system

Abstract

We provide the system. [Solution] A means of collecting information about tourist destinations based on user interests and location information, A means for generating customized guide information using an artificial intelligence model that utilizes collected information, A means of transmitting and displaying the generated guide information to a terminal, A means of collecting user feedback and using that feedback to train a generating artificial intelligence model, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In many tourist facilities, when visitors lack the knowledge to fully understand its value, the tourist experience tends to be insufficient. The current guide service is generally standardized and does not provide information considering the individual interests and location information of visitors. Also, since visitors have to listen to uninteresting information within limited time, there is a problem that it is difficult for visitors to obtain information focused on their own interests.

Means for Solving the Problems

[0005] To solve these problems, this invention collects information about tourist destinations based on the user's interests and location, and generates customized guide information using an artificial intelligence model. The generated guide information is transmitted to the user's device and provided in audio or text format. Furthermore, by collecting user feedback and utilizing this data to train the artificial intelligence model, it is possible to improve the accuracy and personalization of the guide information. In addition, the provided guide information can be multilingual, enabling the provision of appropriate information to users of a wide range of ages and nationalities.

[0006] A "user" refers to an individual or group that uses the system to receive personalized information about their tourism experience.

[0007] "Location information" refers to data about a user's current geographical location, which is obtained using technologies such as GPS.

[0008] A "tourist destination" refers to facilities and places that are open to visitors, such as art museums, museums, and historical buildings.

[0009] "Means of collecting information" refers to a series of systems and technologies used to gather necessary information based on user-related interests and location information.

[0010] A "generative artificial intelligence model" refers to an algorithm or program that generates personalized tourist guide information based on the user's interests and location.

[0011] "Customized guide information" refers to tourist information that has been specially created to suit the user's individual interests and current location.

[0012] "Terminal" refers to a mobile phone, tablet, or other device used by a user to access the system.

[0013] "Feedback" refers to the evaluations and opinions that users give regarding the guide information provided by the system.

[0014] "Multilingualism" refers to the characteristic of being able to provide information in multiple different languages.

[0015] "Means used for learning" refers to the processes and methods for improving system performance based on user feedback. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0019] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

[0020] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used by the processor as a work memory.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

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

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] The system of the present invention operates based on the interaction between a server, a terminal, and a user in order to provide a customized tourist guide based on the user's hobbies, preferences, and location information.

[0038] The server plays a central role in this invention and performs the following functions. First, the server receives registration information transmitted by the user and stores data related to the user's interests and planned tourist destinations in a database. Second, the server receives the user's location information from the terminal and has the ability to dynamically collect and update information on related tourist destinations based on that information. This makes it possible to prepare information that is best suited to the user's interests.

[0039] The device operates through applications installed by the user and tracks the user's current location in real time using GPS. The device also receives guidance information from a server and presents it in a format chosen by the user (e.g., voice or text). Users can provide feedback through the application, which improves the system's user experience.

[0040] Users can set their interests within the application and receive information about their favorite tourist destinations and exhibits. For example, if a user is visiting a museum in a certain city, the server will provide detailed information about a specific exhibit within that museum. This information deepens the user's interest and provides a richer travel experience.

[0041] With the system of the present invention configured in this way, tourists can easily obtain information that matches their interests and enjoy the best possible sightseeing experience within a limited time.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] Users download the application to their device and create a profile. Here, they register their name and categories of interest (e.g., art, architecture).

[0045] Step 2:

[0046] The device periodically uses its GPS function to obtain the user's current location information. This location information is transmitted to the server in real time.

[0047] Step 3:

[0048] The server uses the received user location information to search and collect relevant tourist information from its database. This organizes information about nearby tourist attractions and facilities.

[0049] Step 4:

[0050] The server generates personalized guide information using a generative AI model based on the user's interests and location. The user's past usage history is also taken into consideration during this process.

[0051] Step 5:

[0052] The server sends the generated guide information to the terminal. The transmitted information is formatted according to the user's preferences (voice or text).

[0053] Step 6:

[0054] The terminal presents the user with information received from the server, either via voice or text. This allows the user to obtain detailed and personalized information about tourist destinations.

[0055] Step 7:

[0056] Users can provide feedback on the guide information provided through the app. This feedback will be used to improve the quality of the guide information.

[0057] Step 8:

[0058] The server receives user feedback and incorporates it into the learning process of the generated AI model, improving the accuracy and personalization of future guide generation.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] It is difficult and time-consuming for tourists to obtain efficient and personalized tourist information based on their interests and current location. Furthermore, if the information provided does not meet user expectations, satisfaction levels may decrease. Multilingual support and improvements in information accuracy based on feedback are particularly needed.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] In this invention, the server includes means for receiving user input and storing information on the user's hobbies, preferences, and planned tourist destinations; means for acquiring relevant tourist destination information from external sources based on location information and hobbies, preferences, and storing and updating the information; and means for generating customized guide information using an artificial intelligence model based on the information obtained from external sources. As a result, users can receive appropriate tourist information based on their interests in a timely manner, enabling them to enjoy an efficient and personalized tourist experience.

[0064] "Accepting user input" refers to the process of receiving information such as hobbies, preferences, and planned visits provided by the user through the application.

[0065] "Hobbies and preferences" refers to information that indicates the categories and activities that users are interested in, and serves as a criterion for selecting tourist destinations.

[0066] "Planned tourist destinations" refers to information about places or regions that users plan to visit.

[0067] "Location information acquisition" refers to the process of accurately determining the user's current location using technology built into the device.

[0068] "Relevant tourist destination information" refers to the latest information on tourist destinations that are deemed highly suitable based on the user's location and interests.

[0069] A "generative artificial intelligence model" refers to a machine learning algorithm used to perform advanced analysis based on data and provide personalized information to users.

[0070] "Customized guide information" refers to tourist guide data that is individually tailored to each user, taking into account their interests, preferences, and location.

[0071] "Presentation method" refers to an interface or protocol for displaying guide information on a terminal in a format selected by the user.

[0072] "Feedback" refers to the opinions and impressions that users provide after experiencing a service, and is used to improve the system.

[0073] This invention is a system that provides personalized tourist guide information based on the user's interests, preferences, and current location. Specific embodiments of the invention are described below.

[0074] The server operates in a cloud computing environment and manages user registration information via a database (e.g., PostgreSQL). This information includes hobbies, preferences, and planned tourist destinations entered by the user through the application. The server receives location information from the terminal using the HTTPS protocol to determine the user's current location.

[0075] The device operates via an application installed on the mobile device. The device uses a built-in GPS module to obtain the user's location in real time and transmits the location information to the server.

[0076] The server collects and updates relevant tourist destination information from external services (e.g., geographic information APIs) based on received location information and user preferences. Based on the acquired data, it uses a generative AI model (e.g., an AI-based natural language generation model) to generate personalized guide information for the user. The generated guide information includes details that respond to prompts such as "Please provide information about historical sites in Kyoto."

[0077] The device receives customized guide information sent from the server and displays it in the format selected by the user (audio or text). The user views the information through the application interface and provides feedback on their experience. This feedback is sent to the server and used to improve the system and generate information for future use.

[0078] In this way, users can obtain the latest and most personalized travel information tailored to their interests in real time, resulting in a richer and more efficient travel experience.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The user launches a mobile application and enters information about their hobbies, preferences, and planned travel destinations. This input data includes information such as favorite travel categories and specific places to visit. The server receives this information and saves it in the database as a new user profile.

[0082] Step 2:

[0083] The device uses its built-in GPS function to determine the user's current location. The acquired location information is periodically sent to the server. The server receives this location information and prepares to collect data on relevant tourist destinations based on the user's current location.

[0084] Step 3:

[0085] The server uses the received location information and the user's interests and preferences to collect relevant tourist destination information from external geographic information service APIs. The obtained tourist destination information is stored in a database and updated as needed. During this process, the server filters the information to be appropriate based on location and interests.

[0086] Step 4:

[0087] The server generates customized guide information using a generative AI model based on well-organized tourist destination information. This model creates helpful information in natural language based on prompt statements (e.g., "Please tell me about historical landmarks in Tokyo"). The generated guide information is optimized based on the user profile.

[0088] Step 5:

[0089] The server sends the generated, customized guide information to the terminal. The terminal displays the received information in the format selected by the user. For example, if an audio guide is selected, the text is converted to speech and played.

[0090] Step 6:

[0091] Users experience the provided guide information and then provide feedback through the application. This feedback includes opinions on the accuracy and satisfaction level of the information. The device sends this feedback to the server.

[0092] Step 7:

[0093] The server analyzes the received feedback and uses it to generate information for the next time. The analysis results are reflected in tuning the generation AI model and updating the database, contributing to improvements in the overall accuracy and personalization of the system.

[0094] (Application Example 1)

[0095] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0096] This invention aims to develop an information collection and provision system that takes into account interests and location information in order to provide users with appropriate and personalized guidance information. Current guidance systems have difficulty responding to the frequency of location information updates and the individual interests of users. Furthermore, providing relevant information about exhibits in physical stores often results in insufficient presentation of appropriate detailed information. A system is needed to solve these problems and provide the optimal experience for users.

[0097] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0098] In this invention, the server includes means for collecting location-related information based on the user's interests and location information, means for generating customized guidance information using a generative artificial intelligence model, and means for providing detailed information related to exhibits based on information classified within the physical store. As a result, users can always receive highly relevant information based on their interests and enjoy a richer experience by obtaining detailed background information about the exhibits.

[0099] "User interests" refer to information about the preferences and concerns that users show towards specific things.

[0100] "Location information" is geographical data that indicates the user's current physical location.

[0101] "Information" refers to information that includes detailed descriptions and instructions provided to the user about a specific location or object.

[0102] A "mobile device" refers to a portable electronic device such as a smartphone or tablet, which is a device that users can carry around and use to visualize information.

[0103] A "physical store" refers to a physical store or facility that actually exists and can be visited by users directly.

[0104] "Exhibited items" refer to items or works of art that are displayed to visitors within a physical store, and for which detailed information is provided to users.

[0105] A "generative artificial intelligence model" refers to an algorithm that uses machine learning and data analysis to generate personalized information based on the user's preferences and history.

[0106] "Customization" refers to adjusting the content of information and services to suit the individual needs and interests of users.

[0107] "Feedback" refers to responses and opinions provided by users, and is information used to improve system performance and personalize the system.

[0108] To realize this application, a system will be built in which a server, a terminal, and a user work together. First, the server receives the user's interests and location information, and based on this, collects information about relevant locations from a database. The server uses this information and a generative artificial intelligence model to create personalized guidance information. The guidance information will include details tailored to the individual user's preferences, such as the historical background and explanations of exhibits.

[0109] Next, the device receives this guidance information and displays it in the user's chosen format, such as audio or text. The device can also provide guidance along the user's route within the physical store to view details of specific exhibits. This allows users to receive guidance information using their smartphones when visiting a physical store. Hardware used includes smartphones and tablets, and location information is obtained via the Google® Maps API. Firebase is used for server-side database management.

[0110] User feedback is sent to the server via the device, and this feedback is used to train the generative artificial intelligence model. The model is continuously improved as a result, enabling it to generate more accurate guidance information.

[0111] A concrete example is a user visiting a museum who scans a QR code (registered trademark) displayed on their smartphone screen to obtain detailed historical background information and artist information about a particular painting. In this process, the generating AI model is given a prompt such as, "Generate an audio guide for historical exhibits that the user is interested in, based on their current location," to generate context that matches the user's interests.

[0112] In this way, the system of the present invention becomes capable of providing users with interesting and useful information in real time.

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The server receives interest and location information sent from the user via their device. Based on this input data, it queries the database to retrieve information about relevant locations and exhibits. The data is then filtered to match the location information and the user's interests.

[0116] Step 2:

[0117] The server uses a generative AI model to generate customized guidance information based on the acquired information. In this process, the model is input with the prompt, "Generate an audio guide for historical exhibits that the user is interested in based on their current location," and the output is detailed guidance information tailored to the user's interests.

[0118] Step 3:

[0119] The generated guidance information is sent from the server to the terminal. The terminal receives it and displays it in the format specified by the user, such as text or audio guide. The user's location information is used to highlight guidance for the nearest exhibit or location.

[0120] Step 4:

[0121] When a user reviews the information provided and requests further details about a specific exhibit, the terminal generates a request for additional information. This request is sent to the server, which retrieves the necessary additional data and sends it back to the terminal.

[0122] Step 5:

[0123] Users input feedback into their devices, which is then sent to the server. The server uses this feedback as data to improve its AI model and train it. It is expected that the feedback will improve the accuracy and customization quality of the guidance information generated in the future.

[0124] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0125] This invention provides a tourist guide system that combines an emotion engine that recognizes user emotions, enabling further personalization based on user interests and location information.

[0126] This system operates based on the interaction between a server, a terminal, and a user. The server receives basic user profile information and location information, and searches for tourist information based on this information. The collected information is transformed into guide information optimized for each individual user using a generative AI model. The emotion engine, which is installed in the terminal, uses sensors such as cameras and microphones to monitor the user's facial expressions, tone of voice, and biometric information, and evaluates the user's emotions in real time.

[0127] The device's role is to provide users with customized guide information received from the server. In addition, an emotion engine analyzes the user's emotions and prioritizes presenting information that the user is particularly interested in or likely to find pleasing. For example, if a user looks sad in a museum, the emotion engine will notify the server, which can then add backstories or entertaining content to recreate the appeal of the exhibits.

[0128] User feedback, along with emotional data measured by the emotion engine, is sent to the server and used to train the generative AI model. This continuous learning enables the system to provide more accurate and personalized tourist information. Furthermore, multilingual support and age-appropriate customization make it user-friendly for everyone.

[0129] This system allows users to have a more engaging and emotionally satisfying travel experience, and to deepen their understanding of tourist destinations and cultures.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The user launches the application installed on their device and sets up their profile. The user enters their areas of interest and categories.

[0133] Step 2:

[0134] The device uses GPS to obtain the user's current location information and sends it to the server. Simultaneously, the emotion engine built into the device is activated and analyzes the user's emotions in real time from their facial expressions and voice.

[0135] Step 3:

[0136] The server collects relevant tourist information from a database based on the received location information, user interest data, and sentiment data. Using this data, the server utilizes a generative AI model to generate personalized guide information for the user.

[0137] Step 4:

[0138] The terminal displays guide information received from the server to the user. Based on the analysis results from the emotion engine, it selects and displays information optimized for the user's emotions. For example, if the user shows interest, it emphasizes detailed information to further pique that interest.

[0139] Step 5:

[0140] Users view the presented information and, if they become interested or have feedback, they input that information via their device. The emotion engine continuously monitors the user's emotions throughout this process and collects new data.

[0141] Step 6:

[0142] The server receives user feedback and continuously acquired emotional data, and uses this to update its generating AI model. This update enables the server to personalize guide information with greater accuracy in the future.

[0143] Step 7:

[0144] Based on the feedback received, the device updates the user's profile and interests as needed, preparing for future use. This allows users to receive an even more optimized experience next time.

[0145] (Example 2)

[0146] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0147] Traditional tourist guide systems provide information based on user interests and location, but they lack dynamic adjustments that respond to user emotions and immediate reactions. This results in a limited user experience, insufficient personalized guidance, and difficulty in improving satisfaction at tourist destinations. Furthermore, inadequate use of feedback limits long-term system improvement.

[0148] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0149] In this invention, the server includes means for acquiring tourist information based on the user's interests and location information, means for generating personalized guidance information using a living animal intelligence model with the acquired information, and means for transmitting and presenting the generated guidance information to a terminal. This enables dynamic adjustments based on the user's emotions, realizing the provision of personalized guidance information. Furthermore, these means make it possible to continuously improve user satisfaction through real-time emotion analysis.

[0150] "User interests" refer to the matters or themes that users are interested in, and in the context of travel guides, this refers to content related to places users want to visit or information they want to know.

[0151] "Location information" refers to data indicating the user's current location, obtained through a device. This information forms the basis for personalizing tourist information.

[0152] A "tourist destination" is a region or facility intended for tourists to visit, and includes places with historical, cultural, or natural value.

[0153] The "Living Animal Intelligence Model" is an algorithm developed based on machine learning technology and is used to generate personalized content based on the user's interests and location information.

[0154] "Personalized guidance information" refers to information tailored to each user's individual interests and feelings, meaning that different content is provided to each user.

[0155] A "terminal" refers to an electronic device that the user directly operates, which is used to receive tourist information and perform sentiment analysis.

[0156] "Emotion analysis" is a process that uses the user's facial expressions, voice, and biometric data to evaluate the user's emotional state in real time, and is used to adjust tourist information.

[0157] "Response" refers to the feedback and changes in emotional state that users exhibit through their devices, and the data used to optimize guidance information.

[0158] "Optimization" is the process of adjusting information and operations to make the user experience as good as possible, aiming to provide information that aligns with the user's interests and emotions.

[0159] This system provides optimized tourist information based on the user's interests and emotions. Its main components include a server, terminals, and user interaction.

[0160] The server first receives the user's interests and location information via machine, and then uses this information to obtain information about tourist destinations using a generative AI model. The generative AI model includes a general-purpose natural language processing engine and is likely a commercially available AI tool. In particular, the generative AI model can customize the guidance information based on the input prompt sentence. An example of a prompt sentence could be, "Tell me about the history of temples in Kyoto."

[0161] The device is equipped with an emotion engine that analyzes the user's facial expressions and voice tone in real time through sensors such as cameras and microphones. Furthermore, the device also acquires biometric data using sensors and sends it to a server to provide emotion analysis results. Based on the emotion analysis results, the server dynamically adjusts the guidance information and prioritizes delivering data that the user is likely to be of particular interest to the device.

[0162] Users can view and react to information received through their devices. By sending feedback to the server via their devices, user responses are used to further train the generative AI model. This improves the accuracy and personalization of future guidance information.

[0163] For example, when a user is visiting Kyoto, the emotion engine can provide detailed information about temples and museums that are likely to interest them, in order to increase the user's level of interest. Based on real-time emotion analysis, it may also present interactive content that the user will enjoy. This is an approach aimed at increasing user knowledge and satisfaction.

[0164] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0165] Step 1:

[0166] The user operates the device and logs into the system. The device obtains the user's basic profile information and current location information. This becomes the input data. The device collects this data and sends the status of the tourist recommendation service to the server.

[0167] Step 2:

[0168] The server uses a generative AI model to search for relevant tourist information based on the user's interests and location information received. Data processing here involves converting the user profile and location information into prompt sentences and inputting them into the generative AI model. The output is personalized tourist information tailored to the user.

[0169] Step 3:

[0170] The previously generated guidance information is sent from the server to the terminal. The terminal then displays this customized guidance information to the user. Specific actions include displaying maps, playing audio guides, and presenting text information.

[0171] Step 4:

[0172] The device utilizes an emotion engine to analyze the user's facial expressions, voice tone, and biometric information in real time. Input data includes biometric information obtained from the camera, microphone, and sensors, and the device evaluates the user's emotional state based on this data. The output is the user's emotional diagnosis result.

[0173] Step 5:

[0174] The server receives the sentiment analysis results sent from the terminal and readjusts the guidance information using a generative AI model. The specific data processing involves incorporating the sentiment diagnosis results into the prompt text, and the AI ​​generates new guidance information. This results in the creation of the most appealing information for the user, which is then sent to the terminal.

[0175] Step 6:

[0176] Users enjoy sightseeing using the information presented through their devices. After completing their sightseeing, users enter feedback into their devices. This feedback can be in text or rating format, and the data is sent to the server.

[0177] Step 7:

[0178] The server collects user feedback and sentiment analysis data, which is then used to improve the generating AI model. This data processing improves the accuracy of future guidance and prepares the system to provide a more personalized experience.

[0179] (Application Example 2)

[0180] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0181] In modern commercial facilities, while a wide variety of product information exists, consumers face the challenge of finding relevant information based on their own interests and feelings. Furthermore, conventional sales promotion methods struggle to provide specific, personalized suggestions to individual consumers. To improve the consumer experience, more personalized information provision is necessary.

[0182] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0183] In this invention, the server includes means for collecting information about commercial facilities based on the user's interests and location information, means for generating customized guide information using a generative artificial intelligence model based on the collected information and the user's emotional information, and means for transmitting and presenting the generated guide information to a portable information processing device. This makes it possible to provide product suggestions optimized for each consumer and improve the consumer experience within commercial facilities.

[0184] "User interest" refers to information that indicates the degree of interest an individual consumer has in a particular product or service.

[0185] "Location information" refers to data indicating a user's geographical location, obtained using portable information processing devices, etc.

[0186] A "commercial facility" refers to a physical place where goods and services are sold, and includes stores and shopping malls.

[0187] "Emotional information" refers to data that indicates a user's emotional state, obtained by analyzing their facial expressions, tone of voice, and other biometric information.

[0188] A "generative artificial intelligence model" is an algorithm that utilizes machine learning and deep learning technologies to generate information that is optimal for the user based on collected data.

[0189] A "portable information processing device" is an information device that a user can carry with them, and includes smartphones and tablet devices.

[0190] "Personalized information" refers to information that is tailored based on the individual user's interests and emotions.

[0191] An "emotion engine" is a technology used to analyze a user's emotions in real time, and refers to a device that uses sensors such as cameras and microphones.

[0192] "Feedback" refers to the information and opinions that users provide after using the system, and this data helps to further improve the generative AI model.

[0193] To realize this invention, the system includes the following components and processes: A server collects data about commercial facilities based on location information, interest information, and sentiment information obtained from the user's portable information processing device, and generates personalized guide information using a generative AI model. The generated guide information is transmitted to the user's portable information processing device and displayed to enhance the consumer experience within the commercial facility.

[0194] The emotion engine uses sensors such as cameras and microphones built into the portable information processing device to analyze the user's emotions in real time. This analysis data is sent to a server and used to train a generative artificial intelligence model.

[0195] The specific hardware used will be smartphones and tablet devices. The software incorporates Microsoft's Face API and Google Cloud Speech-to-Text API, which are used for facial recognition and speech analysis. Machine learning libraries such as TENSORFLOW are used for the generative AI model.

[0196] For example, when a user is looking at a product in a specific store within a shopping mall, if the emotion engine detects the user's enjoyment, the server will display a list of recommendations and special campaign information related to that product on the user's smartphone. This makes it easier for the user to purchase the product they are interested in.

[0197] An example of a prompt is, "How can we suggest related offers for products the user is currently interested in?" Based on this prompt, the generative AI model provides optimal information suggestions to improve the user experience.

[0198] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0199] Step 1:

[0200] The server receives location and interest information from the user's portable information device. Based on this information, it searches a database for relevant data within the commercial facility and creates a set of possible information corresponding to the user's current situation. The input is location and interest information, and the output is an initial set of relevant data.

[0201] Step 2:

[0202] The emotion engine built into the device uses the camera and microphone to analyze the user's facial expressions and tone of voice, obtaining emotional information in real time. The input is data of the user's facial expressions and voice, and the output is the analyzed emotional information. Specifically, the AI ​​classifies changes in facial expressions and tone of voice into emotional categories based on the data it has learned.

[0203] Step 3:

[0204] The server inputs emotional information received from the terminal into an AI model that generates optimal product suggestions. This model considers the user's past behavior history and emotional responses to output individually personalized suggestions. The input consists of emotional information and an initial information set, and the output is an optimized product suggestion.

[0205] Step 4:

[0206] The server sends generated product suggestions to the terminal, which then displays this information on the user's portable information processing device. The input is the product suggestions from the server, and the output is the information visually presented to the user. The terminal further prioritizes which information is displayed to the user.

[0207] Step 5:

[0208] The user provides feedback based on the information displayed on their device, which is then sent to the server. This feedback is again used as training data for the generative AI model, which is then utilized for future optimizations. The input is the user's feedback, and the output is the latest training data for the generative AI model. The server uses this to improve the accuracy of future customizations.

[0209] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0210] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0211] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0212] [Second Embodiment]

[0213] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0214] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0215] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0216] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0217] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0218] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0219] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0220] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0221] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0222] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0223] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0224] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0225] The system of the present invention operates based on the interaction between a server, a terminal, and a user in order to provide a customized tourist guide based on the user's hobbies, preferences, and location information.

[0226] The server plays a central role in this invention and performs the following functions. First, the server receives registration information transmitted by the user and stores data related to the user's interests and planned tourist destinations in a database. Second, the server receives the user's location information from the terminal and has the ability to dynamically collect and update information on related tourist destinations based on that information. This makes it possible to prepare information that is best suited to the user's interests.

[0227] The device operates through applications installed by the user and tracks the user's current location in real time using GPS. The device also receives guidance information from a server and presents it in a format chosen by the user (e.g., voice or text). Users can provide feedback through the application, which improves the system's user experience.

[0228] Users can set their interests within the application and receive information about their favorite tourist destinations and exhibits. For example, if a user is visiting a museum in a certain city, the server will provide detailed information about a specific exhibit within that museum. This information deepens the user's interest and provides a richer travel experience.

[0229] With the system of the present invention configured in this way, tourists can easily obtain information that matches their interests and enjoy the best possible sightseeing experience within a limited time.

[0230] The following describes the processing flow.

[0231] Step 1:

[0232] Users download the application to their device and create a profile. Here, they register their name and categories of interest (e.g., art, architecture).

[0233] Step 2:

[0234] The device periodically uses its GPS function to obtain the user's current location information. This location information is transmitted to the server in real time.

[0235] Step 3:

[0236] The server uses the received user location information to search and collect relevant tourist information from its database. This organizes information about nearby tourist attractions and facilities.

[0237] Step 4:

[0238] The server generates personalized guide information using a generative AI model based on the user's interests and location. The user's past usage history is also taken into consideration during this process.

[0239] Step 5:

[0240] The server sends the generated guide information to the terminal. The transmitted information is formatted according to the user's preferences (voice or text).

[0241] Step 6:

[0242] The terminal presents the user with information received from the server, either via voice or text. This allows the user to obtain detailed and personalized information about tourist destinations.

[0243] Step 7:

[0244] Users can provide feedback on the guide information provided through the app. This feedback will be used to improve the quality of the guide information.

[0245] Step 8:

[0246] The server receives user feedback and incorporates it into the learning process of the generated AI model, improving the accuracy and personalization of future guide generation.

[0247] (Example 1)

[0248] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0249] It is difficult and time-consuming for tourists to obtain efficient and personalized tourist information based on their interests and current location. Furthermore, if the information provided does not meet user expectations, satisfaction levels may decrease. Multilingual support and improvements in information accuracy based on feedback are particularly needed.

[0250] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0251] In this invention, the server includes means for receiving user input and storing information on the user's hobbies, preferences, and planned tourist destinations; means for acquiring relevant tourist destination information from external sources based on location information and hobbies, preferences, and storing and updating the information; and means for generating customized guide information using an artificial intelligence model based on the information obtained from external sources. As a result, users can receive appropriate tourist information based on their interests in a timely manner, enabling them to enjoy an efficient and personalized tourist experience.

[0252] "Accepting user input" refers to the process of receiving information such as hobbies, preferences, and planned visits provided by the user through the application.

[0253] "Hobbies and preferences" refers to information that indicates the categories and activities that users are interested in, and serves as a criterion for selecting tourist destinations.

[0254] "Planned tourist destinations" refers to information about places or regions that users plan to visit.

[0255] "Location information acquisition" refers to the process of accurately determining the user's current location using technology built into the device.

[0256] "Relevant tourist destination information" refers to the latest information on tourist destinations that are deemed highly suitable based on the user's location and interests.

[0257] A "generative artificial intelligence model" refers to a machine learning algorithm used to perform advanced analysis based on data and provide personalized information to users.

[0258] "Customized guide information" refers to tourist guide data that is individually tailored to each user, taking into account their interests, preferences, and location.

[0259] "Presentation method" refers to an interface or protocol for displaying guide information on a terminal in a format selected by the user.

[0260] "Feedback" refers to the opinions and impressions that users provide after experiencing a service, and is used to improve the system.

[0261] This invention is a system that provides personalized tourist guide information based on the user's interests, preferences, and current location. Specific embodiments of the invention are described below.

[0262] The server operates in a cloud computing environment and manages user registration information via a database (e.g., PostgreSQL). This information includes hobbies, preferences, and planned tourist destinations entered by the user through the application. The server receives location information from the terminal using the HTTPS protocol to determine the user's current location.

[0263] The device operates via an application installed on the mobile device. The device uses a built-in GPS module to obtain the user's location in real time and transmits the location information to the server.

[0264] The server collects and updates relevant tourist destination information from external services (e.g., geographic information APIs) based on received location information and user preferences. Based on the acquired data, it uses a generative AI model (e.g., an AI-based natural language generation model) to generate personalized guide information for the user. The generated guide information includes details that respond to prompts such as "Please provide information about historical sites in Kyoto."

[0265] The device receives customized guide information sent from the server and displays it in the format selected by the user (audio or text). The user views the information through the application interface and provides feedback on their experience. This feedback is sent to the server and used to improve the system and generate information for future use.

[0266] In this way, users can obtain the latest and most personalized travel information tailored to their interests in real time, resulting in a richer and more efficient travel experience.

[0267] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0268] Step 1:

[0269] The user launches a mobile application and enters information about their hobbies, preferences, and planned travel destinations. This input data includes information such as favorite travel categories and specific places to visit. The server receives this information and saves it in the database as a new user profile.

[0270] Step 2:

[0271] The device uses its built-in GPS function to determine the user's current location. The acquired location information is periodically sent to the server. The server receives this location information and prepares to collect data on relevant tourist destinations based on the user's current location.

[0272] Step 3:

[0273] The server uses the received location information and the user's interests and preferences to collect relevant tourist destination information from external geographic information service APIs. The obtained tourist destination information is stored in a database and updated as needed. During this process, the server filters the information to be appropriate based on location and interests.

[0274] Step 4:

[0275] The server generates customized guide information using a generative AI model based on well-organized tourist destination information. This model creates helpful information in natural language based on prompt statements (e.g., "Please tell me about historical landmarks in Tokyo"). The generated guide information is optimized based on the user profile.

[0276] Step 5:

[0277] The server sends the generated, customized guide information to the terminal. The terminal displays the received information in the format selected by the user. For example, if an audio guide is selected, the text is converted to speech and played.

[0278] Step 6:

[0279] Users experience the provided guide information and then provide feedback through the application. This feedback includes opinions on the accuracy and satisfaction level of the information. The device sends this feedback to the server.

[0280] Step 7:

[0281] The server analyzes the received feedback and uses it to generate information for the next time. The analysis results are reflected in tuning the generation AI model and updating the database, contributing to improvements in the overall accuracy and personalization of the system.

[0282] (Application Example 1)

[0283] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0284] The present invention aims to develop an information collection and provision system that takes into account interests and location information in order to provide appropriate and personalized guidance information to users. In the current guidance system, it is difficult to update location information frequently and to respond to the individualized interests of users. Also, in providing related information about exhibition items within a physical store, the presentation of appropriate detailed information is insufficient. There is a need for a system that solves such problems and provides an optimal experience for users.

[0285] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0286] In this invention, the server includes means for collecting location-related information based on the interests and location information of the user, means for generating customized guidance information by a generative artificial intelligence model, and means for providing detailed information related to exhibition items based on the information classified within a physical store. As a result, the user can always receive highly relevant information based on their interests, and by obtaining detailed background information about exhibition items, it becomes possible to enjoy a richer experience.

[0287] "User's interests" refers to information indicating the preferences and interests shown by the user towards specific things.

[0288] "Location information" refers to geographical data indicating the location where the user is currently physically present.

[0289] "Guidance information" refers to information including detailed explanations and instructions provided to the user about a specific location or object.

[0290] A "mobile device" refers to a portable electronic device such as a smartphone or tablet, which is a device that users can carry around and use to visualize information.

[0291] A "physical store" refers to a physical store or facility that actually exists and can be visited by users directly.

[0292] "Exhibited items" refer to items or works of art that are displayed to visitors within a physical store, and for which detailed information is provided to users.

[0293] A "generative artificial intelligence model" refers to an algorithm that uses machine learning and data analysis to generate personalized information based on the user's preferences and history.

[0294] "Customization" refers to adjusting the content of information and services to suit the individual needs and interests of users.

[0295] "Feedback" refers to responses and opinions provided by users, and is information used to improve system performance and personalize the system.

[0296] To realize this application, a system will be built in which a server, a terminal, and a user work together. First, the server receives the user's interests and location information, and based on this, collects information about relevant locations from a database. The server uses this information and a generative artificial intelligence model to create personalized guidance information. The guidance information will include details tailored to the individual user's preferences, such as the historical background and explanations of exhibits.

[0297] Next, the device receives this guidance information and displays it in the user's chosen format, such as audio or text. The device can also provide guidance along the user's route within the physical store to help them view details of specific exhibits. This allows users to receive guidance information using their smartphones when visiting a physical store. Hardware used includes smartphones and tablets, and location information is obtained via the Google Maps API. Firebase is used for server-side database management.

[0298] User feedback is sent to the server via the device, and this feedback is used to train the generative artificial intelligence model. The model is continuously improved as a result, enabling it to generate more accurate guidance information.

[0299] A concrete example is a user visiting a museum who scans a QR code displayed on their smartphone screen to obtain detailed historical background information and artist information about a particular painting. In this process, the generative AI model is given a prompt such as, "Generate an audio guide for historical exhibits that the user is interested in, based on their current location," to generate context that matches the user's interests.

[0300] In this way, the system of the present invention becomes capable of providing users with interesting and useful information in real time.

[0301] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0302] Step 1:

[0303] The server receives interest and location information sent from the user via their device. Based on this input data, it queries the database to retrieve information about relevant locations and exhibits. The data is then filtered to match the location information and the user's interests.

[0304] Step 2:

[0305] Based on the acquired information, the server uses the generative AI model to generate customized guidance information. In this process, the sentence "Please generate an audio guide for historical exhibits that the user is interested in based on the current location." is input into the model as a prompt sentence, and detailed guidance information tailored to the user's interests is obtained as the output.

[0306] Step 3:

[0307] The generated guidance information is sent from the server to the terminal. The terminal receives this and displays it in the format specified by the user, such as text or an audio guide. Using the user's location information, the guidance regarding the nearest exhibits and locations is highlighted.

[0308] Step 4:

[0309] If the user checks the guidance information and requests more details about a specific exhibit, an additional information request is generated on the terminal. This request is sent to the server, which acquires the necessary additional data and returns it to the terminal.

[0310] Step 5:

[0311] The user inputs feedback into the terminal, and this is sent to the server. The server uses this feedback as data for strengthening the generative AI model and conducts model learning. It is expected that the accuracy and customization quality of the guidance information generated next time will be improved due to the feedback.

[0312] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion. [[ID=D33]]

[0313] This invention provides a tourist guide system that combines an emotion engine that recognizes user emotions, enabling further personalization based on user interests and location information.

[0314] This system operates based on the interaction between a server, a terminal, and a user. The server receives basic user profile information and location information, and searches for tourist information based on this information. The collected information is transformed into guide information optimized for each individual user using a generative AI model. The emotion engine, which is installed in the terminal, uses sensors such as cameras and microphones to monitor the user's facial expressions, tone of voice, and biometric information, and evaluates the user's emotions in real time.

[0315] The device's role is to provide users with customized guide information received from the server. In addition, an emotion engine analyzes the user's emotions and prioritizes presenting information that the user is particularly interested in or likely to find pleasing. For example, if a user looks sad in a museum, the emotion engine will notify the server, which can then add backstories or entertaining content to recreate the appeal of the exhibits.

[0316] User feedback, along with emotional data measured by the emotion engine, is sent to the server and used to train the generative AI model. This continuous learning enables the system to provide more accurate and personalized tourist information. Furthermore, multilingual support and age-appropriate customization make it user-friendly for everyone.

[0317] This system allows users to have a more engaging and emotionally satisfying travel experience, and to deepen their understanding of tourist destinations and cultures.

[0318] The following describes the processing flow.

[0319] Step 1:

[0320] The user launches the application installed on their device and sets up their profile. The user enters their areas of interest and categories.

[0321] Step 2:

[0322] The device uses GPS to obtain the user's current location information and sends it to the server. Simultaneously, the emotion engine built into the device is activated and analyzes the user's emotions in real time from their facial expressions and voice.

[0323] Step 3:

[0324] The server collects relevant tourist information from a database based on the received location information, user interest data, and sentiment data. Using this data, the server utilizes a generative AI model to generate personalized guide information for the user.

[0325] Step 4:

[0326] The terminal displays guide information received from the server to the user. Based on the analysis results from the emotion engine, it selects and displays information optimized for the user's emotions. For example, if the user shows interest, it emphasizes detailed information to further pique that interest.

[0327] Step 5:

[0328] Users view the presented information and, if they become interested or have feedback, they input that information via their device. The emotion engine continuously monitors the user's emotions throughout this process and collects new data.

[0329] Step 6:

[0330] The server receives user feedback and continuously acquired emotional data, and uses this to update its generating AI model. This update enables the server to personalize guide information with greater accuracy in the future.

[0331] Step 7:

[0332] Based on the feedback received, the device updates the user's profile and interests as needed, preparing for future use. This allows users to receive an even more optimized experience next time.

[0333] (Example 2)

[0334] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0335] Traditional tourist guide systems provide information based on user interests and location, but they lack dynamic adjustments that respond to user emotions and immediate reactions. This results in a limited user experience, insufficient personalized guidance, and difficulty in improving satisfaction at tourist destinations. Furthermore, inadequate use of feedback limits long-term system improvement.

[0336] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0337] In this invention, the server includes means for acquiring tourist information based on the user's interests and location information, means for generating personalized guidance information using a living animal intelligence model with the acquired information, and means for transmitting and presenting the generated guidance information to a terminal. This enables dynamic adjustments based on the user's emotions, realizing the provision of personalized guidance information. Furthermore, these means make it possible to continuously improve user satisfaction through real-time emotion analysis.

[0338] "User interests" refer to the matters or themes that users are interested in, and in the context of travel guides, this refers to content related to places users want to visit or information they want to know.

[0339] "Location information" refers to data indicating the user's current location, obtained through a device. This information forms the basis for personalizing tourist information.

[0340] A "tourist destination" is a region or facility intended for tourists to visit, and includes places with historical, cultural, or natural value.

[0341] The "Living Animal Intelligence Model" is an algorithm developed based on machine learning technology and is used to generate personalized content based on the user's interests and location information.

[0342] "Personalized guidance information" refers to information tailored to each user's individual interests and feelings, meaning that different content is provided to each user.

[0343] A "terminal" refers to an electronic device that the user directly operates, which is used to receive tourist information and perform sentiment analysis.

[0344] "Emotion analysis" is a process that uses the user's facial expressions, voice, and biometric data to evaluate the user's emotional state in real time, and is used to adjust tourist information.

[0345] "Response" refers to the feedback and changes in emotional state that users exhibit through their devices, and the data used to optimize guidance information.

[0346] "Optimization" is the process of adjusting information and operations to make the user experience as good as possible, aiming to provide information that aligns with the user's interests and emotions.

[0347] This system provides optimized tourist information based on the user's interests and emotions. Its main components include a server, terminals, and user interaction.

[0348] The server first receives the user's interests and location information via machine, and then uses this information to obtain information about tourist destinations using a generative AI model. The generative AI model includes a general-purpose natural language processing engine and is likely a commercially available AI tool. In particular, the generative AI model can customize the guidance information based on the input prompt sentence. An example of a prompt sentence could be, "Tell me about the history of temples in Kyoto."

[0349] The device is equipped with an emotion engine that analyzes the user's facial expressions and voice tone in real time through sensors such as cameras and microphones. Furthermore, the device also acquires biometric data using sensors and sends it to a server to provide emotion analysis results. Based on the emotion analysis results, the server dynamically adjusts the guidance information and prioritizes delivering data that the user is likely to be of particular interest to the device.

[0350] Users can view and react to information received through their devices. By sending feedback to the server via their devices, user responses are used to further train the generative AI model. This improves the accuracy and personalization of future guidance information.

[0351] For example, when a user is visiting Kyoto, the emotion engine can provide detailed information about temples and museums that are likely to interest them, in order to increase the user's level of interest. Based on real-time emotion analysis, it may also present interactive content that the user will enjoy. This is an approach aimed at increasing user knowledge and satisfaction.

[0352] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0353] Step 1:

[0354] The user operates the device and logs into the system. The device obtains the user's basic profile information and current location information. This becomes the input data. The device collects this data and sends the status of the tourist recommendation service to the server.

[0355] Step 2:

[0356] The server uses a generative AI model to search for relevant tourist information based on the user's interests and location information received. Data processing here involves converting the user profile and location information into prompt sentences and inputting them into the generative AI model. The output is personalized tourist information tailored to the user.

[0357] Step 3:

[0358] The previously generated guidance information is sent from the server to the terminal. The terminal then displays this customized guidance information to the user. Specific actions include displaying maps, playing audio guides, and presenting text information.

[0359] Step 4:

[0360] The device utilizes an emotion engine to analyze the user's facial expressions, voice tone, and biometric information in real time. Input data includes biometric information obtained from the camera, microphone, and sensors, and the device evaluates the user's emotional state based on this data. The output is the user's emotional diagnosis result.

[0361] Step 5:

[0362] The server receives the sentiment analysis results sent from the terminal and readjusts the guidance information using a generative AI model. The specific data processing involves incorporating the sentiment diagnosis results into the prompt text, and the AI ​​generates new guidance information. This results in the creation of the most appealing information for the user, which is then sent to the terminal.

[0363] Step 6:

[0364] Users enjoy sightseeing using the information presented through their devices. After completing their sightseeing, users enter feedback into their devices. This feedback can be in text or rating format, and the data is sent to the server.

[0365] Step 7:

[0366] The server collects user feedback and sentiment analysis data, which is then used to improve the generating AI model. This data processing improves the accuracy of future guidance and prepares the system to provide a more personalized experience.

[0367] (Application Example 2)

[0368] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0369] In modern commercial facilities, while a wide variety of product information exists, consumers face the challenge of finding relevant information based on their own interests and feelings. Furthermore, conventional sales promotion methods struggle to provide specific, personalized suggestions to individual consumers. To improve the consumer experience, more personalized information provision is necessary.

[0370] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0371] In this invention, the server includes means for collecting information about commercial facilities based on the user's interests and location information, means for generating customized guide information using a generative artificial intelligence model based on the collected information and the user's emotional information, and means for transmitting and presenting the generated guide information to a portable information processing device. This makes it possible to provide product suggestions optimized for each consumer and improve the consumer experience within commercial facilities.

[0372] "User interest" refers to information that indicates the degree of interest an individual consumer has in a particular product or service.

[0373] "Location information" refers to data indicating a user's geographical location, obtained using portable information processing devices, etc.

[0374] A "commercial facility" refers to a physical place where goods and services are sold, and includes stores and shopping malls.

[0375] "Emotional information" refers to data that indicates a user's emotional state, obtained by analyzing their facial expressions, tone of voice, and other biometric information.

[0376] A "generative artificial intelligence model" is an algorithm that utilizes machine learning and deep learning technologies to generate information that is optimal for the user based on collected data.

[0377] A "portable information processing device" is an information device that a user can carry with them, and includes smartphones and tablet devices.

[0378] "Personalized information" refers to information that is tailored based on the individual user's interests and emotions.

[0379] An "emotion engine" is a technology used to analyze a user's emotions in real time, and refers to a device that uses sensors such as cameras and microphones.

[0380] "Feedback" refers to the information and opinions that users provide after using the system, and this data helps to further improve the generative AI model.

[0381] To realize this invention, the system includes the following components and processes: A server collects data about commercial facilities based on location information, interest information, and sentiment information obtained from the user's portable information processing device, and generates personalized guide information using a generative AI model. The generated guide information is transmitted to the user's portable information processing device and displayed to enhance the consumer experience within the commercial facility.

[0382] The emotion engine uses sensors such as cameras and microphones built into the portable information processing device to analyze the user's emotions in real time. This analysis data is sent to a server and used to train a generative artificial intelligence model.

[0383] The specific hardware used will be smartphones and tablets. The software will incorporate Microsoft's Face API and Google Cloud Speech-to-Text API, which will be used for facial recognition and speech analysis. Machine learning libraries such as TensorFlow will be used for the generative AI model.

[0384] For example, when a user is looking at a product in a specific store within a shopping mall, if the emotion engine detects the user's enjoyment, the server will display a list of recommendations and special campaign information related to that product on the user's smartphone. This makes it easier for the user to purchase the product they are interested in.

[0385] An example of a prompt is, "How can we suggest related offers for products the user is currently interested in?" Based on this prompt, the generative AI model provides optimal information suggestions to improve the user experience.

[0386] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0387] Step 1:

[0388] The server receives location and interest information from the user's portable information device. Based on this information, it searches a database for relevant data within the commercial facility and creates a set of possible information corresponding to the user's current situation. The input is location and interest information, and the output is an initial set of relevant data.

[0389] Step 2:

[0390] The emotion engine built into the device uses the camera and microphone to analyze the user's facial expressions and tone of voice, obtaining emotional information in real time. The input is data of the user's facial expressions and voice, and the output is the analyzed emotional information. Specifically, the AI ​​classifies changes in facial expressions and tone of voice into emotional categories based on the data it has learned.

[0391] Step 3:

[0392] The server inputs emotional information received from the terminal into an AI model that generates optimal product suggestions. This model considers the user's past behavior history and emotional responses to output individually personalized suggestions. The input consists of emotional information and an initial information set, and the output is an optimized product suggestion.

[0393] Step 4:

[0394] The server sends generated product suggestions to the terminal, which then displays this information on the user's portable information processing device. The input is the product suggestions from the server, and the output is the information visually presented to the user. The terminal further prioritizes which information is displayed to the user.

[0395] Step 5:

[0396] The user provides feedback based on the information displayed on their device, which is then sent to the server. This feedback is again used as training data for the generative AI model, which is then utilized for future optimizations. The input is the user's feedback, and the output is the latest training data for the generative AI model. The server uses this to improve the accuracy of future customizations.

[0397] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0398] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0399] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0400] [Third Embodiment]

[0401] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0402] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0403] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0404] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0405] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0406] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0407] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0408] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0409] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0410] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0411] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0412] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0413] The system of the present invention operates based on the interaction between a server, a terminal, and a user in order to provide a customized tourist guide based on the user's hobbies, preferences, and location information.

[0414] The server plays a central role in this invention and performs the following functions. First, the server receives registration information transmitted by the user and stores data related to the user's interests and planned tourist destinations in a database. Second, the server receives the user's location information from the terminal and has the ability to dynamically collect and update information on related tourist destinations based on that information. This makes it possible to prepare information that is best suited to the user's interests.

[0415] The device operates through applications installed by the user and tracks the user's current location in real time using GPS. The device also receives guidance information from a server and presents it in a format chosen by the user (e.g., voice or text). Users can provide feedback through the application, which improves the system's user experience.

[0416] Users can set their interests within the application and receive information about their favorite tourist destinations and exhibits. For example, if a user is visiting a museum in a certain city, the server will provide detailed information about a specific exhibit within that museum. This information deepens the user's interest and provides a richer travel experience.

[0417] With the system of the present invention configured in this way, tourists can easily obtain information that matches their interests and enjoy the best possible sightseeing experience within a limited time.

[0418] The following describes the processing flow.

[0419] Step 1:

[0420] Users download the application to their device and create a profile. Here, they register their name and categories of interest (e.g., art, architecture).

[0421] Step 2:

[0422] The device periodically uses its GPS function to obtain the user's current location information. This location information is transmitted to the server in real time.

[0423] Step 3:

[0424] The server uses the received user location information to search and collect relevant tourist information from its database. This organizes information about nearby tourist attractions and facilities.

[0425] Step 4:

[0426] The server generates personalized guide information using a generative AI model based on the user's interests and location. This process also takes into account the user's past usage history.

[0427] Step 5:

[0428] The server sends the generated guide information to the terminal. The transmitted information is formatted according to the user's preferences (voice or text).

[0429] Step 6:

[0430] The terminal presents the user with information received from the server, either as audio or text. This allows the user to obtain detailed and personalized information about tourist destinations.

[0431] Step 7:

[0432] Users can provide feedback on the guide information provided through the app. This feedback will be used to improve the quality of the guide information.

[0433] Step 8:

[0434] The server receives user feedback and incorporates it into the learning process of the generated AI model, improving the accuracy and personalization of future guide generation.

[0435] (Example 1)

[0436] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0437] It is difficult and time-consuming for tourists to obtain efficient and personalized tourist information based on their interests and current location. Furthermore, if the information provided does not meet user expectations, satisfaction levels may decrease. Multilingual support and improvements in information accuracy based on feedback are particularly needed.

[0438] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0439] In this invention, the server includes means for receiving user input and storing information on the user's hobbies, preferences, and planned tourist destinations; means for acquiring relevant tourist destination information from external sources based on location information and hobbies, preferences, and storing and updating the information; and means for generating customized guide information using an artificial intelligence model based on the information obtained from external sources. As a result, users can receive appropriate tourist information based on their interests in a timely manner, enabling them to enjoy an efficient and personalized tourist experience.

[0440] "Accepting user input" refers to the process of receiving information such as hobbies, preferences, and planned visits provided by the user through the application.

[0441] "Hobbies and preferences" refers to information that indicates the categories and activities that users are interested in, and serves as a criterion for selecting tourist destinations.

[0442] "Planned tourist destinations" refers to information about places or regions that users plan to visit.

[0443] "Location information acquisition" refers to the process of accurately determining the user's current location using technology built into the device.

[0444] "Relevant tourist destination information" refers to the latest information on tourist destinations that are deemed highly suitable based on the user's location and interests.

[0445] A "generative artificial intelligence model" refers to a machine learning algorithm used to perform advanced analysis based on data and provide personalized information to users.

[0446] "Customized guide information" refers to tourist guide data that is individually tailored to each user, taking into account their interests, preferences, and location.

[0447] "Presentation method" refers to an interface or protocol for displaying guide information on a terminal in a format selected by the user.

[0448] "Feedback" refers to the opinions and impressions that users provide after experiencing a service, and is used to improve the system.

[0449] This invention is a system that provides personalized tourist guide information based on the user's interests, preferences, and current location. Specific embodiments of the invention are described below.

[0450] The server operates in a cloud computing environment and manages user registration information via a database (e.g., PostgreSQL). This information includes hobbies, preferences, and planned tourist destinations entered by the user through the application. The server receives location information from the terminal using the HTTPS protocol to determine the user's current location.

[0451] The device operates via an application installed on the mobile device. The device uses a built-in GPS module to obtain the user's location in real time and transmits the location information to the server.

[0452] The server collects and updates relevant tourist destination information from external services (e.g., geographic information APIs) based on received location information and user preferences. Based on the acquired data, it uses a generative AI model (e.g., an AI-based natural language generation model) to generate personalized guide information for the user. The generated guide information includes details that respond to prompts such as "Please provide information about historical sites in Kyoto."

[0453] The device receives customized guide information sent from the server and displays it in the format selected by the user (audio or text). The user views the information through the application interface and provides feedback on their experience. This feedback is sent to the server and used to improve the system and generate information for future use.

[0454] In this way, users can obtain the latest and most personalized travel information tailored to their interests in real time, resulting in a richer and more efficient travel experience.

[0455] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0456] Step 1:

[0457] The user launches a mobile application and enters information about their hobbies, preferences, and planned travel destinations. This input data includes information such as favorite travel categories and specific places to visit. The server receives this information and saves it in the database as a new user profile.

[0458] Step 2:

[0459] The device uses its built-in GPS function to determine the user's current location. The acquired location information is periodically sent to the server. The server receives this location information and prepares to collect data on relevant tourist destinations based on the user's current location.

[0460] Step 3:

[0461] The server uses the received location information and the user's interests and preferences to collect relevant tourist destination information from external geographic information service APIs. The obtained tourist destination information is stored in a database and updated as needed. During this process, the server filters the information to be appropriate based on location and interests.

[0462] Step 4:

[0463] The server generates customized guide information using a generative AI model based on well-organized tourist destination information. This model creates helpful information in natural language based on prompt statements (e.g., "Please tell me about historical landmarks in Tokyo"). The generated guide information is optimized based on the user profile.

[0464] Step 5:

[0465] The server sends the generated, customized guide information to the terminal. The terminal displays the received information in the format selected by the user. For example, if an audio guide is selected, the text is converted to speech and played.

[0466] Step 6:

[0467] Users experience the provided guide information and then provide feedback through the application. This feedback includes opinions on the accuracy and satisfaction level of the information. The device sends this feedback to the server.

[0468] Step 7:

[0469] The server analyzes the received feedback and uses it to generate information for the next time. The analysis results are reflected in tuning the generation AI model and updating the database, contributing to improvements in the overall accuracy and personalization of the system.

[0470] (Application Example 1)

[0471] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0472] This invention aims to develop an information collection and provision system that takes into account interests and location information in order to provide users with appropriate and personalized guidance information. Current guidance systems have difficulty responding to the frequency of location information updates and the individual interests of users. Furthermore, providing relevant information about exhibits in physical stores often results in insufficient presentation of appropriate detailed information. A system is needed to solve these problems and provide the optimal experience for users.

[0473] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0474] In this invention, the server includes means for collecting location-related information based on the user's interests and location information, means for generating customized guidance information using a generative artificial intelligence model, and means for providing detailed information related to exhibits based on information classified within the physical store. As a result, users can always receive highly relevant information based on their interests and enjoy a richer experience by obtaining detailed background information about the exhibits.

[0475] "User interests" refer to information about the preferences and concerns that users show towards specific things.

[0476] "Location information" is geographical data that indicates the user's current physical location.

[0477] "Information" refers to information that includes detailed descriptions and instructions provided to the user about a specific location or object.

[0478] A "mobile device" refers to a portable electronic device such as a smartphone or tablet, which is a device that users can carry around and use to visualize information.

[0479] A "physical store" refers to a physical store or facility that actually exists and can be visited by users directly.

[0480] "Exhibited items" refer to items or works of art that are displayed to visitors within a physical store, and for which detailed information is provided to users.

[0481] A "generative artificial intelligence model" refers to an algorithm that uses machine learning and data analysis to generate personalized information based on the user's preferences and history.

[0482] "Customization" refers to adjusting the content of information and services to suit the individual needs and interests of users.

[0483] "Feedback" refers to responses and opinions provided by users, and is information used to improve system performance and personalize the system.

[0484] To realize this application, a system will be built in which a server, a terminal, and a user work together. First, the server receives the user's interests and location information, and based on this, collects information about relevant locations from a database. The server uses this information and a generative artificial intelligence model to create personalized guidance information. The guidance information will include details tailored to the individual user's preferences, such as the historical background and explanations of exhibits.

[0485] Next, the device receives this guidance information and displays it in the user's chosen format, such as audio or text. The device can also provide guidance along the user's route within the physical store to help them view details of specific exhibits. This allows users to receive guidance information using their smartphones when visiting a physical store. Hardware used includes smartphones and tablets, and location information is obtained via the Google Maps API. Firebase is used for server-side database management.

[0486] User feedback is sent to the server via the device, and this feedback is used to train the generative artificial intelligence model. The model is continuously improved as a result, enabling it to generate more accurate guidance information.

[0487] A concrete example is a user visiting a museum who scans a QR code displayed on their smartphone screen to obtain detailed historical background information and artist information about a particular painting. In this process, the generative AI model is given a prompt such as, "Generate an audio guide for historical exhibits that the user is interested in, based on their current location," to generate context that matches the user's interests.

[0488] In this way, the system of the present invention becomes capable of providing users with interesting and useful information in real time.

[0489] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0490] Step 1:

[0491] The server receives interest and location information sent from the user via their device. Based on this input data, it queries the database to retrieve information about relevant locations and exhibits. The data is then filtered to match the location information and the user's interests.

[0492] Step 2:

[0493] The server uses a generative AI model to generate customized guidance information based on the acquired information. In this process, the model is input with the prompt, "Generate an audio guide for historical exhibits that the user is interested in based on their current location," and the output is detailed guidance information tailored to the user's interests.

[0494] Step 3:

[0495] The generated guidance information is sent from the server to the terminal. The terminal receives it and displays it in the format specified by the user, such as text or audio guide. The user's location information is used to highlight guidance for the nearest exhibit or location.

[0496] Step 4:

[0497] When a user reviews the information provided and requests further details about a specific exhibit, the terminal generates a request for additional information. This request is sent to the server, which retrieves the necessary additional data and sends it back to the terminal.

[0498] Step 5:

[0499] Users input feedback into their devices, which is then sent to the server. The server uses this feedback as data to improve its AI model and train it. It is expected that the feedback will improve the accuracy and customization quality of the guidance information generated in the future.

[0500] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0501] This invention provides a tourist guide system that combines an emotion engine that recognizes user emotions, enabling further personalization based on user interests and location information.

[0502] This system operates based on the interaction between a server, a terminal, and a user. The server receives basic user profile information and location information, and searches for tourist information based on this information. The collected information is transformed into guide information optimized for each individual user using a generative AI model. The emotion engine, which is installed in the terminal, uses sensors such as cameras and microphones to monitor the user's facial expressions, tone of voice, and biometric information, and evaluates the user's emotions in real time.

[0503] The device's role is to provide users with customized guide information received from the server. In addition, an emotion engine analyzes the user's emotions and prioritizes presenting information that the user is particularly interested in or likely to find pleasing. For example, if a user looks sad in a museum, the emotion engine will notify the server, which can then add backstories or entertaining content to recreate the appeal of the exhibits.

[0504] User feedback, along with emotional data measured by the emotion engine, is sent to the server and used to train the generative AI model. This continuous learning enables the system to provide more accurate and personalized tourist information. Furthermore, multilingual support and age-appropriate customization make it user-friendly for everyone.

[0505] This system allows users to have a more engaging and emotionally satisfying travel experience, and to deepen their understanding of tourist destinations and cultures.

[0506] The following describes the processing flow.

[0507] Step 1:

[0508] The user launches the application installed on their device and sets up their profile. The user enters their areas of interest and categories.

[0509] Step 2:

[0510] The device uses GPS to obtain the user's current location information and sends it to the server. Simultaneously, the emotion engine built into the device is activated and analyzes the user's emotions in real time from their facial expressions and voice.

[0511] Step 3:

[0512] The server collects relevant tourist information from a database based on the received location information, user interest data, and sentiment data. Using this data, the server utilizes a generative AI model to generate personalized guide information for the user.

[0513] Step 4:

[0514] The terminal displays guide information received from the server to the user. Based on the analysis results from the emotion engine, it selects and displays information optimized for the user's emotions. For example, if the user shows interest, it emphasizes detailed information to further pique that interest.

[0515] Step 5:

[0516] Users view the presented information and, if they become interested or have feedback, they input that information via their device. The emotion engine continuously monitors the user's emotions throughout this process and collects new data.

[0517] Step 6:

[0518] The server receives user feedback and continuously acquired emotional data, and uses this to update its generating AI model. This update enables the server to personalize guide information with greater accuracy in the future.

[0519] Step 7:

[0520] Based on the feedback received, the device updates the user's profile and interests as needed, preparing for future use. This allows users to receive an even more optimized experience next time.

[0521] (Example 2)

[0522] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0523] Traditional tourist guide systems provide information based on user interests and location, but they lack dynamic adjustments that respond to user emotions and immediate reactions. This results in a limited user experience, insufficient personalized guidance, and difficulty in improving satisfaction at tourist destinations. Furthermore, inadequate use of feedback limits long-term system improvement.

[0524] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0525] In this invention, the server includes means for acquiring tourist information based on the user's interests and location information, means for generating personalized guidance information using a living animal intelligence model with the acquired information, and means for transmitting and presenting the generated guidance information to a terminal. This enables dynamic adjustments based on the user's emotions, realizing the provision of personalized guidance information. Furthermore, these means make it possible to continuously improve user satisfaction through real-time emotion analysis.

[0526] "User interests" refer to the matters or themes that users are interested in, and in the context of travel guides, this refers to content related to places users want to visit or information they want to know.

[0527] "Location information" refers to data indicating the user's current location, obtained through a device. This information forms the basis for personalizing tourist information.

[0528] A "tourist destination" is a region or facility intended for tourists to visit, and includes places with historical, cultural, or natural value.

[0529] The "Living Animal Intelligence Model" is an algorithm developed based on machine learning technology and is used to generate personalized content based on the user's interests and location information.

[0530] "Personalized guidance information" refers to information tailored to each user's individual interests and feelings, meaning that different content is provided to each user.

[0531] A "terminal" refers to an electronic device that the user directly operates, which is used to receive tourist information and perform sentiment analysis.

[0532] "Emotion analysis" is a process that uses the user's facial expressions, voice, and biometric data to evaluate the user's emotional state in real time, and is used to adjust tourist information.

[0533] "Response" refers to the feedback and changes in emotional state that users exhibit through their devices, and the data used to optimize guidance information.

[0534] "Optimization" is the process of adjusting information and operations to make the user experience as good as possible, aiming to provide information that aligns with the user's interests and emotions.

[0535] This system provides optimized tourist information based on the user's interests and emotions. Its main components include a server, terminals, and user interaction.

[0536] The server first receives the user's interests and location information via machine, and then uses this information to obtain information about tourist destinations using a generative AI model. The generative AI model includes a general-purpose natural language processing engine and is likely a commercially available AI tool. In particular, the generative AI model can customize the guidance information based on the input prompt sentence. An example of a prompt sentence could be, "Tell me about the history of temples in Kyoto."

[0537] The device is equipped with an emotion engine that analyzes the user's facial expressions and voice tone in real time through sensors such as cameras and microphones. Furthermore, the device also acquires biometric data using sensors and sends it to a server to provide emotion analysis results. Based on the emotion analysis results, the server dynamically adjusts the guidance information and prioritizes delivering data that the user is likely to be of particular interest to the device.

[0538] Users can view and react to information received through their devices. By sending feedback to the server via their devices, user responses are used to further train the generative AI model. This improves the accuracy and personalization of future guidance information.

[0539] For example, when a user is visiting Kyoto, the emotion engine can provide detailed information about temples and museums that are likely to interest them, in order to increase the user's level of interest. Based on real-time emotion analysis, it may also present interactive content that the user will enjoy. This is an approach aimed at increasing user knowledge and satisfaction.

[0540] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0541] Step 1:

[0542] The user operates the device and logs into the system. The device obtains the user's basic profile information and current location information. This becomes the input data. The device collects this data and sends the status of the tourist recommendation service to the server.

[0543] Step 2:

[0544] The server uses a generative AI model to search for relevant tourist information based on the user's interests and location information received. Data processing here involves converting the user profile and location information into prompt sentences and inputting them into the generative AI model. The output is personalized tourist information tailored to the user.

[0545] Step 3:

[0546] The previously generated guidance information is sent from the server to the terminal. The terminal then displays this customized guidance information to the user. Specific actions include displaying maps, playing audio guides, and presenting text information.

[0547] Step 4:

[0548] The device utilizes an emotion engine to analyze the user's facial expressions, voice tone, and biometric information in real time. Input data includes biometric information obtained from the camera, microphone, and sensors, and the device evaluates the user's emotional state based on this data. The output is the user's emotional diagnosis result.

[0549] Step 5:

[0550] The server receives the sentiment analysis results sent from the terminal and readjusts the guidance information using a generative AI model. The specific data processing involves incorporating the sentiment diagnosis results into the prompt text, and the AI ​​generates new guidance information. This results in the creation of the most appealing information for the user, which is then sent to the terminal.

[0551] Step 6:

[0552] Users enjoy sightseeing using the information presented through their devices. After completing their sightseeing, users enter feedback into their devices. This feedback can be in text or rating format, and the data is sent to the server.

[0553] Step 7:

[0554] The server collects user feedback and sentiment analysis data, which is then used to improve the generating AI model. This data processing improves the accuracy of future guidance and prepares the system to provide a more personalized experience.

[0555] (Application Example 2)

[0556] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0557] In modern commercial facilities, while a wide variety of product information exists, consumers face the challenge of finding relevant information based on their own interests and feelings. Furthermore, conventional sales promotion methods struggle to provide specific, personalized suggestions to individual consumers. To improve the consumer experience, more personalized information provision is necessary.

[0558] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0559] In this invention, the server includes means for collecting information about commercial facilities based on the user's interests and location information, means for generating customized guide information using a generative artificial intelligence model based on the collected information and the user's emotional information, and means for transmitting and presenting the generated guide information to a portable information processing device. This makes it possible to provide product suggestions optimized for each consumer and improve the consumer experience within commercial facilities.

[0560] "User interest" refers to information that indicates the degree of interest an individual consumer has in a particular product or service.

[0561] "Location information" refers to data indicating a user's geographical location, obtained using portable information processing devices, etc.

[0562] A "commercial facility" refers to a physical place where goods and services are sold, and includes stores and shopping malls.

[0563] "Emotional information" refers to data that indicates a user's emotional state, obtained by analyzing their facial expressions, tone of voice, and other biometric information.

[0564] A "generative artificial intelligence model" is an algorithm that utilizes machine learning and deep learning technologies to generate information that is optimal for the user based on collected data.

[0565] A "portable information processing device" is an information device that a user can carry with them, and includes smartphones and tablet devices.

[0566] "Personalized information" refers to information that is tailored based on the individual user's interests and emotions.

[0567] An "emotion engine" is a technology used to analyze a user's emotions in real time, and refers to a device that uses sensors such as cameras and microphones.

[0568] "Feedback" refers to the information and opinions that users provide after using the system, and this data helps to further improve the generative AI model.

[0569] To realize this invention, the system includes the following components and processes: A server collects data about commercial facilities based on location information, interest information, and sentiment information obtained from the user's portable information processing device, and generates personalized guide information using a generative AI model. The generated guide information is transmitted to the user's portable information processing device and displayed to enhance the consumer experience within the commercial facility.

[0570] The emotion engine uses sensors such as cameras and microphones built into the portable information processing device to analyze the user's emotions in real time. This analysis data is sent to a server and used to train a generative artificial intelligence model.

[0571] The specific hardware used will be smartphones and tablets. The software will incorporate Microsoft's Face API and Google Cloud Speech-to-Text API, which will be used for facial recognition and speech analysis. Machine learning libraries such as TensorFlow will be used for the generative AI model.

[0572] For example, when a user is looking at a product in a specific store within a shopping mall, if the emotion engine detects the user's enjoyment, the server will display a list of recommendations and special campaign information related to that product on the user's smartphone. This makes it easier for the user to purchase the product they are interested in.

[0573] An example of a prompt is, "How can we suggest related offers for products the user is currently interested in?" Based on this prompt, the generative AI model provides optimal information suggestions to improve the user experience.

[0574] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0575] Step 1:

[0576] The server receives location and interest information from the user's portable information device. Based on this information, it searches a database for relevant data within the commercial facility and creates a set of possible information corresponding to the user's current situation. The input is location and interest information, and the output is an initial set of relevant data.

[0577] Step 2:

[0578] The emotion engine built into the device uses the camera and microphone to analyze the user's facial expressions and tone of voice, obtaining emotional information in real time. The input is data of the user's facial expressions and voice, and the output is the analyzed emotional information. Specifically, the AI ​​classifies changes in facial expressions and tone of voice into emotional categories based on the data it has learned.

[0579] Step 3:

[0580] The server inputs emotional information received from the terminal into an AI model that generates optimal product suggestions. This model considers the user's past behavior history and emotional responses to output individually personalized suggestions. The input consists of emotional information and an initial information set, and the output is an optimized product suggestion.

[0581] Step 4:

[0582] The server sends generated product suggestions to the terminal, which then displays this information on the user's portable information processing device. The input is the product suggestions from the server, and the output is the information visually presented to the user. The terminal further prioritizes which information is displayed to the user.

[0583] Step 5:

[0584] The user provides feedback based on the information displayed on their device, which is then sent to the server. This feedback is again used as training data for the generative AI model, which is then utilized for future optimizations. The input is the user's feedback, and the output is the latest training data for the generative AI model. The server uses this to improve the accuracy of future customizations.

[0585] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0586] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0587] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0588] [Fourth Embodiment]

[0589] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0590] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0591] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0592] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0593] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0594] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0595] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0596] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0597] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0600] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0601] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0602] The system of the present invention operates based on the interaction between a server, a terminal, and a user in order to provide a customized tourist guide based on the user's hobbies, preferences, and location information.

[0603] The server plays a central role in this invention and performs the following functions. First, the server receives registration information transmitted by the user and stores data related to the user's interests and planned tourist destinations in a database. Second, the server receives the user's location information from the terminal and has the ability to dynamically collect and update information on related tourist destinations based on that information. This makes it possible to prepare information that is best suited to the user's interests.

[0604] The device operates through applications installed by the user and tracks the user's current location in real time using GPS. The device also receives guidance information from a server and presents it in a format chosen by the user (e.g., voice or text). Users can provide feedback through the application, which improves the system's user experience.

[0605] Users can set their interests within the application and receive information about their favorite tourist destinations and exhibits. For example, if a user is visiting a museum in a certain city, the server will provide detailed information about a specific exhibit within that museum. This information deepens the user's interest and provides a richer travel experience.

[0606] With the system of the present invention configured in this way, tourists can easily obtain information that matches their interests and enjoy the best possible sightseeing experience within a limited time.

[0607] The following describes the processing flow.

[0608] Step 1:

[0609] Users download the application to their device and create a profile. Here, they register their name and categories of interest (e.g., art, architecture).

[0610] Step 2:

[0611] The device periodically uses its GPS function to obtain the user's current location information. This location information is transmitted to the server in real time.

[0612] Step 3:

[0613] The server uses the received user location information to search and collect relevant tourist information from its database. This organizes information about nearby tourist attractions and facilities.

[0614] Step 4:

[0615] The server generates personalized guide information using a generative AI model based on the user's interests and location. This process also takes into account the user's past usage history.

[0616] Step 5:

[0617] The server sends the generated guide information to the terminal. The transmitted information is formatted according to the user's preferences (voice or text).

[0618] Step 6:

[0619] The terminal presents the user with information received from the server, either as audio or text. This allows the user to obtain detailed and personalized information about tourist destinations.

[0620] Step 7:

[0621] Users can provide feedback on the guide information provided through the app. This feedback will be used to improve the quality of the guide information.

[0622] Step 8:

[0623] The server receives user feedback and incorporates it into the learning process of the generated AI model, improving the accuracy and personalization of future guide generation.

[0624] (Example 1)

[0625] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0626] It is difficult and time-consuming for tourists to obtain efficient and personalized tourist information based on their interests and current location. Furthermore, if the information provided does not meet user expectations, satisfaction levels may decrease. Multilingual support and improvements in information accuracy based on feedback are particularly needed.

[0627] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0628] In this invention, the server includes means for receiving user input and storing information on the user's hobbies, preferences, and planned tourist destinations; means for acquiring relevant tourist destination information from external sources based on location information and hobbies, preferences, and storing and updating the information; and means for generating customized guide information using an artificial intelligence model based on the information obtained from external sources. As a result, users can receive appropriate tourist information based on their interests in a timely manner, enabling them to enjoy an efficient and personalized tourist experience.

[0629] "Accepting user input" refers to the process of receiving information such as hobbies, preferences, and planned visits provided by the user through the application.

[0630] "Hobbies and preferences" refers to information that indicates the categories and activities that users are interested in, and serves as a criterion for selecting tourist destinations.

[0631] "Planned tourist destinations" refers to information about places or regions that users plan to visit.

[0632] "Location information acquisition" refers to the process of accurately determining the user's current location using technology built into the device.

[0633] "Relevant tourist destination information" refers to the latest information on tourist destinations that are deemed highly suitable based on the user's location and interests.

[0634] A "generative artificial intelligence model" refers to a machine learning algorithm used to perform advanced analysis based on data and provide personalized information to users.

[0635] "Customized guide information" refers to tourist guide data that is individually tailored to each user, taking into account their interests, preferences, and location.

[0636] "Presentation method" refers to an interface or protocol for displaying guide information on a terminal in a format selected by the user.

[0637] "Feedback" refers to the opinions and impressions that users provide after experiencing a service, and is used to improve the system.

[0638] This invention is a system that provides personalized tourist guide information based on the user's interests, preferences, and current location. Specific embodiments of the invention are described below.

[0639] The server operates in a cloud computing environment and manages user registration information via a database (e.g., PostgreSQL). This information includes hobbies, preferences, and planned tourist destinations entered by the user through the application. The server receives location information from the terminal using the HTTPS protocol to determine the user's current location.

[0640] The device operates via an application installed on the mobile device. The device uses a built-in GPS module to obtain the user's location in real time and transmits the location information to the server.

[0641] The server collects and updates relevant tourist destination information from external services (e.g., geographic information APIs) based on received location information and user preferences. Based on the acquired data, it uses a generative AI model (e.g., an AI-based natural language generation model) to generate personalized guide information for the user. The generated guide information includes details that respond to prompts such as "Please provide information about historical sites in Kyoto."

[0642] The device receives customized guide information sent from the server and displays it in the format selected by the user (audio or text). The user views the information through the application interface and provides feedback on their experience. This feedback is sent to the server and used to improve the system and generate information for future use.

[0643] In this way, users can obtain the latest and most personalized travel information tailored to their interests in real time, resulting in a richer and more efficient travel experience.

[0644] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0645] Step 1:

[0646] The user launches a mobile application and enters information about their hobbies, preferences, and planned travel destinations. This input data includes information such as favorite travel categories and specific places to visit. The server receives this information and saves it in the database as a new user profile.

[0647] Step 2:

[0648] The device uses its built-in GPS function to determine the user's current location. The acquired location information is periodically sent to the server. The server receives this location information and prepares to collect data on relevant tourist destinations based on the user's current location.

[0649] Step 3:

[0650] The server uses the received location information and the user's interests and preferences to collect relevant tourist destination information from external geographic information service APIs. The obtained tourist destination information is stored in a database and updated as needed. During this process, the server filters the information to be appropriate based on location and interests.

[0651] Step 4:

[0652] The server generates customized guide information using a generative AI model based on well-organized tourist destination information. This model creates helpful information in natural language based on prompt statements (e.g., "Please tell me about historical landmarks in Tokyo"). The generated guide information is optimized based on the user profile.

[0653] Step 5:

[0654] The server sends the generated, customized guide information to the terminal. The terminal displays the received information in the format selected by the user. For example, if an audio guide is selected, the text is converted to speech and played.

[0655] Step 6:

[0656] Users experience the provided guide information and then provide feedback through the application. This feedback includes opinions on the accuracy and satisfaction level of the information. The device sends this feedback to the server.

[0657] Step 7:

[0658] The server analyzes the received feedback and uses it to generate information for the next time. The analysis results are reflected in tuning the generation AI model and updating the database, contributing to improvements in the overall accuracy and personalization of the system.

[0659] (Application Example 1)

[0660] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0661] This invention aims to develop an information collection and provision system that takes into account interests and location information in order to provide users with appropriate and personalized guidance information. Current guidance systems have difficulty responding to the frequency of location information updates and the individual interests of users. Furthermore, providing relevant information about exhibits in physical stores often results in insufficient presentation of appropriate detailed information. A system is needed to solve these problems and provide the optimal experience for users.

[0662] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0663] In this invention, the server includes means for collecting location-related information based on the user's interests and location information, means for generating customized guidance information using a generative artificial intelligence model, and means for providing detailed information related to exhibits based on information classified within the physical store. As a result, users can always receive highly relevant information based on their interests and enjoy a richer experience by obtaining detailed background information about the exhibits.

[0664] "User interests" refer to information about the preferences and concerns that users show towards specific things.

[0665] "Location information" is geographical data that indicates the user's current physical location.

[0666] "Information" refers to information that includes detailed descriptions and instructions provided to the user about a specific location or object.

[0667] A "mobile device" refers to a portable electronic device such as a smartphone or tablet, which is a device that users can carry around and use to visualize information.

[0668] A "physical store" refers to a physical store or facility that actually exists and can be visited by users directly.

[0669] "Exhibited items" refer to items or works of art that are displayed to visitors within a physical store, and for which detailed information is provided to users.

[0670] A "generative artificial intelligence model" refers to an algorithm that uses machine learning and data analysis to generate personalized information based on the user's preferences and history.

[0671] "Customization" refers to adjusting the content of information and services to suit the individual needs and interests of users.

[0672] "Feedback" refers to responses and opinions provided by users, and is information used to improve system performance and personalize it.

[0673] To realize this application, a system will be built in which a server, a terminal, and a user work together. First, the server receives the user's interests and location information, and based on this, collects information about relevant locations from a database. The server uses this information and a generative artificial intelligence model to create personalized guidance information. The guidance information will include details tailored to the individual user's preferences, such as the historical background and explanations of exhibits.

[0674] Next, the device receives this guidance information and displays it in the user's chosen format, such as audio or text. The device can also provide guidance along the user's route within the physical store to help them view details of specific exhibits. This allows users to receive guidance information using their smartphones when visiting a physical store. Hardware used includes smartphones and tablets, and location information is obtained via the Google Maps API. Firebase is used for server-side database management.

[0675] User feedback is sent to the server via the device, and this feedback is used to train the generative artificial intelligence model. The model is continuously improved through this process, enabling it to generate more accurate guidance information.

[0676] A concrete example is a user visiting a museum who scans a QR code displayed on their smartphone screen to obtain detailed historical background information and artist information about a particular painting. In this process, the generative AI model is given a prompt such as, "Generate an audio guide for historical exhibits that the user is interested in, based on their current location," to generate context that matches the user's interests.

[0677] In this way, the system of the present invention becomes capable of providing users with interesting and useful information in real time.

[0678] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0679] Step 1:

[0680] The server receives interest and location information sent from the user via their device. Based on this input data, it queries the database to retrieve information about relevant locations and exhibits. The data is then filtered to match the location information and the user's interests.

[0681] Step 2:

[0682] The server uses a generative AI model to generate customized guidance information based on the acquired information. In this process, the model is input with the prompt, "Generate an audio guide for historical exhibits that the user is interested in based on their current location," and the output is detailed guidance information tailored to the user's interests.

[0683] Step 3:

[0684] The generated guidance information is sent from the server to the terminal. The terminal receives it and displays it in the format specified by the user, such as text or audio guide. The user's location information is used to highlight guidance for the nearest exhibit or location.

[0685] Step 4:

[0686] When a user reviews the information provided and requests further details about a specific exhibit, the terminal generates a request for additional information. This request is sent to the server, which retrieves the necessary additional data and sends it back to the terminal.

[0687] Step 5:

[0688] Users input feedback into their devices, which is then sent to the server. The server uses this feedback as data to improve its AI model and train it. It is expected that the feedback will improve the accuracy and customization quality of the guidance information generated in the future.

[0689] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0690] This invention provides a tourist guide system that combines an emotion engine that recognizes user emotions, enabling further personalization based on user interests and location information.

[0691] This system operates based on the interaction between a server, a terminal, and a user. The server receives basic user profile information and location information, and searches for tourist information based on this information. The collected information is transformed into guide information optimized for each individual user using a generative AI model. The emotion engine, which is installed in the terminal, uses sensors such as cameras and microphones to monitor the user's facial expressions, tone of voice, and biometric information, and evaluates the user's emotions in real time.

[0692] The device's role is to provide users with customized guide information received from the server. In addition, an emotion engine analyzes the user's emotions and prioritizes presenting information that the user is particularly interested in or likely to find pleasing. For example, if a user looks sad in a museum, the emotion engine will notify the server, which can then add backstories or entertaining content to recreate the appeal of the exhibits.

[0693] User feedback, along with emotional data measured by the emotion engine, is sent to the server and used to train the generative AI model. This continuous learning enables the system to provide more accurate and personalized tourist information. Furthermore, multilingual support and age-appropriate customization make it user-friendly for everyone.

[0694] This system allows users to have a more engaging and emotionally satisfying travel experience, and to deepen their understanding of tourist destinations and cultures.

[0695] The following describes the processing flow.

[0696] Step 1:

[0697] The user launches the application installed on their device and sets up their profile. The user enters their areas of interest and categories.

[0698] Step 2:

[0699] The device uses GPS to obtain the user's current location information and sends it to the server. Simultaneously, the emotion engine built into the device is activated and analyzes the user's emotions in real time from their facial expressions and voice.

[0700] Step 3:

[0701] The server collects relevant tourist information from a database based on the received location information, user interest data, and sentiment data. Using this data, the server utilizes a generative AI model to generate personalized guide information for the user.

[0702] Step 4:

[0703] The terminal displays guide information received from the server to the user. Based on the analysis results from the emotion engine, it selects and displays information optimized for the user's emotions. For example, if the user shows interest, it emphasizes detailed information to further pique that interest.

[0704] Step 5:

[0705] Users view the presented information and, if they become interested or have feedback, they input that information via their device. The emotion engine continuously monitors the user's emotions throughout this process and collects new data.

[0706] Step 6:

[0707] The server receives user feedback and continuously acquired emotional data, and uses this to update its generating AI model. This update enables the server to personalize guide information with greater accuracy in the future.

[0708] Step 7:

[0709] Based on the feedback received, the device updates the user's profile and interests as needed, preparing for future use. This allows users to receive an even more optimized experience next time.

[0710] (Example 2)

[0711] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0712] Traditional tourist guide systems provide information based on user interests and location, but they lack dynamic adjustments that respond to user emotions and immediate reactions. This results in a limited user experience, insufficient personalized guidance, and difficulty in improving satisfaction at tourist destinations. Furthermore, inadequate use of feedback limits long-term system improvement.

[0713] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0714] In this invention, the server includes means for acquiring tourist information based on the user's interests and location information, means for generating personalized guidance information using a living animal intelligence model with the acquired information, and means for transmitting and presenting the generated guidance information to a terminal. This enables dynamic adjustments based on the user's emotions, realizing the provision of personalized guidance information. Furthermore, these means make it possible to continuously improve user satisfaction through real-time emotion analysis.

[0715] "User interests" refer to the matters or themes that users are interested in, and in the context of travel guides, this refers to content related to places users want to visit or information they want to know.

[0716] "Location information" refers to data indicating the user's current location, obtained through a device. This information forms the basis for personalizing tourist information.

[0717] A "tourist destination" is a region or facility intended for tourists to visit, and includes places with historical, cultural, or natural value.

[0718] The "Living Animal Intelligence Model" is an algorithm developed based on machine learning technology and is used to generate personalized content based on the user's interests and location information.

[0719] "Personalized guidance information" refers to information tailored to each user's individual interests and feelings, meaning that different content is provided to each user.

[0720] A "terminal" refers to an electronic device that the user directly operates, which is used to receive tourist information and perform sentiment analysis.

[0721] "Emotion analysis" is a process that uses the user's facial expressions, voice, and biometric data to evaluate the user's emotional state in real time, and is used to adjust tourist information.

[0722] "Response" refers to the feedback and changes in emotional state that users exhibit through their devices, and the data used to optimize guidance information.

[0723] "Optimization" is the process of adjusting information and operations to make the user experience as good as possible, aiming to provide information that aligns with the user's interests and emotions.

[0724] This system provides optimized tourist information based on the user's interests and emotions. Its main components include a server, terminals, and user interaction.

[0725] The server first receives the user's interests and location information via machine, and then uses this information to obtain information about tourist destinations using a generative AI model. The generative AI model includes a general-purpose natural language processing engine and is likely a commercially available AI tool. In particular, the generative AI model can customize the guidance information based on the input prompt sentence. An example of a prompt sentence could be, "Tell me about the history of temples in Kyoto."

[0726] The device is equipped with an emotion engine that analyzes the user's facial expressions and voice tone in real time through sensors such as cameras and microphones. Furthermore, the device also acquires biometric data using sensors and sends it to a server to provide emotion analysis results. Based on the emotion analysis results, the server dynamically adjusts the guidance information and prioritizes delivering data that the user is likely to be of particular interest to the device.

[0727] Users can view and react to information received through their devices. By sending feedback to the server via their devices, user responses are used to further train the generative AI model. This improves the accuracy and personalization of future guidance information.

[0728] For example, when a user is visiting Kyoto, the emotion engine can provide detailed information about temples and museums that are likely to interest them, in order to increase the user's level of interest. Based on real-time emotion analysis, it may also present interactive content that the user will enjoy. This is an approach aimed at increasing user knowledge and satisfaction.

[0729] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0730] Step 1:

[0731] The user operates the device and logs into the system. The device obtains the user's basic profile information and current location information. This becomes the input data. The device collects this data and sends the status of the tourist recommendation service to the server.

[0732] Step 2:

[0733] The server uses a generative AI model to search for relevant tourist information based on the user's interests and location information received. Data processing here involves converting the user profile and location information into prompt sentences and inputting them into the generative AI model. The output is personalized tourist information tailored to the user.

[0734] Step 3:

[0735] The previously generated guidance information is sent from the server to the terminal. The terminal then displays this customized guidance information to the user. Specific actions include displaying maps, playing audio guides, and presenting text information.

[0736] Step 4:

[0737] The device utilizes an emotion engine to analyze the user's facial expressions, voice tone, and biometric information in real time. Input data includes biometric information obtained from the camera, microphone, and sensors, and the device evaluates the user's emotional state based on this data. The output is the user's emotional diagnosis result.

[0738] Step 5:

[0739] The server receives the sentiment analysis results sent from the terminal and readjusts the guidance information using a generative AI model. The specific data processing involves incorporating the sentiment diagnosis results into the prompt text, and the AI ​​generates new guidance information. This results in the creation of the most appealing information for the user, which is then sent to the terminal.

[0740] Step 6:

[0741] Users enjoy sightseeing using the information presented through their devices. After completing their sightseeing, users enter feedback into their devices. This feedback can be in text or rating format, and the data is sent to the server.

[0742] Step 7:

[0743] The server collects user feedback and sentiment analysis data, which is then used to improve the generating AI model. This data processing improves the accuracy of future guidance and prepares the system to provide a more personalized experience.

[0744] (Application Example 2)

[0745] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0746] In modern commercial facilities, while a wide variety of product information exists, consumers face the challenge of finding relevant information based on their own interests and feelings. Furthermore, conventional sales promotion methods struggle to provide specific, personalized suggestions to individual consumers. To improve the consumer experience, more personalized information provision is necessary.

[0747] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0748] In this invention, the server includes means for collecting information about commercial facilities based on the user's interests and location information, means for generating customized guide information using a generative artificial intelligence model based on the collected information and the user's emotional information, and means for transmitting and presenting the generated guide information to a portable information processing device. This makes it possible to provide product suggestions optimized for each consumer and improve the consumer experience within commercial facilities.

[0749] "User interest" refers to information that indicates the degree of interest an individual consumer has in a particular product or service.

[0750] "Location information" refers to data indicating a user's geographical location, obtained using portable information processing devices, etc.

[0751] A "commercial facility" refers to a physical place where goods and services are sold, and includes stores and shopping malls.

[0752] "Emotional information" refers to data that indicates a user's emotional state, obtained by analyzing their facial expressions, tone of voice, and other biometric information.

[0753] A "generative artificial intelligence model" is an algorithm that utilizes machine learning and deep learning technologies to generate information that is optimal for the user based on collected data.

[0754] A "portable information processing device" is an information device that a user can carry with them, and includes smartphones and tablet devices.

[0755] "Personalized information" refers to information that is tailored based on the individual user's interests and emotions.

[0756] An "emotion engine" is a technology used to analyze a user's emotions in real time, and refers to a device that uses sensors such as cameras and microphones.

[0757] "Feedback" refers to the information and opinions that users provide after using the system, and this data helps to further improve the generative AI model.

[0758] To realize this invention, the system includes the following components and processes: A server collects data about commercial facilities based on location information, interest information, and sentiment information obtained from the user's portable information processing device, and generates personalized guide information using a generative AI model. The generated guide information is transmitted to the user's portable information processing device and displayed to enhance the consumer experience within the commercial facility.

[0759] The emotion engine uses sensors such as cameras and microphones built into the portable information processing device to analyze the user's emotions in real time. This analysis data is sent to a server and used to train a generative artificial intelligence model.

[0760] The specific hardware used will be smartphones and tablets. The software will incorporate Microsoft's Face API and Google Cloud Speech-to-Text API, which will be used for facial recognition and speech analysis. Machine learning libraries such as TensorFlow will be used for the generative AI model.

[0761] For example, when a user is looking at a product in a specific store within a shopping mall, if the emotion engine detects the user's enjoyment, the server will display a list of recommendations and special campaign information related to that product on the user's smartphone. This makes it easier for the user to purchase the product they are interested in.

[0762] An example of a prompt is, "How can we suggest related offers for products the user is currently interested in?" Based on this prompt, the generative AI model provides optimal information suggestions to improve the user experience.

[0763] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0764] Step 1:

[0765] The server receives location and interest information from the user's portable information device. Based on this information, it searches a database for relevant data within the commercial facility and creates a set of possible information corresponding to the user's current situation. The input is location and interest information, and the output is an initial set of relevant data.

[0766] Step 2:

[0767] The emotion engine built into the device uses the camera and microphone to analyze the user's facial expressions and tone of voice, obtaining emotional information in real time. The input is data of the user's facial expressions and voice, and the output is the analyzed emotional information. Specifically, the AI ​​classifies changes in facial expressions and tone of voice into emotional categories based on the data it has learned.

[0768] Step 3:

[0769] The server inputs emotional information received from the terminal into an AI model that generates optimal product suggestions. This model considers the user's past behavior history and emotional responses to output individually personalized suggestions. The input consists of emotional information and an initial information set, and the output is an optimized product suggestion.

[0770] Step 4:

[0771] The server sends generated product suggestions to the terminal, which then displays this information on the user's portable information processing device. The input is the product suggestions from the server, and the output is the information visually presented to the user. The terminal further prioritizes which information is displayed to the user.

[0772] Step 5:

[0773] The user provides feedback based on the information displayed on their device, which is then sent to the server. This feedback is again used as training data for the generative AI model, which is then utilized for future optimizations. The input is the user's feedback, and the output is the latest training data for the generative AI model. The server uses this to improve the accuracy of future customizations.

[0774] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0775] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0776] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0777] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0778] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0779] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0780] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0781] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0782] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0783] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0784] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0785] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0786] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0787] 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.

[0788] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0789] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0790] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0791] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0792] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0793] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0794] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0795] The following is further disclosed regarding the embodiments described above.

[0796] (Claim 1)

[0797] A means of collecting information about tourist destinations based on user interests and location information,

[0798] A means for generating customized guide information using an artificial intelligence model that utilizes collected information,

[0799] A means of transmitting and displaying the generated guide information to a terminal,

[0800] A means of collecting user feedback and using that feedback to train a generating artificial intelligence model,

[0801] A system that includes this.

[0802] (Claim 2)

[0803] The system according to claim 1, further comprising means for providing the customized guide information in multiple languages.

[0804] (Claim 3)

[0805] The system according to claim 1, further comprising means for improving the accuracy and personalization of the guide information generated based on the user's feedback.

[0806] "Example 1"

[0807] (Claim 1)

[0808] A means of receiving user input and accumulating information about their hobbies, preferences, and planned tourist destinations,

[0809] A means for acquiring location information to measure the user's current location,

[0810] A means for acquiring relevant tourist destination information from external sources based on location information and personal preferences, and for storing and updating that information.

[0811] A means for generating customized guide information using an artificial intelligence model obtained from an external source,

[0812] A presentation method that sends the generated customized guide information to the terminal and displays it in a format specified by the user,

[0813] A means of obtaining user feedback, analyzing that feedback, and using it to generate future guide information,

[0814] A system that includes this.

[0815] (Claim 2)

[0816] The system according to claim 1, comprising means for providing customized guide information in multiple languages.

[0817] (Claim 3)

[0818] The system according to claim 1, comprising means for improving the accuracy and personalization of the generated guide information based on user feedback.

[0819] "Application Example 1"

[0820] (Claim 1)

[0821] A means of collecting location-related information based on user interests and location information,

[0822] A means for generating customized guidance information using an artificial intelligence model that utilizes collected information,

[0823] A means of transmitting and displaying the generated guidance information to a mobile device,

[0824] A means of providing detailed information related to exhibited items based on information classified within a physical store,

[0825] A means of collecting user feedback and using that feedback to train a generative artificial intelligence model,

[0826] A system that includes this.

[0827] (Claim 2)

[0828] The system according to claim 1, further comprising means for providing the customized guidance information in multiple languages.

[0829] (Claim 3)

[0830] The system according to claim 1, further comprising means for improving the accuracy and personalization of the guidance information generated based on the user's opinion.

[0831] "Example 2 of combining an emotion engine"

[0832] (Claim 1)

[0833] A means of obtaining information about tourist destinations based on user interests and location information,

[0834] A means for generating personalized guidance information using a living animal intelligence model with acquired information,

[0835] A means of transmitting and displaying the generated guidance information to a terminal,

[0836] A means of analyzing user emotions via a device,

[0837] A means of adjusting and optimizing guidance information based on the results of emotion analysis,

[0838] A means for collecting user responses and utilizing those responses in the learning of a living animal intelligence model,

[0839] A system that includes this.

[0840] (Claim 2)

[0841] The system according to claim 1, further comprising means for providing personalized guidance information in multiple languages.

[0842] (Claim 3)

[0843] The system according to claim 1, further comprising means for improving the accuracy and personalization of guidance information generated based on user responses.

[0844] "Application example 2 when combining with an emotional engine"

[0845] (Claim 1)

[0846] A means of collecting information about commercial facilities based on user interests and location information,

[0847] A means for generating customized guide information using an artificial intelligence model that utilizes collected information and user sentiment information,

[0848] A means for transmitting and displaying the generated guide information on a portable information processing device,

[0849] A means of analyzing user emotions using an emotion engine,

[0850] A means of personalizing product suggestions and providing relevant information based on analysis results,

[0851] A means of collecting user feedback and using that feedback to train a generating artificial intelligence model,

[0852] A system that includes this.

[0853] (Claim 2)

[0854] The system according to claim 1, further comprising means for providing the customized guide information in multiple languages.

[0855] (Claim 3)

[0856] The system according to claim 1, further comprising means for improving the accuracy and personalization of guide information generated based on user feedback and sentiment data. [Explanation of Symbols]

[0857] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting information about tourist destinations based on user interests and location information, A means for generating customized guide information using an artificial intelligence model that utilizes collected information, A means of transmitting and displaying the generated guide information to a terminal, A means of collecting user feedback and using that feedback to train a generating artificial intelligence model, A system that includes this.

2. The system according to claim 1, further comprising means for providing the customized guide information in multiple languages.

3. The system according to claim 1, further comprising means for improving the accuracy and personalization of the guide information generated based on the user's feedback.

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