system

Abstract

Provide a system. A means for reading codes installed at tourist spots; means for transmitting a data request based on information obtained from said code; means for providing a user with information related to the generated tourist destination; A means of providing real-time congestion information for tourist spots, a means of processing reservations for attractions and facilities; The system includes a means to suggest visiting times and alternative tourist destinations.

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] Tourists visiting tourist spots face the challenge of having few opportunities to gain a deeper understanding of the history and related information of the spot. Congestion at tourist spots and the hassle of making reservations are also major problems when sightseeing. The goal of this invention is to help tourists not only visit tourist spots, but also deepen their understanding of what happened at the spot, avoid crowds, and enjoy the spot efficiently. [Means for solving the problem]

[0005] The present invention is a system that includes a means for reading codes installed at tourist attractions, a means for sending a data request based on information obtained from the codes, a means for providing users with generated information related to the tourist attractions, a means for providing real-time congestion information about the tourist attractions, a means for processing reservations for tourist attractions and facilities, and a means for suggesting visiting times and alternative tourist attractions, thereby enabling tourists to have an efficient and rich sightseeing experience.

[0006] "Tourist destination" refers to a historical, cultural or natural place visited by tourists, including a specific area or facility.

[0007] "Code" refers to an identifier for digitally encoding and recording information, such as a QR code (registered trademark), barcode, or RFID tag.

[0008] "Means of reading" refers to technology for obtaining the information embedded in the code using a device such as a smartphone or tablet.

[0009] "Means for sending a data request" refers to a communication technology that allows a terminal to make an access request to a server based on information obtained from the code.

[0010] "Generated information related to tourist destinations" refers to content automatically generated by a generative AI model, including information on the historical background, culture, and attractions of tourist destinations.

[0011] "Means of providing" refers to applications, web services, etc. for displaying the generated information on the user's terminal.

[0012] "Crowd information" refers to the current congestion situation at a tourist destination and future congestion forecasts, and includes information based on real-time and forecast data.

[0013] "Means for processing reservations" refers to an interface that allows users to make reservations for visits to tourist attractions and facilities, and the technology for processing the reservations.

[0014] "Means for suggesting visiting times or alternative tourist destinations" refers to technology for suggesting alternative times or alternative tourist destinations when the user's desired visiting time is crowded. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0023] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0036] This invention is a system that reads QR codes installed at tourist spots to provide historical information and tourist guides related to the tourist spot, and also improves the tourist experience by providing congestion information and reservation functions.

[0037] System Configuration

[0038] 1. Scan the QR code and send a request

[0039] When a user scans a QR code installed at a tourist spot with their smartphone, the device retrieves the URL embedded in the QR code and sends it to the server. Upon receiving the request, the server analyzes the URL and identifies the ID of the corresponding tourist spot.

[0040] 2. Generating related information

[0041] The server retrieves basic information about the designated tourist destination from a database, then uses a generative AI model (e.g., ChatGPT (registered trademark)) to automatically generate detailed content related to the tourist destination, including text, images, audio, and video. This generated information is then provided to the user.

[0042] 3. Providing congestion information

[0043] The server analyzes real-time and past visitor data to predict the congestion status of tourist spots, allowing users to plan their visit based on the current congestion status and future congestion forecasts.

[0044] 4. Reservation function

[0045] Users can request reservations for tourist spots and facilities they are interested in. The server receives the reservation request sent from the device, checks the availability of the facility, and then confirms the reservation. Once the reservation is confirmed, a confirmation notification is sent to the device.

[0046] 5. Re-suggestion function

[0047] If the desired visit time is busy, the server will suggest alternative times or related tourist spots, allowing users to create a more efficient schedule.

[0048] Specific examples

[0049] For example, when a user scans a QR code for historical monument "A" with their smartphone, the device sends the URL "https: / / example.com / monumentA" to a server. The server parses the URL and retrieves information related to monument A from a database. Using a generative AI model, detailed descriptive text, images, audio, and video content about monument A is automatically generated. This information is displayed to the user, including, for example, a detailed explanation of "the history and background of monument A."

[0050] If the user later wishes to visit the neighboring Museum B and makes a reservation, the terminal sends a reservation request to the server. The server checks the availability of Museum B, and if a reservation is possible, the user receives a notification saying, "You have reserved a 10:00 AM slot at Museum B."

[0051] If the planned visit time is busy, the server will use the AI ​​model to suggest alternative tourist spots or different times. For example, "Monument A is currently busy, so we recommend visiting adjacent Garden C at 10:30 AM."

[0052] This allows users to have an efficient and in-depth sightseeing experience. By simply scanning the QR code installed at tourist spots, this system provides detailed information about tourist spots, acquires congestion information, and utilizes reservation and re-suggestion functions.

[0053] The processing flow will be explained below.

[0054] Step 1:

[0055] The user scans a QR code installed at a tourist spot with their smartphone.

[0056] Step 2:

[0057] The device sends the URL obtained from the QR code to the server (e.g., an HTTP GET request).

[0058] Step 3:

[0059] The server analyzes the received URL and identifies the ID of the tourist spot corresponding to the URL.

[0060] Step 4:

[0061] The server retrieves basic information about the tourist destination (historical background, points of interest, etc.) from a database.

[0062] Step 5:

[0063] The server instructs the generative AI model (e.g., ChatGPT) to automatically generate detailed text, image, audio, and video content based on the basic information acquired.

[0064] Step 6:

[0065] The server aggregates the generated related information to form content for presentation to the user.

[0066] Step 7:

[0067] The server runs the congestion prediction model to generate the current congestion status and future congestion predictions for the tourist destination.

[0068] Step 8:

[0069] The server transmits the integrated information and congestion information to the terminal.

[0070] Step 9:

[0071] The terminal displays the information received from the server to the user.

[0072] Step 10:

[0073] Request reservations for attractions and facilities that interest the user.

[0074] Step 11:

[0075] The terminal sends a reservation request to the server.

[0076] Step 12:

[0077] The server checks the availability of the facility in real time.

[0078] Step 13:

[0079] The server confirms that the reservation is possible and processes the reservation, saving the reservation information and sending a confirmation of the reservation to the terminal.

[0080] Step 14:

[0081] If the user is unable to visit at the time they wish, the server uses the generated AI model to suggest alternative times or other tourist destinations.

[0082] Step 15:

[0083] The server transmits the re-proposal information to the terminal, and the terminal notifies the user of it.

[0084] The above are the specific processing steps of this system.

[0085] Example 1

[0086] 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."

[0087] For users visiting tourist destinations, it is difficult to easily obtain information about the history and history of the destination, and to efficiently enjoy sightseeing while understanding the congestion situation. There is also a lack of easy ways to make reservations at tourist destinations and facilities. There is a need for a system that can solve these issues and provide users with a more fulfilling tourist experience.

[0088] 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.

[0089] In this invention, the server includes: [means for identifying a tourist attraction ID and acquiring basic information based on it;] [means for sending a prompt to a generative AI model and generating detailed information related to the tourist attraction; and] [means for analyzing visitor data and providing congestion information about the tourist attraction in real time.] This enables [users to easily obtain detailed information about tourist attractions, understand the congestion situation, and enjoy sightseeing efficiently.] It also enables users to easily make reservations for tourist attractions and facilities, providing a more fulfilling sightseeing experience.

[0090] A "tourist attraction ID" is an identifier that uniquely identifies a tourist attraction.

[0091] "Basic information" refers to basic data about tourist destinations, such as their names, locations, and historical backgrounds.

[0092] A "generative AI model" is an artificial intelligence model that automatically generates content such as text, images, audio, and video based on given prompts.

[0093] A "prompt" is an instruction or question that is input to a generative AI model, and is the input data that the model uses to respond or generate something.

[0094] "Detailed information" refers to additional information such as text, images, audio, and video generated based on basic information about a tourist destination.

[0095] "Visitor Data" means data that indicates the current and past number of visitors to a tourist destination and their trends.

[0096] "Crowd information" is information that includes predictions of the current and future number of visitors to a tourist destination, and is data that is useful for users to plan their visit.

[0097] "Reservation" refers to the process of reserving a time or slot for use of a tourist attraction or facility in advance.

[0098] "Re-suggestion" refers to suggesting an alternative time to visit or a different tourist spot to the user based on the congestion and availability of the spot.

[0099] This invention is a system that reads QR codes installed at tourist spots, and provides detailed information about the tourist spot, congestion status, and reservation and re-suggestion functions. Specific embodiments of this system are described below.

[0100] System configuration and operation

[0101] 1. Scan the QR code and send a request

[0102] When a user scans a QR code installed at a tourist spot with their smartphone, the URL is obtained and sent from the device to the server, which analyzes the URL and identifies the tourist spot ID.

[0103] 2. Generating related information

[0104] The server retrieves basic information from a database based on the tourist attraction ID it identifies. This information is then sent as a prompt to a generative AI model (e.g., ChatGPT) to generate detailed content related to the tourist attraction, including text, images, audio, and video. For example, a prompt might be, "Please provide a detailed historical background for Monument A." The generated information is then sent to the user's device and displayed.

[0105] 3. Providing congestion information

[0106] The server analyzes past and current visitor data for tourist destinations and predicts congestion in real time, which is provided to users as a reference when planning their trip.

[0107] 4. Reservation function

[0108] When a user wishes to visit a tourist spot or facility, they send a reservation request from their device. The server checks the facility's reservation status and confirms the reservation if there is availability. If the reservation is confirmed, a notification is sent to the device. For example, a confirmation may be received saying, "You have reserved a slot at Museum B for 10:00 AM."

[0109] 5. Re-suggestion function

[0110] If the user's desired visit time is busy, the server will suggest alternative tourist spots and time slots. The server uses a generative AI model to make appropriate suggestions. For example, a suggestion might be generated: "Monument A is currently busy, so we recommend visiting adjacent Garden C at 10:30 AM."

[0111] Specific examples

[0112] For example, if a user scans the QR code for historical monument "A" with their smartphone, the device sends the URL "https: / / example.com / monumentA" to the server. The server parses the URL and identifies the ID of monument A. The server then retrieves basic information about monument A from a database and sends a prompt to the generative AI model saying, "Please provide a detailed historical background for monument A." The generated explanatory text, images, audio, and video are then displayed on the user's device.

[0113] If the user later wishes to visit the neighboring Museum B and requests a reservation, the terminal sends the reservation information to the server. The server checks the availability of Museum B, and if a reservation is possible, a notification is sent to the user's terminal stating, "You have reserved a 10:00 AM slot at Museum B."

[0114] Furthermore, if the planned visit time is busy, the server can make an alternative suggestion such as, "Monument A is currently busy, so we recommend visiting adjacent Garden C at 10:30 AM."

[0115] In this way, users can have a fulfilling sightseeing experience by providing detailed information about tourist spots, obtaining congestion information, using the reservation function, and utilizing the re-suggestion function.

[0116] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0117] Step 1:

[0118] Scan the QR code and send a request

[0119] A user scans a QR code installed at a tourist spot with their smartphone camera. The device automatically sends the URL extracted from the QR code (input) to the server (output). In this case, the URL data is sent to the server using an HTTP request. Specifically, the device reads the URL "https: / / example.com / monumentA" and sends it to the server.

[0120] Step 2:

[0121] URL analysis and identification of tourist spot IDs

[0122] The server analyzes the URL received as input and identifies the tourist attraction ID from the path portion of the URL (output). For example, the tourist attraction ID "monumentA" is extracted from the path " / monumentA" of the URL "https: / / example.com / monumentA". This gives the server the unique identifier it needs to use in the next step. Specifically, the server analyzes the path portion of the URL using regular expressions or string manipulation.

[0123] Step 3:

[0124] Obtaining basic information

[0125] The server uses the tourist attraction ID (input) to retrieve basic information (output) of the corresponding tourist attraction from the database. The basic information includes the tourist attraction's name, location, historical background, etc. Specifically, the server executes an SQL query to retrieve basic information about "monumentA" from the database.

[0126] Step 4:

[0127] Generative AI models generate detailed information

[0128] The server sends a prompt to a generative AI model (e.g., ChatGPT) based on basic information (input) to generate content (output) such as detailed text, images, audio, and video related to tourist attractions. For example, a prompt such as "Please explain the detailed historical background of Monument A" is created and sent to the generative AI model. Specifically, the server processes the basic information, generates an appropriate prompt, and passes it to the AI ​​model.

[0129] Step 5:

[0130] Providing generated information

[0131] The server compiles the content (input) obtained from the generative AI model and sends it to the user's device (output). The device receives it and displays it on the screen. Specifically, the server sends the generated text and multimedia content in HTML or JSON format to the device. The device interprets the received data and provides it to the user.

[0132] Step 6:

[0133] Analysis and provision of congestion information

[0134] The server analyzes real-time and past visitor data (input) and generates predictions (output) of the current and future congestion status of tourist destinations, including visitor numbers and time-of-day data. Specifically, the server uses data analysis algorithms to generate congestion predictions based on past visitor trends and provides them to users.

[0135] Step 7:

[0136] Receiving and processing booking requests

[0137] A user sends a reservation request (input) from their terminal for a tourist spot or facility they are interested in. The server receives the reservation request, checks the availability of the facility, and confirms the reservation (output). For example, the server receives a request such as "I would like to visit Museum B at 10:00 AM," checks the availability in the database, and confirms the reservation if it is available.

[0138] Step 8:

[0139] Reservation confirmation notification

[0140] Once the reservation is confirmed, the server sends a reservation confirmation notification (output) to the terminal. The terminal receives this notification and presents it to the user. Specifically, the server sends a message to the terminal saying, "You have reserved a 10:00 AM slot at Museum B," and the terminal displays this to the user.

[0141] Step 9:

[0142] Generate and serve re-proposals

[0143] If the desired visit time is crowded, the server rechecks the crowding situation (input) and suggests alternative tourist spots and time slots (output). Specifically, using a generative AI model, it generates a suggestion such as "Monument A is currently crowded, so we recommend visiting adjacent Garden C at 10:30 AM," and sends it to the user's device.

[0144] (Application example 1)

[0145] 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."

[0146] The shopping experience at physical stores is difficult because it is difficult to grasp detailed product information and congestion status, and it is not easy to check product inventory or reservation status. This makes it difficult for consumers to make appropriate product selections or adjust visit times, and stores also find it difficult to improve customer service.

[0147] 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.

[0148] In this invention, the server includes: [means for reading a code installed in a physical store;] [means for sending a data request based on information obtained from the code;] [means for providing the user with information related to the generated product;] [means for providing real-time information about congestion in the store;] [means for processing reservations for products and services; and] [means for suggesting visiting times and other products.] This allows users to instantly grasp detailed product information and congestion status when shopping in a physical store, enabling them to shop efficiently and plan their visit appropriately.

[0149] A "brick and mortar store" is a commercial establishment located in a physical location where consumers can visit in person to purchase goods or services.

[0150] A "code" is a symbol, character string, or image that is visually expressed to read information, and in this invention refers to a QR code, a barcode, or the like.

[0151] A "data request" is a digital message that requests specific information from a server, through which the required information is obtained.

[0152] "Generated information" refers to detailed text, images, audio, video, and other information that is automatically generated from AI models and databases based on information read from the code.

[0153] "Crowd information" is information that indicates the degree of crowding in a specific location, allowing the user to know in advance how crowded the destination will be.

[0154] A "reservation" is a preliminary procedure for securing a product or service for a specific time period or date, and is confirmed based on the details specified by the user.

[0155] "Suggestion" is the act of providing alternatives or recommendations based on the user's requirements or circumstances, thereby giving the user options.

[0156] A "generative AI model" is an AI system that automatically generates new sentences and content using machine learning and natural language processing techniques, and in this invention is used to generate text, images, audio, video, etc.

[0157] A "prompt sentence" is a sentence that is input into a generative AI model and serves as an instruction for the AI ​​to generate specific content based on that sentence.

[0158] This invention is a system that provides an efficient and satisfying shopping experience by reading codes installed in physical stores, providing detailed product information, crowding status, and a reservation function. Specific embodiments of the system are described below.

[0159] System Configuration

[0160] The system mainly consists of the following components:

[0161] 1. Scan the code and send a request

[0162] When a user scans a QR code or barcode displayed in a physical store with their smartphone, the device retrieves the URL embedded in the code and sends it to the server. Upon receiving the request, the server analyzes the URL and identifies the ID of the corresponding product.

[0163] 2. Generating related information

[0164] The server retrieves basic information about the specified product from a database, then uses a generative AI model (e.g., ChatGPT by OpenAI®) to automatically generate detailed content related to the product, including text, images, audio, and video. This generated information is then provided to the user.

[0165] 3. Providing congestion information

[0166] The server analyzes real-time and past visitor data to predict store congestion, allowing users to plan their visit based on current and future congestion forecasts.

[0167] 4. Reservation function

[0168] Users can request reservations for products or services they are interested in. The server receives the reservation request sent from the terminal, checks the inventory of the product or availability of the service, and then confirms the reservation. Once the reservation is confirmed, a confirmation notification is sent to the terminal.

[0169] 5. Re-suggestion function

[0170] If the desired visit time is busy, the server will suggest alternative times or related products, allowing users to plan a more efficient schedule.

[0171] Hardware and Software

[0172] Hardware: Smartphone (e.g. with QR code reader function), Server (Database server, Calculation server)

[0173] Software: Flask (web framework), MySQL (registered trademark) (database management system), OpenAI API (generative AI model)

[0174] Data processing and calculation

[0175] 1. Code scanning and request submission

[0176] Use your smartphone's QR code reader to scan the QR code and send the URL to the server, which then extracts the product ID from the URL.

[0177] 2. Obtaining product information

[0178] The server connects to the MySQL database and retrieves basic information corresponding to the corresponding product ID.

[0179] 3. Generate detailed information

[0180] The server uses OpenAI's generative AI model to generate detailed text, images, audio, and video about the product, including example prompts such as:

[0181] Please provide more information about the following products:

[0182] Product Name: High Quality Tofu

[0183] Description: This tofu is made from organic soybeans and is of the highest quality.

[0184] 4. Analysis and provision of congestion information

[0185] The server analyzes real-time and past visitor data to predict congestion, allowing users to check the current congestion status of the store and predict future congestion.

[0186] 5. Reservation Processing

[0187] Receives a reservation request from a user, checks the stock status of the relevant product and availability of the service, and if the reservation is available, confirms the reservation and sends a confirmation notification to the user.

[0188] 6. Re-suggestion function

[0189] It provides a more efficient schedule by avoiding busy times and suggesting alternative times and related products to users.

[0190] This system allows users to instantly grasp detailed product information and congestion status in physical stores, enabling them to make appropriate purchase and visit plans.

[0191] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0192] Step 1:

[0193] Scan the code and submit the request

[0194] A user scans a QR code or barcode displayed in a physical store with their smartphone. The smartphone uses a QR code reader to obtain the URL embedded in the code. The obtained URL is sent to the server. The input is the QR code, and the output is the URL sent to the server.

[0195] Step 2:

[0196] Identifying the product ID

[0197] The server analyzes the received URL, extracts the product ID from the URL, and identifies the information corresponding to that product ID. The input is the URL, and the output is the extracted product ID. Specifically, the server analyzes the URL pattern to identify the product ID.

[0198] Step 3:

[0199] Obtaining product information

[0200] The server connects to the MySQL database and retrieves basic information corresponding to the specified product ID. The input is the product ID, and the output is the corresponding basic information. Specifically, it issues an SQL query to retrieve data such as the product name, description, price, and image URL.

[0201] Step 4:

[0202] Generate detailed information

[0203] The server uses a generative AI model (e.g., OpenAI's ChatGPT) to generate detailed information about the product. It sends prompts to the generative AI model to generate detailed text, images, audio, and video. The input is basic information and prompts, and the output is the generated detailed information. Specific examples of prompts are as follows:

[0204] Please provide more information about the following products:

[0205] Product Name: High Quality Tofu

[0206] Description: This tofu is made from organic soybeans and is of the highest quality.

[0207] Step 5:

[0208] Providing more information

[0209] The generated detailed information is displayed on the smartphone screen. The user can view detailed text, images, audio, and video. The input is the generated detailed information, and the output is the detailed information provided to the user. Specifically, the smartphone application receives the detailed information and displays it on the screen.

[0210] Step 6:

[0211] Analysis and provision of congestion status

[0212] The server analyzes the congestion situation based on real-time and past visitor data. It uses a congestion prediction model to calculate the current congestion situation and predict future congestion. The input is visitor data, and the output is congestion information. Specifically, the server analyzes the visitor data, calculates the congestion level, and provides it to the user.

[0213] Step 7:

[0214] Display of congestion information

[0215] Congestion information is displayed on the smartphone screen. Users can check the current congestion situation and future congestion forecasts. The input is congestion situation information, and the output is congestion information displayed to the user. Specifically, the smartphone application receives the congestion information and displays it on the screen.

[0216] Step 8:

[0217] Submit a booking request

[0218] A user requests a reservation for a product or service that they are interested in. The reservation request is sent from the device to the server. The input is the user's reservation request, and the output is the request sent to the server. Specifically, the smartphone application sends the request by pressing the reservation button.

[0219] Step 9:

[0220] Booking confirmation and notifications

[0221] The server processes the received reservation request and checks the inventory of the relevant product and availability of the service. If the reservation is possible, it confirms the reservation and sends a confirmation notice to the terminal. The input is the reservation request and the output is a reservation confirmation notice. Specifically, it accesses the database, updates the reservation information, and sends a notice.

[0222] Step 10:

[0223] Providing a re-proposal

[0224] If the desired visit time is busy, the server will suggest alternative times or related products. The suggestions are displayed on the terminal. The input is the busyness information and the user's request, and the output is an alternative suggestion. Specifically, a generative AI model is used to generate alternative suggestions and provide that information to the user.

[0225] Through the above processing steps, users can instantly grasp detailed product information and congestion status during their shopping experience at a physical store, enabling them to make efficient purchases and plans.

[0226] 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.

[0227] This invention is a system that reads QR codes installed at tourist spots to provide historical information and tourist guides related to the tourist spot, and also improves the user experience by providing congestion information, reservation functions, and an emotion engine.

[0228] System Configuration

[0229] 1. Scan the QR code and send a request

[0230] When a user scans a QR code installed at a tourist spot with their smartphone, the device retrieves the URL embedded in the QR code and sends it to the server. Upon receiving the request, the server analyzes the URL and identifies the ID of the corresponding tourist spot.

[0231] 2. Generating related information

[0232] The server retrieves basic information about the designated tourist destination from a database, then uses a generative AI model (e.g., ChatGPT) to automatically generate detailed content related to the destination, including text, images, audio, and video. This generated information is then provided to the user.

[0233] 3. Use of Emotion Engine

[0234] The device analyzes the user's facial expressions and voice and sends the data to the emotion engine. The server uses the emotion engine to recognize the user's emotional state and collects the results.

[0235] 4. Emotion-based modulation of information

[0236] The server uses data from the emotion engine to suggest tourist spots and provide information that takes the user's emotions into account. For example, if the user is excited, it can suggest tourist spots that are highly exciting.

[0237] 5. Providing congestion information

[0238] The server analyzes real-time and past visitor data to predict the current congestion situation at tourist destinations, allowing users to plan their visit based on the current congestion situation and future congestion predictions.

[0239] 6. Reservation function

[0240] Users can request reservations for tourist spots and facilities they are interested in. The server receives the reservation request sent from the device, checks the availability of the facility, and then confirms the reservation. Once the reservation is confirmed, a confirmation notification is sent to the device.

[0241] 7. Re-suggestion function

[0242] If the desired visit time is busy, the server will suggest alternative times or related tourist spots, allowing users to create a more efficient schedule.

[0243] Specific examples

[0244] For example, when a user scans a QR code for historical monument "A" with their smartphone, the device sends the URL "https: / / example.com / monumentA" to a server. The server parses the URL and retrieves information about monument A from a database. Using a generative AI model, detailed descriptive text, images, audio, and video content about monument A is automatically generated. This information is displayed to the user, including, for example, a detailed explanation of "the history and background of monument A."

[0245] Furthermore, the device collects the user's facial expressions and voice, which the emotion engine analyzes to recognize the user's emotional state. For example, if the emotion engine recognizes that the user is excited, the server will suggest tourist spots that are highly exciting (e.g., active attractions).

[0246] If the user later wishes to visit the neighboring Museum B and makes a reservation, the terminal sends a reservation request to the server. The server checks the availability of Museum B, and if a reservation is possible, the user receives a notification saying, "You have reserved a 10:00 AM slot at Museum B."

[0247] If the planned visit time is busy, the server will use the AI ​​model to suggest alternative tourist spots or different times. For example, "Monument A is currently busy, so we recommend visiting adjacent Garden C at 10:30 AM."

[0248] This allows users to have an efficient and in-depth sightseeing experience. By simply scanning the QR code installed at tourist spots, the system combines an emotion engine to provide more personalized tourist information, providing detailed information about tourist spots, obtaining congestion information, using reservation functions, and utilizing re-suggestion functions.

[0249] The processing flow will be explained below.

[0250] Step 1:

[0251] The user scans a QR code installed at a tourist spot with their smartphone.

[0252] Step 2:

[0253] The device sends the URL obtained from the QR code to the server (e.g., an HTTP GET request).

[0254] Step 3:

[0255] The server analyzes the received URL and identifies the ID of the tourist spot corresponding to the URL.

[0256] Step 4:

[0257] The server retrieves basic information about the tourist destination (historical background, points of interest, etc.) from a database.

[0258] Step 5:

[0259] The server instructs the generative AI model (e.g., ChatGPT) to automatically generate detailed text, image, audio, and video content based on the basic information acquired.

[0260] Step 6:

[0261] The server aggregates the generated related information to form content for presentation to the user.

[0262] Step 7:

[0263] The device collects the user's facial expressions and voice and sends them to the emotion engine.

[0264] Step 8:

[0265] The emotion engine analyzes the user's emotional state and sends the results to the server.

[0266] Step 9:

[0267] Based on the analysis results from the emotion engine, the server adjusts the tourist destination suggestions and information provided, taking into account the user's emotions.

[0268] Step 10:

[0269] The server runs the congestion prediction model to generate the current congestion status and future congestion predictions for the tourist destination.

[0270] Step 11:

[0271] The server transmits the integrated information and congestion information to the terminal.

[0272] Step 12:

[0273] The terminal displays the information received from the server to the user.

[0274] Step 13:

[0275] Request reservations for attractions and facilities that interest the user.

[0276] Step 14:

[0277] The terminal sends a reservation request to the server.

[0278] Step 15:

[0279] The server checks the availability of the facility in real time.

[0280] Step 16:

[0281] The server confirms that the reservation is possible and processes the reservation, saving the reservation information and sending a confirmation of the reservation to the terminal.

[0282] Step 17:

[0283] The server uses the generated AI model to suggest alternative times and tourist destinations based on crowding predictions.

[0284] Step 18:

[0285] The server transmits the re-proposal information to the terminal, and the terminal notifies the user of it.

[0286] The above are the specific processing steps of this system.

[0287] Example 2

[0288] 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."

[0289] When travelers visit tourist destinations, they want to quickly obtain detailed local information and receive tourist guidance tailored to their individual needs. However, conventional tourist information systems only provide one-way information and lack congestion information, reservation functions, and personalized guidance based on individual emotional states. They also lack efficient schedule adjustments based on the current and future congestion situation at tourist destinations. This poses a challenge, reducing the convenience of travelers and the quality of their tourism experience.

[0290] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0291] In this invention, the server includes: [means for reading codes installed at tourist attractions;] [means for sending a data request based on information obtained from the code;] [means for generating information related to tourist attractions using a generative AI model;] [means for providing the generated information related to tourist attractions to the user;] [means for utilizing an emotion engine for analyzing user emotion data obtained from the terminal;] [means for adjusting tourist attraction information based on the user's emotional state;] [means for providing real-time congestion information about tourist attractions;] [means for processing reservations for tourist attractions and facilities; and [means for suggesting visiting times and alternative tourist attractions.] This enables users to obtain detailed information about tourist attractions in real time and receive personalized tourist guidance according to their individual emotional state, and also enables efficient schedule adjustments and reservation management based on congestion levels.

[0292] "Means for reading codes installed at tourist spots" refers to a function that allows users to scan codes such as QR codes installed at tourist spots with devices such as smartphones or tablets and obtain the information contained in the codes.

[0293] The "means for transmitting a data request based on information obtained from the code" is a function for transmitting the information of the obtained code to the server via the terminal.

[0294] "Means for generating information related to tourist destinations using a generative AI model" refers to a function that automatically generates content such as detailed text, images, audio, and video related to tourist destinations using a generative AI model (e.g., a text generation model).

[0295] "Means for providing users with information related to the generated tourist destinations" refers to a function that allows content generated by the generative AI model to be sent to a terminal and for users to receive the information visually or audibly.

[0296] "Means for utilizing an emotion engine that analyzes user emotion data acquired from the terminal" refers to a function in which the terminal collects the user's facial expressions and voice, analyzes them with the server's emotion engine, and recognizes the user's emotional state.

[0297] The "means for adjusting information about tourist attractions based on the user's emotional state" is a function that dynamically adjusts the information provided and the content of suggested tourist attractions according to the user's emotional state recognized by the emotion engine.

[0298] "Means for providing real-time congestion information at tourist destinations" is a function that uses a data analysis model to analyze the current congestion situation and predicted congestion information at tourist destinations and provides it to users in real time.

[0299] "Means for processing reservations for tourist attractions and facilities" refers to the function of receiving a reservation request for a tourist attraction or facility that a user wishes to visit, and the server confirming and confirming the reservation in response to that request.

[0300] "Means for suggesting visiting times and alternative tourist spots" is a function that suggests visiting times and alternative tourist spots depending on the congestion situation and reservation status.

[0301] This invention is a system that uses QR codes installed at tourist spots to provide users with detailed information about tourist spots, congestion information, and reservation functions, and aims to provide personalized guidance based on the user's emotional state.

[0302] System configuration

[0303] The system mainly includes the following components:

[0304] Terminal: A portable device such as a smartphone or tablet that can be used to scan QR codes installed at tourist attractions.

[0305] Server: A central computer system that processes requests sent from user devices and generates information using generative AI models and emotion engines.

[0306] Database: A database system (e.g. MySQL) for storing basic information about tourist destinations.

[0307] Generative AI models: Artificial intelligence models for automatically generating content such as text, images, audio, and video (e.g., ChatGPT).

[0308] Emotion engine: Software that analyzes a user's facial expressions and voice to recognize their emotional state (e.g., Microsoft® Azure® Emotion API).

[0309] Operation overview and examples

[0310] The system begins when a user arrives at a tourist spot and scans a QR code.

[0311] 1. Scan the QR code

[0312] When a user uses a smartphone to scan a QR code installed at a tourist spot, the device retrieves the information, which includes a URL related to the tourist spot.

[0313] 2. Submitting a Data Request

[0314] The device sends the URL (e.g., "https: / / example.com / monumentA") to the server. The device makes this data request using its Internet connection.

[0315] 3. Analysis and Acquisition of Information

[0316] When the server receives the request, it analyzes the URL to identify the tourist spot ID, and then retrieves basic information about the corresponding tourist spot from the database.

[0317] 4. Content generation using generative AI models

[0318] The server inputs a prompt into a generative AI model (e.g., ChatGPT) to generate detailed content related to the tourist attraction, such as text, images, audio, and video. For example, the prompt might be, "Please explain the detailed history and characteristics of Monument A."

[0319] 5. Providing Information to Users

[0320] The generated content is sent from the server to the terminal and displayed on the terminal screen, allowing the user to receive a detailed explanation of "Monument A's history and characteristics" visually or audibly.

[0321] 6. Analysis by Emotion Engine

[0322] The device uses a camera and microphone to collect the user's facial expressions and voice, and sends them to emotion analysis software (e.g., Microsoft Azure Emotion API). The analysis results are then sent to a server.

[0323] 7. Emotion-based modulation of information

[0324] The server provides information based on the user's emotional state based on data from the emotion engine. For example, if the user is excited, it will suggest tourist spots that are likely to excite the user.

[0325] 8. Providing congestion information

[0326] The server analyzes real-time data and past visitor data, and uses a congestion prediction model to predict current and future congestion information for tourist destinations. This information is then provided to users.

[0327] 9. Processing booking requests

[0328] When a user wants to reserve a specific tourist spot or facility, they send a reservation request from their device. The server checks the availability of the facility and confirms the reservation. Once the reservation is complete, a confirmation notification is sent to the device.

[0329] 10. Using the re-suggestion feature

[0330] If reservations are busy or based on crowd predictions, the server will suggest alternative tourist spots and visiting times, such as "Monument A is currently crowded, so we recommend visiting adjacent Garden C at 10:30 AM."

[0331] This allows users to obtain detailed information about tourist spots in real time and receive personalized information according to their emotional state.In addition, the system provides congestion information and reservation functions, enabling efficient schedule adjustment and a rich sightseeing experience.

[0332] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0333] Step 1:

[0334] A user scans a QR code installed at a tourist spot. The input is the QR code scanned using the user's smartphone, and the output is the URL contained in the acquired QR code. Specifically, when the user launches the smartphone's camera app and reads the QR code, the URL (e.g., "https: / / example.com / monumentA") is displayed.

[0335] Step 2:

[0336] The device sends the scanned URL to the server. The input is the URL obtained by the device, and the output is the URL request received by the server. Specifically, the URL information is sent to the server via the smartphone's Internet connection.

[0337] Step 3:

[0338] The server analyzes the received URL. The input is the URL data, and the output is the tourist spot ID. Specifically, the server analyzes the URL using Apache (registered trademark) HTTP Server or similar, and extracts the tourist spot identifier, such as "monumentA."

[0339] Step 4:

[0340] The server retrieves basic information about tourist attractions from a database. The input is the tourist attraction's ID, and the output is the tourist attraction's basic information. Specifically, the server sends a query to the MySQL database to retrieve information such as the tourist attraction's name, location, and brief history.

[0341] Step 5:

[0342] The server generates content using a generative AI model. The input is basic information about the tourist attraction and a prompt, and the output is generated content such as text, images, audio, and video. For example, the prompt "Please explain the detailed history and characteristics of Monument A" is sent to ChatGPT, which generates a detailed explanatory text and related images.

[0343] Step 6:

[0344] The server sends the generated content to the device. The input is the generated content, and the output is the content displayed on the device. Specifically, the server sends the generated text and images to the smartphone and displays them on the smartphone screen.

[0345] Step 7:

[0346] The device collects the user's facial expressions and voice and sends them to the emotion engine. The input is the user's facial expression and voice data, and the output is the emotion analysis results. Specifically, data is collected using the smartphone's camera and microphone and sent to the Microsoft Azure Emotion API.

[0347] Step 8:

[0348] The server receives the emotion analysis results and analyzes the user's emotional state. The input is the emotion analysis results, and the output is a classification of the user's emotional state (e.g., excited, relaxed). The server adjusts the information provided based on the analysis results.

[0349] Step 9:

[0350] The server provides personalized information to the device based on the user's emotional state. The input is the user's emotional state and tourist attraction information, and the output is tailored tourist attraction information and suggestions. For example, if the user is excited, active attractions are suggested.

[0351] Step 10:

[0352] The server analyzes and provides congestion information in real time. The input is real-time visitor data and past visit data, and the output is current and predicted congestion information. The server performs the analysis using data analysis software and sends the results to the terminal.

[0353] Step 11:

[0354] A user requests a reservation for a tourist attraction or facility. The input is the reservation information for the tourist attraction or facility desired by the user, and the output is a reservation request sent to the server. Specifically, the user inputs the reservation request through a smartphone application.

[0355] Step 12:

[0356] The server processes reservation requests. The input is a reservation request from the user, and the output is a reservation confirmation notification. The server accesses the reservation management system, checks availability, and confirms the reservation. Once the reservation is completed, a confirmation notification is sent to the terminal.

[0357] Step 13:

[0358] The server suggests visiting times and alternative tourist spots based on the congestion situation. The input is the congestion situation data and the user's schedule, and the output is an alternative suggestion. The server uses an AI model to calculate the optimal visiting time and alternative tourist spots and notify the user. For example, a suggestion might be, "Monument A is currently crowded, so we recommend visiting adjacent Garden C at 10:30 AM."

[0359] (Application example 2)

[0360] 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."

[0361] In recent years, there has been a demand for systems that can improve the user experience when visiting tourist spots and virtual stores. Conventional systems lack real-time congestion information and reservation functions, and it is difficult to provide personalized information based on the user's emotional state. In addition, generating tourist spot and store information takes time, which results in problems with the quality of the user experience.

[0362] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for reading a code placed at a tourist attraction or virtual store, means for sending a data request based on information obtained from the code, and means for providing the user with the generated information related to the tourist attraction or store. This makes it possible to provide real-time congestion information for the tourist attraction or store and to provide and suggest information based on the user's emotional state. It also processes reservations for tourist attractions, facilities, or products, and suggests reservations for the time period during which the visitor should visit, or for other tourist attractions or different time periods.

[0363] "Tourist attractions or virtual stores" includes not only actual physical tourist attractions or commercial areas, but also stores or tourist attractions recreated in virtual space.

[0364] "Code" means a medium with embedded information, including a QR code, barcode, or other type of code, that can be scanned by a User's device.

[0365] "Means for sending a data request" refers to the method used to send the URL and identifying information obtained by scanning the code to a server to obtain the associated data.

[0366] "Generated Information" includes text, images, audio, and video content created by the server using databases and generative AI models.

[0367] "Crowd information" is information that indicates the congestion status of a target location or store, generated using current visitor numbers, past data, and predictive algorithms.

[0368] "Means for processing reservations" refers to the method by which users make reservations for visit times and facility use at specific tourist attractions and stores, and the associated back-end processing mechanisms.

[0369] "Emotional state" refers to the user's mental and emotional state as identified by analyzing the user's facial expressions and voice.

[0370] "Means for providing or suggesting information" refers to methods that not only display generated information to the user, but also dynamically provide recommended information based on the user's current emotional state.

[0371] This invention provides a system including means for reading QR codes installed at tourist attractions or virtual stores, means for sending a data request based on information obtained from the read code, means for providing the user with generated information related to the tourist attraction or store, means for providing real-time congestion information about the tourist attraction or store, means for processing reservations for tourist attractions, facilities, or products, means for suggesting a visiting time period or reservations for another tourist attraction or a different time period, and means for analyzing the user's emotional state and providing or suggesting information based on the results.

[0372] Hardware and Software

[0373] To realize this system, the following hardware and software are used.

[0374] Smart glasses: Using Microsoft HoloLens® as an example.

[0375] Smartphone: iOS or ANDROID (registered trademark) smartphone.

[0376] Emotion engine: Uses Microsoft Azure Emotion API.

[0377] Generative AI model: Uses OpenAI GPT-4 (registered trademark).

[0378] Backend: Node.js, Express, MongoDB.

[0379] Data processing and calculation

[0380] 1. Scan the QR code and send a data request

[0381] The server obtains the URL by scanning the QR code with the user's smart glasses or smartphone and sends the information to the server, which then analyzes the URL and retrieves the corresponding store and product information from the database.

[0382] 2. Provision of generated information

[0383] The server uses a generative AI model (OpenAI GPT-4) to automatically generate detailed related text, image, audio, and video content based on the information retrieved from the database. The generated information is then sent to the device and provided to the user.

[0384] 3. Use of Emotion Engine

[0385] The server receives facial and voice data acquired from the user's smart glasses or smartphone and sends it to the Microsoft Azure Emotion API to analyze the user's emotional state. The server receives the results of the analysis by the emotion engine.

[0386] 4. Emotionally driven information

[0387] The server uses a generative AI model to create product suggestions or tourist spot recommendations based on the acquired emotional data and provides them to the user. For example, if the user is excited, it will suggest new products or limited-time offers.

[0388] 5. Providing congestion information and reservation functions

[0389] The server analyzes real-time congestion information and notifies users of the congestion situation. If it is crowded, it will suggest less crowded times or other locations and inform users of available reservation times.

[0390] 6. Re-suggestion function

[0391] If the user's desired time slot is crowded, the server will suggest alternative tourist spots and time slots. The server dynamically generates these alternatives through a generative AI model and presents them to the user.

[0392] Specific examples

[0393] For example, when a user scans the QR code of a "virtual fashion store" in a virtual mall, detailed information about the new autumn collection is provided. When using smart glasses, excitement from facial expressions is analyzed through an emotion engine (Microsoft Azure Emotion API), and the most popular items from the new collection are suggested. Crowd information is also provided in real time, allowing users to reserve a suitable time to visit. If the store is crowded, a reservation for a less crowded time slot is suggested.

[0394] Prompt Sentence Examples

[0395] An example of a prompt to be input to the generative AI model is as follows:

[0396] Product information: [Product A: Smartphone, Product B: Smartwatch]

[0397] Emotional state: Highly excited

[0398] Proposal Generation: Suggest the best product offers to highly excited users.

[0399] This can significantly improve the user experience.

[0400] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0401] Step 1:

[0402] The user scans a QR code placed at a tourist spot or virtual store using smart glasses or a smartphone.

[0403] Input: QR code

[0404] Output: URL obtained from QR code

[0405] How it works: A user scans a QR code with their device and retrieves the URL embedded in the code.

[0406] Step 2:

[0407] The terminal sends the acquired URL to the server.

[0408] Input: URL obtained from QR code

[0409] Output: Data request sent to the server

[0410] Operation: The device requests detailed information by sending the URL information it has obtained to the server.

[0411] Step 3:

[0412] The server analyzes the URL and retrieves information about the relevant tourist attraction or store from a database.

[0413] Input: URL information sent to the server

[0414] Output: Tourist attraction or store information retrieved from the database

[0415] How it works: The server parses the URL, identifies the ID of the relevant tourist attraction or store, and retrieves the necessary information from the database based on that ID.

[0416] Step 4:

[0417] The server uses a generative AI model (e.g., OpenAI GPT-4) to generate detailed text, image, audio, and video content based on tourist attraction or store information.

[0418] Input: Tourist attraction or store information retrieved from the database

[0419] Output: Generated detailed content (text, images, audio, video)

[0420] How it works: The server inputs the acquired information into a generative AI model, which automatically generates detailed content based on the prompt.

[0421] Step 5:

[0422] The server sends the generated information to the terminal and displays it to the user.

[0423] Input: Generated detail content

[0424] Output: Detailed content sent to the device, displayed to the user

[0425] How it works: The server sends the content created by the generative AI model to the device and displays it for the user to view.

[0426] Step 6:

[0427] The device collects the user's facial expressions and voice and sends them to an emotion engine (e.g., Microsoft Azure Emotion API).

[0428] Input: User facial and voice data

[0429] Output: Data sent to the emotion engine

[0430] How it works: The device sends the user's facial expression and voice data collected to the emotion engine.

[0431] Step 7:

[0432] The emotion engine analyzes the user's emotional state and sends the results back to the server.

[0433] Input: User facial and voice data

[0434] Output: Emotional state analysis result

[0435] How it works: Analyzes the user's emotional state using waveform analysis and facial expression recognition algorithms. The analysis results are sent to the server.

[0436] Step 8:

[0437] Based on the emotion data, the server uses a generative AI model to regenerate information appropriate for the user and transmits it to the device.

[0438] Input: Emotional state analysis results, generative AI model

[0439] Output: Regenerated personalization information

[0440] How it works: The server adds the acquired emotional data to the prompt text, creates new personalized information using the generative AI model, and suggests it to the user.

[0441] Step 9:

[0442] The server uses the congestion prediction model to analyze the congestion situation at tourist spots or stores and provides real-time information and predicted information to users.

[0443] Input: Current and past visit data

[0444] Output: Real-time and forecast information on congestion status

[0445] Operation: The server analyzes visit data and uses a congestion prediction model to generate real-time and future congestion information, which it then provides to users.

[0446] Step 10:

[0447] The user requests a reservation for a desired tourist spot, facility, or product, and the server processes the reservation.

[0448] Input: User's booking request

[0449] Output: Booking confirmation notice

[0450] How it works: The server receives a reservation request from a user, processes the reservation, checks availability, and then notifies the user that the reservation is confirmed.

[0451] Step 11:

[0452] If the user's desired time is busy, the server will suggest alternative tourist spots or different visiting times.

[0453] Input: Crowd data, generative AI model

[0454] Output: Alternative proposal information

[0455] How it works: The server analyzes the congestion data and uses a generative AI model to generate and provide suggestions for alternative tourist destinations and visiting times to the user.

[0456] This results in a system that improves the user's experience in a more personalized and efficient manner.

[0457] 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.

[0458] 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 (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0459] 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.

[0460] [Second embodiment]

[0461] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0462] 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.

[0463] 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).

[0464] 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.

[0465] 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.

[0466] 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).

[0467] 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.

[0468] 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.

[0469] 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.

[0470] 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.

[0471] 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.

[0472] 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."

[0473] This invention is a system that reads QR codes installed at tourist spots to provide historical information and tourist guides related to the tourist spot, and also improves the tourist experience by providing congestion information and reservation functions.

[0474] System Configuration

[0475] 1. Scan the QR code and send a request

[0476] When a user scans a QR code installed at a tourist spot with their smartphone, the device retrieves the URL embedded in the QR code and sends it to the server. Upon receiving the request, the server analyzes the URL and identifies the ID of the corresponding tourist spot.

[0477] 2. Generating related information

[0478] The server retrieves basic information about the designated tourist destination from a database, then uses a generative AI model (e.g., ChatGPT) to automatically generate detailed content related to the destination, including text, images, audio, and video. This generated information is then provided to the user.

[0479] 3. Providing congestion information

[0480] The server analyzes real-time and past visitor data to predict the congestion status of tourist spots, allowing users to plan their visit based on the current congestion status and future congestion forecasts.

[0481] 4. Reservation function

[0482] Users can request reservations for tourist spots and facilities they are interested in. The server receives the reservation request sent from the device, checks the availability of the facility, and then confirms the reservation. Once the reservation is confirmed, a confirmation notification is sent to the device.

[0483] 5. Re-suggestion function

[0484] If the desired visit time is busy, the server will suggest alternative times or related tourist spots, allowing users to create a more efficient schedule.

[0485] Specific examples

[0486] For example, when a user scans a QR code for historical monument "A" with their smartphone, the device sends the URL "https: / / example.com / monumentA" to a server. The server parses the URL and retrieves information related to monument A from a database. Using a generative AI model, detailed descriptive text, images, audio, and video content about monument A is automatically generated. This information is displayed to the user, including, for example, a detailed explanation of "the history and background of monument A."

[0487] If the user later wishes to visit the neighboring Museum B and makes a reservation, the terminal sends a reservation request to the server. The server checks the availability of Museum B, and if a reservation is possible, the user receives a notification saying, "You have reserved a 10:00 AM slot at Museum B."

[0488] If the planned visit time is busy, the server will use the AI ​​model to suggest alternative tourist spots or different times. For example, "Monument A is currently busy, so we recommend visiting adjacent Garden C at 10:30 AM."

[0489] This allows users to have an efficient and in-depth sightseeing experience. By simply scanning the QR code installed at tourist spots, this system provides detailed information about tourist spots, acquires congestion information, and utilizes reservation and re-suggestion functions.

[0490] The processing flow will be explained below.

[0491] Step 1:

[0492] The user scans a QR code installed at a tourist spot with their smartphone.

[0493] Step 2:

[0494] The device sends the URL obtained from the QR code to the server (e.g., an HTTP GET request).

[0495] Step 3:

[0496] The server analyzes the received URL and identifies the ID of the tourist spot corresponding to the URL.

[0497] Step 4:

[0498] The server retrieves basic information about the tourist destination (historical background, points of interest, etc.) from a database.

[0499] Step 5:

[0500] The server instructs the generative AI model (e.g., ChatGPT) to automatically generate detailed text, image, audio, and video content based on the basic information acquired.

[0501] Step 6:

[0502] The server aggregates the generated related information to form content for presentation to the user.

[0503] Step 7:

[0504] The server runs the congestion prediction model to generate the current congestion status and future congestion predictions for the tourist destination.

[0505] Step 8:

[0506] The server transmits the integrated information and congestion information to the terminal.

[0507] Step 9:

[0508] The terminal displays the information received from the server to the user.

[0509] Step 10:

[0510] Request reservations for attractions and facilities that interest the user.

[0511] Step 11:

[0512] The terminal sends a reservation request to the server.

[0513] Step 12:

[0514] The server checks the availability of the facility in real time.

[0515] Step 13:

[0516] The server confirms that the reservation is possible and processes the reservation, saving the reservation information and sending a confirmation of the reservation to the terminal.

[0517] Step 14:

[0518] If the user is unable to visit at the time they wish, the server uses the generated AI model to suggest alternative times or other tourist destinations.

[0519] Step 15:

[0520] The server transmits the re-proposal information to the terminal, and the terminal notifies the user of it.

[0521] The above are the specific processing steps of this system.

[0522] Example 1

[0523] 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."

[0524] For users visiting tourist destinations, it is difficult to easily obtain information about the history and history of the destination, and to efficiently enjoy sightseeing while understanding the congestion situation. There is also a lack of easy ways to make reservations at tourist destinations and facilities. There is a need for a system that can solve these issues and provide users with a more fulfilling tourist experience.

[0525] 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.

[0526] In this invention, the server includes: [means for identifying a tourist attraction ID and acquiring basic information based on it;] [means for sending a prompt to a generative AI model and generating detailed information related to the tourist attraction; and] [means for analyzing visitor data and providing congestion information about the tourist attraction in real time.] This enables [users to easily obtain detailed information about tourist attractions, understand the congestion situation, and enjoy sightseeing efficiently.] It also enables users to easily make reservations for tourist attractions and facilities, providing a more fulfilling sightseeing experience.

[0527] A "tourist attraction ID" is an identifier that uniquely identifies a tourist attraction.

[0528] "Basic information" refers to basic data about tourist destinations, such as their names, locations, and historical backgrounds.

[0529] A "generative AI model" is an artificial intelligence model that automatically generates content such as text, images, audio, and video based on given prompts.

[0530] A "prompt" is an instruction or question that is input to a generative AI model, and is the input data that the model uses to respond or generate something.

[0531] "Detailed information" refers to additional information such as text, images, audio, and video generated based on basic information about a tourist destination.

[0532] "Visitor Data" means data that indicates the current and past number of visitors to a tourist destination and their trends.

[0533] "Crowd information" is information that includes predictions of the current and future number of visitors to a tourist destination, and is data that is useful for users to plan their visit.

[0534] "Reservation" refers to the process of reserving a time or slot for use of a tourist attraction or facility in advance.

[0535] "Re-suggestion" refers to suggesting an alternative time to visit or a different tourist spot to the user based on the congestion and availability of the spot.

[0536] This invention is a system that reads QR codes installed at tourist spots, and provides detailed information about the tourist spot, congestion status, and reservation and re-suggestion functions. Specific embodiments of this system are described below.

[0537] System configuration and operation

[0538] 1. Scan the QR code and send a request

[0539] When a user scans a QR code installed at a tourist spot with their smartphone, the URL is obtained and sent from the device to the server, which analyzes the URL and identifies the tourist spot ID.

[0540] 2. Generating related information

[0541] The server retrieves basic information from a database based on the tourist attraction ID it identifies. This information is then sent as a prompt to a generative AI model (e.g., ChatGPT) to generate detailed content related to the tourist attraction, including text, images, audio, and video. For example, a prompt might be, "Please provide a detailed historical background for Monument A." The generated information is then sent to the user's device and displayed.

[0542] 3. Providing congestion information

[0543] The server analyzes past and current visitor data for tourist destinations and predicts congestion in real time, which is provided to users as a reference when planning their trip.

[0544] 4. Reservation function

[0545] When a user wishes to visit a tourist spot or facility, they send a reservation request from their device. The server checks the facility's reservation status and confirms the reservation if there is availability. If the reservation is confirmed, a notification is sent to the device. For example, a confirmation may be received saying, "You have reserved a slot at Museum B for 10:00 AM."

[0546] 5. Re-suggestion function

[0547] If the user's desired visit time is busy, the server will suggest alternative tourist spots and time slots. The server uses a generative AI model to make appropriate suggestions. For example, a suggestion might be generated: "Monument A is currently busy, so we recommend visiting adjacent Garden C at 10:30 AM."

[0548] Specific examples

[0549] For example, if a user scans the QR code for historical monument "A" with their smartphone, the device sends the URL "https: / / example.com / monumentA" to the server. The server parses the URL and identifies the ID of monument A. The server then retrieves basic information about monument A from a database and sends a prompt to the generative AI model saying, "Please provide a detailed historical background for monument A." The generated explanatory text, images, audio, and video are then displayed on the user's device.

[0550] If the user later wishes to visit the neighboring Museum B and requests a reservation, the terminal sends the reservation information to the server. The server checks the availability of Museum B, and if a reservation is possible, a notification is sent to the user's terminal stating, "You have reserved a 10:00 AM slot at Museum B."

[0551] Furthermore, if the planned visit time is busy, the server can make an alternative suggestion such as, "Monument A is currently busy, so we recommend visiting adjacent Garden C at 10:30 AM."

[0552] In this way, users can have a fulfilling sightseeing experience by providing detailed information about tourist spots, obtaining congestion information, using the reservation function, and utilizing the re-suggestion function.

[0553] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0554] Step 1:

[0555] Scan the QR code and send a request

[0556] A user scans a QR code installed at a tourist spot with their smartphone camera. The device automatically sends the URL extracted from the QR code (input) to the server (output). In this case, the URL data is sent to the server using an HTTP request. Specifically, the device reads the URL "https: / / example.com / monumentA" and sends it to the server.

[0557] Step 2:

[0558] URL analysis and identification of tourist spot IDs

[0559] The server analyzes the URL received as input and identifies the tourist attraction ID from the path portion of the URL (output). For example, the tourist attraction ID "monumentA" is extracted from the path " / monumentA" of the URL "https: / / example.com / monumentA". This gives the server the unique identifier it needs to use in the next step. Specifically, the server analyzes the path portion of the URL using regular expressions or string manipulation.

[0560] Step 3:

[0561] Obtaining basic information

[0562] The server uses the tourist attraction ID (input) to retrieve basic information (output) of the corresponding tourist attraction from the database. The basic information includes the tourist attraction's name, location, historical background, etc. Specifically, the server executes an SQL query to retrieve basic information about "monumentA" from the database.

[0563] Step 4:

[0564] Generative AI models generate detailed information

[0565] The server sends a prompt to a generative AI model (e.g., ChatGPT) based on basic information (input) to generate content (output) such as detailed text, images, audio, and video related to tourist attractions. For example, a prompt such as "Please explain the detailed historical background of Monument A" is created and sent to the generative AI model. Specifically, the server processes the basic information, generates an appropriate prompt, and passes it to the AI ​​model.

[0566] Step 5:

[0567] Providing generated information

[0568] The server compiles the content (input) obtained from the generative AI model and sends it to the user's device (output). The device receives it and displays it on the screen. Specifically, the server sends the generated text and multimedia content in HTML or JSON format to the device. The device interprets the received data and provides it to the user.

[0569] Step 6:

[0570] Analysis and provision of congestion information

[0571] The server analyzes real-time and past visitor data (input) and generates predictions (output) of the current and future congestion status of tourist destinations, including visitor numbers and time-of-day data. Specifically, the server uses data analysis algorithms to generate congestion predictions based on past visitor trends and provides them to users.

[0572] Step 7:

[0573] Receiving and processing booking requests

[0574] A user sends a reservation request (input) from their terminal for a tourist spot or facility they are interested in. The server receives the reservation request, checks the availability of the facility, and confirms the reservation (output). For example, the server receives a request such as "I would like to visit Museum B at 10:00 AM," checks the availability in the database, and confirms the reservation if it is available.

[0575] Step 8:

[0576] Reservation confirmation notification

[0577] Once the reservation is confirmed, the server sends a reservation confirmation notification (output) to the terminal. The terminal receives this notification and presents it to the user. Specifically, the server sends a message to the terminal saying, "You have reserved a 10:00 AM slot at Museum B," and the terminal displays this to the user.

[0578] Step 9:

[0579] Generate and serve re-proposals

[0580] If the desired visit time is crowded, the server rechecks the crowding situation (input) and suggests alternative tourist spots and time slots (output). Specifically, using a generative AI model, it generates a suggestion such as "Monument A is currently crowded, so we recommend visiting adjacent Garden C at 10:30 AM," and sends it to the user's device.

[0581] (Application example 1)

[0582] 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."

[0583] The shopping experience at physical stores is difficult because it is difficult to grasp detailed product information and congestion status, and it is not easy to check product inventory or reservation status. This makes it difficult for consumers to make appropriate product selections or adjust visit times, and stores also find it difficult to improve customer service.

[0584] 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.

[0585] In this invention, the server includes: [means for reading a code installed in a physical store;] [means for sending a data request based on information obtained from the code;] [means for providing the user with information related to the generated product;] [means for providing real-time information about congestion in the store;] [means for processing reservations for products and services; and] [means for suggesting visiting times and other products.] This allows users to instantly grasp detailed product information and congestion status when shopping in a physical store, enabling them to shop efficiently and plan their visit appropriately.

[0586] A "brick and mortar store" is a commercial establishment located in a physical location where consumers can visit in person to purchase goods or services.

[0587] A "code" is a symbol, character string, or image that is visually expressed to read information, and in this invention refers to a QR code, a barcode, or the like.

[0588] A "data request" is a digital message that requests specific information from a server, through which the required information is obtained.

[0589] "Generated information" refers to detailed text, images, audio, video, and other information that is automatically generated from AI models and databases based on information read from the code.

[0590] "Crowd information" is information that indicates the degree of crowding in a specific location, allowing the user to know in advance how crowded the destination will be.

[0591] A "reservation" is a preliminary procedure for securing a product or service for a specific time period or date, and is confirmed based on the details specified by the user.

[0592] "Suggestion" is the act of providing alternatives or recommendations based on the user's requirements or circumstances, thereby giving the user options.

[0593] A "generative AI model" is an AI system that automatically generates new sentences and content using machine learning and natural language processing techniques, and in this invention is used to generate text, images, audio, video, etc.

[0594] A "prompt sentence" is a sentence that is input into a generative AI model and serves as an instruction for the AI ​​to generate specific content based on that sentence.

[0595] This invention is a system that provides an efficient and satisfying shopping experience by reading codes installed in physical stores, providing detailed product information, crowding status, and a reservation function. Specific embodiments of the system are described below.

[0596] System Configuration

[0597] The system mainly consists of the following components:

[0598] 1. Scan the code and send a request

[0599] When a user scans a QR code or barcode displayed in a physical store with their smartphone, the device retrieves the URL embedded in the code and sends it to the server. Upon receiving the request, the server analyzes the URL and identifies the ID of the corresponding product.

[0600] 2. Generating related information

[0601] The server retrieves basic information about the specified product from a database, then uses a generative AI model (e.g., OpenAI's ChatGPT) to automatically generate detailed content related to the product, including text, images, audio, and video. This generated information is then provided to the user.

[0602] 3. Providing congestion information

[0603] The server analyzes real-time and past visitor data to predict store congestion, allowing users to plan their visit based on current and future congestion forecasts.

[0604] 4. Reservation function

[0605] Users can request reservations for products or services they are interested in. The server receives the reservation request sent from the terminal, checks the inventory of the product or availability of the service, and then confirms the reservation. Once the reservation is confirmed, a confirmation notification is sent to the terminal.

[0606] 5. Re-suggestion function

[0607] If the desired visit time is busy, the server will suggest alternative times or related products, allowing users to plan a more efficient schedule.

[0608] Hardware and Software

[0609] Hardware: Smartphone (e.g. with QR code reader function), Server (Database server, Calculation server)

[0610] Software: Flask (web framework), MySQL (database management system), OpenAI API (generative AI model)

[0611] Data processing and calculation

[0612] 1. Code scanning and request submission

[0613] Use your smartphone's QR code reader to scan the QR code and send the URL to the server, which then extracts the product ID from the URL.

[0614] 2. Obtaining product information

[0615] The server connects to the MySQL database and retrieves basic information corresponding to the corresponding product ID.

[0616] 3. Generate detailed information

[0617] The server uses OpenAI's generative AI model to generate detailed text, images, audio, and video about the product, including example prompts such as:

[0618] Please provide more information about the following products:

[0619] Product Name: High Quality Tofu

[0620] Description: This tofu is made from organic soybeans and is of the highest quality.

[0621] 4. Analysis and provision of congestion information

[0622] The server analyzes real-time and past visitor data to predict congestion, allowing users to check the current congestion status of the store and predict future congestion.

[0623] 5. Reservation Processing

[0624] Receives a reservation request from a user, checks the stock status of the relevant product and availability of the service, and if the reservation is available, confirms the reservation and sends a confirmation notification to the user.

[0625] 6. Re-suggestion function

[0626] It provides a more efficient schedule by avoiding busy times and suggesting alternative times and related products to users.

[0627] This system allows users to instantly grasp detailed product information and congestion status in physical stores, enabling them to make appropriate purchase and visit plans.

[0628] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0629] Step 1:

[0630] Scan the code and submit the request

[0631] A user scans a QR code or barcode displayed in a physical store with their smartphone. The smartphone uses a QR code reader to obtain the URL embedded in the code. The obtained URL is sent to the server. The input is the QR code, and the output is the URL sent to the server.

[0632] Step 2:

[0633] Identifying the product ID

[0634] The server analyzes the received URL, extracts the product ID from the URL, and identifies the information corresponding to that product ID. The input is the URL, and the output is the extracted product ID. Specifically, the server analyzes the URL pattern to identify the product ID.

[0635] Step 3:

[0636] Obtaining product information

[0637] The server connects to the MySQL database and retrieves basic information corresponding to the specified product ID. The input is the product ID, and the output is the corresponding basic information. Specifically, it issues an SQL query to retrieve data such as the product name, description, price, and image URL.

[0638] Step 4:

[0639] Generate detailed information

[0640] The server uses a generative AI model (e.g., OpenAI's ChatGPT) to generate detailed information about the product. It sends prompts to the generative AI model to generate detailed text, images, audio, and video. The input is basic information and prompts, and the output is the generated detailed information. Specific examples of prompts are as follows:

[0641] Please provide more information about the following products:

[0642] Product Name: High Quality Tofu

[0643] Description: This tofu is made from organic soybeans and is of the highest quality.

[0644] Step 5:

[0645] Providing more information

[0646] The generated detailed information is displayed on the smartphone screen. The user can view detailed text, images, audio, and video. The input is the generated detailed information, and the output is the detailed information provided to the user. Specifically, the smartphone application receives the detailed information and displays it on the screen.

[0647] Step 6:

[0648] Analysis and provision of congestion status

[0649] The server analyzes the congestion situation based on real-time and past visitor data. It uses a congestion prediction model to calculate the current congestion situation and predict future congestion. The input is visitor data, and the output is congestion information. Specifically, the server analyzes the visitor data, calculates the congestion level, and provides it to the user.

[0650] Step 7:

[0651] Display of congestion information

[0652] Congestion information is displayed on the smartphone screen. Users can check the current congestion situation and future congestion forecasts. The input is congestion situation information, and the output is congestion information displayed to the user. Specifically, the smartphone application receives the congestion information and displays it on the screen.

[0653] Step 8:

[0654] Submit a booking request

[0655] A user requests a reservation for a product or service that they are interested in. The reservation request is sent from the device to the server. The input is the user's reservation request, and the output is the request sent to the server. Specifically, the smartphone application sends the request by pressing the reservation button.

[0656] Step 9:

[0657] Booking confirmation and notifications

[0658] The server processes the received reservation request and checks the inventory of the relevant product and availability of the service. If the reservation is possible, it confirms the reservation and sends a confirmation notice to the terminal. The input is the reservation request and the output is a reservation confirmation notice. Specifically, it accesses the database, updates the reservation information, and sends a notice.

[0659] Step 10:

[0660] Providing a re-proposal

[0661] If the desired visit time is busy, the server will suggest alternative times or related products. The suggestions are displayed on the terminal. The input is the busyness information and the user's request, and the output is an alternative suggestion. Specifically, a generative AI model is used to generate alternative suggestions and provide that information to the user.

[0662] Through the above processing steps, users can instantly grasp detailed product information and congestion status during their shopping experience at a physical store, enabling them to make efficient purchases and plans.

[0663] 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.

[0664] This invention is a system that reads QR codes installed at tourist spots to provide historical information and tourist guides related to the tourist spot, and also improves the user experience by providing congestion information, reservation functions, and an emotion engine.

[0665] System Configuration

[0666] 1. Scan the QR code and send a request

[0667] When a user scans a QR code installed at a tourist spot with their smartphone, the device retrieves the URL embedded in the QR code and sends it to the server. Upon receiving the request, the server analyzes the URL and identifies the ID of the corresponding tourist spot.

[0668] 2. Generating related information

[0669] The server retrieves basic information about the designated tourist destination from a database, then uses a generative AI model (e.g., ChatGPT) to automatically generate detailed content related to the destination, including text, images, audio, and video. This generated information is then provided to the user.

[0670] 3. Use of Emotion Engine

[0671] The device analyzes the user's facial expressions and voice and sends the data to the emotion engine. The server uses the emotion engine to recognize the user's emotional state and collects the results.

[0672] 4. Emotion-based modulation of information

[0673] The server uses data from the emotion engine to suggest tourist spots and provide information that takes the user's emotions into account. For example, if the user is excited, it can suggest tourist spots that are highly exciting.

[0674] 5. Providing congestion information

[0675] The server analyzes real-time and past visitor data to predict the current congestion situation at tourist destinations, allowing users to plan their visit based on the current congestion situation and future congestion predictions.

[0676] 6. Reservation function

[0677] Users can request reservations for tourist spots and facilities they are interested in. The server receives the reservation request sent from the device, checks the availability of the facility, and then confirms the reservation. Once the reservation is confirmed, a confirmation notification is sent to the device.

[0678] 7. Re-suggestion function

[0679] If the desired visit time is busy, the server will suggest alternative times or related tourist spots, allowing users to create a more efficient schedule.

[0680] Specific examples

[0681] For example, when a user scans a QR code for historical monument "A" with their smartphone, the device sends the URL "https: / / example.com / monumentA" to a server. The server parses the URL and retrieves information about monument A from a database. Using a generative AI model, detailed descriptive text, images, audio, and video content about monument A is automatically generated. This information is displayed to the user, including, for example, a detailed explanation of "the history and background of monument A."

[0682] Furthermore, the device collects the user's facial expressions and voice, which the emotion engine analyzes to recognize the user's emotional state. For example, if the emotion engine recognizes that the user is excited, the server will suggest tourist spots that are highly exciting (e.g., active attractions).

[0683] If the user later wishes to visit the neighboring Museum B and makes a reservation, the terminal sends a reservation request to the server. The server checks the availability of Museum B, and if a reservation is possible, the user receives a notification saying, "You have reserved a 10:00 AM slot at Museum B."

[0684] If the planned visit time is busy, the server will use the AI ​​model to suggest alternative tourist spots or different times. For example, "Monument A is currently busy, so we recommend visiting adjacent Garden C at 10:30 AM."

[0685] This allows users to have an efficient and in-depth sightseeing experience. By simply scanning the QR code installed at tourist spots, the system combines an emotion engine to provide more personalized tourist information, providing detailed information about tourist spots, obtaining congestion information, using reservation functions, and utilizing re-suggestion functions.

[0686] The processing flow will be explained below.

[0687] Step 1:

[0688] The user scans a QR code installed at a tourist spot with their smartphone.

[0689] Step 2:

[0690] The device sends the URL obtained from the QR code to the server (e.g., an HTTP GET request).

[0691] Step 3:

[0692] The server analyzes the received URL and identifies the ID of the tourist spot corresponding to the URL.

[0693] Step 4:

[0694] The server retrieves basic information about the tourist destination (historical background, points of interest, etc.) from a database.

[0695] Step 5:

[0696] The server instructs the generative AI model (e.g., ChatGPT) to automatically generate detailed text, image, audio, and video content based on the basic information acquired.

[0697] Step 6:

[0698] The server aggregates the generated related information to form content for presentation to the user.

[0699] Step 7:

[0700] The device collects the user's facial expressions and voice and sends them to the emotion engine.

[0701] Step 8:

[0702] The emotion engine analyzes the user's emotional state and sends the results to the server.

[0703] Step 9:

[0704] Based on the analysis results from the emotion engine, the server adjusts the tourist destination suggestions and information provided, taking into account the user's emotions.

[0705] Step 10:

[0706] The server runs the congestion prediction model to generate the current congestion status and future congestion predictions for the tourist destination.

[0707] Step 11:

[0708] The server transmits the integrated information and congestion information to the terminal.

[0709] Step 12:

[0710] The terminal displays the information received from the server to the user.

[0711] Step 13:

[0712] Request reservations for attractions and facilities that interest the user.

[0713] Step 14:

[0714] The terminal sends a reservation request to the server.

[0715] Step 15:

[0716] The server checks the availability of the facility in real time.

[0717] Step 16:

[0718] The server confirms that the reservation is possible and processes the reservation, saving the reservation information and sending a confirmation of the reservation to the terminal.

[0719] Step 17:

[0720] The server uses the generated AI model to suggest alternative times and tourist destinations based on crowding predictions.

[0721] Step 18:

[0722] The server transmits the re-proposal information to the terminal, and the terminal notifies the user of it.

[0723] The above are the specific processing steps of this system.

[0724] Example 2

[0725] 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."

[0726] When travelers visit tourist destinations, they want to quickly obtain detailed local information and receive tourist guidance tailored to their individual needs. However, conventional tourist information systems only provide one-way information and lack congestion information, reservation functions, and personalized guidance based on individual emotional states. They also lack efficient schedule adjustments based on the current and future congestion situation at tourist destinations. This poses a challenge, reducing the convenience of travelers and the quality of their tourism experience.

[0727] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0728] In this invention, the server includes: [means for reading codes installed at tourist attractions;] [means for sending a data request based on information obtained from the code;] [means for generating information related to tourist attractions using a generative AI model;] [means for providing the generated information related to tourist attractions to the user;] [means for utilizing an emotion engine for analyzing user emotion data obtained from the terminal;] [means for adjusting tourist attraction information based on the user's emotional state;] [means for providing real-time congestion information about tourist attractions;] [means for processing reservations for tourist attractions and facilities; and [means for suggesting visiting times and alternative tourist attractions.] This enables users to obtain detailed information about tourist attractions in real time and receive personalized tourist guidance according to their individual emotional state, and also enables efficient schedule adjustments and reservation management based on congestion levels.

[0729] "Means for reading codes installed at tourist spots" refers to a function that allows users to scan codes such as QR codes installed at tourist spots with devices such as smartphones or tablets and obtain the information contained in the codes.

[0730] The "means for transmitting a data request based on information obtained from the code" is a function for transmitting the information of the obtained code to the server via the terminal.

[0731] "Means for generating information related to tourist destinations using a generative AI model" refers to a function that automatically generates content such as detailed text, images, audio, and video related to tourist destinations using a generative AI model (e.g., a text generation model).

[0732] "Means for providing users with information related to the generated tourist destinations" refers to a function that allows content generated by the generative AI model to be sent to a terminal and for users to receive the information visually or audibly.

[0733] "Means for utilizing an emotion engine that analyzes user emotion data acquired from the terminal" refers to a function in which the terminal collects the user's facial expressions and voice, analyzes them with the server's emotion engine, and recognizes the user's emotional state.

[0734] The "means for adjusting information about tourist attractions based on the user's emotional state" is a function that dynamically adjusts the information provided and the content of suggested tourist attractions according to the user's emotional state recognized by the emotion engine.

[0735] "Means for providing real-time congestion information at tourist destinations" is a function that uses a data analysis model to analyze the current congestion situation and predicted congestion information at tourist destinations and provides it to users in real time.

[0736] "Means for processing reservations for tourist attractions and facilities" refers to the function of receiving a reservation request for a tourist attraction or facility that a user wishes to visit, and the server confirming and confirming the reservation in response to that request.

[0737] "Means for suggesting visiting times and alternative tourist spots" is a function that suggests visiting times and alternative tourist spots depending on the congestion situation and reservation status.

[0738] This invention is a system that uses QR codes installed at tourist spots to provide users with detailed information about tourist spots, congestion information, and reservation functions, and aims to provide personalized guidance based on the user's emotional state.

[0739] System configuration

[0740] The system mainly includes the following components:

[0741] Terminal: A portable device such as a smartphone or tablet that can be used to scan QR codes installed at tourist attractions.

[0742] Server: A central computer system that processes requests sent from user devices and generates information using generative AI models and emotion engines.

[0743] Database: A database system (e.g. MySQL) for storing basic information about tourist destinations.

[0744] Generative AI models: Artificial intelligence models for automatically generating content such as text, images, audio, and video (e.g., ChatGPT).

[0745] Emotion engine: Software that analyzes a user's facial expressions and voice to recognize their emotional state (e.g., Microsoft Azure Emotion API).

[0746] Operation overview and examples

[0747] The system begins when a user arrives at a tourist spot and scans a QR code.

[0748] 1. Scan the QR code

[0749] When a user uses a smartphone to scan a QR code installed at a tourist spot, the device retrieves the information, which includes a URL related to the tourist spot.

[0750] 2. Submitting a Data Request

[0751] The device sends the URL (e.g., "https: / / example.com / monumentA") to the server. The device makes this data request using its Internet connection.

[0752] 3. Analysis and Acquisition of Information

[0753] When the server receives the request, it analyzes the URL to identify the tourist spot ID, and then retrieves basic information about the corresponding tourist spot from the database.

[0754] 4. Content generation using generative AI models

[0755] The server inputs a prompt into a generative AI model (e.g., ChatGPT) to generate detailed content related to the tourist attraction, such as text, images, audio, and video. For example, the prompt might be, "Please explain the detailed history and characteristics of Monument A."

[0756] 5. Providing Information to Users

[0757] The generated content is sent from the server to the terminal and displayed on the terminal screen, allowing the user to receive a detailed explanation of "Monument A's history and characteristics" visually or audibly.

[0758] 6. Analysis by Emotion Engine

[0759] The device uses a camera and microphone to collect the user's facial expressions and voice, and sends them to emotion analysis software (e.g., Microsoft Azure Emotion API). The analysis results are then sent to a server.

[0760] 7. Emotion-based modulation of information

[0761] The server provides information based on the user's emotional state based on data from the emotion engine. For example, if the user is excited, it will suggest tourist spots that are likely to excite the user.

[0762] 8. Providing congestion information

[0763] The server analyzes real-time data and past visitor data, and uses a congestion prediction model to predict current and future congestion information for tourist destinations. This information is then provided to users.

[0764] 9. Processing booking requests

[0765] When a user wants to reserve a specific tourist spot or facility, they send a reservation request from their device. The server checks the availability of the facility and confirms the reservation. Once the reservation is complete, a confirmation notification is sent to the device.

[0766] 10. Using the re-suggestion feature

[0767] If reservations are busy or based on crowd predictions, the server will suggest alternative tourist spots and visiting times, such as "Monument A is currently crowded, so we recommend visiting adjacent Garden C at 10:30 AM."

[0768] This allows users to obtain detailed information about tourist spots in real time and receive personalized information according to their emotional state.In addition, the system provides congestion information and reservation functions, enabling efficient schedule adjustment and a rich sightseeing experience.

[0769] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0770] Step 1:

[0771] A user scans a QR code installed at a tourist spot. The input is the QR code scanned using the user's smartphone, and the output is the URL contained in the acquired QR code. Specifically, when the user launches the smartphone's camera app and reads the QR code, the URL (e.g., "https: / / example.com / monumentA") is displayed.

[0772] Step 2:

[0773] The device sends the scanned URL to the server. The input is the URL obtained by the device, and the output is the URL request received by the server. Specifically, the URL information is sent to the server via the smartphone's Internet connection.

[0774] Step 3:

[0775] The server analyzes the received URL. The input is the URL data, and the output is the tourist spot ID. Specifically, the server analyzes the URL using Apache HTTP Server or similar and extracts the tourist spot identifier, such as "monumentA."

[0776] Step 4:

[0777] The server retrieves basic information about tourist attractions from a database. The input is the tourist attraction's ID, and the output is the tourist attraction's basic information. Specifically, the server sends a query to the MySQL database to retrieve information such as the tourist attraction's name, location, and brief history.

[0778] Step 5:

[0779] The server generates content using a generative AI model. The input is basic information about the tourist attraction and a prompt, and the output is generated content such as text, images, audio, and video. For example, the prompt "Please explain the detailed history and characteristics of Monument A" is sent to ChatGPT, which generates a detailed explanatory text and related images.

[0780] Step 6:

[0781] The server sends the generated content to the device. The input is the generated content, and the output is the content displayed on the device. Specifically, the server sends the generated text and images to the smartphone and displays them on the smartphone screen.

[0782] Step 7:

[0783] The device collects the user's facial expressions and voice and sends them to the emotion engine. The input is the user's facial expression and voice data, and the output is the emotion analysis results. Specifically, data is collected using the smartphone's camera and microphone and sent to the Microsoft Azure Emotion API.

[0784] Step 8:

[0785] The server receives the emotion analysis results and analyzes the user's emotional state. The input is the emotion analysis results, and the output is a classification of the user's emotional state (e.g., excited, relaxed). The server adjusts the information provided based on the analysis results.

[0786] Step 9:

[0787] The server provides personalized information to the device based on the user's emotional state. The input is the user's emotional state and tourist attraction information, and the output is tailored tourist attraction information and suggestions. For example, if the user is excited, active attractions are suggested.

[0788] Step 10:

[0789] The server analyzes and provides congestion information in real time. The input is real-time visitor data and past visit data, and the output is current and predicted congestion information. The server performs the analysis using data analysis software and sends the results to the terminal.

[0790] Step 11:

[0791] A user requests a reservation for a tourist attraction or facility. The input is the reservation information for the tourist attraction or facility desired by the user, and the output is a reservation request sent to the server. Specifically, the user inputs the reservation request through a smartphone application.

[0792] Step 12:

[0793] The server processes reservation requests. The input is a reservation request from the user, and the output is a reservation confirmation notification. The server accesses the reservation management system, checks availability, and confirms the reservation. Once the reservation is completed, a confirmation notification is sent to the terminal.

[0794] Step 13:

[0795] The server suggests visiting times and alternative tourist spots based on the congestion situation. The input is the congestion situation data and the user's schedule, and the output is an alternative suggestion. The server uses an AI model to calculate the optimal visiting time and alternative tourist spots and notify the user. For example, a suggestion might be, "Monument A is currently crowded, so we recommend visiting adjacent Garden C at 10:30 AM."

[0796] (Application example 2)

[0797] 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."

[0798] In recent years, there has been a demand for systems that can improve the user experience when visiting tourist spots and virtual stores. Conventional systems lack real-time congestion information and reservation functions, and it is difficult to provide personalized information based on the user's emotional state. In addition, generating tourist spot and store information takes time, which results in problems with the quality of the user experience.

[0799] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for reading a code placed at a tourist attraction or virtual store, means for sending a data request based on information obtained from the code, and means for providing the user with the generated information related to the tourist attraction or store. This makes it possible to provide real-time congestion information for the tourist attraction or store and to provide and suggest information based on the user's emotional state. It also processes reservations for tourist attractions, facilities, or products, and suggests reservations for the time period during which the visitor should visit, or for other tourist attractions or different time periods.

[0800] "Tourist attractions or virtual stores" includes not only actual physical tourist attractions or commercial areas, but also stores or tourist attractions recreated in virtual space.

[0801] "Code" means a medium with embedded information, including a QR code, barcode, or other type of code, that can be scanned by a User's device.

[0802] "Means for sending a data request" refers to the method used to send the URL and identifying information obtained by scanning the code to a server to obtain the associated data.

[0803] "Generated Information" includes text, images, audio, and video content created by the server using databases and generative AI models.

[0804] "Crowd information" is information that indicates the congestion status of a target location or store, generated using current visitor numbers, past data, and predictive algorithms.

[0805] "Means for processing reservations" refers to the method by which users make reservations for visit times and facility use at specific tourist attractions and stores, and the associated back-end processing mechanisms.

[0806] "Emotional state" refers to the user's mental and emotional state as identified by analyzing the user's facial expressions and voice.

[0807] "Means for providing or suggesting information" refers to methods that not only display generated information to the user, but also dynamically provide recommended information based on the user's current emotional state.

[0808] This invention provides a system including means for reading QR codes installed at tourist attractions or virtual stores, means for sending a data request based on information obtained from the read code, means for providing the user with generated information related to the tourist attraction or store, means for providing real-time congestion information about the tourist attraction or store, means for processing reservations for tourist attractions, facilities, or products, means for suggesting a visiting time period or reservations for another tourist attraction or a different time period, and means for analyzing the user's emotional state and providing or suggesting information based on the results.

[0809] Hardware and Software

[0810] To realize this system, the following hardware and software are used.

[0811] Smart glasses: Using Microsoft HoloLens as an example.

[0812] Smartphone: iOS or Android smartphone.

[0813] Emotion engine: Uses Microsoft Azure Emotion API.

[0814] Generative AI model: OpenAI GPT-4.

[0815] Backend: Node.js, Express, MongoDB.

[0816] Data processing and calculation

[0817] 1. Scan the QR code and send a data request

[0818] The server obtains the URL by scanning the QR code with the user's smart glasses or smartphone and sends the information to the server, which then analyzes the URL and retrieves the corresponding store and product information from the database.

[0819] 2. Provision of generated information

[0820] The server uses a generative AI model (OpenAI GPT-4) to automatically generate detailed related text, image, audio, and video content based on the information retrieved from the database. The generated information is then sent to the device and provided to the user.

[0821] 3. Use of Emotion Engine

[0822] The server receives facial and voice data acquired from the user's smart glasses or smartphone and sends it to the Microsoft Azure Emotion API to analyze the user's emotional state. The server receives the results of the analysis by the emotion engine.

[0823] 4. Emotionally driven information

[0824] The server uses a generative AI model to create product suggestions or tourist spot recommendations based on the acquired emotional data and provides them to the user. For example, if the user is excited, it will suggest new products or limited-time offers.

[0825] 5. Providing congestion information and reservation functions

[0826] The server analyzes real-time congestion information and notifies users of the congestion situation. If it is crowded, it will suggest less crowded times or other locations and inform users of available reservation times.

[0827] 6. Re-suggestion function

[0828] If the user's desired time slot is crowded, the server will suggest alternative tourist spots and time slots. The server dynamically generates these alternatives through a generative AI model and presents them to the user.

[0829] Specific examples

[0830] For example, when a user scans the QR code of a "virtual fashion store" in a virtual mall, detailed information about the new autumn collection is provided. When using smart glasses, excitement from facial expressions is analyzed through an emotion engine (Microsoft Azure Emotion API), and the most popular items from the new collection are suggested. Crowd information is also provided in real time, allowing users to reserve a suitable time to visit. If the store is crowded, a reservation for a less crowded time slot is suggested.

[0831] Prompt Sentence Examples

[0832] An example of a prompt to be input to the generative AI model is as follows:

[0833] Product information: [Product A: Smartphone, Product B: Smartwatch]

[0834] Emotional state: Highly excited

[0835] Proposal Generation: Suggest the best product offers to highly excited users.

[0836] This can significantly improve the user experience.

[0837] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0838] Step 1:

[0839] The user scans a QR code placed at a tourist spot or virtual store using smart glasses or a smartphone.

[0840] Input: QR code

[0841] Output: URL obtained from QR code

[0842] How it works: A user scans a QR code with their device and retrieves the URL embedded in the code.

[0843] Step 2:

[0844] The terminal sends the acquired URL to the server.

[0845] Input: URL obtained from QR code

[0846] Output: Data request sent to the server

[0847] Operation: The device requests detailed information by sending the URL information it has obtained to the server.

[0848] Step 3:

[0849] The server analyzes the URL and retrieves information about the relevant tourist attraction or store from a database.

[0850] Input: URL information sent to the server

[0851] Output: Tourist attraction or store information retrieved from the database

[0852] How it works: The server parses the URL, identifies the ID of the relevant tourist attraction or store, and retrieves the necessary information from the database based on that ID.

[0853] Step 4:

[0854] The server uses a generative AI model (e.g., OpenAI GPT-4) to generate detailed text, image, audio, and video content based on tourist attraction or store information.

[0855] Input: Tourist attraction or store information retrieved from the database

[0856] Output: Generated detailed content (text, images, audio, video)

[0857] How it works: The server inputs the acquired information into a generative AI model, which automatically generates detailed content based on the prompt.

[0858] Step 5:

[0859] The server sends the generated information to the terminal and displays it to the user.

[0860] Input: Generated detail content

[0861] Output: Detailed content sent to the device, displayed to the user

[0862] How it works: The server sends the content created by the generative AI model to the device and displays it for the user to view.

[0863] Step 6:

[0864] The device collects the user's facial expressions and voice and sends them to an emotion engine (e.g., Microsoft Azure Emotion API).

[0865] Input: User facial and voice data

[0866] Output: Data sent to the emotion engine

[0867] How it works: The device sends the user's facial expression and voice data collected to the emotion engine.

[0868] Step 7:

[0869] The emotion engine analyzes the user's emotional state and sends the results back to the server.

[0870] Input: User facial and voice data

[0871] Output: Emotional state analysis result

[0872] How it works: Analyzes the user's emotional state using waveform analysis and facial expression recognition algorithms. The analysis results are sent to the server.

[0873] Step 8:

[0874] Based on the emotion data, the server uses a generative AI model to regenerate information appropriate for the user and transmits it to the device.

[0875] Input: Emotional state analysis results, generative AI model

[0876] Output: Regenerated personalization information

[0877] How it works: The server adds the acquired emotional data to the prompt text, creates new personalized information using the generative AI model, and suggests it to the user.

[0878] Step 9:

[0879] The server uses the congestion prediction model to analyze the congestion situation at tourist spots or stores and provides real-time information and predicted information to users.

[0880] Input: Current and past visit data

[0881] Output: Real-time and forecast information on congestion status

[0882] Operation: The server analyzes visit data and uses a congestion prediction model to generate real-time and future congestion information, which it then provides to users.

[0883] Step 10:

[0884] The user requests a reservation for a desired tourist spot, facility, or product, and the server processes the reservation.

[0885] Input: User's booking request

[0886] Output: Booking confirmation notice

[0887] How it works: The server receives a reservation request from a user, processes the reservation, checks availability, and then notifies the user that the reservation is confirmed.

[0888] Step 11:

[0889] If the user's desired time is busy, the server will suggest alternative tourist spots or different visiting times.

[0890] Input: Crowd data, generative AI model

[0891] Output: Alternative proposal information

[0892] How it works: The server analyzes the congestion data and uses a generative AI model to generate and provide suggestions for alternative tourist destinations and visiting times to the user.

[0893] This results in a system that improves the user's experience in a more personalized and efficient manner.

[0894] 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.

[0895] 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.

[0896] 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.

[0897] [Third embodiment]

[0898] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0899] 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.

[0900] 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).

[0901] 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.

[0902] 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.

[0903] 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).

[0904] 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.

[0905] 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.

[0906] 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.

[0907] 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.

[0908] 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.

[0909] 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."

[0910] This invention is a system that reads QR codes installed at tourist spots to provide historical information and tourist guides related to the tourist spot, and also improves the tourist experience by providing congestion information and reservation functions.

[0911] System Configuration

[0912] 1. Scan the QR code and send a request

[0913] When a user scans a QR code installed at a tourist spot with their smartphone, the device retrieves the URL embedded in the QR code and sends it to the server. Upon receiving the request, the server analyzes the URL and identifies the ID of the corresponding tourist spot.

[0914] 2. Generating related information

[0915] The server retrieves basic information about the designated tourist destination from a database, then uses a generative AI model (e.g., ChatGPT) to automatically generate detailed content related to the destination, including text, images, audio, and video. This generated information is then provided to the user.

[0916] 3. Providing congestion information

[0917] The server analyzes real-time and past visitor data to predict the congestion status of tourist spots, allowing users to plan their visit based on the current congestion status and future congestion forecasts.

[0918] 4. Reservation function

[0919] Users can request reservations for tourist spots and facilities they are interested in. The server receives the reservation request sent from the device, checks the availability of the facility, and then confirms the reservation. Once the reservation is confirmed, a confirmation notification is sent to the device.

[0920] 5. Re-suggestion function

[0921] If the desired visit time is busy, the server will suggest alternative times or related tourist spots, allowing users to create a more efficient schedule.

[0922] Specific examples

[0923] For example, when a user scans a QR code for historical monument "A" with their smartphone, the device sends the URL "https: / / example.com / monumentA" to a server. The server parses the URL and retrieves information related to monument A from a database. Using a generative AI model, detailed descriptive text, images, audio, and video content about monument A is automatically generated. This information is displayed to the user, including, for example, a detailed explanation of "the history and background of monument A."

[0924] If the user later wishes to visit the neighboring Museum B and makes a reservation, the terminal sends a reservation request to the server. The server checks the availability of Museum B, and if a reservation is possible, the user receives a notification saying, "You have reserved a 10:00 AM slot at Museum B."

[0925] If the planned visit time is busy, the server will use the AI ​​model to suggest alternative tourist spots or different times. For example, "Monument A is currently busy, so we recommend visiting adjacent Garden C at 10:30 AM."

[0926] This allows users to have an efficient and in-depth sightseeing experience. By simply scanning the QR code installed at tourist spots, this system provides detailed information about tourist spots, acquires congestion information, and utilizes reservation and re-suggestion functions.

[0927] The processing flow will be explained below.

[0928] Step 1:

[0929] The user scans a QR code installed at a tourist spot with their smartphone.

[0930] Step 2:

[0931] The device sends the URL obtained from the QR code to the server (e.g., an HTTP GET request).

[0932] Step 3:

[0933] The server analyzes the received URL and identifies the ID of the tourist spot corresponding to the URL.

[0934] Step 4:

[0935] The server retrieves basic information about the tourist destination (historical background, points of interest, etc.) from a database.

[0936] Step 5:

[0937] The server instructs the generative AI model (e.g., ChatGPT) to automatically generate detailed text, image, audio, and video content based on the basic information acquired.

[0938] Step 6:

[0939] The server aggregates the generated related information to form content for presentation to the user.

[0940] Step 7:

[0941] The server runs the congestion prediction model to generate the current congestion status and future congestion predictions for the tourist destination.

[0942] Step 8:

[0943] The server transmits the integrated information and congestion information to the terminal.

[0944] Step 9:

[0945] The terminal displays the information received from the server to the user.

[0946] Step 10:

[0947] Request reservations for attractions and facilities that interest the user.

[0948] Step 11:

[0949] The terminal sends a reservation request to the server.

[0950] Step 12:

[0951] The server checks the availability of the facility in real time.

[0952] Step 13:

[0953] The server confirms that the reservation is possible and processes the reservation, saving the reservation information and sending a confirmation of the reservation to the terminal.

[0954] Step 14:

[0955] If the user is unable to visit at the time they wish, the server uses the generated AI model to suggest alternative times or other tourist destinations.

[0956] Step 15:

[0957] The server transmits the re-proposal information to the terminal, and the terminal notifies the user of it.

[0958] The above are the specific processing steps of this system.

[0959] Example 1

[0960] 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."

[0961] For users visiting tourist destinations, it is difficult to easily obtain information about the history and history of the destination, and to efficiently enjoy sightseeing while understanding the congestion situation. There is also a lack of easy ways to make reservations at tourist destinations and facilities. There is a need for a system that can solve these issues and provide users with a more fulfilling tourist experience.

[0962] 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.

[0963] In this invention, the server includes: [means for identifying a tourist attraction ID and acquiring basic information based on it;] [means for sending a prompt to a generative AI model and generating detailed information related to the tourist attraction; and] [means for analyzing visitor data and providing congestion information about the tourist attraction in real time.] This enables [users to easily obtain detailed information about tourist attractions, understand the congestion situation, and enjoy sightseeing efficiently.] It also enables users to easily make reservations for tourist attractions and facilities, providing a more fulfilling sightseeing experience.

[0964] A "tourist attraction ID" is an identifier that uniquely identifies a tourist attraction.

[0965] "Basic information" refers to basic data about tourist destinations, such as their names, locations, and historical backgrounds.

[0966] A "generative AI model" is an artificial intelligence model that automatically generates content such as text, images, audio, and video based on given prompts.

[0967] A "prompt" is an instruction or question that is input to a generative AI model, and is the input data that the model uses to respond or generate something.

[0968] "Detailed information" refers to additional information such as text, images, audio, and video generated based on basic information about a tourist destination.

[0969] "Visitor Data" means data that indicates the current and past number of visitors to a tourist destination and their trends.

[0970] "Crowd information" is information that includes predictions of the current and future number of visitors to a tourist destination, and is data that is useful for users to plan their visit.

[0971] "Reservation" refers to the process of reserving a time or slot for use of a tourist attraction or facility in advance.

[0972] "Re-suggestion" refers to suggesting an alternative time to visit or a different tourist spot to the user based on the congestion and availability of the spot.

[0973] This invention is a system that reads QR codes installed at tourist spots, and provides detailed information about the tourist spot, congestion status, and reservation and re-suggestion functions. Specific embodiments of this system are described below.

[0974] System configuration and operation

[0975] 1. Scan the QR code and send a request

[0976] When a user scans a QR code installed at a tourist spot with their smartphone, the URL is obtained and sent from the device to the server, which analyzes the URL and identifies the tourist spot ID.

[0977] 2. Generating related information

[0978] The server retrieves basic information from a database based on the tourist attraction ID it identifies. This information is then sent as a prompt to a generative AI model (e.g., ChatGPT) to generate detailed content related to the tourist attraction, including text, images, audio, and video. For example, a prompt might be, "Please provide a detailed historical background for Monument A." The generated information is then sent to the user's device and displayed.

[0979] 3. Providing congestion information

[0980] The server analyzes past and current visitor data for tourist destinations and predicts congestion in real time, which is provided to users as a reference when planning their trip.

[0981] 4. Reservation function

[0982] When a user wishes to visit a tourist spot or facility, they send a reservation request from their device. The server checks the facility's reservation status and confirms the reservation if there is availability. If the reservation is confirmed, a notification is sent to the device. For example, a confirmation may be received saying, "You have reserved a slot at Museum B for 10:00 AM."

[0983] 5. Re-suggestion function

[0984] If the user's desired visit time is busy, the server will suggest alternative tourist spots and time slots. The server uses a generative AI model to make appropriate suggestions. For example, a suggestion might be generated: "Monument A is currently busy, so we recommend visiting adjacent Garden C at 10:30 AM."

[0985] Specific examples

[0986] For example, if a user scans the QR code for historical monument "A" with their smartphone, the device sends the URL "https: / / example.com / monumentA" to the server. The server parses the URL and identifies the ID of monument A. The server then retrieves basic information about monument A from a database and sends a prompt to the generative AI model saying, "Please provide a detailed historical background for monument A." The generated explanatory text, images, audio, and video are then displayed on the user's device.

[0987] If the user later wishes to visit the neighboring Museum B and requests a reservation, the terminal sends the reservation information to the server. The server checks the availability of Museum B, and if a reservation is possible, a notification is sent to the user's terminal stating, "You have reserved a 10:00 AM slot at Museum B."

[0988] Furthermore, if the planned visit time is busy, the server can make an alternative suggestion such as, "Monument A is currently busy, so we recommend visiting adjacent Garden C at 10:30 AM."

[0989] In this way, users can have a fulfilling sightseeing experience by providing detailed information about tourist spots, obtaining congestion information, using the reservation function, and utilizing the re-suggestion function.

[0990] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0991] Step 1:

[0992] Scan the QR code and send a request

[0993] A user scans a QR code installed at a tourist spot with their smartphone camera. The device automatically sends the URL extracted from the QR code (input) to the server (output). In this case, the URL data is sent to the server using an HTTP request. Specifically, the device reads the URL "https: / / example.com / monumentA" and sends it to the server.

[0994] Step 2:

[0995] URL analysis and identification of tourist spot IDs

[0996] The server analyzes the URL received as input and identifies the tourist attraction ID from the path portion of the URL (output). For example, the tourist attraction ID "monumentA" is extracted from the path " / monumentA" of the URL "https: / / example.com / monumentA". This gives the server the unique identifier it needs to use in the next step. Specifically, the server analyzes the path portion of the URL using regular expressions or string manipulation.

[0997] Step 3:

[0998] Obtaining basic information

[0999] The server uses the tourist attraction ID (input) to retrieve basic information (output) of the corresponding tourist attraction from the database. The basic information includes the tourist attraction's name, location, historical background, etc. Specifically, the server executes an SQL query to retrieve basic information about "monumentA" from the database.

[1000] Step 4:

[1001] Generative AI models generate detailed information

[1002] The server sends a prompt to a generative AI model (e.g., ChatGPT) based on basic information (input) to generate content (output) such as detailed text, images, audio, and video related to tourist attractions. For example, a prompt such as "Please explain the detailed historical background of Monument A" is created and sent to the generative AI model. Specifically, the server processes the basic information, generates an appropriate prompt, and passes it to the AI ​​model.

[1003] Step 5:

[1004] Providing generated information

[1005] The server compiles the content (input) obtained from the generative AI model and sends it to the user's device (output). The device receives it and displays it on the screen. Specifically, the server sends the generated text and multimedia content in HTML or JSON format to the device. The device interprets the received data and provides it to the user.

[1006] Step 6:

[1007] Analysis and provision of congestion information

[1008] The server analyzes real-time and past visitor data (input) and generates predictions (output) of the current and future congestion status of tourist destinations, including visitor numbers and time-of-day data. Specifically, the server uses data analysis algorithms to generate congestion predictions based on past visitor trends and provides them to users.

[1009] Step 7:

[1010] Receiving and processing booking requests

[1011] A user sends a reservation request (input) from their terminal for a tourist spot or facility they are interested in. The server receives the reservation request, checks the availability of the facility, and confirms the reservation (output). For example, the server receives a request such as "I would like to visit Museum B at 10:00 AM," checks the availability in the database, and confirms the reservation if it is available.

[1012] Step 8:

[1013] Reservation confirmation notification

[1014] Once the reservation is confirmed, the server sends a reservation confirmation notification (output) to the terminal. The terminal receives this notification and presents it to the user. Specifically, the server sends a message to the terminal saying, "You have reserved a 10:00 AM slot at Museum B," and the terminal displays this to the user.

[1015] Step 9:

[1016] Generate and serve re-proposals

[1017] If the desired visit time is crowded, the server rechecks the crowding situation (input) and suggests alternative tourist spots and time slots (output). Specifically, using a generative AI model, it generates a suggestion such as "Monument A is currently crowded, so we recommend visiting adjacent Garden C at 10:30 AM," and sends it to the user's device.

[1018] (Application example 1)

[1019] 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."

[1020] The shopping experience at physical stores is difficult because it is difficult to grasp detailed product information and congestion status, and it is not easy to check product inventory or reservation status. This makes it difficult for consumers to make appropriate product selections or adjust visit times, and stores also find it difficult to improve customer service.

[1021] 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.

[1022] In this invention, the server includes: [means for reading a code installed in a physical store;] [means for sending a data request based on information obtained from the code;] [means for providing the user with information related to the generated product;] [means for providing real-time information about congestion in the store;] [means for processing reservations for products and services; and] [means for suggesting visiting times and other products.] This allows users to instantly grasp detailed product information and congestion status when shopping in a physical store, enabling them to shop efficiently and plan their visit appropriately.

[1023] A "brick and mortar store" is a commercial establishment located in a physical location where consumers can visit in person to purchase goods or services.

[1024] A "code" is a symbol, character string, or image that is visually expressed to read information, and in this invention refers to a QR code, a barcode, or the like.

[1025] A "data request" is a digital message that requests specific information from a server, through which the required information is obtained.

[1026] "Generated information" refers to detailed text, images, audio, video, and other information that is automatically generated from AI models and databases based on information read from the code.

[1027] "Crowd information" is information that indicates the degree of crowding in a specific location, allowing the user to know in advance how crowded the destination will be.

[1028] A "reservation" is a preliminary procedure for securing a product or service for a specific time period or date, and is confirmed based on the details specified by the user.

[1029] "Suggestion" is the act of providing alternatives or recommendations based on the user's requirements or circumstances, thereby giving the user options.

[1030] A "generative AI model" is an AI system that automatically generates new sentences and content using machine learning and natural language processing techniques, and in this invention is used to generate text, images, audio, video, etc.

[1031] A "prompt sentence" is a sentence that is input into a generative AI model and serves as an instruction for the AI ​​to generate specific content based on that sentence.

[1032] This invention is a system that provides an efficient and satisfying shopping experience by reading codes installed in physical stores, providing detailed product information, crowding status, and a reservation function. Specific embodiments of the system are described below.

[1033] System Configuration

[1034] The system mainly consists of the following components:

[1035] 1. Scan the code and send a request

[1036] When a user scans a QR code or barcode displayed in a physical store with their smartphone, the device retrieves the URL embedded in the code and sends it to the server. Upon receiving the request, the server analyzes the URL and identifies the ID of the corresponding product.

[1037] 2. Generating related information

[1038] The server retrieves basic information about the specified product from a database, then uses a generative AI model (e.g., OpenAI's ChatGPT) to automatically generate detailed content related to the product, including text, images, audio, and video. This generated information is then provided to the user.

[1039] 3. Providing congestion information

[1040] The server analyzes real-time and past visitor data to predict store congestion, allowing users to plan their visit based on current and future congestion forecasts.

[1041] 4. Reservation function

[1042] Users can request reservations for products or services they are interested in. The server receives the reservation request sent from the terminal, checks the inventory of the product or availability of the service, and then confirms the reservation. Once the reservation is confirmed, a confirmation notification is sent to the terminal.

[1043] 5. Re-suggestion function

[1044] If the desired visit time is busy, the server will suggest alternative times or related products, allowing users to plan a more efficient schedule.

[1045] Hardware and Software

[1046] Hardware: Smartphone (e.g. with QR code reader function), Server (Database server, Calculation server)

[1047] Software: Flask (web framework), MySQL (database management system), OpenAI API (generative AI model)

[1048] Data processing and calculation

[1049] 1. Code scanning and request submission

[1050] Use your smartphone's QR code reader to scan the QR code and send the URL to the server, which then extracts the product ID from the URL.

[1051] 2. Obtaining product information

[1052] The server connects to the MySQL database and retrieves basic information corresponding to the corresponding product ID.

[1053] 3. Generate detailed information

[1054] The server uses OpenAI's generative AI model to generate detailed text, images, audio, and video about the product, including example prompts such as:

[1055] Please provide more information about the following products:

[1056] Product Name: High Quality Tofu

[1057] Description: This tofu is made from organic soybeans and is of the highest quality.

[1058] 4. Analysis and provision of congestion information

[1059] The server analyzes real-time and past visitor data to predict congestion, allowing users to check the current congestion status of the store and predict future congestion.

[1060] 5. Reservation Processing

[1061] Receives a reservation request from a user, checks the stock status of the relevant product and availability of the service, and if the reservation is available, confirms the reservation and sends a confirmation notification to the user.

[1062] 6. Re-suggestion function

[1063] It provides a more efficient schedule by avoiding busy times and suggesting alternative times and related products to users.

[1064] This system allows users to instantly grasp detailed product information and congestion status in physical stores, enabling them to make appropriate purchase and visit plans.

[1065] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1066] Step 1:

[1067] Scan the code and submit the request

[1068] A user scans a QR code or barcode displayed in a physical store with their smartphone. The smartphone uses a QR code reader to obtain the URL embedded in the code. The obtained URL is sent to the server. The input is the QR code, and the output is the URL sent to the server.

[1069] Step 2:

[1070] Identifying the product ID

[1071] The server analyzes the received URL, extracts the product ID from the URL, and identifies the information corresponding to that product ID. The input is the URL, and the output is the extracted product ID. Specifically, the server analyzes the URL pattern to identify the product ID.

[1072] Step 3:

[1073] Obtaining product information

[1074] The server connects to the MySQL database and retrieves basic information corresponding to the specified product ID. The input is the product ID, and the output is the corresponding basic information. Specifically, it issues an SQL query to retrieve data such as the product name, description, price, and image URL.

[1075] Step 4:

[1076] Generate detailed information

[1077] The server uses a generative AI model (e.g., OpenAI's ChatGPT) to generate detailed information about the product. It sends prompts to the generative AI model to generate detailed text, images, audio, and video. The input is basic information and prompts, and the output is the generated detailed information. Specific examples of prompts are as follows:

[1078] Please provide more information about the following products:

[1079] Product Name: High Quality Tofu

[1080] Description: This tofu is made from organic soybeans and is of the highest quality.

[1081] Step 5:

[1082] Providing more information

[1083] The generated detailed information is displayed on the smartphone screen. The user can view detailed text, images, audio, and video. The input is the generated detailed information, and the output is the detailed information provided to the user. Specifically, the smartphone application receives the detailed information and displays it on the screen.

[1084] Step 6:

[1085] Analysis and provision of congestion status

[1086] The server analyzes the congestion situation based on real-time and past visitor data. It uses a congestion prediction model to calculate the current congestion situation and predict future congestion. The input is visitor data, and the output is congestion information. Specifically, the server analyzes the visitor data, calculates the congestion level, and provides it to the user.

[1087] Step 7:

[1088] Display of congestion information

[1089] Congestion information is displayed on the smartphone screen. Users can check the current congestion situation and future congestion forecasts. The input is congestion situation information, and the output is congestion information displayed to the user. Specifically, the smartphone application receives the congestion information and displays it on the screen.

[1090] Step 8:

[1091] Submit a booking request

[1092] A user requests a reservation for a product or service that they are interested in. The reservation request is sent from the device to the server. The input is the user's reservation request, and the output is the request sent to the server. Specifically, the smartphone application sends the request by pressing the reservation button.

[1093] Step 9:

[1094] Booking confirmation and notifications

[1095] The server processes the received reservation request and checks the inventory of the relevant product and availability of the service. If the reservation is possible, it confirms the reservation and sends a confirmation notice to the terminal. The input is the reservation request and the output is a reservation confirmation notice. Specifically, it accesses the database, updates the reservation information, and sends a notice.

[1096] Step 10:

[1097] Providing a re-proposal

[1098] If the desired visit time is busy, the server will suggest alternative times or related products. The suggestions are displayed on the terminal. The input is the busyness information and the user's request, and the output is an alternative suggestion. Specifically, a generative AI model is used to generate alternative suggestions and provide that information to the user.

[1099] Through the above processing steps, users can instantly grasp detailed product information and congestion status during their shopping experience at a physical store, enabling them to make efficient purchases and plans.

[1100] 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.

[1101] This invention is a system that reads QR codes installed at tourist spots to provide historical information and tourist guides related to the tourist spot, and also improves the user experience by providing congestion information, reservation functions, and an emotion engine.

[1102] System Configuration

[1103] 1. Scan the QR code and send a request

[1104] When a user scans a QR code installed at a tourist spot with their smartphone, the device retrieves the URL embedded in the QR code and sends it to the server. Upon receiving the request, the server analyzes the URL and identifies the ID of the corresponding tourist spot.

[1105] 2. Generating related information

[1106] The server retrieves basic information about the designated tourist destination from a database, then uses a generative AI model (e.g., ChatGPT) to automatically generate detailed content related to the destination, including text, images, audio, and video. This generated information is then provided to the user.

[1107] 3. Use of Emotion Engine

[1108] The device analyzes the user's facial expressions and voice and sends the data to the emotion engine. The server uses the emotion engine to recognize the user's emotional state and collects the results.

[1109] 4. Emotion-based modulation of information

[1110] The server uses data from the emotion engine to suggest tourist spots and provide information that takes the user's emotions into account. For example, if the user is excited, it can suggest tourist spots that are highly exciting.

[1111] 5. Providing congestion information

[1112] The server analyzes real-time and past visitor data to predict the current congestion situation at tourist destinations, allowing users to plan their visit based on the current congestion situation and future congestion predictions.

[1113] 6. Reservation function

[1114] Users can request reservations for tourist spots and facilities they are interested in. The server receives the reservation request sent from the device, checks the availability of the facility, and then confirms the reservation. Once the reservation is confirmed, a confirmation notification is sent to the device.

[1115] 7. Re-suggestion function

[1116] If the desired visit time is busy, the server will suggest alternative times or related tourist spots, allowing users to create a more efficient schedule.

[1117] Specific examples

[1118] For example, when a user scans a QR code for historical monument "A" with their smartphone, the device sends the URL "https: / / example.com / monumentA" to a server. The server parses the URL and retrieves information about monument A from a database. Using a generative AI model, detailed descriptive text, images, audio, and video content about monument A is automatically generated. This information is displayed to the user, including, for example, a detailed explanation of "the history and background of monument A."

[1119] Furthermore, the device collects the user's facial expressions and voice, which the emotion engine analyzes to recognize the user's emotional state. For example, if the emotion engine recognizes that the user is excited, the server will suggest tourist spots that are highly exciting (e.g., active attractions).

[1120] If the user later wishes to visit the neighboring Museum B and makes a reservation, the terminal sends a reservation request to the server. The server checks the availability of Museum B, and if a reservation is possible, the user receives a notification saying, "You have reserved a 10:00 AM slot at Museum B."

[1121] If the planned visit time is busy, the server will use the AI ​​model to suggest alternative tourist spots or different times. For example, "Monument A is currently busy, so we recommend visiting adjacent Garden C at 10:30 AM."

[1122] This allows users to have an efficient and in-depth sightseeing experience. By simply scanning the QR code installed at tourist spots, the system combines an emotion engine to provide more personalized tourist information, providing detailed information about tourist spots, obtaining congestion information, using reservation functions, and utilizing re-suggestion functions.

[1123] The processing flow will be explained below.

[1124] Step 1:

[1125] The user scans a QR code installed at a tourist spot with their smartphone.

[1126] Step 2:

[1127] The device sends the URL obtained from the QR code to the server (e.g., an HTTP GET request).

[1128] Step 3:

[1129] The server analyzes the received URL and identifies the ID of the tourist spot corresponding to the URL.

[1130] Step 4:

[1131] The server retrieves basic information about the tourist destination (historical background, points of interest, etc.) from a database.

[1132] Step 5:

[1133] The server instructs the generative AI model (e.g., ChatGPT) to automatically generate detailed text, image, audio, and video content based on the basic information acquired.

[1134] Step 6:

[1135] The server aggregates the generated related information to form content for presentation to the user.

[1136] Step 7:

[1137] The device collects the user's facial expressions and voice and sends them to the emotion engine.

[1138] Step 8:

[1139] The emotion engine analyzes the user's emotional state and sends the results to the server.

[1140] Step 9:

[1141] Based on the analysis results from the emotion engine, the server adjusts the tourist destination suggestions and information provided, taking into account the user's emotions.

[1142] Step 10:

[1143] The server runs the congestion prediction model to generate the current congestion status and future congestion predictions for the tourist destination.

[1144] Step 11:

[1145] The server transmits the integrated information and congestion information to the terminal.

[1146] Step 12:

[1147] The terminal displays the information received from the server to the user.

[1148] Step 13:

[1149] Request reservations for attractions and facilities that interest the user.

[1150] Step 14:

[1151] The terminal sends a reservation request to the server.

[1152] Step 15:

[1153] The server checks the availability of the facility in real time.

[1154] Step 16:

[1155] The server confirms that the reservation is possible and processes the reservation, saving the reservation information and sending a confirmation of the reservation to the terminal.

[1156] Step 17:

[1157] The server uses the generated AI model to suggest alternative times and tourist destinations based on crowding predictions.

[1158] Step 18:

[1159] The server transmits the re-proposal information to the terminal, and the terminal notifies the user of it.

[1160] The above are the specific processing steps of this system.

[1161] Example 2

[1162] 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."

[1163] When travelers visit tourist destinations, they want to quickly obtain detailed local information and receive tourist guidance tailored to their individual needs. However, conventional tourist information systems only provide one-way information and lack congestion information, reservation functions, and personalized guidance based on individual emotional states. They also lack efficient schedule adjustments based on the current and future congestion situation at tourist destinations. This poses a challenge, reducing the convenience of travelers and the quality of their tourism experience.

[1164] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1165] In this invention, the server includes: [means for reading codes installed at tourist attractions;] [means for sending a data request based on information obtained from the code;] [means for generating information related to tourist attractions using a generative AI model;] [means for providing the generated information related to tourist attractions to the user;] [means for utilizing an emotion engine for analyzing user emotion data obtained from the terminal;] [means for adjusting tourist attraction information based on the user's emotional state;] [means for providing real-time congestion information about tourist attractions;] [means for processing reservations for tourist attractions and facilities; and [means for suggesting visiting times and alternative tourist attractions.] This enables users to obtain detailed information about tourist attractions in real time and receive personalized tourist guidance according to their individual emotional state, and also enables efficient schedule adjustments and reservation management based on congestion levels.

[1166] "Means for reading codes installed at tourist spots" refers to a function that allows users to scan codes such as QR codes installed at tourist spots with devices such as smartphones or tablets and obtain the information contained in the codes.

[1167] The "means for transmitting a data request based on information obtained from the code" is a function for transmitting the information of the obtained code to the server via the terminal.

[1168] "Means for generating information related to tourist destinations using a generative AI model" refers to a function that automatically generates content such as detailed text, images, audio, and video related to tourist destinations using a generative AI model (e.g., a text generation model).

[1169] "Means for providing users with information related to the generated tourist destinations" refers to a function that allows content generated by the generative AI model to be sent to a terminal and for users to receive the information visually or audibly.

[1170] "Means for utilizing an emotion engine that analyzes user emotion data acquired from the terminal" refers to a function in which the terminal collects the user's facial expressions and voice, analyzes them with the server's emotion engine, and recognizes the user's emotional state.

[1171] The "means for adjusting information about tourist attractions based on the user's emotional state" is a function that dynamically adjusts the information provided and the content of suggested tourist attractions according to the user's emotional state recognized by the emotion engine.

[1172] "Means for providing real-time congestion information at tourist destinations" is a function that uses a data analysis model to analyze the current congestion situation and predicted congestion information at tourist destinations and provides it to users in real time.

[1173] "Means for processing reservations for tourist attractions and facilities" refers to the function of receiving a reservation request for a tourist attraction or facility that a user wishes to visit, and the server confirming and confirming the reservation in response to that request.

[1174] "Means for suggesting visiting times and alternative tourist spots" is a function that suggests visiting times and alternative tourist spots depending on the congestion situation and reservation status.

[1175] This invention is a system that uses QR codes installed at tourist spots to provide users with detailed information about tourist spots, congestion information, and reservation functions, and aims to provide personalized guidance based on the user's emotional state.

[1176] System configuration

[1177] The system mainly includes the following components:

[1178] Terminal: A portable device such as a smartphone or tablet that can be used to scan QR codes installed at tourist attractions.

[1179] Server: A central computer system that processes requests sent from user devices and generates information using generative AI models and emotion engines.

[1180] Database: A database system (e.g. MySQL) for storing basic information about tourist destinations.

[1181] Generative AI models: Artificial intelligence models for automatically generating content such as text, images, audio, and video (e.g., ChatGPT).

[1182] Emotion engine: Software that analyzes a user's facial expressions and voice to recognize their emotional state (e.g., Microsoft Azure Emotion API).

[1183] Operation overview and examples

[1184] The system begins when a user arrives at a tourist spot and scans a QR code.

[1185] 1. Scan the QR code

[1186] When a user uses a smartphone to scan a QR code installed at a tourist spot, the device retrieves the information, which includes a URL related to the tourist spot.

[1187] 2. Submitting a Data Request

[1188] The device sends the URL (e.g., "https: / / example.com / monumentA") to the server. The device makes this data request using its Internet connection.

[1189] 3. Analysis and Acquisition of Information

[1190] When the server receives the request, it analyzes the URL to identify the tourist spot ID, and then retrieves basic information about the corresponding tourist spot from the database.

[1191] 4. Content generation using generative AI models

[1192] The server inputs a prompt into a generative AI model (e.g., ChatGPT) to generate detailed content related to the tourist attraction, such as text, images, audio, and video. For example, the prompt might be, "Please explain the detailed history and characteristics of Monument A."

[1193] 5. Providing Information to Users

[1194] The generated content is sent from the server to the terminal and displayed on the terminal screen, allowing the user to receive a detailed explanation of "Monument A's history and characteristics" visually or audibly.

[1195] 6. Analysis by Emotion Engine

[1196] The device uses a camera and microphone to collect the user's facial expressions and voice, and sends them to emotion analysis software (e.g., Microsoft Azure Emotion API). The analysis results are then sent to a server.

[1197] 7. Emotion-based modulation of information

[1198] The server provides information based on the user's emotional state based on data from the emotion engine. For example, if the user is excited, it will suggest tourist spots that are likely to excite the user.

[1199] 8. Providing congestion information

[1200] The server analyzes real-time data and past visitor data, and uses a congestion prediction model to predict current and future congestion information for tourist destinations. This information is then provided to users.

[1201] 9. Processing booking requests

[1202] When a user wants to reserve a specific tourist spot or facility, they send a reservation request from their device. The server checks the availability of the facility and confirms the reservation. Once the reservation is complete, a confirmation notification is sent to the device.

[1203] 10. Using the re-suggestion feature

[1204] If reservations are busy or based on crowd predictions, the server will suggest alternative tourist spots and visiting times, such as "Monument A is currently crowded, so we recommend visiting adjacent Garden C at 10:30 AM."

[1205] This allows users to obtain detailed information about tourist spots in real time and receive personalized information according to their emotional state.In addition, the system provides congestion information and reservation functions, enabling efficient schedule adjustment and a rich sightseeing experience.

[1206] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1207] Step 1:

[1208] A user scans a QR code installed at a tourist spot. The input is the QR code scanned using the user's smartphone, and the output is the URL contained in the acquired QR code. Specifically, when the user launches the smartphone's camera app and reads the QR code, the URL (e.g., "https: / / example.com / monumentA") is displayed.

[1209] Step 2:

[1210] The device sends the scanned URL to the server. The input is the URL obtained by the device, and the output is the URL request received by the server. Specifically, the URL information is sent to the server via the smartphone's Internet connection.

[1211] Step 3:

[1212] The server analyzes the received URL. The input is the URL data, and the output is the tourist spot ID. Specifically, the server analyzes the URL using Apache HTTP Server or similar and extracts the tourist spot identifier, such as "monumentA."

[1213] Step 4:

[1214] The server retrieves basic information about tourist attractions from a database. The input is the tourist attraction's ID, and the output is the tourist attraction's basic information. Specifically, the server sends a query to the MySQL database to retrieve information such as the tourist attraction's name, location, and brief history.

[1215] Step 5:

[1216] The server generates content using a generative AI model. The input is basic information about the tourist attraction and a prompt, and the output is generated content such as text, images, audio, and video. For example, the prompt "Please explain the detailed history and characteristics of Monument A" is sent to ChatGPT, which generates a detailed explanatory text and related images.

[1217] Step 6:

[1218] The server sends the generated content to the device. The input is the generated content, and the output is the content displayed on the device. Specifically, the server sends the generated text and images to the smartphone and displays them on the smartphone screen.

[1219] Step 7:

[1220] The device collects the user's facial expressions and voice and sends them to the emotion engine. The input is the user's facial expression and voice data, and the output is the emotion analysis results. Specifically, data is collected using the smartphone's camera and microphone and sent to the Microsoft Azure Emotion API.

[1221] Step 8:

[1222] The server receives the emotion analysis results and analyzes the user's emotional state. The input is the emotion analysis results, and the output is a classification of the user's emotional state (e.g., excited, relaxed). The server adjusts the information provided based on the analysis results.

[1223] Step 9:

[1224] The server provides personalized information to the device based on the user's emotional state. The input is the user's emotional state and tourist attraction information, and the output is tailored tourist attraction information and suggestions. For example, if the user is excited, active attractions are suggested.

[1225] Step 10:

[1226] The server analyzes and provides congestion information in real time. The input is real-time visitor data and past visit data, and the output is current and predicted congestion information. The server performs the analysis using data analysis software and sends the results to the terminal.

[1227] Step 11:

[1228] A user requests a reservation for a tourist attraction or facility. The input is the reservation information for the tourist attraction or facility desired by the user, and the output is a reservation request sent to the server. Specifically, the user inputs the reservation request through a smartphone application.

[1229] Step 12:

[1230] The server processes reservation requests. The input is a reservation request from the user, and the output is a reservation confirmation notification. The server accesses the reservation management system, checks availability, and confirms the reservation. Once the reservation is completed, a confirmation notification is sent to the terminal.

[1231] Step 13:

[1232] The server suggests visiting times and alternative tourist spots based on the congestion situation. The input is the congestion situation data and the user's schedule, and the output is an alternative suggestion. The server uses an AI model to calculate the optimal visiting time and alternative tourist spots and notify the user. For example, a suggestion might be, "Monument A is currently crowded, so we recommend visiting adjacent Garden C at 10:30 AM."

[1233] (Application example 2)

[1234] 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."

[1235] In recent years, there has been a demand for systems that can improve the user experience when visiting tourist spots and virtual stores. Conventional systems lack real-time congestion information and reservation functions, and it is difficult to provide personalized information based on the user's emotional state. In addition, generating tourist spot and store information takes time, which results in problems with the quality of the user experience.

[1236] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for reading a code placed at a tourist attraction or virtual store, means for sending a data request based on information obtained from the code, and means for providing the user with the generated information related to the tourist attraction or store. This makes it possible to provide real-time congestion information for the tourist attraction or store and to provide and suggest information based on the user's emotional state. It also processes reservations for tourist attractions, facilities, or products, and suggests reservations for the time period during which the visitor should visit, or for other tourist attractions or different time periods.

[1237] "Tourist attractions or virtual stores" includes not only actual physical tourist attractions or commercial areas, but also stores or tourist attractions recreated in virtual space.

[1238] "Code" means a medium with embedded information, including a QR code, barcode, or other type of code, that can be scanned by a User's device.

[1239] "Means for sending a data request" refers to the method used to send the URL and identifying information obtained by scanning the code to a server to obtain the associated data.

[1240] "Generated Information" includes text, images, audio, and video content created by the server using databases and generative AI models.

[1241] "Crowd information" is information that indicates the congestion status of a target location or store, generated using current visitor numbers, past data, and predictive algorithms.

[1242] "Means for processing reservations" refers to the method by which users make reservations for visit times and facility use at specific tourist attractions and stores, and the associated back-end processing mechanisms.

[1243] "Emotional state" refers to the user's mental and emotional state as identified by analyzing the user's facial expressions and voice.

[1244] "Means for providing or suggesting information" refers to methods that not only display generated information to the user, but also dynamically provide recommended information based on the user's current emotional state.

[1245] This invention provides a system including means for reading QR codes installed at tourist attractions or virtual stores, means for sending a data request based on information obtained from the read code, means for providing the user with generated information related to the tourist attraction or store, means for providing real-time congestion information about the tourist attraction or store, means for processing reservations for tourist attractions, facilities, or products, means for suggesting a visiting time period or reservations for another tourist attraction or a different time period, and means for analyzing the user's emotional state and providing or suggesting information based on the results.

[1246] Hardware and Software

[1247] To realize this system, the following hardware and software are used.

[1248] Smart glasses: Using Microsoft HoloLens as an example.

[1249] Smartphone: iOS or Android smartphone.

[1250] Emotion engine: Uses Microsoft Azure Emotion API.

[1251] Generative AI model: OpenAI GPT-4.

[1252] Backend: Node.js, Express, MongoDB.

[1253] Data processing and calculation

[1254] 1. Scan the QR code and send a data request

[1255] The server obtains the URL by scanning the QR code with the user's smart glasses or smartphone and sends the information to the server, which then analyzes the URL and retrieves the corresponding store and product information from the database.

[1256] 2. Provision of generated information

[1257] The server uses a generative AI model (OpenAI GPT-4) to automatically generate detailed related text, image, audio, and video content based on the information retrieved from the database. The generated information is then sent to the device and provided to the user.

[1258] 3. Use of Emotion Engine

[1259] The server receives facial and voice data acquired from the user's smart glasses or smartphone and sends it to the Microsoft Azure Emotion API to analyze the user's emotional state. The server receives the results of the analysis by the emotion engine.

[1260] 4. Emotionally driven information

[1261] The server uses a generative AI model to create product suggestions or tourist spot recommendations based on the acquired emotional data and provides them to the user. For example, if the user is excited, it will suggest new products or limited-time offers.

[1262] 5. Providing congestion information and reservation functions

[1263] The server analyzes real-time congestion information and notifies users of the congestion situation. If it is crowded, it will suggest less crowded times or other locations and inform users of available reservation times.

[1264] 6. Re-suggestion function

[1265] If the user's desired time slot is crowded, the server will suggest alternative tourist spots and time slots. The server dynamically generates these alternatives through a generative AI model and presents them to the user.

[1266] Specific examples

[1267] For example, when a user scans the QR code of a "virtual fashion store" in a virtual mall, detailed information about the new autumn collection is provided. When using smart glasses, excitement from facial expressions is analyzed through an emotion engine (Microsoft Azure Emotion API), and the most popular items from the new collection are suggested. Crowd information is also provided in real time, allowing users to reserve a suitable time to visit. If the store is crowded, a reservation for a less crowded time slot is suggested.

[1268] Prompt Sentence Examples

[1269] An example of a prompt to be input to the generative AI model is as follows:

[1270] Product information: [Product A: Smartphone, Product B: Smartwatch]

[1271] Emotional state: Highly excited

[1272] Proposal Generation: Suggest the best product offers to highly excited users.

[1273] This can significantly improve the user experience.

[1274] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1275] Step 1:

[1276] The user scans a QR code placed at a tourist spot or virtual store using smart glasses or a smartphone.

[1277] Input: QR code

[1278] Output: URL obtained from QR code

[1279] How it works: A user scans a QR code with their device and retrieves the URL embedded in the code.

[1280] Step 2:

[1281] The terminal sends the acquired URL to the server.

[1282] Input: URL obtained from QR code

[1283] Output: Data request sent to the server

[1284] Operation: The device requests detailed information by sending the URL information it has obtained to the server.

[1285] Step 3:

[1286] The server analyzes the URL and retrieves information about the relevant tourist attraction or store from a database.

[1287] Input: URL information sent to the server

[1288] Output: Tourist attraction or store information retrieved from the database

[1289] How it works: The server parses the URL, identifies the ID of the relevant tourist attraction or store, and retrieves the necessary information from the database based on that ID.

[1290] Step 4:

[1291] The server uses a generative AI model (e.g., OpenAI GPT-4) to generate detailed text, image, audio, and video content based on tourist attraction or store information.

[1292] Input: Tourist attraction or store information retrieved from the database

[1293] Output: Generated detailed content (text, images, audio, video)

[1294] How it works: The server inputs the acquired information into a generative AI model, which automatically generates detailed content based on the prompt.

[1295] Step 5:

[1296] The server sends the generated information to the terminal and displays it to the user.

[1297] Input: Generated detail content

[1298] Output: Detailed content sent to the device, displayed to the user

[1299] How it works: The server sends the content created by the generative AI model to the device and displays it for the user to view.

[1300] Step 6:

[1301] The device collects the user's facial expressions and voice and sends them to an emotion engine (e.g., Microsoft Azure Emotion API).

[1302] Input: User facial and voice data

[1303] Output: Data sent to the emotion engine

[1304] How it works: The device sends the user's facial expression and voice data collected to the emotion engine.

[1305] Step 7:

[1306] The emotion engine analyzes the user's emotional state and sends the results back to the server.

[1307] Input: User facial and voice data

[1308] Output: Emotional state analysis result

[1309] How it works: Analyzes the user's emotional state using waveform analysis and facial expression recognition algorithms. The analysis results are sent to the server.

[1310] Step 8:

[1311] Based on the emotion data, the server uses a generative AI model to regenerate information appropriate for the user and transmits it to the device.

[1312] Input: Emotional state analysis results, generative AI model

[1313] Output: Regenerated personalization information

[1314] How it works: The server adds the acquired emotional data to the prompt text, creates new personalized information using the generative AI model, and suggests it to the user.

[1315] Step 9:

[1316] The server uses the congestion prediction model to analyze the congestion situation at tourist spots or stores and provides real-time information and predicted information to users.

[1317] Input: Current and past visit data

[1318] Output: Real-time and forecast information on congestion status

[1319] Operation: The server analyzes visit data and uses a congestion prediction model to generate real-time and future congestion information, which it then provides to users.

[1320] Step 10:

[1321] The user requests a reservation for a desired tourist spot, facility, or product, and the server processes the reservation.

[1322] Input: User's booking request

[1323] Output: Booking confirmation notice

[1324] How it works: The server receives a reservation request from a user, processes the reservation, checks availability, and then notifies the user that the reservation is confirmed.

[1325] Step 11:

[1326] If the user's desired time is busy, the server will suggest alternative tourist spots or different visiting times.

[1327] Input: Crowd data, generative AI model

[1328] Output: Alternative proposal information

[1329] How it works: The server analyzes the congestion data and uses a generative AI model to generate and provide suggestions for alternative tourist destinations and visiting times to the user.

[1330] This results in a system that improves the user's experience in a more personalized and efficient manner.

[1331] 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.

[1332] 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.

[1333] 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.

[1334] [Fourth embodiment]

[1335] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1336] 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.

[1337] 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).

[1338] 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.

[1339] 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.

[1340] 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).

[1341] 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.

[1342] 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.

[1343] 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.

[1344] 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.

[1345] 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.

[1346] 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.

[1347] 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."

[1348] This invention is a system that reads QR codes installed at tourist spots to provide historical information and tourist guides related to the tourist spot, and also improves the tourist experience by providing congestion information and reservation functions.

[1349] System Configuration

[1350] 1. Scan the QR code and send a request

[1351] When a user scans a QR code installed at a tourist spot with their smartphone, the device retrieves the URL embedded in the QR code and sends it to the server. Upon receiving the request, the server analyzes the URL and identifies the ID of the corresponding tourist spot.

[1352] 2. Generating related information

[1353] The server retrieves basic information about the designated tourist destination from a database, then uses a generative AI model (e.g., ChatGPT) to automatically generate detailed content related to the destination, including text, images, audio, and video. This generated information is then provided to the user.

[1354] 3. Providing congestion information

[1355] The server analyzes real-time and past visitor data to predict the congestion status of tourist spots, allowing users to plan their visit based on the current congestion status and future congestion forecasts.

[1356] 4. Reservation function

[1357] Users can request reservations for tourist spots and facilities they are interested in. The server receives the reservation request sent from the device, checks the availability of the facility, and then confirms the reservation. Once the reservation is confirmed, a confirmation notification is sent to the device.

[1358] 5. Re-suggestion function

[1359] If the desired visit time is busy, the server will suggest alternative times or related tourist spots, allowing users to create a more efficient schedule.

[1360] Specific examples

[1361] For example, when a user scans a QR code for historical monument "A" with their smartphone, the device sends the URL "https: / / example.com / monumentA" to a server. The server parses the URL and retrieves information related to monument A from a database. Using a generative AI model, detailed descriptive text, images, audio, and video content about monument A is automatically generated. This information is displayed to the user, including, for example, a detailed explanation of "the history and background of monument A."

[1362] If the user later wishes to visit the neighboring Museum B and makes a reservation, the terminal sends a reservation request to the server. The server checks the availability of Museum B, and if a reservation is possible, the user receives a notification saying, "You have reserved a 10:00 AM slot at Museum B."

[1363] If the planned visit time is busy, the server will use the AI ​​model to suggest alternative tourist spots or different times. For example, "Monument A is currently busy, so we recommend visiting adjacent Garden C at 10:30 AM."

[1364] This allows users to have an efficient and in-depth sightseeing experience. By simply scanning the QR code installed at tourist spots, this system provides detailed information about tourist spots, acquires congestion information, and utilizes reservation and re-suggestion functions.

[1365] The processing flow will be explained below.

[1366] Step 1:

[1367] The user scans a QR code installed at a tourist spot with their smartphone.

[1368] Step 2:

[1369] The device sends the URL obtained from the QR code to the server (e.g., an HTTP GET request).

[1370] Step 3:

[1371] The server analyzes the received URL and identifies the ID of the tourist spot corresponding to the URL.

[1372] Step 4:

[1373] The server retrieves basic information about the tourist destination (historical background, points of interest, etc.) from a database.

[1374] Step 5:

[1375] The server instructs the generative AI model (e.g., ChatGPT) to automatically generate detailed text, image, audio, and video content based on the basic information acquired.

[1376] Step 6:

[1377] The server aggregates the generated related information to form content for presentation to the user.

[1378] Step 7:

[1379] The server runs the congestion prediction model to generate the current congestion status and future congestion predictions for the tourist destination.

[1380] Step 8:

[1381] The server transmits the integrated information and congestion information to the terminal.

[1382] Step 9:

[1383] The terminal displays the information received from the server to the user.

[1384] Step 10:

[1385] Request reservations for attractions and facilities that interest the user.

[1386] Step 11:

[1387] The terminal sends a reservation request to the server.

[1388] Step 12:

[1389] The server checks the availability of the facility in real time.

[1390] Step 13:

[1391] The server confirms that the reservation is possible and processes the reservation, saving the reservation information and sending a confirmation of the reservation to the terminal.

[1392] Step 14:

[1393] If the user is unable to visit at the time they wish, the server uses the generated AI model to suggest alternative times or other tourist destinations.

[1394] Step 15:

[1395] The server transmits the re-proposal information to the terminal, and the terminal notifies the user of it.

[1396] The above are the specific processing steps of this system.

[1397] Example 1

[1398] 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."

[1399] For users visiting tourist destinations, it is difficult to easily obtain information about the history and history of the destination, and to efficiently enjoy sightseeing while understanding the congestion situation. There is also a lack of easy ways to make reservations at tourist destinations and facilities. There is a need for a system that can solve these issues and provide users with a more fulfilling tourist experience.

[1400] 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.

[1401] In this invention, the server includes: [means for identifying a tourist attraction ID and acquiring basic information based on it;] [means for sending a prompt to a generative AI model and generating detailed information related to the tourist attraction; and] [means for analyzing visitor data and providing congestion information about the tourist attraction in real time.] This enables [users to easily obtain detailed information about tourist attractions, understand the congestion situation, and enjoy sightseeing efficiently.] It also enables users to easily make reservations for tourist attractions and facilities, providing a more fulfilling sightseeing experience.

[1402] A "tourist attraction ID" is an identifier that uniquely identifies a tourist attraction.

[1403] "Basic information" refers to basic data about tourist destinations, such as their names, locations, and historical backgrounds.

[1404] A "generative AI model" is an artificial intelligence model that automatically generates content such as text, images, audio, and video based on given prompts.

[1405] A "prompt" is an instruction or question that is input to a generative AI model, and is the input data that the model uses to respond or generate something.

[1406] "Detailed information" refers to additional information such as text, images, audio, and video generated based on basic information about a tourist destination.

[1407] "Visitor Data" means data that indicates the current and past number of visitors to a tourist destination and their trends.

[1408] "Crowd information" is information that includes predictions of the current and future number of visitors to a tourist destination, and is data that is useful for users to plan their visit.

[1409] "Reservation" refers to the process of reserving a time or slot for use of a tourist attraction or facility in advance.

[1410] "Re-suggestion" refers to suggesting an alternative time to visit or a different tourist spot to the user based on the congestion and availability of the spot.

[1411] This invention is a system that reads QR codes installed at tourist spots, and provides detailed information about the tourist spot, congestion status, and reservation and re-suggestion functions. Specific embodiments of this system are described below.

[1412] System configuration and operation

[1413] 1. Scan the QR code and send a request

[1414] When a user scans a QR code installed at a tourist spot with their smartphone, the URL is obtained and sent from the device to the server, which analyzes the URL and identifies the tourist spot ID.

[1415] 2. Generating related information

[1416] The server retrieves basic information from a database based on the tourist attraction ID it identifies. This information is then sent as a prompt to a generative AI model (e.g., ChatGPT) to generate detailed content related to the tourist attraction, including text, images, audio, and video. For example, a prompt might be, "Please provide a detailed historical background for Monument A." The generated information is then sent to the user's device and displayed.

[1417] 3. Providing congestion information

[1418] The server analyzes past and current visitor data for tourist destinations and predicts congestion in real time, which is provided to users as a reference when planning their trip.

[1419] 4. Reservation function

[1420] When a user wishes to visit a tourist spot or facility, they send a reservation request from their device. The server checks the facility's reservation status and confirms the reservation if there is availability. If the reservation is confirmed, a notification is sent to the device. For example, a confirmation may be received saying, "You have reserved a slot at Museum B for 10:00 AM."

[1421] 5. Re-suggestion function

[1422] If the user's desired visit time is busy, the server will suggest alternative tourist spots and time slots. The server uses a generative AI model to make appropriate suggestions. For example, a suggestion might be generated: "Monument A is currently busy, so we recommend visiting adjacent Garden C at 10:30 AM."

[1423] Specific examples

[1424] For example, if a user scans the QR code for historical monument "A" with their smartphone, the device sends the URL "https: / / example.com / monumentA" to the server. The server parses the URL and identifies the ID of monument A. The server then retrieves basic information about monument A from a database and sends a prompt to the generative AI model saying, "Please provide a detailed historical background for monument A." The generated explanatory text, images, audio, and video are then displayed on the user's device.

[1425] If the user later wishes to visit the neighboring Museum B and requests a reservation, the terminal sends the reservation information to the server. The server checks the availability of Museum B, and if a reservation is possible, a notification is sent to the user's terminal stating, "You have reserved a 10:00 AM slot at Museum B."

[1426] Furthermore, if the planned visit time is busy, the server can make an alternative suggestion such as, "Monument A is currently busy, so we recommend visiting adjacent Garden C at 10:30 AM."

[1427] In this way, users can have a fulfilling sightseeing experience by providing detailed information about tourist spots, obtaining congestion information, using the reservation function, and utilizing the re-suggestion function.

[1428] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1429] Step 1:

[1430] Scan the QR code and send a request

[1431] A user scans a QR code installed at a tourist spot with their smartphone camera. The device automatically sends the URL extracted from the QR code (input) to the server (output). In this case, the URL data is sent to the server using an HTTP request. Specifically, the device reads the URL "https: / / example.com / monumentA" and sends it to the server.

[1432] Step 2:

[1433] URL analysis and identification of tourist spot IDs

[1434] The server analyzes the URL received as input and identifies the tourist attraction ID from the path portion of the URL (output). For example, the tourist attraction ID "monumentA" is extracted from the path " / monumentA" of the URL "https: / / example.com / monumentA". This gives the server the unique identifier it needs to use in the next step. Specifically, the server analyzes the path portion of the URL using regular expressions or string manipulation.

[1435] Step 3:

[1436] Obtaining basic information

[1437] The server uses the tourist attraction ID (input) to retrieve basic information (output) of the corresponding tourist attraction from the database. The basic information includes the tourist attraction's name, location, historical background, etc. Specifically, the server executes an SQL query to retrieve basic information about "monumentA" from the database.

[1438] Step 4:

[1439] Generative AI models generate detailed information

[1440] The server sends a prompt to a generative AI model (e.g., ChatGPT) based on basic information (input) to generate content (output) such as detailed text, images, audio, and video related to tourist attractions. For example, a prompt such as "Please explain the detailed historical background of Monument A" is created and sent to the generative AI model. Specifically, the server processes the basic information, generates an appropriate prompt, and passes it to the AI ​​model.

[1441] Step 5:

[1442] Providing generated information

[1443] The server compiles the content (input) obtained from the generative AI model and sends it to the user's device (output). The device receives it and displays it on the screen. Specifically, the server sends the generated text and multimedia content in HTML or JSON format to the device. The device interprets the received data and provides it to the user.

[1444] Step 6:

[1445] Analysis and provision of congestion information

[1446] The server analyzes real-time and past visitor data (input) and generates predictions (output) of the current and future congestion status of tourist destinations, including visitor numbers and time-of-day data. Specifically, the server uses data analysis algorithms to generate congestion predictions based on past visitor trends and provides them to users.

[1447] Step 7:

[1448] Receiving and processing booking requests

[1449] A user sends a reservation request (input) from their terminal for a tourist spot or facility they are interested in. The server receives the reservation request, checks the availability of the facility, and confirms the reservation (output). For example, the server receives a request such as "I would like to visit Museum B at 10:00 AM," checks the availability in the database, and confirms the reservation if it is available.

[1450] Step 8:

[1451] Reservation confirmation notification

[1452] Once the reservation is confirmed, the server sends a reservation confirmation notification (output) to the terminal. The terminal receives this notification and presents it to the user. Specifically, the server sends a message to the terminal saying, "You have reserved a 10:00 AM slot at Museum B," and the terminal displays this to the user.

[1453] Step 9:

[1454] Generate and serve re-proposals

[1455] If the desired visit time is crowded, the server rechecks the crowding situation (input) and suggests alternative tourist spots and time slots (output). Specifically, using a generative AI model, it generates a suggestion such as "Monument A is currently crowded, so we recommend visiting adjacent Garden C at 10:30 AM," and sends it to the user's device.

[1456] (Application example 1)

[1457] 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."

[1458] The shopping experience at physical stores is difficult because it is difficult to grasp detailed product information and congestion status, and it is not easy to check product inventory or reservation status. This makes it difficult for consumers to make appropriate product selections or adjust visit times, and stores also find it difficult to improve customer service.

[1459] 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.

[1460] In this invention, the server includes: [means for reading a code installed in a physical store;] [means for sending a data request based on information obtained from the code;] [means for providing the user with information related to the generated product;] [means for providing real-time information about congestion in the store;] [means for processing reservations for products and services; and] [means for suggesting visiting times and other products.] This allows users to instantly grasp detailed product information and congestion status when shopping in a physical store, enabling them to shop efficiently and plan their visit appropriately.

[1461] A "brick and mortar store" is a commercial establishment located in a physical location where consumers can visit in person to purchase goods or services.

[1462] A "code" is a symbol, character string, or image that is visually expressed to read information, and in this invention refers to a QR code, a barcode, or the like.

[1463] A "data request" is a digital message that requests specific information from a server, through which the required information is obtained.

[1464] "Generated information" refers to detailed text, images, audio, video, and other information that is automatically generated from AI models and databases based on information read from the code.

[1465] "Crowd information" is information that indicates the degree of crowding in a specific location, allowing the user to know in advance how crowded the destination will be.

[1466] A "reservation" is a preliminary procedure for securing a product or service for a specific time period or date, and is confirmed based on the details specified by the user.

[1467] "Suggestion" is the act of providing alternatives or recommendations based on the user's requirements or circumstances, thereby giving the user options.

[1468] A "generative AI model" is an AI system that automatically generates new sentences and content using machine learning and natural language processing techniques, and in this invention is used to generate text, images, audio, video, etc.

[1469] A "prompt sentence" is a sentence that is input into a generative AI model and serves as an instruction for the AI ​​to generate specific content based on that sentence.

[1470] This invention is a system that provides an efficient and satisfying shopping experience by reading codes installed in physical stores, providing detailed product information, crowding status, and a reservation function. Specific embodiments of the system are described below.

[1471] System Configuration

[1472] The system mainly consists of the following components:

[1473] 1. Scan the code and send a request

[1474] When a user scans a QR code or barcode displayed in a physical store with their smartphone, the device retrieves the URL embedded in the code and sends it to the server. Upon receiving the request, the server analyzes the URL and identifies the ID of the corresponding product.

[1475] 2. Generating related information

[1476] The server retrieves basic information about the specified product from a database, then uses a generative AI model (e.g., OpenAI's ChatGPT) to automatically generate detailed content related to the product, including text, images, audio, and video. This generated information is then provided to the user.

[1477] 3. Providing congestion information

[1478] The server analyzes real-time and past visitor data to predict store congestion, allowing users to plan their visit based on current and future congestion forecasts.

[1479] 4. Reservation function

[1480] Users can request reservations for products or services they are interested in. The server receives the reservation request sent from the terminal, checks the inventory of the product or availability of the service, and then confirms the reservation. Once the reservation is confirmed, a confirmation notification is sent to the terminal.

[1481] 5. Re-suggestion function

[1482] If the desired visit time is busy, the server will suggest alternative times or related products, allowing users to plan a more efficient schedule.

[1483] Hardware and Software

[1484] Hardware: Smartphone (e.g. with QR code reader function), Server (Database server, Calculation server)

[1485] Software: Flask (web framework), MySQL (database management system), OpenAI API (generative AI model)

[1486] Data processing and calculation

[1487] 1. Code scanning and request submission

[1488] Use your smartphone's QR code reader to scan the QR code and send the URL to the server, which then extracts the product ID from the URL.

[1489] 2. Obtaining product information

[1490] The server connects to the MySQL database and retrieves basic information corresponding to the corresponding product ID.

[1491] 3. Generate detailed information

[1492] The server uses OpenAI's generative AI model to generate detailed text, images, audio, and video about the product, including example prompts such as:

[1493] Please provide more information about the following products:

[1494] Product Name: High Quality Tofu

[1495] Description: This tofu is made from organic soybeans and is of the highest quality.

[1496] 4. Analysis and provision of congestion information

[1497] The server analyzes real-time and past visitor data to predict congestion, allowing users to check the current congestion status of the store and predict future congestion.

[1498] 5. Reservation Processing

[1499] Receives a reservation request from a user, checks the stock status of the relevant product and availability of the service, and if the reservation is available, confirms the reservation and sends a confirmation notification to the user.

[1500] 6. Re-suggestion function

[1501] It provides a more efficient schedule by avoiding busy times and suggesting alternative times and related products to users.

[1502] This system allows users to instantly grasp detailed product information and congestion status in physical stores, enabling them to make appropriate purchase and visit plans.

[1503] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1504] Step 1:

[1505] Scan the code and submit the request

[1506] A user scans a QR code or barcode displayed in a physical store with their smartphone. The smartphone uses a QR code reader to obtain the URL embedded in the code. The obtained URL is sent to the server. The input is the QR code, and the output is the URL sent to the server.

[1507] Step 2:

[1508] Identifying the product ID

[1509] The server analyzes the received URL, extracts the product ID from the URL, and identifies the information corresponding to that product ID. The input is the URL, and the output is the extracted product ID. Specifically, the server analyzes the URL pattern to identify the product ID.

[1510] Step 3:

[1511] Obtaining product information

[1512] The server connects to the MySQL database and retrieves basic information corresponding to the specified product ID. The input is the product ID, and the output is the corresponding basic information. Specifically, it issues an SQL query to retrieve data such as the product name, description, price, and image URL.

[1513] Step 4:

[1514] Generate detailed information

[1515] The server uses a generative AI model (e.g., OpenAI's ChatGPT) to generate detailed information about the product. It sends prompts to the generative AI model to generate detailed text, images, audio, and video. The input is basic information and prompts, and the output is the generated detailed information. Specific examples of prompts are as follows:

[1516] Please provide more information about the following products:

[1517] Product Name: High Quality Tofu

[1518] Description: This tofu is made from organic soybeans and is of the highest quality.

[1519] Step 5:

[1520] Providing more information

[1521] The generated detailed information is displayed on the smartphone screen. The user can view detailed text, images, audio, and video. The input is the generated detailed information, and the output is the detailed information provided to the user. Specifically, the smartphone application receives the detailed information and displays it on the screen.

[1522] Step 6:

[1523] Analysis and provision of congestion status

[1524] The server analyzes the congestion situation based on real-time and past visitor data. It uses a congestion prediction model to calculate the current congestion situation and predict future congestion. The input is visitor data, and the output is congestion information. Specifically, the server analyzes the visitor data, calculates the congestion level, and provides it to the user.

[1525] Step 7:

[1526] Display of congestion information

[1527] Congestion information is displayed on the smartphone screen. Users can check the current congestion situation and future congestion forecasts. The input is congestion situation information, and the output is congestion information displayed to the user. Specifically, the smartphone application receives the congestion information and displays it on the screen.

[1528] Step 8:

[1529] Submit a booking request

[1530] A user requests a reservation for a product or service that they are interested in. The reservation request is sent from the device to the server. The input is the user's reservation request, and the output is the request sent to the server. Specifically, the smartphone application sends the request by pressing the reservation button.

[1531] Step 9:

[1532] Booking confirmation and notifications

[1533] The server processes the received reservation request and checks the inventory of the relevant product and availability of the service. If the reservation is possible, it confirms the reservation and sends a confirmation notice to the terminal. The input is the reservation request and the output is a reservation confirmation notice. Specifically, it accesses the database, updates the reservation information, and sends a notice.

[1534] Step 10:

[1535] Providing a re-proposal

[1536] If the desired visit time is busy, the server will suggest alternative times or related products. The suggestions are displayed on the terminal. The input is the busyness information and the user's request, and the output is an alternative suggestion. Specifically, a generative AI model is used to generate alternative suggestions and provide that information to the user.

[1537] Through the above processing steps, users can instantly grasp detailed product information and congestion status during their shopping experience at a physical store, enabling them to make efficient purchases and plans.

[1538] 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.

[1539] This invention is a system that reads QR codes installed at tourist spots to provide historical information and tourist guides related to the tourist spot, and also improves the user experience by providing congestion information, reservation functions, and an emotion engine.

[1540] System Configuration

[1541] 1. Scan the QR code and send a request

[1542] When a user scans a QR code installed at a tourist spot with their smartphone, the device retrieves the URL embedded in the QR code and sends it to the server. Upon receiving the request, the server analyzes the URL and identifies the ID of the corresponding tourist spot.

[1543] 2. Generating related information

[1544] The server retrieves basic information about the designated tourist destination from a database, then uses a generative AI model (e.g., ChatGPT) to automatically generate detailed content related to the destination, including text, images, audio, and video. This generated information is then provided to the user.

[1545] 3. Use of Emotion Engine

[1546] The device analyzes the user's facial expressions and voice and sends the data to the emotion engine. The server uses the emotion engine to recognize the user's emotional state and collects the results.

[1547] 4. Emotion-based modulation of information

[1548] The server uses data from the emotion engine to suggest tourist spots and provide information that takes the user's emotions into account. For example, if the user is excited, it can suggest tourist spots that are highly exciting.

[1549] 5. Providing congestion information

[1550] The server analyzes real-time and past visitor data to predict the current congestion situation at tourist destinations, allowing users to plan their visit based on the current congestion situation and future congestion predictions.

[1551] 6. Reservation function

[1552] Users can request reservations for tourist spots and facilities they are interested in. The server receives the reservation request sent from the device, checks the availability of the facility, and then confirms the reservation. Once the reservation is confirmed, a confirmation notification is sent to the device.

[1553] 7. Re-suggestion function

[1554] If the desired visit time is busy, the server will suggest alternative times or related tourist spots, allowing users to create a more efficient schedule.

[1555] Specific examples

[1556] For example, when a user scans a QR code for historical monument "A" with their smartphone, the device sends the URL "https: / / example.com / monumentA" to a server. The server parses the URL and retrieves information about monument A from a database. Using a generative AI model, detailed descriptive text, images, audio, and video content about monument A is automatically generated. This information is displayed to the user, including, for example, a detailed explanation of "the history and background of monument A."

[1557] Furthermore, the device collects the user's facial expressions and voice, which the emotion engine analyzes to recognize the user's emotional state. For example, if the emotion engine recognizes that the user is excited, the server will suggest tourist spots that are highly exciting (e.g., active attractions).

[1558] If the user later wishes to visit the neighboring Museum B and makes a reservation, the terminal sends a reservation request to the server. The server checks the availability of Museum B, and if a reservation is possible, the user receives a notification saying, "You have reserved a 10:00 AM slot at Museum B."

[1559] If the planned visit time is busy, the server will use the AI ​​model to suggest alternative tourist spots or different times. For example, "Monument A is currently busy, so we recommend visiting adjacent Garden C at 10:30 AM."

[1560] This allows users to have an efficient and in-depth sightseeing experience. By simply scanning the QR code installed at tourist spots, the system combines an emotion engine to provide more personalized tourist information, providing detailed information about tourist spots, obtaining congestion information, using reservation functions, and utilizing re-suggestion functions.

[1561] The processing flow will be explained below.

[1562] Step 1:

[1563] The user scans a QR code installed at a tourist spot with their smartphone.

[1564] Step 2:

[1565] The device sends the URL obtained from the QR code to the server (e.g., an HTTP GET request).

[1566] Step 3:

[1567] The server analyzes the received URL and identifies the ID of the tourist spot corresponding to the URL.

[1568] Step 4:

[1569] The server retrieves basic information about the tourist destination (historical background, points of interest, etc.) from a database.

[1570] Step 5:

[1571] The server instructs the generative AI model (e.g., ChatGPT) to automatically generate detailed text, image, audio, and video content based on the basic information acquired.

[1572] Step 6:

[1573] The server aggregates the generated related information to form content for presentation to the user.

[1574] Step 7:

[1575] The device collects the user's facial expressions and voice and sends them to the emotion engine.

[1576] Step 8:

[1577] The emotion engine analyzes the user's emotional state and sends the results to the server.

[1578] Step 9:

[1579] Based on the analysis results from the emotion engine, the server adjusts the tourist destination suggestions and information provided, taking into account the user's emotions.

[1580] Step 10:

[1581] The server runs the congestion prediction model to generate the current congestion status and future congestion predictions for the tourist destination.

[1582] Step 11:

[1583] The server transmits the integrated information and congestion information to the terminal.

[1584] Step 12:

[1585] The terminal displays the information received from the server to the user.

[1586] Step 13:

[1587] Request reservations for attractions and facilities that interest the user.

[1588] Step 14:

[1589] The terminal sends a reservation request to the server.

[1590] Step 15:

[1591] The server checks the availability of the facility in real time.

[1592] Step 16:

[1593] The server confirms that the reservation is possible and processes the reservation, saving the reservation information and sending a confirmation of the reservation to the terminal.

[1594] Step 17:

[1595] The server uses the generated AI model to suggest alternative times and tourist destinations based on crowding predictions.

[1596] Step 18:

[1597] The server transmits the re-proposal information to the terminal, and the terminal notifies the user of it.

[1598] The above are the specific processing steps of this system.

[1599] Example 2

[1600] 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."

[1601] When travelers visit tourist destinations, they want to quickly obtain detailed local information and receive tourist guidance tailored to their individual needs. However, conventional tourist information systems only provide one-way information and lack congestion information, reservation functions, and personalized guidance based on individual emotional states. They also lack efficient schedule adjustments based on the current and future congestion situation at tourist destinations. This poses a challenge, reducing the convenience of travelers and the quality of their tourism experience.

[1602] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1603] In this invention, the server includes: [means for reading codes installed at tourist attractions;] [means for sending a data request based on information obtained from the code;] [means for generating information related to tourist attractions using a generative AI model;] [means for providing the generated information related to tourist attractions to the user;] [means for utilizing an emotion engine for analyzing user emotion data obtained from the terminal;] [means for adjusting tourist attraction information based on the user's emotional state;] [means for providing real-time congestion information about tourist attractions;] [means for processing reservations for tourist attractions and facilities; and [means for suggesting visiting times and alternative tourist attractions.] This enables users to obtain detailed information about tourist attractions in real time and receive personalized tourist guidance according to their individual emotional state, and also enables efficient schedule adjustments and reservation management based on congestion levels.

[1604] "Means for reading codes installed at tourist spots" refers to a function that allows users to scan codes such as QR codes installed at tourist spots with devices such as smartphones or tablets and obtain the information contained in the codes.

[1605] The "means for transmitting a data request based on information obtained from the code" is a function for transmitting the information of the obtained code to the server via the terminal.

[1606] "Means for generating information related to tourist destinations using a generative AI model" refers to a function that automatically generates content such as detailed text, images, audio, and video related to tourist destinations using a generative AI model (e.g., a text generation model).

[1607] "Means for providing users with information related to the generated tourist destinations" refers to a function that allows content generated by the generative AI model to be sent to a terminal and for users to receive the information visually or audibly.

[1608] "Means for utilizing an emotion engine that analyzes user emotion data acquired from the terminal" refers to a function in which the terminal collects the user's facial expressions and voice, analyzes them with the server's emotion engine, and recognizes the user's emotional state.

[1609] The "means for adjusting information about tourist attractions based on the user's emotional state" is a function that dynamically adjusts the information provided and the content of suggested tourist attractions according to the user's emotional state recognized by the emotion engine.

[1610] "Means for providing real-time congestion information at tourist destinations" is a function that uses a data analysis model to analyze the current congestion situation and predicted congestion information at tourist destinations and provides it to users in real time.

[1611] "Means for processing reservations for tourist attractions and facilities" refers to the function of receiving a reservation request for a tourist attraction or facility that a user wishes to visit, and the server confirming and confirming the reservation in response to that request.

[1612] "Means for suggesting visiting times and alternative tourist spots" is a function that suggests visiting times and alternative tourist spots depending on the congestion situation and reservation status.

[1613] This invention is a system that uses QR codes installed at tourist spots to provide users with detailed information about tourist spots, congestion information, and reservation functions, and aims to provide personalized guidance based on the user's emotional state.

[1614] System configuration

[1615] The system mainly includes the following components:

[1616] Terminal: A portable device such as a smartphone or tablet that can be used to scan QR codes installed at tourist attractions.

[1617] Server: A central computer system that processes requests sent from user devices and generates information using generative AI models and emotion engines.

[1618] Database: A database system (e.g. MySQL) for storing basic information about tourist destinations.

[1619] Generative AI models: Artificial intelligence models for automatically generating content such as text, images, audio, and video (e.g., ChatGPT).

[1620] Emotion engine: Software that analyzes a user's facial expressions and voice to recognize their emotional state (e.g., Microsoft Azure Emotion API).

[1621] Operation overview and examples

[1622] The system begins when a user arrives at a tourist spot and scans a QR code.

[1623] 1. Scan the QR code

[1624] When a user uses a smartphone to scan a QR code installed at a tourist spot, the device retrieves the information, which includes a URL related to the tourist spot.

[1625] 2. Submitting a Data Request

[1626] The device sends the URL (e.g., "https: / / example.com / monumentA") to the server. The device makes this data request using its Internet connection.

[1627] 3. Analysis and Acquisition of Information

[1628] When the server receives the request, it analyzes the URL to identify the tourist spot ID, and then retrieves basic information about the corresponding tourist spot from the database.

[1629] 4. Content generation using generative AI models

[1630] The server inputs a prompt into a generative AI model (e.g., ChatGPT) to generate detailed content related to the tourist attraction, such as text, images, audio, and video. For example, the prompt might be, "Please explain the detailed history and characteristics of Monument A."

[1631] 5. Providing Information to Users

[1632] The generated content is sent from the server to the terminal and displayed on the terminal screen, allowing the user to receive a detailed explanation of "Monument A's history and characteristics" visually or audibly.

[1633] 6. Analysis by Emotion Engine

[1634] The device uses a camera and microphone to collect the user's facial expressions and voice, and sends them to emotion analysis software (e.g., Microsoft Azure Emotion API). The analysis results are then sent to a server.

[1635] 7. Emotion-based modulation of information

[1636] The server provides information based on the user's emotional state based on data from the emotion engine. For example, if the user is excited, it will suggest tourist spots that are likely to excite the user.

[1637] 8. Providing congestion information

[1638] The server analyzes real-time data and past visitor data, and uses a congestion prediction model to predict current and future congestion information for tourist destinations. This information is then provided to users.

[1639] 9. Processing booking requests

[1640] When a user wants to reserve a specific tourist spot or facility, they send a reservation request from their device. The server checks the availability of the facility and confirms the reservation. Once the reservation is complete, a confirmation notification is sent to the device.

[1641] 10. Using the re-suggestion feature

[1642] If reservations are busy or based on crowd predictions, the server will suggest alternative tourist spots and visiting times, such as "Monument A is currently crowded, so we recommend visiting adjacent Garden C at 10:30 AM."

[1643] This allows users to obtain detailed information about tourist spots in real time and receive personalized information according to their emotional state.In addition, the system provides congestion information and reservation functions, enabling efficient schedule adjustment and a rich sightseeing experience.

[1644] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1645] Step 1:

[1646] A user scans a QR code installed at a tourist spot. The input is the QR code scanned using the user's smartphone, and the output is the URL contained in the acquired QR code. Specifically, when the user launches the smartphone's camera app and reads the QR code, the URL (e.g., "https: / / example.com / monumentA") is displayed.

[1647] Step 2:

[1648] The device sends the scanned URL to the server. The input is the URL obtained by the device, and the output is the URL request received by the server. Specifically, the URL information is sent to the server via the smartphone's Internet connection.

[1649] Step 3:

[1650] The server analyzes the received URL. The input is the URL data, and the output is the tourist spot ID. Specifically, the server analyzes the URL using Apache HTTP Server or similar and extracts the tourist spot identifier, such as "monumentA."

[1651] Step 4:

[1652] The server retrieves basic information about tourist attractions from a database. The input is the tourist attraction's ID, and the output is the tourist attraction's basic information. Specifically, the server sends a query to the MySQL database to retrieve information such as the tourist attraction's name, location, and brief history.

[1653] Step 5:

[1654] The server generates content using a generative AI model. The input is basic information about the tourist attraction and a prompt, and the output is generated content such as text, images, audio, and video. For example, the prompt "Please explain the detailed history and characteristics of Monument A" is sent to ChatGPT, which generates a detailed explanatory text and related images.

[1655] Step 6:

[1656] The server sends the generated content to the device. The input is the generated content, and the output is the content displayed on the device. Specifically, the server sends the generated text and images to the smartphone and displays them on the smartphone screen.

[1657] Step 7:

[1658] The device collects the user's facial expressions and voice and sends them to the emotion engine. The input is the user's facial expression and voice data, and the output is the emotion analysis results. Specifically, data is collected using the smartphone's camera and microphone and sent to the Microsoft Azure Emotion API.

[1659] Step 8:

[1660] The server receives the emotion analysis results and analyzes the user's emotional state. The input is the emotion analysis results, and the output is a classification of the user's emotional state (e.g., excited, relaxed). The server adjusts the information provided based on the analysis results.

[1661] Step 9:

[1662] The server provides personalized information to the device based on the user's emotional state. The input is the user's emotional state and tourist attraction information, and the output is tailored tourist attraction information and suggestions. For example, if the user is excited, active attractions are suggested.

[1663] Step 10:

[1664] The server analyzes and provides congestion information in real time. The input is real-time visitor data and past visit data, and the output is current and predicted congestion information. The server performs the analysis using data analysis software and sends the results to the terminal.

[1665] Step 11:

[1666] A user requests a reservation for a tourist attraction or facility. The input is the reservation information for the tourist attraction or facility desired by the user, and the output is a reservation request sent to the server. Specifically, the user inputs the reservation request through a smartphone application.

[1667] Step 12:

[1668] The server processes reservation requests. The input is a reservation request from the user, and the output is a reservation confirmation notification. The server accesses the reservation management system, checks availability, and confirms the reservation. Once the reservation is completed, a confirmation notification is sent to the terminal.

[1669] Step 13:

[1670] The server suggests visiting times and alternative tourist spots based on the congestion situation. The input is the congestion situation data and the user's schedule, and the output is an alternative suggestion. The server uses an AI model to calculate the optimal visiting time and alternative tourist spots and notify the user. For example, a suggestion might be, "Monument A is currently crowded, so we recommend visiting adjacent Garden C at 10:30 AM."

[1671] (Application example 2)

[1672] 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."

[1673] In recent years, there has been a demand for systems that can improve the user experience when visiting tourist spots and virtual stores. Conventional systems lack real-time congestion information and reservation functions, and it is difficult to provide personalized information based on the user's emotional state. In addition, generating tourist spot and store information takes time, which results in problems with the quality of the user experience.

[1674] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for reading a code placed at a tourist attraction or virtual store, means for sending a data request based on information obtained from the code, and means for providing the user with the generated information related to the tourist attraction or store. This makes it possible to provide real-time congestion information for the tourist attraction or store and to provide and suggest information based on the user's emotional state. It also processes reservations for tourist attractions, facilities, or products, and suggests reservations for the time period during which the visitor should visit, or for other tourist attractions or different time periods.

[1675] "Tourist attractions or virtual stores" includes not only actual physical tourist attractions or commercial areas, but also stores or tourist attractions recreated in virtual space.

[1676] "Code" means a medium with embedded information, including a QR code, barcode, or other type of code, that can be scanned by a User's device.

[1677] "Means for sending a data request" refers to the method used to send the URL and identifying information obtained by scanning the code to a server to obtain the associated data.

[1678] "Generated Information" includes text, images, audio, and video content created by the server using databases and generative AI models.

[1679] "Crowd information" is information that indicates the congestion status of a target location or store, generated using current visitor numbers, past data, and predictive algorithms.

[1680] "Means for processing reservations" refers to the method by which users make reservations for visit times and facility use at specific tourist attractions and stores, and the associated back-end processing mechanisms.

[1681] "Emotional state" refers to the user's mental and emotional state as identified by analyzing the user's facial expressions and voice.

[1682] "Means for providing or suggesting information" refers to methods that not only display generated information to the user, but also dynamically provide recommended information based on the user's current emotional state.

[1683] This invention provides a system including means for reading QR codes installed at tourist attractions or virtual stores, means for sending a data request based on information obtained from the read code, means for providing the user with generated information related to the tourist attraction or store, means for providing real-time congestion information about the tourist attraction or store, means for processing reservations for tourist attractions, facilities, or products, means for suggesting a visiting time period or reservations for another tourist attraction or a different time period, and means for analyzing the user's emotional state and providing or suggesting information based on the results.

[1684] Hardware and Software

[1685] To realize this system, the following hardware and software are used.

[1686] Smart glasses: Using Microsoft HoloLens as an example.

[1687] Smartphone: iOS or Android smartphone.

[1688] Emotion engine: Uses Microsoft Azure Emotion API.

[1689] Generative AI model: OpenAI GPT-4.

[1690] Backend: Node.js, Express, MongoDB.

[1691] Data processing and calculation

[1692] 1. Scan the QR code and send a data request

[1693] The server obtains the URL by scanning the QR code with the user's smart glasses or smartphone and sends the information to the server, which then analyzes the URL and retrieves the corresponding store and product information from the database.

[1694] 2. Provision of generated information

[1695] The server uses a generative AI model (OpenAI GPT-4) to automatically generate detailed related text, image, audio, and video content based on the information retrieved from the database. The generated information is then sent to the device and provided to the user.

[1696] 3. Use of Emotion Engine

[1697] The server receives facial and voice data acquired from the user's smart glasses or smartphone and sends it to the Microsoft Azure Emotion API to analyze the user's emotional state. The server receives the results of the analysis by the emotion engine.

[1698] 4. Emotionally driven information

[1699] The server uses a generative AI model to create product suggestions or tourist spot recommendations based on the acquired emotional data and provides them to the user. For example, if the user is excited, it will suggest new products or limited-time offers.

[1700] 5. Providing congestion information and reservation functions

[1701] The server analyzes real-time congestion information and notifies users of the congestion situation. If it is crowded, it will suggest less crowded times or other locations and inform users of available reservation times.

[1702] 6. Re-suggestion function

[1703] If the user's desired time slot is crowded, the server will suggest alternative tourist spots and time slots. The server dynamically generates these alternatives through a generative AI model and presents them to the user.

[1704] Specific examples

[1705] For example, when a user scans the QR code of a "virtual fashion store" in a virtual mall, detailed information about the new autumn collection is provided. When using smart glasses, excitement from facial expressions is analyzed through an emotion engine (Microsoft Azure Emotion API), and the most popular items from the new collection are suggested. Crowd information is also provided in real time, allowing users to reserve a suitable time to visit. If the store is crowded, a reservation for a less crowded time slot is suggested.

[1706] Prompt Sentence Examples

[1707] An example of a prompt to be input to the generative AI model is as follows:

[1708] Product information: [Product A: Smartphone, Product B: Smartwatch]

[1709] Emotional state: Highly excited

[1710] Proposal Generation: Suggest the best product offers to highly excited users.

[1711] This can significantly improve the user experience.

[1712] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1713] Step 1:

[1714] The user scans a QR code placed at a tourist spot or virtual store using smart glasses or a smartphone.

[1715] Input: QR code

[1716] Output: URL obtained from QR code

[1717] How it works: A user scans a QR code with their device and retrieves the URL embedded in the code.

[1718] Step 2:

[1719] The terminal sends the acquired URL to the server.

[1720] Input: URL obtained from QR code

[1721] Output: Data request sent to the server

[1722] Operation: The device requests detailed information by sending the URL information it has obtained to the server.

[1723] Step 3:

[1724] The server analyzes the URL and retrieves information about the relevant tourist attraction or store from a database.

[1725] Input: URL information sent to the server

[1726] Output: Tourist attraction or store information retrieved from the database

[1727] How it works: The server parses the URL, identifies the ID of the relevant tourist attraction or store, and retrieves the necessary information from the database based on that ID.

[1728] Step 4:

[1729] The server uses a generative AI model (e.g., OpenAI GPT-4) to generate detailed text, image, audio, and video content based on tourist attraction or store information.

[1730] Input: Tourist attraction or store information retrieved from the database

[1731] Output: Generated detailed content (text, images, audio, video)

[1732] How it works: The server inputs the acquired information into a generative AI model, which automatically generates detailed content based on the prompt.

[1733] Step 5:

[1734] The server sends the generated information to the terminal and displays it to the user.

[1735] Input: Generated detail content

[1736] Output: Detailed content sent to the device, displayed to the user

[1737] How it works: The server sends the content created by the generative AI model to the device and displays it for the user to view.

[1738] Step 6:

[1739] The device collects the user's facial expressions and voice and sends them to an emotion engine (e.g., Microsoft Azure Emotion API).

[1740] Input: User facial and voice data

[1741] Output: Data sent to the emotion engine

[1742] How it works: The device sends the user's facial expression and voice data collected to the emotion engine.

[1743] Step 7:

[1744] The emotion engine analyzes the user's emotional state and sends the results back to the server.

[1745] Input: User facial and voice data

[1746] Output: Emotional state analysis result

[1747] How it works: Analyzes the user's emotional state using waveform analysis and facial expression recognition algorithms. The analysis results are sent to the server.

[1748] Step 8:

[1749] Based on the emotion data, the server uses a generative AI model to regenerate information appropriate for the user and transmits it to the device.

[1750] Input: Emotional state analysis results, generative AI model

[1751] Output: Regenerated personalization information

[1752] How it works: The server adds the acquired emotional data to the prompt text, creates new personalized information using the generative AI model, and suggests it to the user.

[1753] Step 9:

[1754] The server uses the congestion prediction model to analyze the congestion situation at tourist spots or stores and provides real-time information and predicted information to users.

[1755] Input: Current and past visit data

[1756] Output: Real-time and forecast information on congestion status

[1757] Operation: The server analyzes visit data and uses a congestion prediction model to generate real-time and future congestion information, which it then provides to users.

[1758] Step 10:

[1759] The user requests a reservation for a desired tourist spot, facility, or product, and the server processes the reservation.

[1760] Input: User's booking request

[1761] Output: Booking confirmation notice

[1762] How it works: The server receives a reservation request from a user, processes the reservation, checks availability, and then notifies the user that the reservation is confirmed.

[1763] Step 11:

[1764] If the user's desired time is busy, the server will suggest alternative tourist spots or different visiting times.

[1765] Input: Crowd data, generative AI model

[1766] Output: Alternative proposal information

[1767] How it works: The server analyzes the congestion data and uses a generative AI model to generate and provide suggestions for alternative tourist destinations and visiting times to the user.

[1768] This results in a system that improves the user's experience in a more personalized and efficient manner.

[1769] 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.

[1770] 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.

[1771] 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.

[1772] 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.

[1773] 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.

[1774] 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.

[1775] 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).

[1776] 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.

[1777] 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."

[1778] 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.

[1779] 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).

[1780] 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.

[1781] 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.

[1782] 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.

[1783] 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.

[1784] 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.

[1785] 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.

[1786] 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.

[1787] 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.

[1788] 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.

[1789] 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.

[1790] The following is further disclosed regarding the above embodiment.

[1791] (Claim 1)

[1792] [Means for reading the codes installed at tourist spots,

[1793] means for sending a data request based on information obtained from said code;

[1794] [Means for providing users with information related to the generated tourist destinations;

[1795] [Means of providing real-time congestion information for tourist spots,

[1796] [Means of processing reservations for tourist attractions and facilities;

[1797] [A system that includes a means to suggest visiting times and alternative tourist spots.

[1798] (Claim 2)

[1799] The system of claim 1, wherein the generated information includes text, images, audio, and video.

[1800] (Claim 3)

[1801] [The system according to claim 1 provides congestion information for tourist destinations using a congestion prediction model.

[1802] "Example 1"

[1803] (Claim 1)

[1804] [Means for reading the codes installed at tourist spots,

[1805] means for sending a data request based on information obtained from said code;

[1806] [Means for identifying tourist destination IDs and obtaining basic information based on them;

[1807] [Means for sending prompts to a generative AI model to generate detailed information related to tourist attractions; and

[1808] [means for providing the generated information to a user; and

[1809] [Means of analyzing visitor data to provide real-time congestion information for tourist spots, and

[1810] [Means of processing reservations for tourist attractions and facilities;

[1811] [A system that includes a means to suggest visiting times and alternative tourist spots.

[1812] (Claim 2)

[1813] The system of claim 1, wherein the generated information includes text, images, audio, and video.

[1814] (Claim 3)

[1815] [The system according to claim 1 provides congestion information for tourist destinations using a congestion prediction model.

[1816] "Application Example 1"

[1817] (Claim 1)

[1818] [Means to read the code installed in the physical store,

[1819] means for sending a data request based on information obtained from said code;

[1820] [means for providing information related to the generated product to the user;

[1821] [Means of providing real-time information on congestion in stores,

[1822] [Means of processing reservations for products and services;

[1823] [A system that includes a means to suggest visiting times and alternative products.

[1824] (Claim 2)

[1825] The system of claim 1, wherein the generated information includes text, images, audio, and video.

[1826] (Claim 3)

[1827] [The system according to claim 1 provides congestion information within a store using a congestion prediction model.

[1828] "Example 2: Combining Emotion Engines"

[1829] (Claim 1)

[1830] [Means for reading the codes installed at tourist spots,

[1831] means for sending a data request based on information obtained from said code;

[1832] [Means for generating information related to tourist destinations using a generative AI model;

[1833] [Means for providing users with information related to the generated tourist destinations;

[1834] [Means for utilizing an emotion engine that analyzes user emotion data acquired from the terminal;

[1835] [Means for adjusting tourist attraction information based on the user's emotional state;

[1836] [Means of providing real-time congestion information for tourist spots,

[1837] [Means of processing reservations for tourist attractions and facilities;

[1838] [A system that includes a means to suggest visiting times and alternative tourist spots.

[1839] (Claim 2)

[1840] The system of claim 1, wherein the generated information includes text, images, audio, and video.

[1841] (Claim 3)

[1842] [The system according to claim 1 provides congestion information for tourist destinations using a congestion prediction model.

[1843] "Application example 2 when combining emotion engines"

[1844] (Claim 1)

[1845] [Means for reading codes installed at tourist spots or virtual stores;

[1846] means for sending a data request based on information obtained from said code;

[1847] [Means for providing users with information related to the generated tourist spots or stores;

[1848] [Means of providing real-time information on crowds at tourist spots or stores;

[1849] [Means for processing reservations for tourist attractions, facilities or products;

[1850] [Means to suggest visiting times, alternative tourist spots or reservations at different times,

[1851] [A system including a means for analyzing a user's emotional state and providing or suggesting information based on the results.

[1852] (Claim 2)

[1853] The system of claim 1, wherein the generated information includes text, images, audio, and video.

[1854] (Claim 3)

[1855] [The system according to claim 1 provides congestion information for tourist spots or stores using a congestion prediction model. [Explanation of symbols]

[1856] 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 reading the codes installed at tourist spots; means for transmitting a data request based on information obtained from said code; means for providing a user with information related to the generated tourist destination; A means of providing real-time congestion information for tourist spots, a means of processing reservations for attractions and facilities; The system includes a means to suggest visiting times and alternative tourist destinations.

2. The system of claim 1 , wherein the generated information includes text, images, audio, and video.

3. The system according to claim 1, wherein congestion information of tourist spots is provided using a congestion prediction model.

Images