Paris." The website allows the user to input the request via a browser over the Internet.

[0495] When a user inputs a request, the device receives the request and sends it to the server. The server analyzes the request and starts a process to collect relevant information. Specifically, the server uses web crawling tools such as Google's search API, BeautifulSoup, and Selenium to gather the necessary data from multilingual sources.

[0496] The collected data is analyzed by a generative AI model, which uses natural language processing algorithms such as GPT-3 and BERT. The generative AI model classifies the collected information in multiple languages, extracts important information, summarizes it, and generates a detailed experience report to provide to users. This report includes related photos, event information, directions, and evaluation reviews.

[0497] The generated report is generated by the server in HTML or PDF format, allowing users to receive information in a visually easy-to-understand format. The report is sent to the user's device via encrypted communication (HTTPS), ensuring security and privacy.

[0498] Users can view the reports sent to their devices and make travel plans or purchasing decisions based on the detailed information. For example, a user can view a detailed report on the Eiffel Tower to plan the time to visit and the sightseeing route, or make a reservation decision based on information on high-end restaurants in Rome.

[0499] Specific examples

[0500] Example 1: Pre-visiting a tourist spot

[0501] 1. The user opens the dedicated application and enters a request: "Tell me about the Eiffel Tower in Paris."

[0502] 2. The server receives this request and collects information from relevant multilingual websites.

[0503] 3. A generative AI model analyzes the collected information and generates a detailed experience report, including up-to-date event information, directions, visitor reviews, and more.

[0504] 4. The server sends the generated report to the user's device using encrypted communication.

[0505] 5. The user views the provided report and uses it to plan their trip.

[0506] Example 2: Previewing a luxury restaurant

[0507] 1. A user visits a website and types, "Please give me the latest information on fine dining restaurants in Rome."

[0508] 2. The server collects information about the restaurant from relevant multilingual websites.

[0509] 3. A generative AI model analyzes the collected information and generates a detailed report, including menu items, chef highlights, and visitor experiences.

[0510] 4. The server sends the generated report to the user's device.

[0511] 5. The user reviews the report and makes a booking decision.

[0512] This invention allows users to efficiently gather detailed information they need and receive it in a visually easy-to-understand report format, which helps them make better decisions when it comes to travel and luxury purchases.

[0513] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0514] Step 1:

[0515] The user accesses a dedicated application or website and enters a request.

[0516] Specifically, the user enters a prompt such as "Tell me about the Eiffel Tower in Paris" into the app's text input field and presses the send button.

[0517] Input: User text input (prompt)

[0518] Output: The request data is sent from the terminal to the server.

[0519] Step 2:

[0520] The server receives the request sent from the terminal.

[0521] The server analyzes the request data and generates a search query to gather the required information.

[0522] Specifically, a search query is generated to collect "information about the Eiffel Tower" based on the request data.

[0523] Input: Request data

[0524] Output: Search query

[0525] Step 3:

[0526] A server uses the generated search query to gather relevant information on the Internet in multiple languages.

[0527] The server uses Google's search API and web crawling tools such as BeautifulSoup and Selenium to gather relevant information in multiple languages.

[0528] Specifically, it scrapes the latest information, visitor reviews, photos, etc. about the Eiffel Tower from English, Japanese, and French websites.

[0529] Input: Search query

[0530] Output: Collected multilingual web data

[0531] Step 4:

[0532] The server passes the collected multilingual information to the generative AI model.

[0533] A generative AI model analyzes the collected information and generates a detailed experience report.

[0534] Specifically, it uses natural language processing algorithms such as GPT-3 and BERT to classify, extract, and summarize important information to create an experience report.

[0535] Input: Collected multilingual web data

[0536] Output: Parsed experience report

[0537] Step 5:

[0538] The server compiles the generated experience reports and generates a user-friendly report in HTML or PDF format.

[0539] Specifically, the system creates a visually easy-to-understand report based on the collected information, including high-resolution photos and charts.

[0540] Input: Parsed experience report

[0541] Output: Report in HTML or PDF format

[0542] Step 6:

[0543] The server sends the generated report to the user's terminal.

[0544] In this case, encrypted communication (HTTPS) is used to ensure data privacy and security.

[0545] Input: Report in HTML or PDF format

[0546] Output: Report sent to user's terminal

[0547] Step 7:

[0548] The user views the provided report on the device.

[0549] Specifically, users can open the report on their smartphone or computer and use it to help plan their trip or make purchasing decisions.

[0550] Input: Report submitted

[0551] Output: Viewed details

[0552] (Application example 1)

[0553] 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."

[0554] Conventional tourist information systems have the problem that it is time-consuming for users to quickly obtain specific information. Furthermore, they are not able to adequately respond to real-time information requests from inside a vehicle. As a result, it is difficult to efficiently gather information while traveling, resulting in an unsatisfactory experience. Therefore, there is a need for a system that allows users to obtain detailed multilingual tourist reports in real time while on the move.

[0555] 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.

[0556] In this invention, the server includes means for a user to input a request, means for the server to receive the request and collect related information in multiple languages, means for a generation AI to analyze the related information in multiple languages ​​and generate a detailed experience report, means for the server to provide the generated experience report to the user, means for the server to receive the request via an interface in the vehicle and for an on-board computer to collect and analyze related information in multiple languages, and means for displaying the generated experience report on an on-board display. This allows users to obtain detailed information in real time even while on the move and to efficiently plan their sightseeing trips.

[0557] "User" refers to any individual or entity using the Service.

[0558] The "means for inputting a request" refers to an interface that a user uses to request information, and includes a touch panel, a voice input system, and the like.

[0559] "Server" refers to a computer system that receives requests from users and collects, processes, and provides relevant information in multiple languages ​​in response to those requests.

[0560] "Means of collecting relevant information in multiple languages" refers to means of collecting necessary information written in multiple languages ​​via the Internet or databases.

[0561] "Generative AI" refers to an artificial intelligence model that analyzes collected multilingual information and generates detailed experience reports based on it.

[0562] An "Experience Report" is a report containing detailed information about a specific subject requested by a user, including photos, reviews, event information, etc.

[0563] "Interfaces inside the vehicle" refers to input and display devices installed inside the vehicle, including touch panels and displays.

[0564] "On-board computer" refers to a computer installed inside a vehicle, and is hardware used to collect, analyze, and display information.

[0565] An "in-vehicle display" is a screen installed inside a vehicle and refers to a device for displaying information to the user.

[0566] The present invention is a system for providing detailed tourist information in real time within a vehicle. The system starts when a user inputs a request through an in-vehicle interface. Specific embodiments for realizing this system are described below.

[0567] 1. User request input

[0568] The user requests detailed information about tourist attractions, restaurants, etc. through an in-car interface. The interface can be a touch panel or a voice input system. For example, the user might say, "I'd like more information about a famous tower in a specific city."

[0569] 2. Server receives requests and collects information

[0570] The server receives user requests and collects relevant information in multiple languages ​​from the internet using web scraping tools (such as Scrapy) and database APIs (such as Google Places API). The server organizes this information and sends it to the generation AI for analysis.

[0571] 3. Information analysis and report generation using generative AI

[0572] The generative AI (e.g., OpenAI's GPT-4) analyzes relevant information in multiple languages ​​and generates a detailed experience report, including the history of the target tourist destination, the latest events, visitor reviews, photos, etc. The generative AI uses prompt sentences such as:

[0573] "Gather up-to-date information about famous towers in the designated cities and prepare a detailed tourist guide report. Please include event information and visitor reviews, among other things."

[0574] "Generate a report containing the history and highlights of famous towers in selected cities, as well as the latest visitor experiences."

[0575] 4. Serving and Displaying Reports by the Server

[0576] The generated experience report is sent to the user's in-vehicle display via the server and displayed. The user can view the report on the dashboard display and use it as a reference for planning their trip. For example, the user can use the provided report to plan their visit schedule and sightseeing route in detail.

[0577] Specific example explanation

[0578] When a user types "I want to know about famous riverside towers" into the car's touch panel, the on-board computer sends this request to the server. The server collects multilingual information from the internet and sends it to the generation AI. The generation AI generates a detailed experience report based on the prompt, "Collect the latest information about famous riverside towers and create a report as a detailed tourist guide. Please include event information and visitor reviews in particular." The generated report is displayed on the car's display, allowing the user to obtain detailed tourist information in real time.

[0579] This system allows users to obtain detailed tourist information in real time while on the move, enabling them to efficiently plan their sightseeing trips.

[0580] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0581] Step 1:

[0582] The user inputs a request via the car's interface (touchscreen or voice input system), for example, "I want more information about a famous tower in a specific city." Once this request is input, it is sent to the car's computer.

[0583] Input: "Detailed information about famous towers in a given city"

[0584] Output: Request data sent to the on-board computer

[0585] Step 2:

[0586] The server receives requests sent from the vehicle's onboard computer and generates queries based on the received requests to collect relevant information in multiple languages ​​from the internet and databases. This information is collected using web scraping tools (such as Scrapy) and database APIs (such as Google Places API).

[0587] Input: Request data sent from the on-board computer

[0588] Output: Collected multilingual related information (HTML data, API response)

[0589] Step 3:

[0590] The server organizes the collected multilingual related information and sends it to a generation AI (e.g., OpenAI's GPT-4). At this time, a prompt for analysis is also generated. The prompt is, "Collect the latest information about famous towers in the specified city and create a report as a detailed tourist guide. Please include event information and visitor reviews in particular."

[0591] Input: Collected multilingual related information

[0592] Output: Prompts and related information sent to the generation AI for analysis.

[0593] Step 4:

[0594] Based on the prompts sent and the collected information, the generative AI analyzes the data and generates a detailed experience report, including the history of the target tourist destination, the latest events, visitor reviews, photos, and more.

[0595] Input: Prompt text, related information in multiple languages

[0596] Output: A detailed experience report (text data, images)

[0597] Step 5:

[0598] The server organizes the generated experience reports and formats them for display on the vehicle's in-vehicle display, using HTML / CSS formatting to create an easy-to-read report.

[0599] Input: Generated experience report (text data, images)

[0600] Output: The report in a viewable format

[0601] Step 6:

[0602] The server then sends the formatted report to the in-vehicle display for display, allowing users to view detailed tourist information on the in-vehicle display while traveling.

[0603] Input: Formatted report

[0604] Output: Detailed experience report displayed on the in-car display

[0605] 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.

[0606] This invention combines a system that collects and analyzes related information in multiple languages ​​in response to requests entered by users, and generates and provides detailed experience reports, with an emotion engine that recognizes the user's emotions. The program processing required to achieve this specific operation is explained below in natural language.

[0607] overview

[0608] When a user requests detailed information about a specific tourist spot, luxury restaurant, luxury hotel, car, or property, the server receives the request and collects related information in multiple languages ​​from the Internet. The emotion engine recognizes the user's emotion and dynamically changes the scope and content of the information collected based on that emotion. The generation AI analyzes the collected information and generates a detailed experience report. This report is sent to the user's device via the server and provided to the user.

[0609] Program processing overview

[0610] Receiving request input and emotion recognition

[0611] A user inputs a request for a specific tourist spot or luxury item through a dedicated app or website. At this time, the emotion engine recognizes the user's emotions from their facial expressions and voice input. For example, if a user inputs "I want to know more about the Eiffel Tower in Paris," it will recognize emotions such as excitement and anticipation.

[0612] Information collection and generation AI analysis

[0613] The server receives requests sent from the device and collects multilingual information from related websites and databases. During this collection process, the emotion engine dynamically changes the scope and content of the information collection based on the user's emotions. For example, if the user is excited, it will prioritize collecting information about particularly inspiring attractions and events. Next, the generation AI analyzes this multilingual information and generates detailed experience reports of tourist spots and high-value items. The reports include the latest events, distinctive photos, visitor reviews, and more.

[0614] Report Generation

[0615] The server organizes the experience reports created by the generative AI and creates an easy-to-read report containing both text and images. Based on the recognition results of the emotion engine, the report is optimized to include expressions and content appropriate to the user's emotions.

[0616] Providing reports

[0617] The server sends the generated report to the user's device and displays it. The user can then view the report and use it to plan their trip or make a purchase. For example, a user can use the detailed report on the Eiffel Tower to plan their visit and sightseeing route.

[0618] Specific examples

[0619] Example 1: Pre-visiting a tourist spot

[0620] 1. A user is planning a trip to Paris, France and wants to know more about the Eiffel Tower.

[0621] 2. The user enters "More information about the Eiffel Tower in Paris" into the app's input form and submits a request. At this time, the emotion engine recognizes the user's expectations.

[0622] 3. The server receives the request and collects information related to the Eiffel Tower in multiple languages ​​from the Internet. The emotion engine prioritizes collecting particularly impressive sights and reviews based on the perceived expectations.

[0623] 4. Generative AI analyzes this information and creates a detailed experience report, including high-resolution photos, tips for visiting, and highlights.

[0624] 5. The server sends the generated report to the user's device and displays it. Based on the results of the emotion engine, it includes expressions that heighten the sense of anticipation.

[0625] 6. The user browses the detailed information provided and makes efficient and satisfying travel plans.

[0626] Example 2: Previewing a luxury restaurant

[0627] 1. A user wants to make a reservation at a highly rated restaurant in Rome, Italy, but wants more information about the restaurant.

[0628] 2. The user enters the name of a specific restaurant into the app to request more information, and the emotion engine recognizes the user's expectations.

[0629] 3. Based on the request, the server collects information about the restaurant in multiple languages ​​from the Internet, analyzing the latest menu, reviews, and the restaurant's atmosphere. The emotion engine prioritizes information that is particularly interesting to the user based on perceived expectations.

[0630] 4. Generative AI uses the collected information to create a detailed report, including menu items, chef highlights, and visitor experiences.

[0631] 5. The server sends the generated report to the user's device and displays it. Depending on the results of the emotion engine, interesting details are highlighted.

[0632] 6. The user can use the provided report to check the restaurant's atmosphere and menu in detail before making a reservation, making the best choice.

[0633] In this way, by combining emotion engines, it becomes possible to provide optimal information tailored to the user's emotions, supporting decision-making when planning trips or purchasing high-value items.

[0634] The processing flow will be explained below.

[0635] Step 1:

[0636] Users input requests for specific tourist attractions or luxury items through a dedicated app or website. For example, a user might input a request such as, "I want more information about the Eiffel Tower in Paris."

[0637] Step 2:

[0638] The emotion engine uses a facial recognition camera and microphone to analyze the user's emotions as they input. For example, it can detect "expectation" or "excitement" from the user's facial expressions and tone of voice.

[0639] Step 3:

[0640] The device sends the request along with the emotion data analyzed by the emotion engine to the server. For example, the request is formatted as "Request content: Eiffel Tower, Emotion: Anticipation."

[0641] Step 4:

[0642] The server analyzes the request and emotion data received from the device and identifies information about the target tourist spot. For example, set "collect information about the Eiffel Tower."

[0643] Step 5:

[0644] The server dynamically changes the priority and scope of information collection based on the results of the emotion engine, and collects related information in multiple languages ​​from the Internet. For example, it may prioritize collecting "impressive sights" and "latest event information" related to the Eiffel Tower.

[0645] Step 6:

[0646] The server preprocesses the collected raw multilingual data and converts it into a format that can be analyzed by the generative AI.

[0647] Step 7:

[0648] Generative AI analyzes the pre-processed data and generates detailed experience reports for tourist attractions and high-ticket items, such as a detailed report including "Eiffel Tower tourist information," "latest events," and "visitor reviews."

[0649] Step 8:

[0650] Based on the results of the emotion engine, the generative AI optimizes the report's expression and content to suit the user's emotions. For example, it highlights recommended attractions for users with "expectations."

[0651] Step 9:

[0652] The server organizes and formats the generated experience reports for presentation to the user, for example, in an easy-to-read layout including text and high-resolution photos.

[0653] Step 10:

[0654] The server sends the generated detailed report to the user's terminal.

[0655] Step 11:

[0656] The device receives the report sent from the server and displays it to the user, for example, providing detailed information about the Eiffel Tower in an app or browser.

[0657] Step 12:

[0658] Users can view the detailed reports provided to help inform their travel planning and purchasing decisions, for example, to plan their visit to the Eiffel Tower.

[0659] Example 2

[0660] 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."

[0661] When collecting information about tourist spots or luxury goods, the information provided may not be suited to the user's emotions, resulting in a poor user experience. Furthermore, conventional information collection systems often collect uniform information in response to user requests, making it difficult to provide information optimized for individual users.

[0662] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for the user to input a request, a means for the terminal to recognize the user's emotion, a means for the server to receive the request and dynamically collect related information in multiple languages ​​based on the emotion information, a means for the generative AI model to analyze the related information in multiple languages ​​and generate a detailed experience report that matches the emotion, and a means for the server to provide the generated experience report to the user. This makes it possible to provide optimal information based on the user's emotion.

[0663] "User" refers to the end user who requests information from the System and views and uses the information provided.

[0664] "Terminal" refers to an electronic device that allows a user to input information and communicate with a server through an application or website.

[0665] "Means for recognizing emotions" refers to the function that enables the device to analyze the user's facial expressions and voice and identify their emotional state.

[0666] "Server" refers to the computer system that receives requests from users, collects necessary information, and creates and provides reports using generative AI models.

[0667] "Means for dynamically collecting related information in multiple languages" refers to a function that allows a server to collect related information provided in different languages ​​from the Internet and adjust the scope and content according to the user's feelings.

[0668] "Generative AI model" refers to an artificial intelligence model that analyzes related multilingual information collected by the server and automatically generates a detailed experience report that matches the user's emotions.

[0669] An "experience report" is a detailed report created by a generative AI model based on collected information, and includes a wide range of information such as tourist destinations and luxury items.

[0670] This invention combines a system that collects and analyzes related information in multiple languages ​​in response to requests entered by users, and generates and provides detailed experience reports with an emotion engine that recognizes the user's emotions.

[0671] System configuration

[0672] Hardware

[0673] Device: An electronic device, such as a smartphone, tablet, or computer, through which a user inputs requests and performs emotion recognition.

[0674] Server: A high-performance computer system that collects and analyzes data, runs generative AI models, and generates reports.

[0675] software

[0676] Dedicated application or website: Software that allows users to enter requests and view related information and reports.

[0677] Emotion engine: Software containing algorithms for recognizing emotions by analyzing a user's facial expressions and voice.

[0678] Generative AI model: An artificial intelligence model for generating detailed experience reports based on collected multilingual related information, for example, using an advanced natural language processing model such as GPT-4.

[0679] Program processing

[0680] A user requests detailed information about a specific tourist spot or luxury item through a dedicated app or website. The device passes the user's facial expressions and voice input to the emotion engine to recognize the user's emotions. For example, if the user requests "I want more information about the Eiffel Tower in Paris," the emotion engine can identify the user's anticipation and excitement.

[0681] The server receives the user's request and emotion information, and collects relevant multilingual information from related websites and databases (e.g., Wikipedia, travel review sites, etc.). Based on the results of the emotion engine, it prioritizes the collection of particularly moving or interesting information.

[0682] The generative AI model analyzes the collected information and generates a detailed experience report that matches the user's emotions. An example prompt might be, "Please tell me the latest information about attractions and events at the Eiffel Tower."

[0683] The server organizes the generated report and sends it to the user's device in an easy-to-read format. The report includes special expressions based on the user's emotions, and the user makes travel plans and purchase decisions based on these.

[0684] Specific examples

[0685] Example 1: Pre-visiting a tourist spot

[0686] A user opens the app, types in "More information about the Eiffel Tower in Paris," and submits the request.

[0687] The device records the user's facial expressions and voice in real time, and an emotion engine identifies "expectations."

[0688] The server receives the request and emotion information and collects information in multiple languages, prioritizing particularly inspiring sights and the latest event information.

[0689] A generative AI model analyzes the collected information and generates a detailed report.

[0690] The server sends the report to the user's terminal, and the user views it to make a visiting plan.

[0691] Example 2: Previewing a luxury restaurant

[0692] A user types the name of a specific restaurant into the app and requests more information.

[0693] The device recognizes the user's emotions, and the emotion engine identifies "excitement."

[0694] The server collects multilingual information based on requests and sentiment information, prioritizing menus and reviews that are particularly interesting.

[0695] The generative AI model generates a detailed report, which the server sends to the user's device.

[0696] Users can view the report and check restaurant details before booking.

[0697] In this way, the system of the present invention can dynamically collect relevant information based on the user's emotions and generate and provide a detailed and personalized experience report to support the user's decision-making.

[0698] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0699] Specific processing steps of the system

[0700] Step 1: Enter your request

[0701] Users use a dedicated app or website to enter information about specific tourist destinations or luxury items.

[0702] Input: User types "I want to know more about the Eiffel Tower."

[0703] Output: The input request text is sent to the system.

[0704] Step 2: Recognize emotions

[0705] The device uses a camera and microphone to record facial expressions and voice to recognize the user's emotions.

[0706] Input: User's facial expression data, voice data.

[0707] Data processing: Emotion recognition algorithms analyze facial and audio data to identify emotional states (e.g., anticipation, excitement).

[0708] Output: The recognized emotion data is sent to the system.

[0709] Step 3: Receiving requests and emotions

[0710] The server receives the user's request text and emotion data.

[0711] Input: User request text, emotion data.

[0712] Data operation: Log a combination of request text and emotion data.

[0713] Output: The combined data is passed on to the next process.

[0714] Step 4: Gather information

[0715] The server collects relevant information from the internet in multiple languages, and dynamically adjusts the scope and content of the information collected based on emotional data during the collection process.

[0716] Input: Request text, emotion data.

[0717] Data processing: Scrip multilingual information from relevant websites and databases to prioritize sentiment-driven search terms.

[0718] Output: Collected multilingual information data.

[0719] Step 5: Analyze the information

[0720] The server uses a generative AI model to analyze the collected information and generate a detailed experience report.

[0721] Input: Collected multilingual information data.

[0722] Prompt generation: The generative AI model generates prompts such as, "Please tell me the latest information about attractions and events at the Eiffel Tower."

[0723] Data calculation: A generative AI model analyzes based on prompts and generates a detailed experience report.

[0724] Output: The generated experience report.

[0725] Step 6: Organize your reports

[0726] The server organizes the generated reports, formats them into an easy-to-read format, and adds the most appropriate expressions based on the results of the emotion engine.

[0727] Input: Generated experience reports, sentiment data.

[0728] Data manipulation: Formatting reports containing text and images. Adding sentiment-based emphasis.

[0729] Output: A nicely formatted report.

[0730] Step 7: Submit the report

[0731] The server sends the generated report to the user's terminal.

[0732] Input: The formatted report.

[0733] Data calculation: Sends data to the user's terminal according to the report transmission protocol.

[0734] Output: Report data ready for viewing on the user's device.

[0735] Step 8: View the report

[0736] The user views the provided report on the device.

[0737] Input: Submitted report data.

[0738] Data calculation: The rendering process for displaying the report.

[0739] Output: The report is presented in a user-readable format.

[0740] These steps will result in a system that collects and analyzes optimal information based on the user's emotions, and generates and provides a detailed experience report.

[0741] (Application example 2)

[0742] 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."

[0743] Conventional information gathering systems are limited to providing simple information based on user input requests, and do not provide customized information based on the user's emotions or interests. As a result, it is difficult for users to obtain the specific information they need that is relevant to their emotions, and they are unable to support highly satisfying decision-making. It is also difficult to effectively collect and analyze information in multiple languages.

[0744] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0745] In this invention, the server includes a means for a user to input a request, a means for the server to receive the request and collect related information in multiple languages, a means for an emotion recognition engine to recognize the user's emotion and dynamically change the range and content of the information collection based on the emotion, a means for a generation AI to analyze the related information in multiple languages ​​and generate a detailed experience report, and a means for the server to provide the generated experience report to the user. This makes it possible to provide detailed information customized according to the user's emotion, thereby supporting decision-making with a high level of satisfaction.

[0746] A "user" is an end-user who utilizes the system of the present invention to request information.

[0747] A "request" is a request or inquiry made by a user for specific information.

[0748] "Server" means a computer system that receives requests from users, collects and analyzes relevant information based on those requests, and provides generated reports.

[0749] "Multilingual" means including multiple different languages, such as collecting and analyzing information in English, Japanese, and French.

[0750] "Related information" is data related to tourist spots, luxury restaurants, luxury hotels, products, etc., collected based on user requests.

[0751] An "emotion recognition engine" is a component that has the function of detecting and analyzing emotions from the user's facial expressions and voice input.

[0752] "Emotion" refers to the psychological state expressed when a user wants to know specific information, and includes, for example, a sense of expectation, excitement, etc.

[0753] "Generative AI" refers to an artificial intelligence model that analyzes collected multilingual related information and generates detailed experience reports to provide to users.

[0754] An "Experience Report" is a detailed report generated based on relevant information, including up-to-date event information, visitor reviews, and high-resolution photos.

[0755] A "terminal" is a device used by a user to input a request, such as a smartphone, tablet, or computer.

[0756] "Dynamic change" means that the scope and content of information to be collected can be flexibly adjusted according to the user's emotions and real-time situations.

[0757] This invention relates to a system that collects and analyzes related information in multiple languages ​​based on user requests, and generates and provides detailed experience reports. This system is combined with an emotion recognition engine that recognizes the user's emotions, enabling the provision of information according to the user's emotions.

[0758] Overall system configuration

[0759] The system consists of a terminal that receives user input requests, a server that receives the requests and collects related information, an emotion recognition engine that recognizes the user's emotions, a generation AI that analyzes the collected information and generates a detailed experience report, and a means for providing the report to the terminal.

[0760] Program processing overview

[0761] User request input

[0762] Users use devices such as smartphones or tablets to input requests about specific tourist spots or luxury items. At this time, the emotion recognition engine analyzes the user's facial expressions and voice input to recognize their emotions. For example, a user can input a request such as, "I'd like to know product reviews of the latest smartphones."

[0763] Server information collection

[0764] The request sent from the device is received by the server, which automatically collects multilingual information from related websites and databases. During this collection process, the emotion recognition engine dynamically adjusts the scope and content of the information collection based on the user's emotions.

[0765] Generative AI analysis and report generation

[0766] The collected multilingual related information is analyzed by the Generative AI, which then generates a detailed experience report of tourist attractions and luxury products. Based on the results of the emotion recognition engine, the report is optimized to include expressions and content that match the user's emotions. The report includes the latest event information, high-resolution photos, visitor reviews, and more.

[0767] Providing reports

[0768] The generated experience report is sent from the server to the user's device and displayed. The user can view detailed information and use it as a reference for travel planning and purchasing decisions.

[0769] Specific examples

[0770] For example, if a user requests, "I'm traveling to Paris, France, and I'd like detailed information about the Eiffel Tower," the emotion recognition engine will recognize the user's expectations. The server will collect multilingual information about the Eiffel Tower, and the generation AI will analyze it to generate a detailed experience report, including high-resolution photos, highlights, and the latest event information.

[0771] Prompt Sentence Examples

[0772] "I'd like to learn more about the latest smartphones. Product reviews, photos, feature recommendations, etc."

[0773] In this way, the system of the present invention can provide information customized according to the user's emotions, thereby supporting decision-making with a high level of satisfaction.

[0774] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0775] Step 1:

[0776] A user inputs a request using a device such as a smartphone or tablet. The input request is for specific information, and the user inputs a prompt such as, "I am traveling to Paris, France, and would like detailed information about the Eiffel Tower." This becomes the input data on the device.

[0777] Step 2:

[0778] The emotion recognition engine recognizes the user's emotions based on the user's facial expressions and voice input collected by the device. The input data is the user's facial expression data and voice data, and the output data is the user's emotional state (for example, anticipation or excitement). The emotion recognition engine analyzes this data to identify the user's current emotion.

[0779] Step 3:

[0780] The request and recognized emotional information are sent from the device to the server. The input data at this time is the user's input request and emotional information, which the server receives. Based on the request and emotional information, the server collects multilingual information from related websites and databases.

[0781] Step 4:

[0782] The server dynamically adjusts the scope and content of the information it collects based on the results of the emotion recognition engine. Specifically, if the user expresses anticipation, it prioritizes collecting information about particularly inspiring attractions and events. The input data for this process is information from the websites and databases to be collected, and the output data is related information corresponding to the emotion.

[0783] Step 5:

[0784] The collected multilingual information is analyzed by a generation AI, which uses this input data to generate detailed experience reports of tourist spots and luxury products. The output data is a customized, detailed experience report based on related information.

[0785] Step 6:

[0786] The server receives the generated experience report and optimizes it to include expressions and content appropriate to the user's emotions. This optimization process takes into account the results of the emotion recognition engine. The optimized experience report is the output data.

[0787] Step 7:

[0788] The server finally sends the generated and optimized experience report to the user's device, which receives it and displays it to the user. The user can then view the detailed experience report provided and use it as a reference for travel planning and purchasing decisions, thereby enabling the user to make a satisfactory decision.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] [Third embodiment]

[0793] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0794] 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.

[0795] 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).

[0796] 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.

[0797] 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.

[0798] 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).

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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."

[0805] This invention is a system that collects and analyzes related information in multiple languages ​​in response to requests entered by users, and generates and provides detailed experience reports. The program processing required to realize the operation of this system is explained below in natural language.

[0806] overview

[0807] When a user requests detailed information about a specific tourist spot, luxury restaurant, luxury hotel, car, or property, the server receives the request and collects related information in multiple languages ​​from the Internet. The generative AI then analyzes the collected information and creates a detailed experience report. This report is then sent to the user's device via the server and provided to the user.

[0808] Program processing overview

[0809] Receiving request input

[0810] Users input requests for specific tourist attractions or luxury items through a dedicated app or website. For example, a user might request, "I want more information about the Eiffel Tower in Paris."

[0811] Information collection and generation AI analysis

[0812] The server receives requests sent from devices and collects multilingual information from relevant websites and databases. For example, it collects information related to the "Eiffel Tower" in English, Japanese, and French from the internet. Next, the generative AI analyzes this multilingual information and generates a detailed experience report of tourist attractions and high-ticket items. The report includes recent events, featured photos, visitor reviews, and more.

[0813] Report Generation

[0814] The server organizes the experience reports created by the generative AI and creates an easy-to-read report containing text and images.

[0815] Providing reports

[0816] The server then sends the generated report to the user's device for display. The user can then view the report and use it to help plan their trip or make a purchase. For example, a user can use a detailed report about the Eiffel Tower to plan their visit and sightseeing route.

[0817] Specific examples

[0818] Example 1: Pre-visiting a tourist spot

[0819] 1. A user is planning a trip to Paris, France and wants to know more about the Eiffel Tower.

[0820] 2. The user fills in the app's input form with "More information about the Eiffel Tower in Paris" and submits the request.

[0821] 3. The server receives this request and collects information related to the Eiffel Tower (e.g., latest events, directions, visitor reviews) from across the Internet in multiple languages.

[0822] 4. Generative AI analyzes the collected information and creates a detailed experience report, including high-resolution photos, tips for visiting, and highlights.

[0823] 5. The server sends the generated report to the user's terminal for display.

[0824] 6. The user browses the detailed information provided and makes efficient and satisfying travel plans.

[0825] Example 2: Previewing a luxury restaurant

[0826] 1. A user wants to make a reservation at a highly rated restaurant in Rome, Italy, but wants more information about the restaurant.

[0827] 2. The user enters the name of a specific restaurant into the app and requests more information.

[0828] 3. Based on the request, the server collects information about the restaurant in multiple languages ​​from the Internet and analyzes the latest menu, reviews, and the atmosphere of the restaurant.

[0829] 4. Generative AI creates a detailed report based on the collected information, including menu items, chef characteristics, and visitor experiences.

[0830] 5. The server sends the generated report to the user's terminal for display.

[0831] 6. Users can avoid mistakes by referring to the provided report and checking the restaurant's atmosphere and menu in detail before making a reservation.

[0832] In this way, the present invention provides powerful support for users to efficiently obtain detailed information and make optimal travel plans and purchasing choices.

[0833] The processing flow will be explained below.

[0834] Step 1:

[0835] A user enters a request for a specific tourist attraction or luxury item through a dedicated app or website, for example, "I'd like more information about the Eiffel Tower in Paris."

[0836] Step 2:

[0837] The device takes the user's input and formats it into an appropriate form, for example, "Sightseeing spot: Eiffel Tower."

[0838] Step 3:

[0839] The terminal sends the formatted request over the network to the server.

[0840] Step 4:

[0841] The server analyzes the request received from the device and identifies the target information. For example, it sets a task to "collect information related to the Eiffel Tower."

[0842] Step 5:

[0843] The server generates queries to gather relevant information from multilingual sources on the Net (e.g., English, Japanese, and French websites).

[0844] Step 6:

[0845] The server queries and gathers multilingual information from relevant websites and databases, such as "latest event information and visitor reviews about the Eiffel Tower."

[0846] Step 7:

[0847] The server preprocesses the collected raw multilingual data and converts it into a format that can be analyzed by the generative AI.

[0848] Step 8:

[0849] Generative AI analyzes the pre-processed data and generates detailed experience reports for tourist attractions and high-value items, such as the Eiffel Tower, including tourist information, highlights, and things to watch out for.

[0850] Step 9:

[0851] Generative AI organizes the generated report, arranging text and high-resolution images in an easy-to-read layout.

[0852] Step 10:

[0853] The server transmits the generated experience report to the terminal for providing to the user.

[0854] Step 11:

[0855] The terminal receives the report sent from the server.

[0856] Step 12:

[0857] The device then displays the received report to the user, for example providing detailed information about the Eiffel Tower in an app or browser.

[0858] Step 13:

[0859] Users can view the detailed reports provided to help them plan their trips and make purchasing decisions, for example, by planning their visit to the Eiffel Tower.

[0860] Example 1

[0861] 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."

[0862] Today's users want to quickly obtain detailed information when planning a trip or purchasing luxury goods. However, traditional information gathering methods lacked multilingual support and detailed analysis, making it difficult for users to obtain the precise, multifaceted information they needed. Furthermore, there was a lack of a way to efficiently analyze the information requested by users and provide it in a visually easy-to-understand report format. This left users unable to make informed decisions and making efficient plans.

[0863] 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.

[0864] In this invention, the server includes a means for a user to input a request, a means for collecting related information in multiple languages, a means for a generative AI model to analyze the collected related information in multiple languages ​​and generate a detailed experience report, and a means for transmitting the report to the user's device via encrypted communication. This makes it possible to quickly and multifacetedly collect and analyze the information requested by the user and provide it in a visually easy-to-understand report format.

[0865] The "means for a user to input a request" refers to an interface that allows a user to request specific information using an input device, such as an input form on a dedicated application or website.

[0866] A "server" is a computer system that receives user requests and processes related information. It communicates with user terminals via a network.

[0867] "Means for collecting relevant information in multiple languages" refers to a system that uses web crawling tools and APIs to collect necessary data from multiple language sources on the Internet.

[0868] A "generative AI model" is an algorithm that analyzes collected information and generates a detailed report to provide to users. Examples include generative models such as GPT-3 and BERT.

[0869] The "means of analysis" refers to the process by which the generative AI model categorizes, extracts, and summarizes the information collected. Natural language processing algorithms are used.

[0870] "Means for generating experience reports" refers to the process of creating a report in a visually understandable format based on the information analyzed by the generative AI model, including output in HTML and PDF formats.

[0871] The "means for providing to the user" refers to a communication means for providing the generated experience report to the user, including sending the report to the user's terminal using an encrypted communication protocol (HTTPS).

[0872] "Terminal" refers to a device on which a user inputs requests and receives and views reports sent from the server. For example, this applies to a smartphone or a PC.

[0873] "Encrypted communication" refers to a protocol used to securely transmit and receive data. Data is encrypted before transmission to protect user privacy.

[0874] A "dedicated application" is a specific piece of software that allows users to input requests. It is installed on a smartphone or tablet and is specialized for a specific function.

[0875] A "website" is an online interface through which users enter requests over the Internet, accessed via a browser.

[0876] "Natural language processing algorithms" are programs that analyze text data and perform tasks such as semantic analysis, summarization, and classification. They are included in generative AI models.

[0877] "HTML" is a markup language for building web pages. It is used to present the experience report in a visually understandable format.

[0878] "PDF" is a file format for viewing documents on various devices. It is used to save the generated experience report.

[0879] In this invention, a user uses a dedicated application or website to input a request. The dedicated application is installed on a smartphone or tablet and has an interactive UI. The user inputs a prompt, for example, "I want more information about the Eiffel Tower in Paris." The website allows the user to input the request via a browser over the Internet.

[0880] When a user inputs a request, the device receives the request and sends it to the server. The server analyzes the request and starts a process to collect relevant information. Specifically, the server uses web crawling tools such as Google's search API, BeautifulSoup, and Selenium to gather the necessary data from multilingual sources.

[0881] The collected data is analyzed by a generative AI model, which uses natural language processing algorithms such as GPT-3 and BERT. The generative AI model classifies the collected information in multiple languages, extracts important information, summarizes it, and generates a detailed experience report to provide to users. This report includes related photos, event information, directions, and evaluation reviews.

[0882] The generated report is generated by the server in HTML or PDF format, allowing users to receive information in a visually easy-to-understand format. The report is sent to the user's device via encrypted communication (HTTPS), ensuring security and privacy.

[0883] Users can view the reports sent to their devices and make travel plans or purchasing decisions based on the detailed information. For example, a user can view a detailed report on the Eiffel Tower to plan the time to visit and the sightseeing route, or make a reservation decision based on information on high-end restaurants in Rome.

[0884] Specific examples

[0885] Example 1: Pre-visiting a tourist spot

[0886] 1. The user opens the dedicated application and enters a request: "Tell me about the Eiffel Tower in Paris."

[0887] 2. The server receives this request and collects information from relevant multilingual websites.

[0888] 3. A generative AI model analyzes the collected information and generates a detailed experience report, including up-to-date event information, directions, visitor reviews, and more.

[0889] 4. The server sends the generated report to the user's device using encrypted communication.

[0890] 5. The user views the provided report and uses it to plan their trip.

[0891] Example 2: Previewing a luxury restaurant

[0892] 1. A user visits a website and types, "Please give me the latest information on fine dining restaurants in Rome."

[0893] 2. The server collects information about the restaurant from relevant multilingual websites.

[0894] 3. A generative AI model analyzes the collected information and generates a detailed report, including menu items, chef highlights, and visitor experiences.

[0895] 4. The server sends the generated report to the user's device.

[0896] 5. The user reviews the report and makes a booking decision.

[0897] This invention allows users to efficiently gather detailed information they need and receive it in a visually easy-to-understand report format, which helps them make better decisions when it comes to travel and luxury purchases.

[0898] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0899] Step 1:

[0900] The user accesses a dedicated application or website and enters a request.

[0901] Specifically, the user enters a prompt such as "Tell me about the Eiffel Tower in Paris" into the app's text input field and presses the send button.

[0902] Input: User text input (prompt)

[0903] Output: The request data is sent from the terminal to the server.

[0904] Step 2:

[0905] The server receives the request sent from the terminal.

[0906] The server analyzes the request data and generates a search query to gather the required information.

[0907] Specifically, a search query is generated to collect "information about the Eiffel Tower" based on the request data.

[0908] Input: Request data

[0909] Output: Search query

[0910] Step 3:

[0911] A server uses the generated search query to gather relevant information on the Internet in multiple languages.

[0912] The server uses Google's search API and web crawling tools such as BeautifulSoup and Selenium to gather relevant information in multiple languages.

[0913] Specifically, it scrapes the latest information, visitor reviews, photos, etc. about the Eiffel Tower from English, Japanese, and French websites.

[0914] Input: Search query

[0915] Output: Collected multilingual web data

[0916] Step 4:

[0917] The server passes the collected multilingual information to the generative AI model.

[0918] A generative AI model analyzes the collected information and generates a detailed experience report.

[0919] Specifically, it uses natural language processing algorithms such as GPT-3 and BERT to classify, extract, and summarize important information to create an experience report.

[0920] Input: Collected multilingual web data

[0921] Output: Parsed experience report

[0922] Step 5:

[0923] The server compiles the generated experience reports and generates a user-friendly report in HTML or PDF format.

[0924] Specifically, the system creates a visually easy-to-understand report based on the collected information, including high-resolution photos and charts.

[0925] Input: Parsed experience report

[0926] Output: Report in HTML or PDF format

[0927] Step 6:

[0928] The server sends the generated report to the user's terminal.

[0929] In this case, encrypted communication (HTTPS) is used to ensure data privacy and security.

[0930] Input: Report in HTML or PDF format

[0931] Output: Report sent to user's terminal

[0932] Step 7:

[0933] The user views the provided report on the device.

[0934] Specifically, users can open the report on their smartphone or computer and use it to help plan their trip or make purchasing decisions.

[0935] Input: Report submitted

[0936] Output: Viewed details

[0937] (Application example 1)

[0938] 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."

[0939] Conventional tourist information systems have the problem that it is time-consuming for users to quickly obtain specific information. Furthermore, they are not able to adequately respond to real-time information requests from inside a vehicle. As a result, it is difficult to efficiently gather information while traveling, resulting in an unsatisfactory experience. Therefore, there is a need for a system that allows users to obtain detailed multilingual tourist reports in real time while on the move.

[0940] 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.

[0941] In this invention, the server includes means for a user to input a request, means for the server to receive the request and collect related information in multiple languages, means for a generation AI to analyze the related information in multiple languages ​​and generate a detailed experience report, means for the server to provide the generated experience report to the user, means for the server to receive the request via an interface in the vehicle and for an on-board computer to collect and analyze related information in multiple languages, and means for displaying the generated experience report on an on-board display. This allows users to obtain detailed information in real time even while on the move and to efficiently plan their sightseeing trips.

[0942] "User" refers to any individual or entity using the Service.

[0943] The "means for inputting a request" refers to an interface that a user uses to request information, and includes a touch panel, a voice input system, and the like.

[0944] "Server" refers to a computer system that receives requests from users and collects, processes, and provides relevant information in multiple languages ​​in response to those requests.

[0945] "Means of collecting relevant information in multiple languages" refers to means of collecting necessary information written in multiple languages ​​via the Internet or databases.

[0946] "Generative AI" refers to an artificial intelligence model that analyzes collected multilingual information and generates detailed experience reports based on it.

[0947] An "Experience Report" is a report containing detailed information about a specific subject requested by a user, including photos, reviews, event information, etc.

[0948] "Interfaces inside the vehicle" refers to input and display devices installed inside the vehicle, including touch panels and displays.

[0949] "On-board computer" refers to a computer installed inside a vehicle, and is hardware used to collect, analyze, and display information.

[0950] An "in-vehicle display" is a screen installed inside a vehicle and refers to a device for displaying information to the user.

[0951] The present invention is a system for providing detailed tourist information in real time within a vehicle. The system starts when a user inputs a request through an in-vehicle interface. Specific embodiments for realizing this system are described below.

[0952] 1. User request input

[0953] The user requests detailed information about tourist attractions, restaurants, etc. through an in-car interface. The interface can be a touch panel or a voice input system. For example, the user might say, "I'd like more information about a famous tower in a specific city."

[0954] 2. Server receives requests and collects information

[0955] The server receives user requests and collects relevant information in multiple languages ​​from the internet using web scraping tools (such as Scrapy) and database APIs (such as Google Places API). The server organizes this information and sends it to the generation AI for analysis.

[0956] 3. Information analysis and report generation using generative AI

[0957] The generative AI (e.g., OpenAI's GPT-4) analyzes relevant information in multiple languages ​​and generates a detailed experience report, including the history of the target tourist destination, the latest events, visitor reviews, photos, etc. The generative AI uses prompt sentences such as:

[0958] "Gather up-to-date information about famous towers in the designated cities and prepare a detailed tourist guide report. Please include event information and visitor reviews, among other things."

[0959] "Generate a report containing the history and highlights of famous towers in selected cities, as well as the latest visitor experiences."

[0960] 4. Serving and Displaying Reports by the Server

[0961] The generated experience report is sent to the user's in-vehicle display via the server and displayed. The user can view the report on the dashboard display and use it as a reference for planning their trip. For example, the user can use the provided report to plan their visit schedule and sightseeing route in detail.

[0962] Specific example explanation

[0963] When a user types "I want to know about famous riverside towers" into the car's touch panel, the on-board computer sends this request to the server. The server collects multilingual information from the internet and sends it to the generation AI. The generation AI generates a detailed experience report based on the prompt, "Collect the latest information about famous riverside towers and create a report as a detailed tourist guide. Please include event information and visitor reviews in particular." The generated report is displayed on the car's display, allowing the user to obtain detailed tourist information in real time.

[0964] This system allows users to obtain detailed tourist information in real time while on the move, enabling them to efficiently plan their sightseeing trips.

[0965] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0966] Step 1:

[0967] The user inputs a request via the car's interface (touchscreen or voice input system), for example, "I want more information about a famous tower in a specific city." Once this request is input, it is sent to the car's computer.

[0968] Input: "Detailed information about famous towers in a given city"

[0969] Output: Request data sent to the on-board computer

[0970] Step 2:

[0971] The server receives requests sent from the vehicle's onboard computer and generates queries based on the received requests to collect relevant information in multiple languages ​​from the internet and databases. This information is collected using web scraping tools (such as Scrapy) and database APIs (such as Google Places API).

[0972] Input: Request data sent from the on-board computer

[0973] Output: Collected multilingual related information (HTML data, API response)

[0974] Step 3:

[0975] The server organizes the collected multilingual related information and sends it to a generation AI (e.g., OpenAI's GPT-4). At this time, a prompt for analysis is also generated. The prompt is, "Collect the latest information about famous towers in the specified city and create a report as a detailed tourist guide. Please include event information and visitor reviews in particular."

[0976] Input: Collected multilingual related information

[0977] Output: Prompts and related information sent to the generation AI for analysis.

[0978] Step 4:

[0979] Based on the prompts sent and the collected information, the generative AI analyzes the data and generates a detailed experience report, including the history of the target tourist destination, the latest events, visitor reviews, photos, and more.

[0980] Input: Prompt text, related information in multiple languages

[0981] Output: A detailed experience report (text data, images)

[0982] Step 5:

[0983] The server organizes the generated experience reports and formats them for display on the vehicle's in-vehicle display, using HTML / CSS formatting to create an easy-to-read report.

[0984] Input: Generated experience report (text data, images)

[0985] Output: The report in a viewable format

[0986] Step 6:

[0987] The server then sends the formatted report to the in-vehicle display for display, allowing users to view detailed tourist information on the in-vehicle display while traveling.

[0988] Input: Formatted report

[0989] Output: Detailed experience report displayed on the in-car display

[0990] 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.

[0991] This invention combines a system that collects and analyzes related information in multiple languages ​​in response to requests entered by users, and generates and provides detailed experience reports, with an emotion engine that recognizes the user's emotions. The program processing required to achieve this specific operation is explained below in natural language.

[0992] overview

[0993] When a user requests detailed information about a specific tourist spot, luxury restaurant, luxury hotel, car, or property, the server receives the request and collects related information in multiple languages ​​from the Internet. The emotion engine recognizes the user's emotion and dynamically changes the scope and content of the information collected based on that emotion. The generation AI analyzes the collected information and generates a detailed experience report. This report is sent to the user's device via the server and provided to the user.

[0994] Program processing overview

[0995] Receiving request input and emotion recognition

[0996] A user inputs a request for a specific tourist spot or luxury item through a dedicated app or website. At this time, the emotion engine recognizes the user's emotions from their facial expressions and voice input. For example, if a user inputs "I want to know more about the Eiffel Tower in Paris," it will recognize emotions such as excitement and anticipation.

[0997] Information collection and generation AI analysis

[0998] The server receives requests sent from the device and collects multilingual information from related websites and databases. During this collection process, the emotion engine dynamically changes the scope and content of the information collection based on the user's emotions. For example, if the user is excited, it will prioritize collecting information about particularly inspiring attractions and events. Next, the generation AI analyzes this multilingual information and generates detailed experience reports of tourist spots and high-value items. The reports include the latest events, distinctive photos, visitor reviews, and more.

[0999] Report Generation

[1000] The server organizes the experience reports created by the generative AI and creates an easy-to-read report containing both text and images. Based on the recognition results of the emotion engine, the report is optimized to include expressions and content appropriate to the user's emotions.

[1001] Providing reports

[1002] The server sends the generated report to the user's device and displays it. The user can then view the report and use it to plan their trip or make a purchase. For example, a user can use the detailed report on the Eiffel Tower to plan their visit and sightseeing route.

[1003] Specific examples

[1004] Example 1: Pre-visiting a tourist spot

[1005] 1. A user is planning a trip to Paris, France and wants to know more about the Eiffel Tower.

[1006] 2. The user enters "More information about the Eiffel Tower in Paris" into the app's input form and submits a request. At this time, the emotion engine recognizes the user's expectations.

[1007] 3. The server receives the request and collects information related to the Eiffel Tower in multiple languages ​​from the Internet. The emotion engine prioritizes collecting particularly impressive sights and reviews based on the perceived expectations.

[1008] 4. Generative AI analyzes this information and creates a detailed experience report, including high-resolution photos, tips for visiting, and highlights.

[1009] 5. The server sends the generated report to the user's device and displays it. Based on the results of the emotion engine, it includes expressions that heighten the sense of anticipation.

[1010] 6. The user browses the detailed information provided and makes efficient and satisfying travel plans.

[1011] Example 2: Previewing a luxury restaurant

[1012] 1. A user wants to make a reservation at a highly rated restaurant in Rome, Italy, but wants more information about the restaurant.

[1013] 2. The user enters the name of a specific restaurant into the app to request more information, and the emotion engine recognizes the user's expectations.

[1014] 3. Based on the request, the server collects information about the restaurant in multiple languages ​​from the Internet, analyzing the latest menu, reviews, and the restaurant's atmosphere. The emotion engine prioritizes information that is particularly interesting to the user based on perceived expectations.

[1015] 4. Generative AI uses the collected information to create a detailed report, including menu items, chef highlights, and visitor experiences.

[1016] 5. The server sends the generated report to the user's device and displays it. Depending on the results of the emotion engine, interesting details are highlighted.

[1017] 6. The user can use the provided report to check the restaurant's atmosphere and menu in detail before making a reservation, making the best choice.

[1018] In this way, by combining emotion engines, it becomes possible to provide optimal information tailored to the user's emotions, supporting decision-making when planning trips or purchasing high-value items.

[1019] The processing flow will be explained below.

[1020] Step 1:

[1021] Users input requests for specific tourist attractions or luxury items through a dedicated app or website. For example, a user might input a request such as, "I want more information about the Eiffel Tower in Paris."

[1022] Step 2:

[1023] The emotion engine uses a facial recognition camera and microphone to analyze the user's emotions as they input. For example, it can detect "expectation" or "excitement" from the user's facial expressions and tone of voice.

[1024] Step 3:

[1025] The device sends the request along with the emotion data analyzed by the emotion engine to the server. For example, the request is formatted as "Request content: Eiffel Tower, Emotion: Anticipation."

[1026] Step 4:

[1027] The server analyzes the request and emotion data received from the device and identifies information about the target tourist spot. For example, set "collect information about the Eiffel Tower."

[1028] Step 5:

[1029] The server dynamically changes the priority and scope of information collection based on the results of the emotion engine, and collects related information in multiple languages ​​from the Internet. For example, it may prioritize collecting "impressive sights" and "latest event information" related to the Eiffel Tower.

[1030] Step 6:

[1031] The server preprocesses the collected raw multilingual data and converts it into a format that can be analyzed by the generative AI.

[1032] Step 7:

[1033] Generative AI analyzes the pre-processed data and generates detailed experience reports for tourist attractions and high-ticket items, such as a detailed report including "Eiffel Tower tourist information," "latest events," and "visitor reviews."

[1034] Step 8:

[1035] Based on the results of the emotion engine, the generative AI optimizes the report's expression and content to suit the user's emotions. For example, it highlights recommended attractions for users with "expectations."

[1036] Step 9:

[1037] The server organizes and formats the generated experience reports for presentation to the user, for example, in an easy-to-read layout including text and high-resolution photos.

[1038] Step 10:

[1039] The server sends the generated detailed report to the user's terminal.

[1040] Step 11:

[1041] The device receives the report sent from the server and displays it to the user, for example, providing detailed information about the Eiffel Tower in an app or browser.

[1042] Step 12:

[1043] Users can view the detailed reports provided to help inform their travel planning and purchasing decisions, for example, to plan their visit to the Eiffel Tower.

[1044] Example 2

[1045] 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."

[1046] When collecting information about tourist spots or luxury goods, the information provided may not be suited to the user's emotions, resulting in a poor user experience. Furthermore, conventional information collection systems often collect uniform information in response to user requests, making it difficult to provide information optimized for individual users.

[1047] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for the user to input a request, a means for the terminal to recognize the user's emotion, a means for the server to receive the request and dynamically collect related information in multiple languages ​​based on the emotion information, a means for the generative AI model to analyze the related information in multiple languages ​​and generate a detailed experience report that matches the emotion, and a means for the server to provide the generated experience report to the user. This makes it possible to provide optimal information based on the user's emotion.

[1048] "User" refers to the end user who requests information from the System and views and uses the information provided.

[1049] "Terminal" refers to an electronic device that allows a user to input information and communicate with a server through an application or website.

[1050] "Means for recognizing emotions" refers to the function that enables the device to analyze the user's facial expressions and voice and identify their emotional state.

[1051] "Server" refers to the computer system that receives requests from users, collects necessary information, and creates and provides reports using generative AI models.

[1052] "Means for dynamically collecting related information in multiple languages" refers to a function that allows a server to collect related information provided in different languages ​​from the Internet and adjust the scope and content according to the user's feelings.

[1053] "Generative AI model" refers to an artificial intelligence model that analyzes related multilingual information collected by the server and automatically generates a detailed experience report that matches the user's emotions.

[1054] An "experience report" is a detailed report created by a generative AI model based on collected information, and includes a wide range of information such as tourist destinations and luxury items.

[1055] This invention combines a system that collects and analyzes related information in multiple languages ​​in response to requests entered by users, and generates and provides detailed experience reports with an emotion engine that recognizes the user's emotions.

[1056] System configuration

[1057] Hardware

[1058] Device: An electronic device, such as a smartphone, tablet, or computer, through which a user inputs requests and performs emotion recognition.

[1059] Server: A high-performance computer system that collects and analyzes data, runs generative AI models, and generates reports.

[1060] software

[1061] Dedicated application or website: Software that allows users to enter requests and view related information and reports.

[1062] Emotion engine: Software containing algorithms for recognizing emotions by analyzing a user's facial expressions and voice.

[1063] Generative AI model: An artificial intelligence model for generating detailed experience reports based on collected multilingual related information, for example, using an advanced natural language processing model such as GPT-4.

[1064] Program processing

[1065] A user requests detailed information about a specific tourist spot or luxury item through a dedicated app or website. The device passes the user's facial expressions and voice input to the emotion engine to recognize the user's emotions. For example, if the user requests "I want more information about the Eiffel Tower in Paris," the emotion engine can identify the user's anticipation and excitement.

[1066] The server receives the user's request and emotion information, and collects relevant multilingual information from related websites and databases (e.g., Wikipedia, travel review sites, etc.). Based on the results of the emotion engine, it prioritizes the collection of particularly moving or interesting information.

[1067] The generative AI model analyzes the collected information and generates a detailed experience report that matches the user's emotions. An example prompt might be, "Please tell me the latest information about attractions and events at the Eiffel Tower."

[1068] The server organizes the generated report and sends it to the user's device in an easy-to-read format. The report includes special expressions based on the user's emotions, and the user makes travel plans and purchase decisions based on these.

[1069] Specific examples

[1070] Example 1: Pre-visiting a tourist spot

[1071] A user opens the app, types in "More information about the Eiffel Tower in Paris," and submits the request.

[1072] The device records the user's facial expressions and voice in real time, and an emotion engine identifies "expectations."

[1073] The server receives the request and emotion information and collects information in multiple languages, prioritizing particularly inspiring sights and the latest event information.

[1074] A generative AI model analyzes the collected information and generates a detailed report.

[1075] The server sends the report to the user's terminal, and the user views it to make a visiting plan.

[1076] Example 2: Previewing a luxury restaurant

[1077] A user types the name of a specific restaurant into the app and requests more information.

[1078] The device recognizes the user's emotions, and the emotion engine identifies "excitement."

[1079] The server collects multilingual information based on requests and sentiment information, prioritizing menus and reviews that are particularly interesting.

[1080] The generative AI model generates a detailed report, which the server sends to the user's device.

[1081] Users can view the report and check restaurant details before booking.

[1082] In this way, the system of the present invention can dynamically collect relevant information based on the user's emotions and generate and provide a detailed and personalized experience report to support the user's decision-making.

[1083] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1084] Specific processing steps of the system

[1085] Step 1: Enter your request

[1086] Users use a dedicated app or website to enter information about specific tourist destinations or luxury items.

[1087] Input: User types "I want to know more about the Eiffel Tower."

[1088] Output: The input request text is sent to the system.

[1089] Step 2: Recognize emotions

[1090] The device uses a camera and microphone to record facial expressions and voice to recognize the user's emotions.

[1091] Input: User's facial expression data, voice data.

[1092] Data processing: Emotion recognition algorithms analyze facial and audio data to identify emotional states (e.g., anticipation, excitement).

[1093] Output: The recognized emotion data is sent to the system.

[1094] Step 3: Receiving requests and emotions

[1095] The server receives the user's request text and emotion data.

[1096] Input: User request text, emotion data.

[1097] Data operation: Log a combination of request text and emotion data.

[1098] Output: The combined data is passed on to the next process.

[1099] Step 4: Gather information

[1100] The server collects relevant information from the internet in multiple languages, and dynamically adjusts the scope and content of the information collected based on emotional data during the collection process.

[1101] Input: Request text, emotion data.

[1102] Data processing: Scrip multilingual information from relevant websites and databases to prioritize sentiment-driven search terms.

[1103] Output: Collected multilingual information data.

[1104] Step 5: Analyze the information

[1105] The server uses a generative AI model to analyze the collected information and generate a detailed experience report.

[1106] Input: Collected multilingual information data.

[1107] Prompt generation: The generative AI model generates prompts such as, "Please tell me the latest information about attractions and events at the Eiffel Tower."

[1108] Data calculation: A generative AI model analyzes based on prompts and generates a detailed experience report.

[1109] Output: The generated experience report.

[1110] Step 6: Organize your reports

[1111] The server organizes the generated reports, formats them into an easy-to-read format, and adds the most appropriate expressions based on the results of the emotion engine.

[1112] Input: Generated experience reports, sentiment data.

[1113] Data manipulation: Formatting reports containing text and images. Adding sentiment-based emphasis.

[1114] Output: A nicely formatted report.

[1115] Step 7: Submit the report

[1116] The server sends the generated report to the user's terminal.

[1117] Input: The formatted report.

[1118] Data calculation: Sends data to the user's terminal according to the report transmission protocol.

[1119] Output: Report data ready for viewing on the user's device.

[1120] Step 8: View the report

[1121] The user views the provided report on the device.

[1122] Input: Submitted report data.

[1123] Data calculation: The rendering process for displaying the report.

[1124] Output: The report is presented in a user-readable format.

[1125] These steps will result in a system that collects and analyzes optimal information based on the user's emotions, and generates and provides a detailed experience report.

[1126] (Application example 2)

[1127] 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."

[1128] Conventional information gathering systems are limited to providing simple information based on user input requests, and do not provide customized information based on the user's emotions or interests. As a result, it is difficult for users to obtain the specific information they need that is relevant to their emotions, and they are unable to support highly satisfying decision-making. It is also difficult to effectively collect and analyze information in multiple languages.

[1129] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1130] In this invention, the server includes a means for a user to input a request, a means for the server to receive the request and collect related information in multiple languages, a means for an emotion recognition engine to recognize the user's emotion and dynamically change the range and content of the information collection based on the emotion, a means for a generation AI to analyze the related information in multiple languages ​​and generate a detailed experience report, and a means for the server to provide the generated experience report to the user. This makes it possible to provide detailed information customized according to the user's emotion, thereby supporting decision-making with a high level of satisfaction.

[1131] A "user" is an end-user who utilizes the system of the present invention to request information.

[1132] A "request" is a request or inquiry made by a user for specific information.

[1133] "Server" means a computer system that receives requests from users, collects and analyzes relevant information based on those requests, and provides generated reports.

[1134] "Multilingual" means including multiple different languages, such as collecting and analyzing information in English, Japanese, and French.

[1135] "Related information" is data related to tourist spots, luxury restaurants, luxury hotels, products, etc., collected based on user requests.

[1136] An "emotion recognition engine" is a component that has the function of detecting and analyzing emotions from the user's facial expressions and voice input.

[1137] "Emotion" refers to the psychological state expressed when a user wants to know specific information, and includes, for example, a sense of expectation, excitement, etc.

[1138] "Generative AI" refers to an artificial intelligence model that analyzes collected multilingual related information and generates detailed experience reports to provide to users.

[1139] An "Experience Report" is a detailed report generated based on relevant information, including up-to-date event information, visitor reviews, and high-resolution photos.

[1140] A "terminal" is a device used by a user to input a request, such as a smartphone, tablet, or computer.

[1141] "Dynamic change" means that the scope and content of information to be collected can be flexibly adjusted according to the user's emotions and real-time situations.

[1142] This invention relates to a system that collects and analyzes related information in multiple languages ​​based on user requests, and generates and provides detailed experience reports. This system is combined with an emotion recognition engine that recognizes the user's emotions, enabling the provision of information according to the user's emotions.

[1143] Overall system configuration

[1144] The system consists of a terminal that receives user input requests, a server that receives the requests and collects related information, an emotion recognition engine that recognizes the user's emotions, a generation AI that analyzes the collected information and generates a detailed experience report, and a means for providing the report to the terminal.

[1145] Program processing overview

[1146] User request input

[1147] Users use devices such as smartphones or tablets to input requests about specific tourist spots or luxury items. At this time, the emotion recognition engine analyzes the user's facial expressions and voice input to recognize their emotions. For example, a user can input a request such as, "I'd like to know product reviews of the latest smartphones."

[1148] Server information collection

[1149] The request sent from the device is received by the server, which automatically collects multilingual information from related websites and databases. During this collection process, the emotion recognition engine dynamically adjusts the scope and content of the information collection based on the user's emotions.

[1150] Generative AI analysis and report generation

[1151] The collected multilingual related information is analyzed by the Generative AI, which then generates a detailed experience report of tourist attractions and luxury products. Based on the results of the emotion recognition engine, the report is optimized to include expressions and content that match the user's emotions. The report includes the latest event information, high-resolution photos, visitor reviews, and more.

[1152] Providing reports

[1153] The generated experience report is sent from the server to the user's device and displayed. The user can view detailed information and use it as a reference for travel planning and purchasing decisions.

[1154] Specific examples

[1155] For example, if a user requests, "I'm traveling to Paris, France, and I'd like detailed information about the Eiffel Tower," the emotion recognition engine will recognize the user's expectations. The server will collect multilingual information about the Eiffel Tower, and the generation AI will analyze it to generate a detailed experience report, including high-resolution photos, highlights, and the latest event information.

[1156] Prompt Sentence Examples

[1157] "I'd like to learn more about the latest smartphones. Product reviews, photos, feature recommendations, etc."

[1158] In this way, the system of the present invention can provide information customized according to the user's emotions, thereby supporting decision-making with a high level of satisfaction.

[1159] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1160] Step 1:

[1161] A user inputs a request using a device such as a smartphone or tablet. The input request is for specific information, and the user inputs a prompt such as, "I am traveling to Paris, France, and would like detailed information about the Eiffel Tower." This becomes the input data on the device.

[1162] Step 2:

[1163] The emotion recognition engine recognizes the user's emotions based on the user's facial expressions and voice input collected by the device. The input data is the user's facial expression data and voice data, and the output data is the user's emotional state (for example, anticipation or excitement). The emotion recognition engine analyzes this data to identify the user's current emotion.

[1164] Step 3:

[1165] The request and recognized emotional information are sent from the device to the server. The input data at this time is the user's input request and emotional information, which the server receives. Based on the request and emotional information, the server collects multilingual information from related websites and databases.

[1166] Step 4:

[1167] The server dynamically adjusts the scope and content of the information it collects based on the results of the emotion recognition engine. Specifically, if the user expresses anticipation, it prioritizes collecting information about particularly inspiring attractions and events. The input data for this process is information from the websites and databases to be collected, and the output data is related information corresponding to the emotion.

[1168] Step 5:

[1169] The collected multilingual information is analyzed by a generation AI, which uses this input data to generate detailed experience reports of tourist spots and luxury products. The output data is a customized, detailed experience report based on related information.

[1170] Step 6:

[1171] The server receives the generated experience report and optimizes it to include expressions and content appropriate to the user's emotions. This optimization process takes into account the results of the emotion recognition engine. The optimized experience report is the output data.

[1172] Step 7:

[1173] The server finally sends the generated and optimized experience report to the user's device, which receives it and displays it to the user. The user can then view the detailed experience report provided and use it as a reference for travel planning and purchasing decisions, thereby enabling the user to make a satisfactory decision.

[1174] 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.

[1175] 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.

[1176] 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.

[1177] [Fourth embodiment]

[1178] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1179] 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.

[1180] 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).

[1181] 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.

[1182] 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.

[1183] 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).

[1184] 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.

[1185] 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.

[1186] 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.

[1187] 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.

[1188] 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.

[1189] 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.

[1190] 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."

[1191] This invention is a system that collects and analyzes related information in multiple languages ​​in response to requests entered by users, and generates and provides detailed experience reports. The program processing required to realize the operation of this system is explained below in natural language.

[1192] overview

[1193] When a user requests detailed information about a specific tourist spot, luxury restaurant, luxury hotel, car, or property, the server receives the request and collects related information in multiple languages ​​from the Internet. The generative AI then analyzes the collected information and creates a detailed experience report. This report is then sent to the user's device via the server and provided to the user.

[1194] Program processing overview

[1195] Receiving request input

[1196] Users input requests for specific tourist attractions or luxury items through a dedicated app or website. For example, a user might request, "I want more information about the Eiffel Tower in Paris."

[1197] Information collection and generation AI analysis

[1198] The server receives requests sent from devices and collects multilingual information from relevant websites and databases. For example, it collects information related to the "Eiffel Tower" in English, Japanese, and French from the internet. Next, the generative AI analyzes this multilingual information and generates a detailed experience report of tourist attractions and high-ticket items. The report includes recent events, featured photos, visitor reviews, and more.

[1199] Report Generation

[1200] The server organizes the experience reports created by the generative AI and creates an easy-to-read report containing text and images.

[1201] Providing reports

[1202] The server then sends the generated report to the user's device for display. The user can then view the report and use it to help plan their trip or make a purchase. For example, a user can use a detailed report about the Eiffel Tower to plan their visit and sightseeing route.

[1203] Specific examples

[1204] Example 1: Pre-visiting a tourist spot

[1205] 1. A user is planning a trip to Paris, France and wants to know more about the Eiffel Tower.

[1206] 2. The user fills in the app's input form with "More information about the Eiffel Tower in Paris" and submits the request.

[1207] 3. The server receives this request and collects information related to the Eiffel Tower (e.g., latest events, directions, visitor reviews) from across the Internet in multiple languages.

[1208] 4. Generative AI analyzes the collected information and creates a detailed experience report, including high-resolution photos, tips for visiting, and highlights.

[1209] 5. The server sends the generated report to the user's terminal for display.

[1210] 6. The user browses the detailed information provided and makes efficient and satisfying travel plans.

[1211] Example 2: Previewing a luxury restaurant

[1212] 1. A user wants to make a reservation at a highly rated restaurant in Rome, Italy, but wants more information about the restaurant.

[1213] 2. The user enters the name of a specific restaurant into the app and requests more information.

[1214] 3. Based on the request, the server collects information about the restaurant in multiple languages ​​from the Internet and analyzes the latest menu, reviews, and the atmosphere of the restaurant.

[1215] 4. Generative AI creates a detailed report based on the collected information, including menu items, chef characteristics, and visitor experiences.

[1216] 5. The server sends the generated report to the user's terminal for display.

[1217] 6. Users can avoid mistakes by referring to the provided report and checking the restaurant's atmosphere and menu in detail before making a reservation.

[1218] In this way, the present invention provides powerful support for users to efficiently obtain detailed information and make optimal travel plans and purchasing choices.

[1219] The processing flow will be explained below.

[1220] Step 1:

[1221] A user enters a request for a specific tourist attraction or luxury item through a dedicated app or website, for example, "I'd like more information about the Eiffel Tower in Paris."

[1222] Step 2:

[1223] The device takes the user's input and formats it into an appropriate form, for example, "Sightseeing spot: Eiffel Tower."

[1224] Step 3:

[1225] The terminal sends the formatted request over the network to the server.

[1226] Step 4:

[1227] The server analyzes the request received from the device and identifies the target information. For example, it sets a task to "collect information related to the Eiffel Tower."

[1228] Step 5:

[1229] The server generates queries to gather relevant information from multilingual sources on the Net (e.g., English, Japanese, and French websites).

[1230] Step 6:

[1231] The server queries and gathers multilingual information from relevant websites and databases, such as "latest event information and visitor reviews about the Eiffel Tower."

[1232] Step 7:

[1233] The server preprocesses the collected raw multilingual data and converts it into a format that can be analyzed by the generative AI.

[1234] Step 8:

[1235] Generative AI analyzes the pre-processed data and generates detailed experience reports for tourist attractions and high-value items, such as the Eiffel Tower, including tourist information, highlights, and things to watch out for.

[1236] Step 9:

[1237] Generative AI organizes the generated report, arranging text and high-resolution images in an easy-to-read layout.

[1238] Step 10:

[1239] The server transmits the generated experience report to the terminal for providing to the user.

[1240] Step 11:

[1241] The terminal receives the report sent from the server.

[1242] Step 12:

[1243] The device then displays the received report to the user, for example providing detailed information about the Eiffel Tower in an app or browser.

[1244] Step 13:

[1245] Users can view the detailed reports provided to help them plan their trips and make purchasing decisions, for example, by planning their visit to the Eiffel Tower.

[1246] Example 1

[1247] 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."

[1248] Today's users want to quickly obtain detailed information when planning a trip or purchasing luxury goods. However, traditional information gathering methods lacked multilingual support and detailed analysis, making it difficult for users to obtain the precise, multifaceted information they needed. Furthermore, there was a lack of a way to efficiently analyze the information requested by users and provide it in a visually easy-to-understand report format. This left users unable to make informed decisions and making efficient plans.

[1249] 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.

[1250] In this invention, the server includes a means for a user to input a request, a means for collecting related information in multiple languages, a means for a generative AI model to analyze the collected related information in multiple languages ​​and generate a detailed experience report, and a means for transmitting the report to the user's device via encrypted communication. This makes it possible to quickly and multifacetedly collect and analyze the information requested by the user and provide it in a visually easy-to-understand report format.

[1251] The "means for a user to input a request" refers to an interface that allows a user to request specific information using an input device, such as an input form on a dedicated application or website.

[1252] A "server" is a computer system that receives user requests and processes related information. It communicates with user terminals via a network.

[1253] "Means for collecting relevant information in multiple languages" refers to a system that uses web crawling tools and APIs to collect necessary data from multiple language sources on the Internet.

[1254] A "generative AI model" is an algorithm that analyzes collected information and generates a detailed report to provide to users. Examples include generative models such as GPT-3 and BERT.

[1255] The "means of analysis" refers to the process by which the generative AI model categorizes, extracts, and summarizes the information collected. Natural language processing algorithms are used.

[1256] "Means for generating experience reports" refers to the process of creating a report in a visually understandable format based on the information analyzed by the generative AI model, including output in HTML and PDF formats.

[1257] The "means for providing to the user" refers to a communication means for providing the generated experience report to the user, including sending the report to the user's terminal using an encrypted communication protocol (HTTPS).

[1258] "Terminal" refers to a device on which a user inputs requests and receives and views reports sent from the server. For example, this applies to a smartphone or a PC.

[1259] "Encrypted communication" refers to a protocol used to securely transmit and receive data. Data is encrypted before transmission to protect user privacy.

[1260] A "dedicated application" is a specific piece of software that allows users to input requests. It is installed on a smartphone or tablet and is specialized for a specific function.

[1261] A "website" is an online interface through which users enter requests over the Internet, accessed via a browser.

[1262] "Natural language processing algorithms" are programs that analyze text data and perform tasks such as semantic analysis, summarization, and classification. They are included in generative AI models.

[1263] "HTML" is a markup language for building web pages. It is used to present the experience report in a visually understandable format.

[1264] "PDF" is a file format for viewing documents on various devices. It is used to save the generated experience report.

[1265] In this invention, a user uses a dedicated application or website to input a request. The dedicated application is installed on a smartphone or tablet and has an interactive UI. The user inputs a prompt, for example, "I want more information about the Eiffel Tower in Paris." The website allows the user to input the request via a browser over the Internet.

[1266] When a user inputs a request, the device receives the request and sends it to the server. The server analyzes the request and starts a process to collect relevant information. Specifically, the server uses web crawling tools such as Google's search API, BeautifulSoup, and Selenium to gather the necessary data from multilingual sources.

[1267] The collected data is analyzed by a generative AI model, which uses natural language processing algorithms such as GPT-3 and BERT. The generative AI model classifies the collected information in multiple languages, extracts important information, summarizes it, and generates a detailed experience report to provide to users. This report includes related photos, event information, directions, and evaluation reviews.

[1268] The generated report is generated by the server in HTML or PDF format, allowing users to receive information in a visually easy-to-understand format. The report is sent to the user's device via encrypted communication (HTTPS), ensuring security and privacy.

[1269] Users can view the reports sent to their devices and make travel plans or purchasing decisions based on the detailed information. For example, a user can view a detailed report on the Eiffel Tower to plan the time to visit and the sightseeing route, or make a reservation decision based on information on high-end restaurants in Rome.

[1270] Specific examples

[1271] Example 1: Pre-visiting a tourist spot

[1272] 1. The user opens the dedicated application and enters a request: "Tell me about the Eiffel Tower in Paris."

[1273] 2. The server receives this request and collects information from relevant multilingual websites.

[1274] 3. A generative AI model analyzes the collected information and generates a detailed experience report, including up-to-date event information, directions, visitor reviews, and more.

[1275] 4. The server sends the generated report to the user's device using encrypted communication.

[1276] 5. The user views the provided report and uses it to plan their trip.

[1277] Example 2: Previewing a luxury restaurant

[1278] 1. A user visits a website and types, "Please give me the latest information on fine dining restaurants in Rome."

[1279] 2. The server collects information about the restaurant from relevant multilingual websites.

[1280] 3. A generative AI model analyzes the collected information and generates a detailed report, including menu items, chef highlights, and visitor experiences.

[1281] 4. The server sends the generated report to the user's device.

[1282] 5. The user reviews the report and makes a booking decision.

[1283] This invention allows users to efficiently gather detailed information they need and receive it in a visually easy-to-understand report format, which helps them make better decisions when it comes to travel and luxury purchases.

[1284] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1285] Step 1:

[1286] The user accesses a dedicated application or website and enters a request.

[1287] Specifically, the user enters a prompt such as "Tell me about the Eiffel Tower in Paris" into the app's text input field and presses the send button.

[1288] Input: User text input (prompt)

[1289] Output: The request data is sent from the terminal to the server.

[1290] Step 2:

[1291] The server receives the request sent from the terminal.

[1292] The server analyzes the request data and generates a search query to gather the required information.

[1293] Specifically, a search query is generated to collect "information about the Eiffel Tower" based on the request data.

[1294] Input: Request data

[1295] Output: Search query

[1296] Step 3:

[1297] A server uses the generated search query to gather relevant information on the Internet in multiple languages.

[1298] The server uses Google's search API and web crawling tools such as BeautifulSoup and Selenium to gather relevant information in multiple languages.

[1299] Specifically, it scrapes the latest information, visitor reviews, photos, etc. about the Eiffel Tower from English, Japanese, and French websites.

[1300] Input: Search query

[1301] Output: Collected multilingual web data

[1302] Step 4:

[1303] The server passes the collected multilingual information to the generative AI model.

[1304] A generative AI model analyzes the collected information and generates a detailed experience report.

[1305] Specifically, it uses natural language processing algorithms such as GPT-3 and BERT to classify, extract, and summarize important information to create an experience report.

[1306] Input: Collected multilingual web data

[1307] Output: Parsed experience report

[1308] Step 5:

[1309] The server compiles the generated experience reports and generates a user-friendly report in HTML or PDF format.

[1310] Specifically, the system creates a visually easy-to-understand report based on the collected information, including high-resolution photos and charts.

[1311] Input: Parsed experience report

[1312] Output: Report in HTML or PDF format

[1313] Step 6:

[1314] The server sends the generated report to the user's terminal.

[1315] In this case, encrypted communication (HTTPS) is used to ensure data privacy and security.

[1316] Input: Report in HTML or PDF format

[1317] Output: Report sent to user's terminal

[1318] Step 7:

[1319] The user views the provided report on the device.

[1320] Specifically, users can open the report on their smartphone or computer and use it to help plan their trip or make purchasing decisions.

[1321] Input: Report submitted

[1322] Output: Viewed details

[1323] (Application example 1)

[1324] 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."

[1325] Conventional tourist information systems have the problem that it is time-consuming for users to quickly obtain specific information. Furthermore, they are not able to adequately respond to real-time information requests from inside a vehicle. As a result, it is difficult to efficiently gather information while traveling, resulting in an unsatisfactory experience. Therefore, there is a need for a system that allows users to obtain detailed multilingual tourist reports in real time while on the move.

[1326] 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.

[1327] In this invention, the server includes means for a user to input a request, means for the server to receive the request and collect related information in multiple languages, means for a generation AI to analyze the related information in multiple languages ​​and generate a detailed experience report, means for the server to provide the generated experience report to the user, means for the server to receive the request via an interface in the vehicle and for an on-board computer to collect and analyze related information in multiple languages, and means for displaying the generated experience report on an on-board display. This allows users to obtain detailed information in real time even while on the move and to efficiently plan their sightseeing trips.

[1328] "User" refers to any individual or entity using the Service.

[1329] The "means for inputting a request" refers to an interface that a user uses to request information, and includes a touch panel, a voice input system, and the like.

[1330] "Server" refers to a computer system that receives requests from users and collects, processes, and provides relevant information in multiple languages ​​in response to those requests.

[1331] "Means of collecting relevant information in multiple languages" refers to means of collecting necessary information written in multiple languages ​​via the Internet or databases.

[1332] "Generative AI" refers to an artificial intelligence model that analyzes collected multilingual information and generates detailed experience reports based on it.

[1333] An "Experience Report" is a report containing detailed information about a specific subject requested by a user, including photos, reviews, event information, etc.

[1334] "Interfaces inside the vehicle" refers to input and display devices installed inside the vehicle, including touch panels and displays.

[1335] "On-board computer" refers to a computer installed inside a vehicle, and is hardware used to collect, analyze, and display information.

[1336] An "in-vehicle display" is a screen installed inside a vehicle and refers to a device for displaying information to the user.

[1337] The present invention is a system for providing detailed tourist information in real time within a vehicle. The system starts when a user inputs a request through an in-vehicle interface. Specific embodiments for realizing this system are described below.

[1338] 1. User request input

[1339] The user requests detailed information about tourist attractions, restaurants, etc. through an in-car interface. The interface can be a touch panel or a voice input system. For example, the user might say, "I'd like more information about a famous tower in a specific city."

[1340] 2. Server receives requests and collects information

[1341] The server receives user requests and collects relevant information in multiple languages ​​from the internet using web scraping tools (such as Scrapy) and database APIs (such as Google Places API). The server organizes this information and sends it to the generation AI for analysis.

[1342] 3. Information analysis and report generation using generative AI

[1343] The generative AI (e.g., OpenAI's GPT-4) analyzes relevant information in multiple languages ​​and generates a detailed experience report, including the history of the target tourist destination, the latest events, visitor reviews, photos, etc. The generative AI uses prompt sentences such as:

[1344] "Gather up-to-date information about famous towers in the designated cities and prepare a detailed tourist guide report. Please include event information and visitor reviews, among other things."

[1345] "Generate a report containing the history and highlights of famous towers in selected cities, as well as the latest visitor experiences."

[1346] 4. Serving and Displaying Reports by the Server

[1347] The generated experience report is sent to the user's in-vehicle display via the server and displayed. The user can view the report on the dashboard display and use it as a reference for planning their trip. For example, the user can use the provided report to plan their visit schedule and sightseeing route in detail.

[1348] Specific example explanation

[1349] When a user types "I want to know about famous riverside towers" into the car's touch panel, the on-board computer sends this request to the server. The server collects multilingual information from the internet and sends it to the generation AI. The generation AI generates a detailed experience report based on the prompt, "Collect the latest information about famous riverside towers and create a report as a detailed tourist guide. Please include event information and visitor reviews in particular." The generated report is displayed on the car's display, allowing the user to obtain detailed tourist information in real time.

[1350] This system allows users to obtain detailed tourist information in real time while on the move, enabling them to efficiently plan their sightseeing trips.

[1351] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1352] Step 1:

[1353] The user inputs a request via the car's interface (touchscreen or voice input system), for example, "I want more information about a famous tower in a specific city." Once this request is input, it is sent to the car's computer.

[1354] Input: "Detailed information about famous towers in a given city"

[1355] Output: Request data sent to the on-board computer

[1356] Step 2:

[1357] The server receives requests sent from the vehicle's onboard computer and generates queries based on the received requests to collect relevant information in multiple languages ​​from the internet and databases. This information is collected using web scraping tools (such as Scrapy) and database APIs (such as Google Places API).

[1358] Input: Request data sent from the on-board computer

[1359] Output: Collected multilingual related information (HTML data, API response)

[1360] Step 3:

[1361] The server organizes the collected multilingual related information and sends it to a generation AI (e.g., OpenAI's GPT-4). At this time, a prompt for analysis is also generated. The prompt is, "Collect the latest information about famous towers in the specified city and create a report as a detailed tourist guide. Please include event information and visitor reviews in particular."

[1362] Input: Collected multilingual related information

[1363] Output: Prompts and related information sent to the generation AI for analysis.

[1364] Step 4:

[1365] Based on the prompts sent and the collected information, the generative AI analyzes the data and generates a detailed experience report, including the history of the target tourist destination, the latest events, visitor reviews, photos, and more.

[1366] Input: Prompt text, related information in multiple languages

[1367] Output: A detailed experience report (text data, images)

[1368] Step 5:

[1369] The server organizes the generated experience reports and formats them for display on the vehicle's in-vehicle display, using HTML / CSS formatting to create an easy-to-read report.

[1370] Input: Generated experience report (text data, images)

[1371] Output: The report in a viewable format

[1372] Step 6:

[1373] The server then sends the formatted report to the in-vehicle display for display, allowing users to view detailed tourist information on the in-vehicle display while traveling.

[1374] Input: Formatted report

[1375] Output: Detailed experience report displayed on the in-car display

[1376] 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.

[1377] This invention combines a system that collects and analyzes related information in multiple languages ​​in response to requests entered by users, and generates and provides detailed experience reports, with an emotion engine that recognizes the user's emotions. The program processing required to achieve this specific operation is explained below in natural language.

[1378] overview

[1379] When a user requests detailed information about a specific tourist spot, luxury restaurant, luxury hotel, car, or property, the server receives the request and collects related information in multiple languages ​​from the Internet. The emotion engine recognizes the user's emotion and dynamically changes the scope and content of the information collected based on that emotion. The generation AI analyzes the collected information and generates a detailed experience report. This report is sent to the user's device via the server and provided to the user.

[1380] Program processing overview

[1381] Receiving request input and emotion recognition

[1382] A user inputs a request for a specific tourist spot or luxury item through a dedicated app or website. At this time, the emotion engine recognizes the user's emotions from their facial expressions and voice input. For example, if a user inputs "I want to know more about the Eiffel Tower in Paris," it will recognize emotions such as excitement and anticipation.

[1383] Information collection and generation AI analysis

[1384] The server receives requests sent from the device and collects multilingual information from related websites and databases. During this collection process, the emotion engine dynamically changes the scope and content of the information collection based on the user's emotions. For example, if the user is excited, it will prioritize collecting information about particularly inspiring attractions and events. Next, the generation AI analyzes this multilingual information and generates detailed experience reports of tourist spots and high-value items. The reports include the latest events, distinctive photos, visitor reviews, and more.

[1385] Report Generation

[1386] The server organizes the experience reports created by the generative AI and creates an easy-to-read report containing both text and images. Based on the recognition results of the emotion engine, the report is optimized to include expressions and content appropriate to the user's emotions.

[1387] Providing reports

[1388] The server sends the generated report to the user's device and displays it. The user can then view the report and use it to plan their trip or make a purchase. For example, a user can use the detailed report on the Eiffel Tower to plan their visit and sightseeing route.

[1389] Specific examples

[1390] Example 1: Pre-visiting a tourist spot

[1391] 1. A user is planning a trip to Paris, France and wants to know more about the Eiffel Tower.

[1392] 2. The user enters "More information about the Eiffel Tower in Paris" into the app's input form and submits a request. At this time, the emotion engine recognizes the user's expectations.

[1393] 3. The server receives the request and collects information related to the Eiffel Tower in multiple languages ​​from the Internet. The emotion engine prioritizes collecting particularly impressive sights and reviews based on the perceived expectations.

[1394] 4. Generative AI analyzes this information and creates a detailed experience report, including high-resolution photos, tips for visiting, and highlights.

[1395] 5. The server sends the generated report to the user's device and displays it. Based on the results of the emotion engine, it includes expressions that heighten the sense of anticipation.

[1396] 6. The user browses the detailed information provided and makes efficient and satisfying travel plans.

[1397] Example 2: Previewing a luxury restaurant

[1398] 1. A user wants to make a reservation at a highly rated restaurant in Rome, Italy, but wants more information about the restaurant.

[1399] 2. The user enters the name of a specific restaurant into the app to request more information, and the emotion engine recognizes the user's expectations.

[1400] 3. Based on the request, the server collects information about the restaurant in multiple languages ​​from the Internet, analyzing the latest menu, reviews, and the restaurant's atmosphere. The emotion engine prioritizes information that is particularly interesting to the user based on perceived expectations.

[1401] 4. Generative AI uses the collected information to create a detailed report, including menu items, chef highlights, and visitor experiences.

[1402] 5. The server sends the generated report to the user's device and displays it. Depending on the results of the emotion engine, interesting details are highlighted.

[1403] 6. The user can use the provided report to check the restaurant's atmosphere and menu in detail before making a reservation, making the best choice.

[1404] In this way, by combining emotion engines, it becomes possible to provide optimal information tailored to the user's emotions, supporting decision-making when planning trips or purchasing high-value items.

[1405] The processing flow will be explained below.

[1406] Step 1:

[1407] Users input requests for specific tourist attractions or luxury items through a dedicated app or website. For example, a user might input a request such as, "I want more information about the Eiffel Tower in Paris."

[1408] Step 2:

[1409] The emotion engine uses a facial recognition camera and microphone to analyze the user's emotions as they input. For example, it can detect "expectation" or "excitement" from the user's facial expressions and tone of voice.

[1410] Step 3:

[1411] The device sends the request along with the emotion data analyzed by the emotion engine to the server. For example, the request is formatted as "Request content: Eiffel Tower, Emotion: Anticipation."

[1412] Step 4:

[1413] The server analyzes the request and emotion data received from the device and identifies information about the target tourist spot. For example, set "collect information about the Eiffel Tower."

[1414] Step 5:

[1415] The server dynamically changes the priority and scope of information collection based on the results of the emotion engine, and collects related information in multiple languages ​​from the Internet. For example, it may prioritize collecting "impressive sights" and "latest event information" related to the Eiffel Tower.

[1416] Step 6:

[1417] The server preprocesses the collected raw multilingual data and converts it into a format that can be analyzed by the generative AI.

[1418] Step 7:

[1419] Generative AI analyzes the pre-processed data and generates detailed experience reports for tourist attractions and high-ticket items, such as a detailed report including "Eiffel Tower tourist information," "latest events," and "visitor reviews."

[1420] Step 8:

[1421] Based on the results of the emotion engine, the generative AI optimizes the report's expression and content to suit the user's emotions. For example, it highlights recommended attractions for users with "expectations."

[1422] Step 9:

[1423] The server organizes and formats the generated experience reports for presentation to the user, for example, in an easy-to-read layout including text and high-resolution photos.

[1424] Step 10:

[1425] The server sends the generated detailed report to the user's terminal.

[1426] Step 11:

[1427] The device receives the report sent from the server and displays it to the user, for example, providing detailed information about the Eiffel Tower in an app or browser.

[1428] Step 12:

[1429] Users can view the detailed reports provided to help inform their travel planning and purchasing decisions, for example, to plan their visit to the Eiffel Tower.

[1430] Example 2

[1431] 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."

[1432] When collecting information about tourist spots or luxury goods, the information provided may not be suited to the user's emotions, resulting in a poor user experience. Furthermore, conventional information collection systems often collect uniform information in response to user requests, making it difficult to provide information optimized for individual users.

[1433] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for the user to input a request, a means for the terminal to recognize the user's emotion, a means for the server to receive the request and dynamically collect related information in multiple languages ​​based on the emotion information, a means for the generative AI model to analyze the related information in multiple languages ​​and generate a detailed experience report that matches the emotion, and a means for the server to provide the generated experience report to the user. This makes it possible to provide optimal information based on the user's emotion.

[1434] "User" refers to the end user who requests information from the System and views and uses the information provided.

[1435] "Terminal" refers to an electronic device that allows a user to input information and communicate with a server through an application or website.

[1436] "Means for recognizing emotions" refers to the function that enables the device to analyze the user's facial expressions and voice and identify their emotional state.

[1437] "Server" refers to the computer system that receives requests from users, collects necessary information, and creates and provides reports using generative AI models.

[1438] "Means for dynamically collecting related information in multiple languages" refers to a function that allows a server to collect related information provided in different languages ​​from the Internet and adjust the scope and content according to the user's feelings.

[1439] "Generative AI model" refers to an artificial intelligence model that analyzes related multilingual information collected by the server and automatically generates a detailed experience report that matches the user's emotions.

[1440] An "experience report" is a detailed report created by a generative AI model based on collected information, and includes a wide range of information such as tourist destinations and luxury items.

[1441] This invention combines a system that collects and analyzes related information in multiple languages ​​in response to requests entered by users, and generates and provides detailed experience reports with an emotion engine that recognizes the user's emotions.

[1442] System configuration

[1443] Hardware

[1444] Device: An electronic device, such as a smartphone, tablet, or computer, through which a user inputs requests and performs emotion recognition.

[1445] Server: A high-performance computer system that collects and analyzes data, runs generative AI models, and generates reports.

[1446] software

[1447] Dedicated application or website: Software that allows users to enter requests and view related information and reports.

[1448] Emotion engine: Software containing algorithms for recognizing emotions by analyzing a user's facial expressions and voice.

[1449] Generative AI model: An artificial intelligence model for generating detailed experience reports based on collected multilingual related information, for example, using an advanced natural language processing model such as GPT-4.

[1450] Program processing

[1451] A user requests detailed information about a specific tourist spot or luxury item through a dedicated app or website. The device passes the user's facial expressions and voice input to the emotion engine to recognize the user's emotions. For example, if the user requests "I want more information about the Eiffel Tower in Paris," the emotion engine can identify the user's anticipation and excitement.

[1452] The server receives the user's request and emotion information, and collects relevant multilingual information from related websites and databases (e.g., Wikipedia, travel review sites, etc.). Based on the results of the emotion engine, it prioritizes the collection of particularly moving or interesting information.

[1453] The generative AI model analyzes the collected information and generates a detailed experience report that matches the user's emotions. An example prompt might be, "Please tell me the latest information about attractions and events at the Eiffel Tower."

[1454] The server organizes the generated report and sends it to the user's device in an easy-to-read format. The report includes special expressions based on the user's emotions, and the user makes travel plans and purchase decisions based on these.

[1455] Specific examples

[1456] Example 1: Pre-visiting a tourist spot

[1457] A user opens the app, types in "More information about the Eiffel Tower in Paris," and submits the request.

[1458] The device records the user's facial expressions and voice in real time, and an emotion engine identifies "expectations."

[1459] The server receives the request and emotion information and collects information in multiple languages, prioritizing particularly inspiring sights and the latest event information.

[1460] A generative AI model analyzes the collected information and generates a detailed report.

[1461] The server sends the report to the user's terminal, and the user views it to make a visiting plan.

[1462] Example 2: Previewing a luxury restaurant

[1463] A user types the name of a specific restaurant into the app and requests more information.

[1464] The device recognizes the user's emotions, and the emotion engine identifies "excitement."

[1465] The server collects multilingual information based on requests and sentiment information, prioritizing menus and reviews that are particularly interesting.

[1466] The generative AI model generates a detailed report, which the server sends to the user's device.

[1467] Users can view the report and check restaurant details before booking.

[1468] In this way, the system of the present invention can dynamically collect relevant information based on the user's emotions and generate and provide a detailed and personalized experience report to support the user's decision-making.

[1469] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1470] Specific processing steps of the system

[1471] Step 1: Enter your request

[1472] Users use a dedicated app or website to enter information about specific tourist destinations or luxury items.

[1473] Input: User types "I want to know more about the Eiffel Tower."

[1474] Output: The input request text is sent to the system.

[1475] Step 2: Recognize emotions

[1476] The device uses a camera and microphone to record facial expressions and voice to recognize the user's emotions.

[1477] Input: User's facial expression data, voice data.

[1478] Data processing: Emotion recognition algorithms analyze facial and audio data to identify emotional states (e.g., anticipation, excitement).

[1479] Output: The recognized emotion data is sent to the system.

[1480] Step 3: Receiving requests and emotions

[1481] The server receives the user's request text and emotion data.

[1482] Input: User request text, emotion data.

[1483] Data operation: Log a combination of request text and emotion data.

[1484] Output: The combined data is passed on to the next process.

[1485] Step 4: Gather information

[1486] The server collects relevant information from the internet in multiple languages, and dynamically adjusts the scope and content of the information collected based on emotional data during the collection process.

[1487] Input: Request text, emotion data.

[1488] Data processing: Scrip multilingual information from relevant websites and databases to prioritize sentiment-driven search terms.

[1489] Output: Collected multilingual information data.

[1490] Step 5: Analyze the information

[1491] The server uses a generative AI model to analyze the collected information and generate a detailed experience report.

[1492] Input: Collected multilingual information data.

[1493] Prompt generation: The generative AI model generates prompts such as, "Please tell me the latest information about attractions and events at the Eiffel Tower."

[1494] Data calculation: A generative AI model analyzes based on prompts and generates a detailed experience report.

[1495] Output: The generated experience report.

[1496] Step 6: Organize your reports

[1497] The server organizes the generated reports, formats them into an easy-to-read format, and adds the most appropriate expressions based on the results of the emotion engine.

[1498] Input: Generated experience reports, sentiment data.

[1499] Data manipulation: Formatting reports containing text and images. Adding sentiment-based emphasis.

[1500] Output: A nicely formatted report.

[1501] Step 7: Submit the report

[1502] The server sends the generated report to the user's terminal.

[1503] Input: The formatted report.

[1504] Data calculation: Sends data to the user's terminal according to the report transmission protocol.

[1505] Output: Report data ready for viewing on the user's device.

[1506] Step 8: View the report

[1507] The user views the provided report on the device.

[1508] Input: Submitted report data.

[1509] Data calculation: The rendering process for displaying the report.

[1510] Output: The report is presented in a user-readable format.

[1511] These steps will result in a system that collects and analyzes optimal information based on the user's emotions, and generates and provides a detailed experience report.

[1512] (Application example 2)

[1513] 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."

[1514] Conventional information gathering systems are limited to providing simple information based on user input requests, and do not provide customized information based on the user's emotions or interests. As a result, it is difficult for users to obtain the specific information they need that is relevant to their emotions, and they are unable to support highly satisfying decision-making. It is also difficult to effectively collect and analyze information in multiple languages.

[1515] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1516] In this invention, the server includes a means for a user to input a request, a means for the server to receive the request and collect related information in multiple languages, a means for an emotion recognition engine to recognize the user's emotion and dynamically change the range and content of the information collection based on the emotion, a means for a generation AI to analyze the related information in multiple languages ​​and generate a detailed experience report, and a means for the server to provide the generated experience report to the user. This makes it possible to provide detailed information customized according to the user's emotion, thereby supporting decision-making with a high level of satisfaction.

[1517] A "user" is an end-user who utilizes the system of the present invention to request information.

[1518] A "request" is a request or inquiry made by a user for specific information.

[1519] "Server" means a computer system that receives requests from users, collects and analyzes relevant information based on those requests, and provides generated reports.

[1520] "Multilingual" means including multiple different languages, such as collecting and analyzing information in English, Japanese, and French.

[1521] "Related information" is data related to tourist spots, luxury restaurants, luxury hotels, products, etc., collected based on user requests.

[1522] An "emotion recognition engine" is a component that has the function of detecting and analyzing emotions from the user's facial expressions and voice input.

[1523] "Emotion" refers to the psychological state expressed when a user wants to know specific information, and includes, for example, a sense of expectation, excitement, etc.

[1524] "Generative AI" refers to an artificial intelligence model that analyzes collected multilingual related information and generates detailed experience reports to provide to users.

[1525] An "Experience Report" is a detailed report generated based on relevant information, including up-to-date event information, visitor reviews, and high-resolution photos.

[1526] A "terminal" is a device used by a user to input a request, such as a smartphone, tablet, or computer.

[1527] "Dynamic change" means that the scope and content of information to be collected can be flexibly adjusted according to the user's emotions and real-time situations.

[1528] This invention relates to a system that collects and analyzes related information in multiple languages ​​based on user requests, and generates and provides detailed experience reports. This system is combined with an emotion recognition engine that recognizes the user's emotions, enabling the provision of information according to the user's emotions.

[1529] Overall system configuration

[1530] The system consists of a terminal that receives user input requests, a server that receives the requests and collects related information, an emotion recognition engine that recognizes the user's emotions, a generation AI that analyzes the collected information and generates a detailed experience report, and a means for providing the report to the terminal.

[1531] Program processing overview

[1532] User request input

[1533] Users use devices such as smartphones or tablets to input requests about specific tourist spots or luxury items. At this time, the emotion recognition engine analyzes the user's facial expressions and voice input to recognize their emotions. For example, a user can input a request such as, "I'd like to know product reviews of the latest smartphones."

[1534] Server information collection

[1535] The request sent from the device is received by the server, which automatically collects multilingual information from related websites and databases. During this collection process, the emotion recognition engine dynamically adjusts the scope and content of the information collection based on the user's emotions.

[1536] Generative AI analysis and report generation

[1537] The collected multilingual related information is analyzed by the Generative AI, which then generates a detailed experience report of tourist attractions and luxury products. Based on the results of the emotion recognition engine, the report is optimized to include expressions and content that match the user's emotions. The report includes the latest event information, high-resolution photos, visitor reviews, and more.

[1538] Providing reports

[1539] The generated experience report is sent from the server to the user's device and displayed. The user can view detailed information and use it as a reference for travel planning and purchasing decisions.

[1540] Specific examples

[1541] For example, if a user requests, "I'm traveling to Paris, France, and I'd like detailed information about the Eiffel Tower," the emotion recognition engine will recognize the user's expectations. The server will collect multilingual information about the Eiffel Tower, and the generation AI will analyze it to generate a detailed experience report, including high-resolution photos, highlights, and the latest event information.

[1542] Prompt Sentence Examples

[1543] "I'd like to learn more about the latest smartphones. Product reviews, photos, feature recommendations, etc."

[1544] In this way, the system of the present invention can provide information customized according to the user's emotions, thereby supporting decision-making with a high level of satisfaction.

[1545] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1546] Step 1:

[1547] A user inputs a request using a device such as a smartphone or tablet. The input request is for specific information, and the user inputs a prompt such as, "I am traveling to Paris, France, and would like detailed information about the Eiffel Tower." This becomes the input data on the device.

[1548] Step 2:

[1549] The emotion recognition engine recognizes the user's emotions based on the user's facial expressions and voice input collected by the device. The input data is the user's facial expression data and voice data, and the output data is the user's emotional state (for example, anticipation or excitement). The emotion recognition engine analyzes this data to identify the user's current emotion.

[1550] Step 3:

[1551] The request and recognized emotional information are sent from the device to the server. The input data at this time is the user's input request and emotional information, which the server receives. Based on the request and emotional information, the server collects multilingual information from related websites and databases.

[1552] Step 4:

[1553] The server dynamically adjusts the scope and content of the information it collects based on the results of the emotion recognition engine. Specifically, if the user expresses anticipation, it prioritizes collecting information about particularly inspiring attractions and events. The input data for this process is information from the websites and databases to be collected, and the output data is related information corresponding to the emotion.

[1554] Step 5:

[1555] The collected multilingual information is analyzed by a generation AI, which uses this input data to generate detailed experience reports of tourist spots and luxury products. The output data is a customized, detailed experience report based on related information.

[1556] Step 6:

[1557] The server receives the generated experience report and optimizes it to include expressions and content appropriate to the user's emotions. This optimization process takes into account the results of the emotion recognition engine. The optimized experience report is the output data.

[1558] Step 7:

[1559] The server finally sends the generated and optimized experience report to the user's device, which receives it and displays it to the user. The user can then view the detailed experience report provided and use it as a reference for travel planning and purchasing decisions, thereby enabling the user to make a satisfactory decision.

[1560] 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.

[1561] 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.

[1562] 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.

[1563] 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.

[1564] 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.

[1565] 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.

[1566] 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).

[1567] 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.

[1568] 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."

[1569] 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.

[1570] 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).

[1571] 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.

[1572] 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.

[1573] 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.

[1574] 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.

[1575] 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.

[1576] 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.

[1577] 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.

[1578] 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.

[1579] 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.

[1580] 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.

[1581] The following is further disclosed regarding the above embodiment.

[1582] (Claim 1)

[1583] a means for a user to input a request;

[1584] a server receiving the request and collecting relevant information in multiple languages;

[1585] A means for the generation AI to analyze the multilingual related information and generate a detailed experience report;

[1586] The system includes a means for the server to provide the generated experience report to the user.

[1587] (Claim 2)

[1588] a terminal at which a user inputs a request;

[1589] 2. The system of claim 1, further comprising means for the server to collect related information in multiple languages ​​in response to the request.

[1590] (Claim 3)

[1591] The system of claim 1, wherein the generation AI has the function of generating a detailed experience report based on the collected multilingual related information.

[1592] "Example 1"

[1593] (Claim 1)

[1594] a means for a user to input a request;

[1595] a server receiving the request and collecting relevant information in multiple languages;

[1596] A means for a generative AI model to analyze the multilingual related information and generate a detailed experience report;

[1597] means for the server to provide the generated experience report to the user;

[1598] means for transmitting a report to a user's terminal via encrypted communication;

[1599] A means of entering requests through a dedicated application or website;

[1600] a means for analyzing the information using natural language processing algorithms;

[1601] The system includes a means for generating experience reports in HTML and PDF formats.

[1602] (Claim 2)

[1603] a terminal at which a user inputs a request;

[1604] A means for the server to collect relevant information in multiple languages ​​in response to the request;

[1605] 2. The system of claim 1, further comprising means for transmitting the report to a user terminal through encrypted communication.

[1606] (Claim 3)

[1607] 2. The system of claim 1, wherein the generative AI model has a means for generating a detailed experience report based on the collected multilingual related information using a natural language processing algorithm.

[1608] "Application Example 1"

[1609] (Claim 1)

[1610] a means for a user to input a request;

[1611] a server receiving the request and collecting related information in multiple languages;

[1612] A means for the generation AI to analyze the multilingual related information and generate a detailed experience report;

[1613] means for the server to provide the generated experience report to the user;

[1614] means for receiving the request via an interface within the vehicle and for the on-board computer to collect and analyze relevant information in multiple languages;

[1615] a means for displaying the generated experience report on an in-vehicle display;

[1616] A system including:

[1617] (Claim 2)

[1618] 2. The system according to claim 1, further comprising a terminal for a user to input a request, and a means for a server to collect related information in multiple languages ​​in response to the request.

[1619] (Claim 3)

[1620] The system of claim 1, wherein the generation AI has the function of generating a detailed experience report based on the collected multilingual related information.

[1621] "Example 2: Combining Emotion Engines"

[1622] (Claim 1)

[1623] a means for a user to input a request;

[1624] A means for the terminal to recognize the user's emotion;

[1625] a server receiving the request and dynamically collecting related information in multiple languages ​​based on the emotion information;

[1626] A means for a generative AI model to analyze the multilingual related information and generate a detailed experience report that matches the emotions;

[1627] The system includes a means for the server to provide the generated experience report to the user.

[1628] (Claim 2)

[1629] 2. The system according to claim 1, further comprising means for dynamically changing the scope and content of information collection based on the user's emotions.

[1630] (Claim 3)

[1631] The system of claim 1, wherein the generative AI model generates prompt sentences based on emotion recognition and generates a detailed experience report.

[1632] "Application example 2 when combining emotion engines"

[1633] (Claim 1)

[1634] a means for a user to input a request;

[1635] a server receiving the request and collecting relevant information in multiple languages;

[1636] an emotion recognition engine that recognizes the emotion of a user and dynamically changes the range and content of information collection based on the emotion;

[1637] A means for the generation AI to analyze the multilingual related information and generate a detailed experience report;

[1638] The system includes a means for the server to provide the generated experience report to the user.

[1639] (Claim 2)

[1640] a terminal at which a user inputs a request;

[1641] 2. The system according to claim 1, further comprising means for the server to collect related information in multiple languages ​​in response to the request, and for the emotion recognition engine to dynamically change the range and content of the collected information based on the emotion.

[1642] (Claim 3)

[1643] The system of claim 1, wherein the generation AI has the function of generating a detailed experience report based on the collected multilingual related information and taking into account the results of the emotion recognition engine. [Explanation of symbols]

[1644] 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 a user to input a request; a server receiving the request and collecting relevant information in multiple languages; A means for the generation AI to analyze the multilingual related information and generate a detailed experience report; The system includes a means for the server to provide the generated experience report to the user.

2. a terminal at which a user inputs a request; 2. The system of claim 1, further comprising means for the server to collect related information in multiple languages ​​in response to the request.

3. The system according to claim 1, wherein the generation AI has the function of generating a detailed experience report based on the collected multilingual related information.

Images