system

Abstract

We provide the system. [Solution] A means for obtaining user interest information and learning objective information, and for generating or updating a user profile based on this information, A means for selecting multiple exhibits based on the user profile and using a generation model to generate explanatory information about the selected exhibits, A means for generating a personalized tour plan that includes generated explanatory information and presenting it via the user's terminal device, A means of providing users with the experience of the aforementioned tour plan using virtual reality technology, A means for obtaining user feedback information and optimizing the content of the next visit based on said feedback information, 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 method for controlling a persona chatbot, which is performed by at least one processor, 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 cultural facilities such as museums, there is a problem that it is difficult for visitors to effectively and efficiently select items that match their interests from a large number of exhibits. In addition, there is a demand to provide an immersive experience for people who cannot visit these facilities due to physical distance or physical constraints. Furthermore, in educational institutions, efficient learning using exhibits related to the content of lessons is demanded, but there is a lack of means to achieve this. To solve these problems, a system that provides a more personalized exhibition experience is needed.

Means for Solving the Problems

[0005] This invention provides means for acquiring user interest information and learning objectives, and for creating or updating individual profiles. Based on these profiles, it selects multiple exhibits and generates detailed explanatory information using a generative model employing natural language processing technology. Furthermore, by generating and presenting a personalized tour plan to the user, it enables the user to enjoy museum exhibits with a sense of presence even from home. In addition, it provides continuous learning support by acquiring user feedback and utilizing it to optimize the content provided for subsequent visits.

[0006] "User interest information" refers to information about areas or themes that users are particularly interested in.

[0007] "Learning objective information" refers to information about the goals and objectives that a user wants to achieve through learning.

[0008] A "user profile" is a collection of information that summarizes a user's characteristics, including their interests, learning objectives, and past behavioral history.

[0009] "Exhibits" refer to objects or works of art that are on public display in museums and cultural facilities.

[0010] "Explanatory information" refers to descriptive text that includes background information, historical significance, and technical details about the exhibits.

[0011] A "generative model" is a computational model designed to automatically generate output in a specific format from given information.

[0012] A "personalized tour plan" is a customized itinerary and route based on the individual user's profile.

[0013] "Feedback information" refers to information in which users express their opinions and suggestions for improvement regarding their experiences.

[0014] "Learning progress information" refers to information that shows how far a user has progressed in their learning. [Brief explanation of the drawing]

[0015] [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] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying out the Invention

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

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

[0018] In the following embodiments, a numbered 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 a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

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

[0021] 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).

[0022] 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."

[0023] [First Embodiment]

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

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0030] 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.

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

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] This invention is a system that provides users with a personalized museum experience by selecting exhibits according to their interests and learning objectives and providing detailed explanations about those exhibits. This system operates with close coordination between the server, terminal, and user elements. Specific embodiments are shown below.

[0037] First, the user enters their areas of interest and learning objectives into the device. For example, themes such as "Medieval Art" or "Dinosaur Evolution" might be considered. Next, the device sends this information to the server, which then creates or updates the user profile.

[0038] The server selects highly relevant exhibits based on the user's profile information. For the selected exhibits, a generative model is used to generate explanatory information. For example, in the case of "Medieval Art," detailed information about representative medieval painters, the background of their works, and techniques is generated.

[0039] Next, the server uses the generated explanatory information to create a tour plan optimized for each individual user and sends it to the device. The device then presents this personalized tour plan to the user through a VR headset or display. The user can walk around the virtual space and receive detailed explanations about selected exhibits in audio or text format.

[0040] After the tour ends, users enter feedback about their tour experience into a terminal. This includes comments on the exhibits and suggestions for future visits. The terminal then sends this feedback information to the server.

[0041] Finally, the server analyzes the feedback information to evaluate the user's learning progress and changes in interests. Based on this, it optimizes the content for subsequent visits to provide a more enriching museum experience.

[0042] In this way, this system can make the traditional museum experience more personalized and educationally valuable by providing exhibits and explanations tailored to each user's individual interests and learning goals.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] Users input information about their interests and learning objectives through their device. The device then sends this input information to the server.

[0046] Step 2:

[0047] The server creates or updates the user profile based on the received information. If there is past activity history or existing profile information, this is also taken into consideration to optimize the profile.

[0048] Step 3:

[0049] The server selects relevant exhibits from the database based on the user profile. The selection criteria reflect the user's interests and learning objectives.

[0050] Step 4:

[0051] The server uses a generative model to generate detailed explanatory information for selected exhibits. This model leverages natural language processing techniques to generate historical background and technical details of the exhibits.

[0052] Step 5:

[0053] The server uses the generated explanatory information to create a personalized tour plan optimized for the user and sends it to the device.

[0054] Step 6:

[0055] The device displays a personalized tour plan, presenting it to the user through a VR space or screen. This allows the user to experience selected exhibits and obtain detailed explanations.

[0056] Step 7:

[0057] After the tour ends, users enter feedback about their experience and send it to the server via their device.

[0058] Step 8:

[0059] The server analyzes the collected feedback to evaluate the user's learning progress and changes in interests. Based on this, it prepares to optimize the content provided on the next visit.

[0060] (Example 1)

[0061] 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."

[0062] Traditional museum experiences often limit themselves to general information provision, making it difficult to offer personalized experiences tailored to individual users' interests and learning objectives. Furthermore, the lack of flexible content updates to reflect changes in visitors' interests and knowledge makes it challenging to maximize user learning. Additionally, there is a need to provide efficient methods for stimulating interest using virtual reality technology.

[0063] 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.

[0064] In this invention, the server includes means for acquiring user interest information and learning objective information and generating or updating user information based on this information; means for selecting multiple exhibits based on the user information and using a generative model to generate explanatory information about the selected exhibits; and means for generating a personalized visit plan including the generated explanatory information and providing it to the user through virtual reality technology. This makes it possible to provide an optimized learning experience for each individual user and effectively promote interest and learning.

[0065] "User interest information" refers to data related to specific themes or topics that users are interested in.

[0066] "Learning objective information" refers to data that indicates what a user wants to learn and what kind of knowledge they want to acquire.

[0067] "User information" refers to comprehensive data that combines user interest information and learning objectives information, and forms the basis for creating a user profile.

[0068] "Exhibits" refer to items, works, and materials displayed for educational purposes in museums and other educational institutions.

[0069] A "generative model" is an algorithm or system used to generate relevant information from given input information using machine learning techniques.

[0070] A "personalized visit plan" is an optimized schedule of exhibit visits built based on each user's individual interests and learning objectives.

[0071] "Virtual reality technology" is a technology that uses computer graphics to allow users to experience environments different from the real world.

[0072] "Evaluation information" refers to data collected from users, including their impressions and opinions on exhibits and visit plans.

[0073] The system for implementing this invention consists of a server, a terminal, and a user. First, the user uses the terminal to input their interests and learning objectives. The terminal consists of hardware devices such as tablets, smartphones, and personal computers, and automatically transmits the input information to the server.

[0074] The server generates or updates a user profile based on interest and learning objective information received from the user. A database system (e.g., MySQL®) is used for this profile generation. The server then selects relevant exhibits based on the user profile. Database management and information retrieval technologies are used for this selection.

[0075] For selected exhibits, the server uses a generative AI model to generate detailed explanatory information. This generative AI model utilizes natural language processing techniques (e.g., GPT). The generative AI model takes a prompt as input and outputs relevant detailed explanatory text. For example, the prompt might be "Please provide detailed background information on Renaissance art."

[0076] Based on the generated explanatory information, the server creates a personalized visit plan and sends it to the terminal. This plan includes the order of exhibits, points of explanation, and suggested duration of stay. In addition, the terminal also uses a VR headset to present the visit plan to the user through virtual reality technology. This allows the user to enjoy information about the exhibits visually and aurally through an experience in a virtual space.

[0077] After the tour ends, users enter feedback using a device. This feedback is sent to the server as evaluation data. The server analyzes this information to optimize the content offered for subsequent visits. This results in a more enriching experience tailored to each user's individual interests and knowledge level.

[0078] This system provides a personalized learning experience and delivers meaningful information to visitors.

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

[0080] Step 1:

[0081] The user inputs information about their interests and learning objectives into the device. Using a touchscreen or keyboard, the user enters topics such as "ancient Egyptian pyramids" or "dinosaur evolution." This input is the output of this step and becomes the input data sent by the device to the next step.

[0082] Step 2:

[0083] The terminal converts the interest and learning objective information entered by the user into packet format and sends it to the server using a secure protocol (e.g., HTTPS). This data transfer is the specific operation of the terminal. The output of this step is the input data that the server will then process.

[0084] Step 3:

[0085] The server generates or updates a user profile based on the received user interest and learning objectives information. The server utilizes a database system to process the information and store it in storage. The output of this step is the generated or updated user profile.

[0086] Step 4:

[0087] The server selects relevant exhibits from the database based on the generated user profile. The server uses information retrieval techniques to retrieve the appropriate exhibits. The output of this step is a list of the selected exhibits.

[0088] Step 5:

[0089] The server utilizes a generative AI model to generate explanatory information related to the selected exhibits. The model receives a prompt and generates the explanatory text through natural language processing. An example prompt is, "Please provide detailed background information on Renaissance art." The output of this step is the generated explanatory information.

[0090] Step 6:

[0091] The server uses the generated explanatory information to create a personalized visit plan. The plan includes the order of exhibits and points of explanation. The server sends this plan to the terminal. The output obtained in this step is the visit plan, and displaying it on the terminal is the next step.

[0092] Step 7:

[0093] The terminal presents the received visit plan to the user via a VR headset or display. The terminal displays the contents of the visit plan in a virtual space and provides dynamic information display in response to the user's movements and choices. The output of this step leads to feedback as part of the user experience.

[0094] Step 8:

[0095] After completing their visit, users enter feedback information into a terminal. This feedback includes their impressions of the exhibits and their wishes for future visits, and this data is sent from the terminal to the server.

[0096] Step 9:

[0097] The server receives and analyzes user feedback to optimize content for the next visit. The server uses a learning algorithm to analyze the feedback. The output of this step is an optimized content plan for the next visit.

[0098] (Application Example 1)

[0099] 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."

[0100] Current exhibition facilities face challenges in providing exhibits tailored to the individual interests and learning objectives of visitors, and fixed exhibit content limits visitors' opportunities to deepen their knowledge. Furthermore, physical limitations restrict the amount of information that can be presented at one time, resulting in a lack of appeal, particularly to repeat visitors.

[0101] 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.

[0102] This invention includes a server that acquires user interest information and learning objective information and generates or updates a user profile based on this information; a server that uses a generative model to select multiple exhibits based on the user profile and generate explanatory information about the selected exhibits; and a server that uses virtual reality technology to provide the user with an experience of the tour plan. This makes it possible to provide a personalized virtual tour optimized for each individual user and to realize a deep museum experience tailored to each visitor.

[0103] A "user" is someone who uses the system to engage in virtual experiences, and the content provided is tailored to their interests and learning objectives.

[0104] "Interest information" refers to information about themes and fields that users are particularly interested in, and this information is used by the system to select content.

[0105] "Learning objectives information" refers to data that represents the educational goals and intentions of the user when using the system, and is used to determine the direction of the content that the system provides.

[0106] A "user profile" refers to a collection of data about an individual user that is generated or updated based on the user's interests and learning objectives.

[0107] "Exhibition content" refers to educational or exhibitional content selected based on user profiles and presented to users in a virtual space.

[0108] "Explanatory information" refers to detailed information about the selected exhibits, and is provided through a generative model.

[0109] A "generative model" refers to an algorithm or system used to generate explanatory information based on the user's interests.

[0110] A "tour plan" refers to a virtual itinerary or schedule that combines a series of exhibits and explanatory information presented to the user.

[0111] "Virtual reality technology" is a general term for technologies that enable users to experience a three-dimensional space different from the real world, and is used for experiencing exhibits in virtual spaces.

[0112] "Feedback information" refers to information about opinions and impressions that users provide after an experience, and is data that the system uses to improve the user experience and optimize subsequent visits.

[0113] The system for implementing this invention consists of a user, a terminal device, and a server. In this system, the user first inputs information about their interests and learning objectives into the terminal device. For example, they can input specific themes such as "contemporary art" or "dinosaur ecology."

[0114] The terminal device transmits the acquired information to a server. This server operates on a cloud platform (such as Google Cloud or AWS) and generates or updates a user profile based on the input information. Based on the user profile, the server uses a generative AI model to select exhibits related to the theme entered by the user. This model uses natural language processing technology and can select relevant exhibits even from complex interest information.

[0115] The server generates detailed explanatory information about the selected exhibits. This information includes details such as the history behind a particular artwork and the techniques used to create it. The generated information is then transmitted to a terminal device as a personalized tour plan using virtual reality technology. The user can then use the terminal device to experience VR and gain a sensory understanding of the selected exhibits.

[0116] After the tour experience, users input feedback information using a terminal device. This feedback information is sent to a server and used to optimize the content provided during future visits.

[0117] For example, if a user is interested in "Renaissance sculpture" and uses the system on that theme, the server will use an AI model to select works by Michelangelo and Donatello and generate detailed explanatory information about their historical background and technical methods. An example of a prompt to the generating AI model would be, "Generate a list of the most suitable exhibits related to 'Renaissance sculpture,' which the user is interested in, and provide detailed explanations for each exhibit."

[0118] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0119] Step 1:

[0120] Users input their interest information and learning objectives into the terminal device. The input information is text data indicating specific hobbies and areas of interest. When the terminal device receives this information, it forms the basis for sending data to the next server.

[0121] Step 2:

[0122] The terminal device transmits interest information and learning objective information obtained from the user to the server. The input here is the user's interest-related information registered as text, which is then passed to the server as a dataset to generate or update the user profile.

[0123] Step 3:

[0124] The server generates or updates user profiles based on the received information. It analyzes the received dataset as input, integrates it with existing user information, and constructs a new interest profile. This results in a profile tailored to the user's specific interests.

[0125] Step 4:

[0126] The server uses a generative AI model to select exhibits based on the user profile. In this processing step, the input profile data is provided to the AI ​​model, which selects highly relevant exhibits. The AI ​​model uses natural language processing techniques to output an optimized exhibit list based on complex interest information.

[0127] Step 5:

[0128] The server generates detailed explanatory information about the exhibits it has created. Based on the selected list of exhibits, the AI ​​model constructs detailed explanatory text. The input is the selected list of exhibits, and the output is explanatory text about those exhibits. You can use prompts to instruct the AI ​​and generate the explanations.

[0129] Step 6:

[0130] The server uses the generated explanatory information to create a personalized tour plan and sends it to the terminal device. The tour plan is constructed based on the generated explanations and sent to the terminal device, where it is output as a dataset to prepare for the VR experience.

[0131] Step 7:

[0132] The terminal device provides the user with a VR experience based on a tour plan received from the server. The terminal device utilizes virtual reality technology to visualize the exhibits, allowing the user to explore them viscerally. The input for this step is the tour plan from the server, and the output is the user's visual experience.

[0133] Step 8:

[0134] After the VR experience ends, users input feedback information into a terminal device. The user's impressions and requests for future visits are saved in text format on the terminal device. This input forms the basis for sending the next data to the server.

[0135] Step 9:

[0136] The terminal device sends feedback information to the server, and optimizations are made for the next visit based on the previous experience. This feedback information is recorded on the server as an evaluation of the user experience and output as preparation data for the next visit.

[0137] 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.

[0138] This invention provides a more personalized museum experience by recognizing the user's emotions in addition to their interests and learning objectives. This system works in cooperation with its server, terminal, and user components to generate content that accurately reflects the user's interests and emotions.

[0139] First, the user enters their desired field of study and areas of interest through their device. This information is sent from the device to the server in order to generate or update the user profile.

[0140] Based on the information received, the server selects relevant exhibits from the database and simultaneously generates explanations about the selected exhibits using a generative model. During this process, the emotion engine analyzes the user's emotions, evaluating their emotional state through user feedback and facial expression analysis during the experience.

[0141] Using the generated explanatory information and emotion evaluation results, the server creates a personalized tour plan based on the user's profile and sends it to the device. The device presents this tour plan to the user and provides explanations of the exhibits visually or audibly through the VR space or screen. By analyzing the user's emotions towards specific exhibits in real time, the content provided is tailored to the user's emotional state.

[0142] After the tour ends, users enter feedback about their experience and send it to the server via their device. The server then combines this feedback with the results of the emotion engine to optimize the content provided to the user on their next visit.

[0143] For example, if a user expresses interest in "Impressionist paintings" and a tour plan is generated, the server will select several exhibits related to Impressionism and create explanations for them using a generation model. If the user rates a particular artwork as "moved and delighted," the system will prioritize showing additional information related to that artwork or other works by the same artist on their next visit.

[0144] In this way, by combining information technology and emotion analysis technology, this system can provide users with a scientifically-based, personalized museum experience, supporting sustained learning and the stimulation of interest.

[0145] The following describes the processing flow.

[0146] Step 1:

[0147] Users input their areas of interest and learning objectives using their devices. This information is necessary for building a user profile and is therefore transmitted from the device to the server in real time.

[0148] Step 2:

[0149] The server creates or updates user profiles based on the received interest and learning objectives information. This profile also takes into account the user's past behavior history and feedback information.

[0150] Step 3:

[0151] The server references the user profile and selects relevant exhibits from the database. A generative model is used to automatically generate descriptions and background information for the selected exhibits.

[0152] Step 4:

[0153] To evaluate in real time the emotions a user expresses in response to the displayed exhibits, the device sends data of the user's facial expressions and voice to the emotion engine. The emotion engine analyzes this data and evaluates the user's emotional state.

[0154] Step 5:

[0155] The server uses emotion evaluation results obtained from the emotion engine to optimize a personalized tour plan. This tour plan includes the order in which exhibits are presented and the content of explanations tailored to the user's emotions.

[0156] Step 6:

[0157] The device displays an optimized tour plan to the user and provides explanations of the exhibits through the VR space or screen. Based on emotional feedback, users can have a more moving and interesting experience.

[0158] Step 7:

[0159] After the tour ends, users enter their impressions and evaluations of the experience into a feedback form and send it to the server via their device.

[0160] Step 8:

[0161] The server integrates feedback information and the sentiment engine's evaluation results to prepare for optimizing content for the next visit. This process maintains the user's deep learning and improves their interest.

[0162] (Example 2)

[0163] 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 will be referred to as the "terminal."

[0164] Traditional museum experiences provide users with uniform information, resulting in insufficient personalization tailored to individual interests and emotional states. This makes it difficult to improve user learning effectiveness and satisfaction, leading to challenges in sustained learning and interest stimulation.

[0165] 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.

[0166] In this invention, the server includes means for acquiring user interest information, learning objective information, and sentiment information, and generating or updating a user profile based on this information; means for selecting a plurality of exhibits based on the user profile and sentiment information, and using a generative model to generate explanatory information about the selected exhibits; and means for generating and presenting a personalized tour plan to the user, which includes the generated explanatory information and the user's sentiment evaluation results.

[0167] This makes it possible to provide a museum experience optimized for each individual user, improve learning effectiveness and satisfaction, and stimulate sustainable learning and interest.

[0168] A "user profile" is a dataset created based on information such as a user's interests, learning objectives, and emotional state, representing the individual characteristics and preferences of each user.

[0169] A "generative model" is an algorithm that utilizes natural language processing techniques to generate explanatory information about relevant exhibits based on user profiles and interest information.

[0170] "Emotional information" refers to data that analyzes the emotions and psychological states a user exhibits during an experience and expresses them as numerical values ​​or categories.

[0171] A "personalized tour plan" is an experience plan in which specific exhibits and explanations are selected and arranged based on each user's individual profile and emotional evaluation results.

[0172] "Natural language processing technology" is a field of information technology that enables computers to understand, generate, and analyze human language.

[0173] This invention is a system that utilizes user interest information, learning objectives, and emotional information to provide a personalized museum experience. This system involves the collaboration of a server, terminal, and user to provide customized information to the user.

[0174] First, the user enters their areas of interest and learning objectives into the device. The device then organizes this information and sends it to the server. The interface provided by this device is designed to be intuitive for the user.

[0175] The server uses a database to select exhibits that match the user's interests. A database management system like MySQL is used in this process. A generative AI model is then used to generate detailed explanations for the selected exhibits. The generative model implements natural language processing technology, allowing for flexible information generation based on input prompts. For example, a prompt might be, "Generate a detailed explanation of Impressionist paintings."

[0176] The server further analyzes the user's emotions from multiple perspectives using an emotion engine. It analyzes the user's facial expressions and voice data in real time and records the evaluation results. This information is used as a basis for making decisions to provide the user with a more personalized experience.

[0177] Using the generated explanatory information and sentiment evaluation results, the server creates a personalized tour plan and sends it to the device. When presenting this information to the user, the device can utilize VR technology to provide a more immersive museum experience.

[0178] As an example, if a user is interested in "Impressionist paintings," the server selects several relevant exhibits and generates explanations for each using an AI model. Additional information is provided for works that particularly impressed the user, and these are prioritized for subsequent visits, thereby continuously enhancing the user's interest and learning.

[0179] Thus, this invention combines information technology, generative AI models, and emotion analysis to provide users with an innovative museum experience.

[0180] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0181] Step 1:

[0182] Users input their interests and learning goals using the device's interface. Specific examples of input include selecting themes such as "Impressionist paintings" or "cutting-edge science and technology." The device then collects the user's interest and learning objectives and prepares to send this information to the server.

[0183] Step 2:

[0184] The terminal formats the information entered by the user and sends it to the server using a communication protocol. At this time, the user ID and session information are also sent to ensure the uniqueness of the user information on the server side. The output here is formatted data containing the user's interest information and learning objectives.

[0185] Step 3:

[0186] The server selects relevant exhibits from the database based on the user's interest and learning objectives information received from the terminal. This database search process filters exhibits to match keywords such as "Impressionism." The output at this stage is a list of selected exhibits.

[0187] Step 4:

[0188] The server uses a generative AI model to generate explanations for the selected exhibits. It takes prompts such as "Please create a detailed explanation for the selected exhibits" and uses natural language processing techniques to generate the explanations. As a result, explanations are generated for each exhibit, and these become the output.

[0189] Step 5:

[0190] The server uses an emotion engine to analyze the user's emotions. Specifically, it analyzes the user's facial expression data and voice data to quantify their emotional state. This results in the output of emotional information about the user's experience.

[0191] Step 6:

[0192] The server combines the generated commentary and sentiment information to create a personalized tour plan based on the user's profile information. This tour plan includes exhibits and commentary that are likely to keep the user interested. The output here is the customized tour plan.

[0193] Step 7:

[0194] The terminal presents the user with a personalized tour plan received from the server. The user can view the selected exhibits and their explanations through a VR device or display. At the same time, the terminal also collects data to analyze the user's reactions again for sentiment analysis.

[0195] Step 8:

[0196] Users enter feedback into a terminal after the experience ends. This feedback may include their satisfaction with the experience and requests for additional information. This information will be used to improve the service on their next visit.

[0197] Step 9:

[0198] The device sends feedback received from the user to the server. The server integrates the feedback information with the results of sentiment analysis and retains it as data to further optimize the experience for the next visit. This allows for a more personalized experience to be provided on subsequent visits.

[0199] (Application Example 2)

[0200] 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".

[0201] There is a growing need to improve the quality of the user experience by providing personalized content in real time that responds to the diverse preferences and emotional states of users. However, conventional systems rely on fixed content and have the challenge of not being able to fully address the individual needs of users. Against this backdrop, there is a demand for a more adaptive and personalized information delivery system that takes into account users' interest information and emotional data.

[0202] 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.

[0203] In this invention, the server includes means for acquiring user interest information and sentiment data and generating or updating a user profile based on this information; means for selecting a plurality of information elements based on the user profile and using a generative model to generate explanations about the selected information elements; and means for generating personalized suggestions including the generated explanations and presenting them to the user. This enables adaptive information provision that meets the individual needs of the user.

[0204] A "user" refers to an individual who shows interest in and experiences exhibits or informational elements.

[0205] "Interest information" refers to information related to the fields or subjects that the user is interested in.

[0206] "Emotional data" refers to information that indicates a user's emotional state, and includes data that is analyzed in real time.

[0207] A "user profile" is a record of personalized information generated based on a user's interests and emotional data.

[0208] "Information elements" refer to items such as exhibits and products offered to users, which are selected or suggested during the experience.

[0209] A "generative model" is a system based on artificial intelligence technology used to generate output for a specific task.

[0210] "Explanation" refers to descriptive text or audio information about specific information elements, provided to deepen the user's understanding.

[0211] "Personalized suggestions" refer to the provision of information that is individually tailored based on the user's interests and sentiment data.

[0212] This invention constructs a system that acquires user interest information and sentiment data, and generates or updates a personalized user profile based on this information. Users input information of interest using a smartphone or wearable device, and the device transmits the acquired data to a server. The server utilizes a generative AI model to select multiple information elements based on the user profile and generates explanations about them.

[0213] The core of this system is a generative model that utilizes natural language processing technology to create personalized explanations for each user. This generative model employs AI technology to provide information tailored to the user's preferences and emotions. The emotion engine analyzes the user's emotional state in real time and obtains feedback information. Using this analyzed information and the generated explanations, the server generates personalized suggestions and presents them to the user through their device.

[0214] As a concrete example, consider a case where a user shows strong interest in "the latest smartphone." The server selects relevant product information and creates a description of its features using a generative model. If the user expresses interest in this product, the system adjusts to prioritize showing additional accessory information on their next visit.

[0215] As an example of a prompt, input the following into the generative AI model:

[0216] Please generate product descriptions based on the following user information:

[0217] Product Category: Smartphones

[0218] Interests: Latest technology

[0219] User sentiment: Interesting

[0220] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0221] Step 1:

[0222] Users input their interest information through the device, and the device sends this information to the server. The input data includes the user's interest categories and keywords, and the user profile is updated based on this. The device plays a role in appropriately formatting the necessary data and transferring it to the server using a secure communication method.

[0223] Step 2:

[0224] The server analyzes the received interest information and updates the user profile. Using the entered interest information and past data, it defines the user's preferences in more detail and stores them in the database so that they can be used for future information provision. At this time, the database optimizes the profile based on the newly entered information.

[0225] Step 3:

[0226] The server uses a generative AI model to select information elements that fit the user profile. Based on the user profile and interest information as input, the generative model extracts relevant information elements and generates explanations using defined prompts.

[0227] Step 4:

[0228] The generated explanations are packaged by the server as personalized suggestions and sent to the device. The server receives the text data generated by the generative AI model and converts it into a user-friendly format. This output highlights informational elements that align with the user's emotions and interests.

[0229] Step 5:

[0230] The user reviews the suggestions sent from the device and enters feedback and emotional data about the experience. At this time, the user records their emotions regarding the suggested information in data format, and the device sends this data back to the server.

[0231] Step 6:

[0232] The server analyzes the feedback and sentiment data received and updates the user profile for further personalization. Based on the feedback information, the profile is adjusted to provide the most relevant information elements on the next visit and saved as new data.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] [Second Embodiment]

[0237] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0238] 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.

[0239] 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).

[0240] 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.

[0241] 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.

[0242] 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).

[0243] 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.

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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".

[0249] This invention is a system that provides users with a personalized museum experience by selecting exhibits according to their interests and learning objectives and providing detailed explanations about those exhibits. This system operates with close coordination between the server, terminal, and user elements. Specific embodiments are shown below.

[0250] First, the user enters their areas of interest and learning objectives into the device. For example, themes such as "Medieval Art" or "Dinosaur Evolution" might be considered. Next, the device sends this information to the server, which then creates or updates the user profile.

[0251] The server selects highly relevant exhibits based on the user's profile information. For the selected exhibits, a generative model is used to generate explanatory information. For example, in the case of "Medieval Art," detailed information about representative medieval painters, the background of their works, and techniques is generated.

[0252] Next, the server uses the generated explanatory information to create a tour plan optimized for each individual user and sends it to the device. The device then presents this personalized tour plan to the user through a VR headset or display. The user can walk around the virtual space and receive detailed explanations about selected exhibits in audio or text format.

[0253] After the tour ends, users enter feedback about their tour experience into a terminal. This includes comments on the exhibits and suggestions for future visits. The terminal then sends this feedback information to the server.

[0254] Finally, the server analyzes the feedback information to evaluate the user's learning progress and changes in interests. Based on this, it optimizes the content for subsequent visits to provide a more enriching museum experience.

[0255] In this way, this system can make the traditional museum experience more personalized and educationally valuable by providing exhibits and explanations tailored to each user's individual interests and learning goals.

[0256] The following describes the processing flow.

[0257] Step 1:

[0258] Users input information about their interests and learning objectives through their device. The device then sends this input information to the server.

[0259] Step 2:

[0260] The server creates or updates the user profile based on the received information. If there is past activity history or existing profile information, this is also taken into consideration to optimize the profile.

[0261] Step 3:

[0262] The server selects relevant exhibits from the database based on the user profile. The selection criteria reflect the user's interests and learning objectives.

[0263] Step 4:

[0264] The server uses a generative model to generate detailed explanatory information for selected exhibits. This model leverages natural language processing techniques to generate historical background and technical details of the exhibits.

[0265] Step 5:

[0266] The server uses the generated explanatory information to create a personalized tour plan optimized for the user and sends it to the device.

[0267] Step 6:

[0268] The device displays a personalized tour plan, presenting it to the user through a VR space or screen. This allows the user to experience selected exhibits and obtain detailed explanations.

[0269] Step 7:

[0270] After the tour ends, users enter feedback about their experience and send it to the server via their device.

[0271] Step 8:

[0272] The server analyzes the collected feedback to evaluate the user's learning progress and changes in interests. Based on this, it prepares to optimize the content provided on the next visit.

[0273] (Example 1)

[0274] 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."

[0275] Traditional museum experiences often limit themselves to general information provision, making it difficult to offer personalized experiences tailored to individual users' interests and learning objectives. Furthermore, the lack of flexible content updates to reflect changes in visitors' interests and knowledge makes it challenging to maximize user learning. Additionally, there is a need to provide efficient methods for stimulating interest using virtual reality technology.

[0276] 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.

[0277] In this invention, the server includes means for acquiring user interest information and learning objective information and generating or updating user information based on this information; means for selecting multiple exhibits based on the user information and using a generative model to generate explanatory information about the selected exhibits; and means for generating a personalized visit plan including the generated explanatory information and providing it to the user through virtual reality technology. This makes it possible to provide an optimized learning experience for each individual user and effectively promote interest and learning.

[0278] "User interest information" refers to data related to specific themes or topics that users are interested in.

[0279] "Learning objective information" refers to data that indicates what a user wants to learn and what kind of knowledge they want to acquire.

[0280] "User information" refers to comprehensive data that combines user interest information and learning objectives information, and forms the basis for creating a user profile.

[0281] The "exhibit" refers to articles, works, and materials displayed for educational purposes in museums and other educational institutions.

[0282] The "generative model" is an algorithm or system used to generate related information from given input information using machine learning techniques.

[0283] The "individualized visit plan" is an optimized schedule for visiting exhibits constructed based on the individual interests and learning purposes of users.

[0284] The "virtual reality technology" is a technology that uses computer graphics to enable users to experience an environment different from the real world.

[0285] The "evaluation information" is data including the feelings and opinions of users regarding exhibits and visit plans collected from users.

[0286] The system for implementing this invention consists of a server, a terminal, and user elements. First, the user uses the terminal to input their interests and learning purposes. The terminal is composed of hardware devices such as tablets, smartphones, and personal computers, and automatically transmits the input information to the server when it receives it.

[0287] The server generates or updates a user profile based on the interest information and learning purpose information received from the user. A database system (e.g., MySQL) is used for this profile generation. Next, the server selects relevant exhibits based on the user profile. Database management and information retrieval techniques are used for this selection.

[0288] For selected exhibits, the server uses a generative AI model to generate detailed explanatory information. This generative AI model utilizes natural language processing techniques (e.g., GPT). The generative AI model takes a prompt as input and outputs relevant detailed explanatory text. For example, the prompt might be "Please provide detailed background information on Renaissance art."

[0289] Based on the generated explanatory information, the server creates a personalized visit plan and sends it to the terminal. This plan includes the order of exhibits, points of explanation, and suggested duration of stay. In addition, the terminal also uses a VR headset to present the visit plan to the user through virtual reality technology. This allows the user to enjoy information about the exhibits visually and aurally through an experience in a virtual space.

[0290] After the tour ends, users enter feedback using a device. This feedback is sent to the server as evaluation data. The server analyzes this information to optimize the content offered for subsequent visits. This results in a more enriching experience tailored to each user's individual interests and knowledge level.

[0291] This system provides a personalized learning experience and delivers meaningful information to visitors.

[0292] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0293] Step 1:

[0294] The user inputs information about their interests and learning objectives into the device. Using a touchscreen or keyboard, the user enters topics such as "ancient Egyptian pyramids" or "dinosaur evolution." This input is the output of this step and becomes the input data sent by the device to the next step.

[0295] Step 2:

[0296] The terminal converts the interest and learning objective information entered by the user into packet format and sends it to the server using a secure protocol (e.g., HTTPS). This data transfer is the specific operation of the terminal. The output of this step is the input data that the server will then process.

[0297] Step 3:

[0298] The server generates or updates a user profile based on the received user interest and learning objectives information. The server utilizes a database system to process the information and store it in storage. The output of this step is the generated or updated user profile.

[0299] Step 4:

[0300] The server selects relevant exhibits from the database based on the generated user profile. The server uses information retrieval techniques to retrieve the appropriate exhibits. The output of this step is a list of the selected exhibits.

[0301] Step 5:

[0302] The server utilizes a generative AI model to generate explanatory information related to the selected exhibits. The model receives a prompt and generates the explanatory text through natural language processing. An example prompt is, "Please provide detailed background information on Renaissance art." The output of this step is the generated explanatory information.

[0303] Step 6:

[0304] The server uses the generated explanatory information to create a personalized visit plan. The plan includes the order of exhibits and points of explanation. The server sends this plan to the terminal. The output obtained in this step is the visit plan, and displaying it on the terminal is the next step.

[0305] Step 7:

[0306] The terminal presents the received visit plan to the user through a VR headset or a display. The terminal displays the content of the visit plan in a virtual space and performs dynamic information display according to the user's movement and selection. The output of this step leads to feedback as the user's experience.

[0307] Step 8:

[0308] After the visit experience, the user inputs feedback information to the terminal. Opinions on the exhibits and next-time expectations become the input information, and this data is sent from the terminal to the server.

[0309] Step 9:

[0310] The server receives the feedback information from the user and optimizes the content for the next visit by performing analysis. The server analyzes using a learning algorithm based on the feedback. The output of this step is an optimized content plan for the next use.

[0311] (Application Example 1)

[0312] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0313] In the current exhibition facility, there are problems such as it being difficult to provide exhibits according to the individual interests and learning purposes of visitors, and the fixed exhibition content limiting the opportunities for visitors to deepen new knowledge. Furthermore, due to physical limitations, the amount of information that can be provided at one time is also limited, so there is an issue of lacking attraction especially for repeat visitors.

[0314] 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.

[0315] This invention includes a server that acquires user interest information and learning objective information and generates or updates a user profile based on this information; a server that uses a generative model to select multiple exhibits based on the user profile and generate explanatory information about the selected exhibits; and a server that uses virtual reality technology to provide the user with an experience of the tour plan. This makes it possible to provide a personalized virtual tour optimized for each individual user and to realize a deep museum experience tailored to each visitor.

[0316] A "user" is someone who uses the system to engage in virtual experiences, and the content provided is tailored to their interests and learning objectives.

[0317] "Interest information" refers to information about themes and fields that users are particularly interested in, and this information is used by the system to select content.

[0318] "Learning objectives information" refers to data that represents the educational goals and intentions of the user when using the system, and is used to determine the direction of the content that the system provides.

[0319] A "user profile" refers to a collection of data about an individual user that is generated or updated based on the user's interests and learning objectives.

[0320] "Exhibition content" refers to educational or exhibitional content selected based on user profiles and presented to users in a virtual space.

[0321] "Explanatory information" refers to detailed information about the selected exhibits, and is provided through a generative model.

[0322] A "generative model" refers to an algorithm or system used to generate explanatory information based on the user's interests.

[0323] A "tour plan" refers to a virtual itinerary or schedule that combines a series of exhibits and explanatory information presented to the user.

[0324] "Virtual reality technology" is a general term for technologies that enable users to experience a three-dimensional space different from the real world, and is used for experiencing exhibits in virtual spaces.

[0325] "Feedback information" refers to information about opinions and impressions that users provide after an experience, and is data that the system uses to improve the user experience and optimize subsequent visits.

[0326] The system for implementing this invention consists of a user, a terminal device, and a server. In this system, the user first inputs information about their interests and learning objectives into the terminal device. For example, they can input specific themes such as "contemporary art" or "dinosaur ecology."

[0327] The terminal device transmits the acquired information to a server. This server operates on a cloud platform (such as Google Cloud or AWS) and generates or updates a user profile based on the input information. Based on the user profile, the server uses a generative AI model to select exhibits related to the theme entered by the user. This model uses natural language processing technology and can select relevant exhibits even from complex interest information.

[0328] The server generates detailed explanatory information about the selected exhibits. This information includes details such as the history behind a particular artwork and the techniques used to create it. The generated information is then transmitted to a terminal device as a personalized tour plan using virtual reality technology. The user can then use the terminal device to experience VR and gain a sensory understanding of the selected exhibits.

[0329] After the tour experience, users input feedback information using a terminal device. This feedback information is sent to a server and used to optimize the content provided during future visits.

[0330] For example, if a user is interested in "Renaissance sculpture" and uses the system on that theme, the server will use an AI model to select works by Michelangelo and Donatello and generate detailed explanatory information about their historical background and technical methods. An example of a prompt to the generating AI model would be, "Generate a list of the most suitable exhibits related to 'Renaissance sculpture,' which the user is interested in, and provide detailed explanations for each exhibit."

[0331] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0332] Step 1:

[0333] Users input their interest information and learning objectives into the terminal device. The input information is text data indicating specific hobbies and areas of interest. When the terminal device receives this information, it forms the basis for sending data to the next server.

[0334] Step 2:

[0335] The terminal device transmits interest information and learning objective information obtained from the user to the server. The input here is the user's interest-related information registered as text, which is then passed to the server as a dataset to generate or update the user profile.

[0336] Step 3:

[0337] The server generates or updates user profiles based on the received information. It analyzes the received dataset as input, integrates it with existing user information, and constructs a new interest profile. This results in a profile tailored to the user's specific interests.

[0338] Step 4:

[0339] The server uses a generative AI model to select exhibits based on the user profile. In this processing step, the input profile data is provided to the AI ​​model, which selects highly relevant exhibits. The AI ​​model uses natural language processing techniques to output an optimized exhibit list based on complex interest information.

[0340] Step 5:

[0341] The server generates detailed explanatory information about the exhibits it has created. Based on the selected list of exhibits, the AI ​​model constructs detailed explanatory text. The input is the selected list of exhibits, and the output is explanatory text about those exhibits. You can use prompts to instruct the AI ​​and generate the explanations.

[0342] Step 6:

[0343] The server uses the generated explanatory information to create a personalized tour plan and sends it to the terminal device. The tour plan is constructed based on the generated explanations and sent to the terminal device, where it is output as a dataset to prepare for the VR experience.

[0344] Step 7:

[0345] The terminal device provides the user with a VR experience based on a tour plan received from the server. The terminal device utilizes virtual reality technology to visualize the exhibits, allowing the user to explore them viscerally. The input for this step is the tour plan from the server, and the output is the user's visual experience.

[0346] Step 8:

[0347] After the VR experience ends, users input feedback information into a terminal device. The user's impressions and requests for future visits are saved in text format on the terminal device. This input forms the basis for sending the next data to the server.

[0348] Step 9:

[0349] The terminal device sends feedback information to the server, and optimizations are made for the next visit based on the previous experience. This feedback information is recorded on the server as an evaluation of the user experience and output as preparation data for the next visit.

[0350] 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.

[0351] This invention provides a more personalized museum experience by recognizing the user's emotions in addition to their interests and learning objectives. This system works in cooperation with its server, terminal, and user components to generate content that accurately reflects the user's interests and emotions.

[0352] First, the user enters their desired field of study and areas of interest through their device. This information is sent from the device to the server in order to generate or update the user profile.

[0353] Based on the information received, the server selects relevant exhibits from the database and simultaneously generates explanations about the selected exhibits using a generative model. During this process, the emotion engine analyzes the user's emotions, evaluating their emotional state through user feedback and facial expression analysis during the experience.

[0354] Using the generated explanatory information and emotion evaluation results, the server creates a personalized tour plan based on the user's profile and sends it to the device. The device presents this tour plan to the user and provides explanations of the exhibits visually or audibly through the VR space or screen. By analyzing the user's emotions towards specific exhibits in real time, the content provided is tailored to the user's emotional state.

[0355] After the tour ends, users enter feedback about their experience and send it to the server via their device. The server then combines this feedback with the results of the emotion engine to optimize the content provided to the user on their next visit.

[0356] For example, if a user expresses interest in "Impressionist paintings" and a tour plan is generated, the server will select several exhibits related to Impressionism and create explanations for them using a generation model. If the user rates a particular artwork as "moved and delighted," the system will prioritize showing additional information related to that artwork or other works by the same artist on their next visit.

[0357] In this way, by combining information technology and emotion analysis technology, this system can provide users with a scientifically-based, personalized museum experience, supporting sustained learning and the stimulation of interest.

[0358] The following describes the processing flow.

[0359] Step 1:

[0360] Users input their areas of interest and learning objectives using their devices. This information is necessary for building a user profile and is therefore transmitted from the device to the server in real time.

[0361] Step 2:

[0362] The server creates or updates user profiles based on the received interest and learning objectives information. This profile also takes into account the user's past behavior history and feedback information.

[0363] Step 3:

[0364] The server references the user profile and selects relevant exhibits from the database. A generative model is used to automatically generate descriptions and background information for the selected exhibits.

[0365] Step 4:

[0366] To evaluate in real time the emotions a user expresses in response to the displayed exhibits, the device sends data of the user's facial expressions and voice to the emotion engine. The emotion engine analyzes this data and evaluates the user's emotional state.

[0367] Step 5:

[0368] The server uses emotion evaluation results obtained from the emotion engine to optimize a personalized tour plan. This tour plan includes the order in which exhibits are presented and the content of explanations tailored to the user's emotions.

[0369] Step 6:

[0370] The device displays an optimized tour plan to the user and provides explanations of the exhibits through the VR space or screen. Based on emotional feedback, users can have a more moving and interesting experience.

[0371] Step 7:

[0372] After the tour ends, users enter their impressions and evaluations of the experience into a feedback form and send it to the server via their device.

[0373] Step 8:

[0374] The server integrates feedback information and the sentiment engine's evaluation results to prepare for optimizing content for the next visit. This process maintains the user's deep learning and improves their interest.

[0375] (Example 2)

[0376] 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".

[0377] Traditional museum experiences provide users with uniform information, resulting in insufficient personalization tailored to individual interests and emotional states. This makes it difficult to improve user learning effectiveness and satisfaction, leading to challenges in sustained learning and interest stimulation.

[0378] 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.

[0379] In this invention, the server includes means for acquiring user interest information, learning objective information, and sentiment information, and generating or updating a user profile based on this information; means for selecting a plurality of exhibits based on the user profile and sentiment information, and using a generative model to generate explanatory information about the selected exhibits; and means for generating and presenting a personalized tour plan to the user, which includes the generated explanatory information and the user's sentiment evaluation results.

[0380] This makes it possible to provide a museum experience optimized for each individual user, improve learning effectiveness and satisfaction, and stimulate sustainable learning and interest.

[0381] A "user profile" is a dataset created based on information such as a user's interests, learning objectives, and emotional state, representing the individual characteristics and preferences of each user.

[0382] A "generative model" is an algorithm that utilizes natural language processing techniques to generate explanatory information about relevant exhibits based on user profiles and interest information.

[0383] "Emotional information" refers to data that analyzes the emotions and psychological states a user exhibits during an experience and expresses them as numerical values ​​or categories.

[0384] A "personalized tour plan" is an experience plan in which specific exhibits and explanations are selected and arranged based on each user's individual profile and emotional evaluation results.

[0385] "Natural language processing technology" is a field of information technology that enables computers to understand, generate, and analyze human language.

[0386] This invention is a system that utilizes user interest information, learning objectives, and emotional information to provide a personalized museum experience. This system involves the collaboration of a server, terminal, and user to provide customized information to the user.

[0387] First, the user enters their areas of interest and learning objectives into the device. The device then organizes this information and sends it to the server. The interface provided by this device is designed to be intuitive for the user.

[0388] The server uses a database to select exhibits that match the user's interests. A database management system like MySQL is used in this process. A generative AI model is then used to generate detailed explanations for the selected exhibits. The generative model implements natural language processing technology, allowing for flexible information generation based on input prompts. For example, a prompt might be, "Generate a detailed explanation of Impressionist paintings."

[0389] The server further analyzes the user's emotions from multiple perspectives using an emotion engine. It analyzes the user's facial expressions and voice data in real time and records the evaluation results. This information is used as a basis for making decisions to provide the user with a more personalized experience.

[0390] Using the generated explanatory information and sentiment evaluation results, the server creates a personalized tour plan and sends it to the device. When presenting this information to the user, the device can utilize VR technology to provide a more immersive museum experience.

[0391] As an example, if a user is interested in "Impressionist paintings," the server selects several relevant exhibits and generates explanations for each using an AI model. Additional information is provided for works that particularly impressed the user, and these are prioritized for subsequent visits, thereby continuously enhancing the user's interest and learning.

[0392] Thus, this invention combines information technology, generative AI models, and emotion analysis to provide users with an innovative museum experience.

[0393] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0394] Step 1:

[0395] Users input their interests and learning goals using the device's interface. Specific examples of input include selecting themes such as "Impressionist paintings" or "cutting-edge science and technology." The device then collects the user's interest and learning objectives and prepares to send this information to the server.

[0396] Step 2:

[0397] The terminal formats the information entered by the user and sends it to the server using a communication protocol. At this time, the user ID and session information are also sent to ensure the uniqueness of the user information on the server side. The output here is formatted data containing the user's interest information and learning objectives.

[0398] Step 3:

[0399] The server selects relevant exhibits from the database based on the user's interest and learning objectives information received from the terminal. This database search process filters exhibits to match keywords such as "Impressionism." The output at this stage is a list of selected exhibits.

[0400] Step 4:

[0401] The server uses a generative AI model to generate explanations for the selected exhibits. It takes prompts such as "Please create a detailed explanation for the selected exhibits" and uses natural language processing techniques to generate the explanations. As a result, explanations are generated for each exhibit, and these become the output.

[0402] Step 5:

[0403] The server uses an emotion engine to analyze the user's emotions. Specifically, it analyzes the user's facial expression data and voice data to quantify their emotional state. This results in the output of emotional information about the user's experience.

[0404] Step 6:

[0405] The server combines the generated commentary and sentiment information to create a personalized tour plan based on the user's profile information. This tour plan includes exhibits and commentary that are likely to keep the user interested. The output here is the customized tour plan.

[0406] Step 7:

[0407] The terminal presents the user with a personalized tour plan received from the server. The user can view the selected exhibits and their explanations through a VR device or display. At the same time, the terminal also collects data to analyze the user's reactions again for sentiment analysis.

[0408] Step 8:

[0409] Users enter feedback into a terminal after the experience ends. This feedback may include their satisfaction with the experience and requests for additional information. This information will be used to improve the service on their next visit.

[0410] Step 9:

[0411] The device sends feedback received from the user to the server. The server integrates the feedback information with the results of sentiment analysis and retains it as data to further optimize the experience for the next visit. This allows for a more personalized experience to be provided on subsequent visits.

[0412] (Application Example 2)

[0413] 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".

[0414] There is a growing need to improve the quality of the user experience by providing personalized content in real time that responds to the diverse preferences and emotional states of users. However, conventional systems rely on fixed content and have the challenge of not being able to fully address the individual needs of users. Against this backdrop, there is a demand for a more adaptive and personalized information delivery system that takes into account users' interest information and emotional data.

[0415] 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.

[0416] In this invention, the server includes means for acquiring user interest information and sentiment data and generating or updating a user profile based on this information; means for selecting a plurality of information elements based on the user profile and using a generative model to generate explanations about the selected information elements; and means for generating personalized suggestions including the generated explanations and presenting them to the user. This enables adaptive information provision that meets the individual needs of the user.

[0417] A "user" refers to an individual who shows interest in and experiences exhibits or informational elements.

[0418] "Interest information" refers to information related to the fields or subjects that the user is interested in.

[0419] "Emotional data" refers to information that indicates a user's emotional state, and includes data that is analyzed in real time.

[0420] A "user profile" is a record of personalized information generated based on a user's interests and emotional data.

[0421] "Information elements" refer to items such as exhibits and products offered to users, which are selected or suggested during the experience.

[0422] A "generative model" is a system based on artificial intelligence technology used to generate output for a specific task.

[0423] "Explanation" refers to descriptive text or audio information about specific information elements, provided to deepen the user's understanding.

[0424] "Personalized suggestions" refer to the provision of information that is individually tailored based on the user's interests and sentiment data.

[0425] This invention constructs a system that acquires user interest information and sentiment data, and generates or updates a personalized user profile based on this information. Users input information of interest using a smartphone or wearable device, and the device transmits the acquired data to a server. The server utilizes a generative AI model to select multiple information elements based on the user profile and generates explanations about them.

[0426] The core of this system is a generative model that utilizes natural language processing technology to create personalized explanations for each user. This generative model employs AI technology to provide information tailored to the user's preferences and emotions. The emotion engine analyzes the user's emotional state in real time and obtains feedback information. Using this analyzed information and the generated explanations, the server generates personalized suggestions and presents them to the user through their device.

[0427] As a concrete example, consider a case where a user shows strong interest in "the latest smartphone." The server selects relevant product information and creates a description of its features using a generative model. If the user expresses interest in this product, the system adjusts to prioritize showing additional accessory information on their next visit.

[0428] As an example of a prompt, input the following into the generative AI model:

[0429] Please generate product descriptions based on the following user information:

[0430] Product Category: Smartphones

[0431] Interests: Latest technology

[0432] User sentiment: Interesting

[0433] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0434] Step 1:

[0435] Users input their interest information through the device, and the device sends this information to the server. The input data includes the user's interest categories and keywords, and the user profile is updated based on this. The device plays a role in appropriately formatting the necessary data and transferring it to the server using a secure communication method.

[0436] Step 2:

[0437] The server analyzes the received interest information and updates the user profile. Using the entered interest information and past data, it defines the user's preferences in more detail and stores them in the database so that they can be used for future information provision. At this time, the database optimizes the profile based on the newly entered information.

[0438] Step 3:

[0439] The server uses a generative AI model to select information elements that fit the user profile. Based on the user profile and interest information as input, the generative model extracts relevant information elements and generates explanations using defined prompts.

[0440] Step 4:

[0441] The generated explanations are packaged by the server as personalized suggestions and sent to the device. The server receives the text data generated by the generative AI model and converts it into a user-friendly format. This output highlights informational elements that align with the user's emotions and interests.

[0442] Step 5:

[0443] The user reviews the suggestions sent from the device and enters feedback and emotional data about the experience. At this time, the user records their emotions regarding the suggested information in data format, and the device sends this data back to the server.

[0444] Step 6:

[0445] The server analyzes the feedback and sentiment data received and updates the user profile for further personalization. Based on the feedback information, the profile is adjusted to provide the most relevant information elements on the next visit and saved as new data.

[0446] 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.

[0447] 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.

[0448] 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.

[0449] [Third Embodiment]

[0450] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0451] 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.

[0452] 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).

[0453] 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.

[0454] 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.

[0455] 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).

[0456] 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.

[0457] 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.

[0458] 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.

[0459] 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.

[0460] 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.

[0461] 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".

[0462] This invention is a system that provides users with a personalized museum experience by selecting exhibits according to their interests and learning objectives and providing detailed explanations about those exhibits. This system operates with close coordination between the server, terminal, and user elements. Specific embodiments are shown below.

[0463] First, the user enters their areas of interest and learning objectives into the device. For example, themes such as "Medieval Art" or "Dinosaur Evolution" might be considered. Next, the device sends this information to the server, which then creates or updates the user profile.

[0464] The server selects highly relevant exhibits based on the user's profile information. For the selected exhibits, a generative model is used to generate explanatory information. For example, in the case of "Medieval Art," detailed information about representative medieval painters, the background of their works, and techniques is generated.

[0465] Next, the server uses the generated explanatory information to create a tour plan optimized for each individual user and sends it to the device. The device then presents this personalized tour plan to the user through a VR headset or display. The user can walk around the virtual space and receive detailed explanations about selected exhibits in audio or text format.

[0466] After the tour ends, users enter feedback about their tour experience into a terminal. This includes comments on the exhibits and suggestions for future visits. The terminal then sends this feedback information to the server.

[0467] Finally, the server analyzes the feedback information to evaluate the user's learning progress and changes in interests. Based on this, it optimizes the content for subsequent visits to provide a more enriching museum experience.

[0468] In this way, this system can make the traditional museum experience more personalized and educationally valuable by providing exhibits and explanations tailored to each user's individual interests and learning goals.

[0469] The following describes the processing flow.

[0470] Step 1:

[0471] Users input information about their interests and learning objectives through their device. The device then sends this input information to the server.

[0472] Step 2:

[0473] The server creates or updates the user profile based on the received information. If there is past activity history or existing profile information, this is also taken into consideration to optimize the profile.

[0474] Step 3:

[0475] The server selects relevant exhibits from the database based on the user profile. The selection criteria reflect the user's interests and learning objectives.

[0476] Step 4:

[0477] The server uses a generative model to generate detailed explanatory information for selected exhibits. This model leverages natural language processing techniques to generate historical background and technical details of the exhibits.

[0478] Step 5:

[0479] The server uses the generated explanatory information to create a personalized tour plan optimized for the user and sends it to the device.

[0480] Step 6:

[0481] The device displays a personalized tour plan, presenting it to the user through a VR space or screen. This allows the user to experience selected exhibits and obtain detailed explanations.

[0482] Step 7:

[0483] After the tour ends, users enter feedback about their experience and send it to the server via their device.

[0484] Step 8:

[0485] The server analyzes the collected feedback to evaluate the user's learning progress and changes in interests. Based on this, it prepares to optimize the content provided on the next visit.

[0486] (Example 1)

[0487] 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."

[0488] Traditional museum experiences often limit themselves to general information provision, making it difficult to offer personalized experiences tailored to individual users' interests and learning objectives. Furthermore, the lack of flexible content updates to reflect changes in visitors' interests and knowledge makes it challenging to maximize user learning. Additionally, there is a need to provide efficient methods for stimulating interest using virtual reality technology.

[0489] 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.

[0490] In this invention, the server includes means for acquiring user interest information and learning objective information and generating or updating user information based on this information; means for selecting multiple exhibits based on the user information and using a generative model to generate explanatory information about the selected exhibits; and means for generating a personalized visit plan including the generated explanatory information and providing it to the user through virtual reality technology. This makes it possible to provide an optimized learning experience for each individual user and effectively promote interest and learning.

[0491] "User interest information" refers to data related to specific themes or topics that users are interested in.

[0492] "Learning objective information" refers to data that indicates what a user wants to learn and what kind of knowledge they want to acquire.

[0493] "User information" refers to comprehensive data that combines user interest information and learning objectives information, and forms the basis for creating a user profile.

[0494] "Exhibits" refer to items, works, and materials displayed for educational purposes in museums and other educational institutions.

[0495] A "generative model" is an algorithm or system used to generate relevant information from given input information using machine learning techniques.

[0496] A "personalized visit plan" is an optimized schedule of exhibit visits built based on each user's individual interests and learning objectives.

[0497] "Virtual reality technology" is a technology that uses computer graphics to allow users to experience environments different from the real world.

[0498] "Evaluation information" refers to data collected from users, including their impressions and opinions on exhibits and visit plans.

[0499] The system for implementing this invention consists of a server, a terminal, and a user. First, the user uses the terminal to input their interests and learning objectives. The terminal consists of hardware devices such as tablets, smartphones, and personal computers, and automatically transmits the input information to the server.

[0500] The server generates or updates a user profile based on interest and learning objective information received from the user. A database system (e.g., MySQL) is used for this profile generation. The server then selects relevant exhibits based on the user profile. Database management and information retrieval technologies are used for this selection.

[0501] For selected exhibits, the server uses a generative AI model to generate detailed explanatory information. This generative AI model utilizes natural language processing techniques (e.g., GPT). The generative AI model takes a prompt as input and outputs relevant detailed explanatory text. For example, the prompt might be "Please provide detailed background information on Renaissance art."

[0502] Based on the generated explanatory information, the server creates a personalized visit plan and sends it to the terminal. This plan includes the order of exhibits, points of explanation, and suggested duration of stay. In addition, the terminal also uses a VR headset to present the visit plan to the user through virtual reality technology. This allows the user to enjoy information about the exhibits visually and aurally through an experience in a virtual space.

[0503] After the tour ends, users enter feedback using a device. This feedback is sent to the server as evaluation data. The server analyzes this information to optimize the content offered for subsequent visits. This results in a more enriching experience tailored to each user's individual interests and knowledge level.

[0504] This system provides a personalized learning experience and delivers meaningful information to visitors.

[0505] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0506] Step 1:

[0507] The user inputs information about their interests and learning objectives into the device. Using a touchscreen or keyboard, the user enters topics such as "ancient Egyptian pyramids" or "dinosaur evolution." This input is the output of this step and becomes the input data sent by the device to the next step.

[0508] Step 2:

[0509] The terminal converts the interest and learning objective information entered by the user into packet format and sends it to the server using a secure protocol (e.g., HTTPS). This data transfer is the specific operation of the terminal. The output of this step is the input data that the server will then process.

[0510] Step 3:

[0511] The server generates or updates a user profile based on the received user interest and learning objectives information. The server utilizes a database system to process the information and store it in storage. The output of this step is the generated or updated user profile.

[0512] Step 4:

[0513] The server selects relevant exhibits from the database based on the generated user profile. The server uses information retrieval techniques to retrieve the appropriate exhibits. The output of this step is a list of the selected exhibits.

[0514] Step 5:

[0515] The server utilizes a generative AI model to generate explanatory information related to the selected exhibits. The model receives a prompt and generates the explanatory text through natural language processing. An example prompt is, "Please provide detailed background information on Renaissance art." The output of this step is the generated explanatory information.

[0516] Step 6:

[0517] The server uses the generated explanatory information to create a personalized visit plan. The plan includes the order of exhibits and points of explanation. The server sends this plan to the terminal. The output obtained in this step is the visit plan, and displaying it on the terminal is the next step.

[0518] Step 7:

[0519] The terminal presents the received visit plan to the user via a VR headset or display. The terminal displays the contents of the visit plan in a virtual space and provides dynamic information display in response to the user's movements and choices. The output of this step leads to feedback as part of the user experience.

[0520] Step 8:

[0521] After completing their visit, users enter feedback information into a terminal. This feedback includes their impressions of the exhibits and their wishes for future visits, and this data is sent from the terminal to the server.

[0522] Step 9:

[0523] The server receives and analyzes user feedback to optimize content for the next visit. The server uses a learning algorithm to analyze the feedback. The output of this step is an optimized content plan for the next visit.

[0524] (Application Example 1)

[0525] 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."

[0526] Current exhibition facilities face challenges in providing exhibits tailored to the individual interests and learning objectives of visitors, and fixed exhibit content limits visitors' opportunities to deepen their knowledge. Furthermore, physical limitations restrict the amount of information that can be presented at one time, resulting in a lack of appeal, particularly to repeat visitors.

[0527] 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.

[0528] This invention includes a server that acquires user interest information and learning objective information and generates or updates a user profile based on this information; a server that uses a generative model to select multiple exhibits based on the user profile and generate explanatory information about the selected exhibits; and a server that uses virtual reality technology to provide the user with an experience of the tour plan. This makes it possible to provide a personalized virtual tour optimized for each individual user and to realize a deep museum experience tailored to each visitor.

[0529] A "user" is someone who uses the system to engage in virtual experiences, and the content provided is tailored to their interests and learning objectives.

[0530] "Interest information" refers to information about themes and fields that users are particularly interested in, and this information is used by the system to select content.

[0531] "Learning objectives information" refers to data that represents the educational goals and intentions of the user when using the system, and is used to determine the direction of the content that the system provides.

[0532] A "user profile" refers to a collection of data about an individual user that is generated or updated based on the user's interests and learning objectives.

[0533] "Exhibition content" refers to educational or exhibitional content selected based on user profiles and presented to users in a virtual space.

[0534] "Explanatory information" refers to detailed information about the selected exhibits, and is provided through a generative model.

[0535] A "generative model" refers to an algorithm or system used to generate explanatory information based on the user's interests.

[0536] A "tour plan" refers to a virtual itinerary or schedule that combines a series of exhibits and explanatory information presented to the user.

[0537] "Virtual reality technology" is a general term for technologies that enable users to experience a three-dimensional space different from the real world, and is used for experiencing exhibits in virtual spaces.

[0538] "Feedback information" refers to information about opinions and impressions that users provide after an experience, and is data that the system uses to improve the user experience and optimize subsequent visits.

[0539] The system for implementing this invention consists of a user, a terminal device, and a server. In this system, the user first inputs information about their interests and learning objectives into the terminal device. For example, they can input specific themes such as "contemporary art" or "dinosaur ecology."

[0540] The terminal device transmits the acquired information to a server. This server operates on a cloud platform (such as Google Cloud or AWS) and generates or updates a user profile based on the input information. Based on the user profile, the server uses a generative AI model to select exhibits related to the theme entered by the user. This model uses natural language processing technology and can select relevant exhibits even from complex interest information.

[0541] The server generates detailed explanatory information about the selected exhibits. This information includes details such as the history behind a particular artwork and the techniques used to create it. The generated information is then transmitted to a terminal device as a personalized tour plan using virtual reality technology. The user can then use the terminal device to experience VR and gain a sensory understanding of the selected exhibits.

[0542] After the tour experience, users input feedback information using a terminal device. This feedback information is sent to a server and used to optimize the content provided during future visits.

[0543] For example, if a user is interested in "Renaissance sculpture" and uses the system on that theme, the server will use an AI model to select works by Michelangelo and Donatello and generate detailed explanatory information about their historical background and technical methods. An example of a prompt to the generating AI model would be, "Generate a list of the most suitable exhibits related to 'Renaissance sculpture,' which the user is interested in, and provide detailed explanations for each exhibit."

[0544] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0545] Step 1:

[0546] Users input their interest information and learning objectives into the terminal device. The input information is text data indicating specific hobbies and areas of interest. When the terminal device receives this information, it forms the basis for sending data to the next server.

[0547] Step 2:

[0548] The terminal device transmits interest information and learning objective information obtained from the user to the server. The input here is the user's interest-related information registered as text, which is then passed to the server as a dataset to generate or update the user profile.

[0549] Step 3:

[0550] The server generates or updates user profiles based on the received information. It analyzes the received dataset as input, integrates it with existing user information, and constructs a new interest profile. This results in a profile tailored to the user's specific interests.

[0551] Step 4:

[0552] The server uses a generative AI model to select exhibits based on the user profile. In this processing step, the input profile data is provided to the AI ​​model, which selects highly relevant exhibits. The AI ​​model uses natural language processing techniques to output an optimized exhibit list based on complex interest information.

[0553] Step 5:

[0554] The server generates detailed explanatory information about the exhibits it has created. Based on the selected list of exhibits, the AI ​​model constructs detailed explanatory text. The input is the selected list of exhibits, and the output is explanatory text about those exhibits. You can use prompts to instruct the AI ​​and generate the explanations.

[0555] Step 6:

[0556] The server uses the generated explanatory information to create a personalized tour plan and sends it to the terminal device. The tour plan is constructed based on the generated explanations and sent to the terminal device, where it is output as a dataset to prepare for the VR experience.

[0557] Step 7:

[0558] The terminal device provides the user with a VR experience based on a tour plan received from the server. The terminal device utilizes virtual reality technology to visualize the exhibits, allowing the user to explore them viscerally. The input for this step is the tour plan from the server, and the output is the user's visual experience.

[0559] Step 8:

[0560] After the VR experience ends, users input feedback information into a terminal device. The user's impressions and requests for future visits are saved in text format on the terminal device. This input forms the basis for sending the next data to the server.

[0561] Step 9:

[0562] The terminal device sends feedback information to the server, and optimizations are made for the next visit based on the previous experience. This feedback information is recorded on the server as an evaluation of the user experience and output as preparation data for the next visit.

[0563] 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.

[0564] This invention provides a more personalized museum experience by recognizing the user's emotions in addition to their interests and learning objectives. This system works in cooperation with its server, terminal, and user components to generate content that accurately reflects the user's interests and emotions.

[0565] First, the user enters their desired field of study and areas of interest through their device. This information is sent from the device to the server in order to generate or update the user profile.

[0566] Based on the information received, the server selects relevant exhibits from the database and simultaneously generates explanations about the selected exhibits using a generative model. During this process, the emotion engine analyzes the user's emotions, evaluating their emotional state through user feedback and facial expression analysis during the experience.

[0567] Using the generated explanatory information and emotion evaluation results, the server creates a personalized tour plan based on the user's profile and sends it to the device. The device presents this tour plan to the user and provides explanations of the exhibits visually or audibly through the VR space or screen. By analyzing the user's emotions towards specific exhibits in real time, the content provided is tailored to the user's emotional state.

[0568] After the tour ends, users enter feedback about their experience and send it to the server via their device. The server then combines this feedback with the results of the emotion engine to optimize the content provided to the user on their next visit.

[0569] For example, if a user expresses interest in "Impressionist paintings" and a tour plan is generated, the server will select several exhibits related to Impressionism and create explanations for them using a generation model. If the user rates a particular artwork as "moved and delighted," the system will prioritize showing additional information related to that artwork or other works by the same artist on their next visit.

[0570] In this way, by combining information technology and emotion analysis technology, this system can provide users with a scientifically-based, personalized museum experience, supporting sustained learning and the stimulation of interest.

[0571] The following describes the processing flow.

[0572] Step 1:

[0573] Users input their areas of interest and learning objectives using their devices. This information is necessary for building a user profile and is therefore transmitted from the device to the server in real time.

[0574] Step 2:

[0575] The server creates or updates user profiles based on the received interest and learning objectives information. This profile also takes into account the user's past behavior history and feedback information.

[0576] Step 3:

[0577] The server references the user profile and selects relevant exhibits from the database. A generative model is used to automatically generate descriptions and background information for the selected exhibits.

[0578] Step 4:

[0579] To evaluate in real time the emotions a user expresses in response to the displayed exhibits, the device sends data of the user's facial expressions and voice to the emotion engine. The emotion engine analyzes this data and evaluates the user's emotional state.

[0580] Step 5:

[0581] The server uses emotion evaluation results obtained from the emotion engine to optimize a personalized tour plan. This tour plan includes the order in which exhibits are presented and the content of explanations tailored to the user's emotions.

[0582] Step 6:

[0583] The device displays an optimized tour plan to the user and provides explanations of the exhibits through the VR space or screen. Based on emotional feedback, users can have a more moving and interesting experience.

[0584] Step 7:

[0585] After the tour ends, users enter their impressions and evaluations of the experience into a feedback form and send it to the server via their device.

[0586] Step 8:

[0587] The server integrates feedback information and the sentiment engine's evaluation results to prepare for optimizing content for the next visit. This process maintains the user's deep learning and improves their interest.

[0588] (Example 2)

[0589] 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."

[0590] Traditional museum experiences provide users with uniform information, resulting in insufficient personalization tailored to individual interests and emotional states. This makes it difficult to improve user learning effectiveness and satisfaction, leading to challenges in sustained learning and interest stimulation.

[0591] 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.

[0592] In this invention, the server includes means for acquiring user interest information, learning objective information, and sentiment information, and generating or updating a user profile based on this information; means for selecting a plurality of exhibits based on the user profile and sentiment information, and using a generative model to generate explanatory information about the selected exhibits; and means for generating and presenting a personalized tour plan to the user, which includes the generated explanatory information and the user's sentiment evaluation results.

[0593] This makes it possible to provide a museum experience optimized for each individual user, improve learning effectiveness and satisfaction, and stimulate sustainable learning and interest.

[0594] A "user profile" is a dataset created based on information such as a user's interests, learning objectives, and emotional state, representing the individual characteristics and preferences of each user.

[0595] A "generative model" is an algorithm that utilizes natural language processing techniques to generate explanatory information about relevant exhibits based on user profiles and interest information.

[0596] "Emotional information" refers to data that analyzes the emotions and psychological states a user exhibits during an experience and expresses them as numerical values ​​or categories.

[0597] A "personalized tour plan" is an experience plan in which specific exhibits and explanations are selected and arranged based on each user's individual profile and emotional evaluation results.

[0598] "Natural language processing technology" is a field of information technology that enables computers to understand, generate, and analyze human language.

[0599] This invention is a system that utilizes user interest information, learning objectives, and emotional information to provide a personalized museum experience. This system involves the collaboration of a server, terminal, and user to provide customized information to the user.

[0600] First, the user enters their areas of interest and learning objectives into the device. The device then organizes this information and sends it to the server. The interface provided by this device is designed to be intuitive for the user.

[0601] The server uses a database to select exhibits that match the user's interests. A database management system like MySQL is used in this process. A generative AI model is then used to generate detailed explanations for the selected exhibits. The generative model implements natural language processing technology, allowing for flexible information generation based on input prompts. For example, a prompt might be, "Generate a detailed explanation of Impressionist paintings."

[0602] The server further analyzes the user's emotions from multiple perspectives using an emotion engine. It analyzes the user's facial expressions and voice data in real time and records the evaluation results. This information is used as a basis for making decisions to provide the user with a more personalized experience.

[0603] Using the generated explanatory information and sentiment evaluation results, the server creates a personalized tour plan and sends it to the device. When presenting this information to the user, the device can utilize VR technology to provide a more immersive museum experience.

[0604] As an example, if a user is interested in "Impressionist paintings," the server selects several relevant exhibits and generates explanations for each using an AI model. Additional information is provided for works that particularly impressed the user, and these are prioritized for subsequent visits, thereby continuously enhancing the user's interest and learning.

[0605] Thus, this invention combines information technology, generative AI models, and emotion analysis to provide users with an innovative museum experience.

[0606] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0607] Step 1:

[0608] Users input their interests and learning goals using the device's interface. Specific examples of input include selecting themes such as "Impressionist paintings" or "cutting-edge science and technology." The device then collects the user's interest and learning objectives and prepares to send this information to the server.

[0609] Step 2:

[0610] The terminal formats the information entered by the user and sends it to the server using a communication protocol. At this time, the user ID and session information are also sent to ensure the uniqueness of the user information on the server side. The output here is formatted data containing the user's interest information and learning objectives.

[0611] Step 3:

[0612] The server selects relevant exhibits from the database based on the user's interest and learning objectives information received from the terminal. This database search process filters exhibits to match keywords such as "Impressionism." The output at this stage is a list of selected exhibits.

[0613] Step 4:

[0614] The server uses a generative AI model to generate explanations for the selected exhibits. It takes prompts such as "Please create a detailed explanation for the selected exhibits" and uses natural language processing techniques to generate the explanations. As a result, explanations are generated for each exhibit, and these become the output.

[0615] Step 5:

[0616] The server uses an emotion engine to analyze the user's emotions. Specifically, it analyzes the user's facial expression data and voice data to quantify their emotional state. This results in the output of emotional information about the user's experience.

[0617] Step 6:

[0618] The server combines the generated commentary and sentiment information to create a personalized tour plan based on the user's profile information. This tour plan includes exhibits and commentary that are likely to keep the user interested. The output here is the customized tour plan.

[0619] Step 7:

[0620] The terminal presents the user with a personalized tour plan received from the server. The user can view the selected exhibits and their explanations through a VR device or display. At the same time, the terminal also collects data to analyze the user's reactions again for sentiment analysis.

[0621] Step 8:

[0622] Users enter feedback into a terminal after the experience ends. This feedback may include their satisfaction with the experience and requests for additional information. This information will be used to improve the service on their next visit.

[0623] Step 9:

[0624] The device sends feedback received from the user to the server. The server integrates the feedback information with the results of sentiment analysis and retains it as data to further optimize the experience for the next visit. This allows for a more personalized experience to be provided on subsequent visits.

[0625] (Application Example 2)

[0626] 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."

[0627] There is a growing need to improve the quality of the user experience by providing personalized content in real time that responds to the diverse preferences and emotional states of users. However, conventional systems rely on fixed content and have the challenge of not being able to fully address the individual needs of users. Against this backdrop, there is a demand for a more adaptive and personalized information delivery system that takes into account users' interest information and emotional data.

[0628] 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.

[0629] In this invention, the server includes means for acquiring user interest information and sentiment data and generating or updating a user profile based on this information; means for selecting a plurality of information elements based on the user profile and using a generative model to generate explanations about the selected information elements; and means for generating personalized suggestions including the generated explanations and presenting them to the user. This enables adaptive information provision that meets the individual needs of the user.

[0630] A "user" refers to an individual who shows interest in and experiences exhibits or informational elements.

[0631] "Interest information" refers to information related to the fields or subjects that the user is interested in.

[0632] "Emotional data" refers to information that indicates a user's emotional state, and includes data that is analyzed in real time.

[0633] A "user profile" is a record of personalized information generated based on a user's interests and emotional data.

[0634] "Information elements" refer to items such as exhibits and products offered to users, which are selected or suggested during the experience.

[0635] A "generative model" is a system based on artificial intelligence technology used to generate output for a specific task.

[0636] "Explanation" refers to descriptive text or audio information about specific information elements, provided to deepen the user's understanding.

[0637] "Personalized suggestions" refer to the provision of information that is individually tailored based on the user's interests and sentiment data.

[0638] This invention constructs a system that acquires user interest information and sentiment data, and generates or updates a personalized user profile based on this information. Users input information of interest using a smartphone or wearable device, and the device transmits the acquired data to a server. The server utilizes a generative AI model to select multiple information elements based on the user profile and generates explanations about them.

[0639] The core of this system is a generative model that utilizes natural language processing technology to create personalized explanations for each user. This generative model employs AI technology to provide information tailored to the user's preferences and emotions. The emotion engine analyzes the user's emotional state in real time and obtains feedback information. Using this analyzed information and the generated explanations, the server generates personalized suggestions and presents them to the user through their device.

[0640] As a concrete example, consider a case where a user shows strong interest in "the latest smartphone." The server selects relevant product information and creates a description of its features using a generative model. If the user expresses interest in this product, the system adjusts to prioritize showing additional accessory information on their next visit.

[0641] As an example of a prompt, input the following into the generative AI model:

[0642] Please generate product descriptions based on the following user information:

[0643] Product Category: Smartphones

[0644] Interests: Latest technology

[0645] User sentiment: Interesting

[0646] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0647] Step 1:

[0648] Users input their interest information through the device, and the device sends this information to the server. The input data includes the user's interest categories and keywords, and the user profile is updated based on this. The device plays a role in appropriately formatting the necessary data and transferring it to the server using a secure communication method.

[0649] Step 2:

[0650] The server analyzes the received interest information and updates the user profile. Using the entered interest information and past data, it defines the user's preferences in more detail and stores them in the database so that they can be used for future information provision. At this time, the database optimizes the profile based on the newly entered information.

[0651] Step 3:

[0652] The server uses a generative AI model to select information elements that fit the user profile. Based on the user profile and interest information as input, the generative model extracts relevant information elements and generates explanations using defined prompts.

[0653] Step 4:

[0654] The generated explanations are packaged by the server as personalized suggestions and sent to the device. The server receives the text data generated by the generative AI model and converts it into a user-friendly format. This output highlights informational elements that align with the user's emotions and interests.

[0655] Step 5:

[0656] The user reviews the suggestions sent from the device and enters feedback and emotional data about the experience. At this time, the user records their emotions regarding the suggested information in data format, and the device sends this data back to the server.

[0657] Step 6:

[0658] The server analyzes the feedback and sentiment data received and updates the user profile for further personalization. Based on the feedback information, the profile is adjusted to provide the most relevant information elements on the next visit and saved as new data.

[0659] 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.

[0660] 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.

[0661] 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.

[0662] [Fourth Embodiment]

[0663] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0664] 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.

[0665] 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).

[0666] 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.

[0667] 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.

[0668] 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).

[0669] 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.

[0670] 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.

[0671] 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.

[0672] 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.

[0673] 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.

[0674] 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.

[0675] 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".

[0676] This invention is a system that provides users with a personalized museum experience by selecting exhibits according to their interests and learning objectives and providing detailed explanations about those exhibits. This system operates with close coordination between the server, terminal, and user elements. Specific embodiments are shown below.

[0677] First, the user enters their areas of interest and learning objectives into the device. For example, themes such as "Medieval Art" or "Dinosaur Evolution" might be considered. Next, the device sends this information to the server, which then creates or updates the user profile.

[0678] The server selects highly relevant exhibits based on the user's profile information. For the selected exhibits, a generative model is used to generate explanatory information. For example, in the case of "Medieval Art," detailed information about representative medieval painters, the background of their works, and techniques is generated.

[0679] Next, the server uses the generated explanatory information to create a tour plan optimized for each individual user and sends it to the device. The device then presents this personalized tour plan to the user through a VR headset or display. The user can walk around the virtual space and receive detailed explanations about selected exhibits in audio or text format.

[0680] After the tour ends, users enter feedback about their tour experience into a terminal. This includes comments on the exhibits and suggestions for future visits. The terminal then sends this feedback information to the server.

[0681] Finally, the server analyzes the feedback information to evaluate the user's learning progress and changes in interests. Based on this, it optimizes the content for subsequent visits to provide a more enriching museum experience.

[0682] In this way, this system can make the traditional museum experience more personalized and educationally valuable by providing exhibits and explanations tailored to each user's individual interests and learning goals.

[0683] The following describes the processing flow.

[0684] Step 1:

[0685] Users input information about their interests and learning objectives through their device. The device then sends this input information to the server.

[0686] Step 2:

[0687] The server creates or updates the user profile based on the received information. If there is past activity history or existing profile information, this is also taken into consideration to optimize the profile.

[0688] Step 3:

[0689] The server selects relevant exhibits from the database based on the user profile. The selection criteria reflect the user's interests and learning objectives.

[0690] Step 4:

[0691] The server uses a generative model to generate detailed explanatory information for selected exhibits. This model leverages natural language processing techniques to generate historical background and technical details of the exhibits.

[0692] Step 5:

[0693] The server uses the generated explanatory information to create a personalized tour plan optimized for the user and sends it to the device.

[0694] Step 6:

[0695] The device displays a personalized tour plan, presenting it to the user through a VR space or screen. This allows the user to experience selected exhibits and obtain detailed explanations.

[0696] Step 7:

[0697] After the tour ends, users enter feedback about their experience and send it to the server via their device.

[0698] Step 8:

[0699] The server analyzes the collected feedback to evaluate the user's learning progress and changes in interests. Based on this, it prepares to optimize the content provided on the next visit.

[0700] (Example 1)

[0701] 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".

[0702] Traditional museum experiences often limit themselves to general information provision, making it difficult to offer personalized experiences tailored to individual users' interests and learning objectives. Furthermore, the lack of flexible content updates to reflect changes in visitors' interests and knowledge makes it challenging to maximize user learning. Additionally, there is a need to provide efficient methods for stimulating interest using virtual reality technology.

[0703] 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.

[0704] In this invention, the server includes means for acquiring user interest information and learning objective information and generating or updating user information based on this information; means for selecting multiple exhibits based on the user information and using a generative model to generate explanatory information about the selected exhibits; and means for generating a personalized visit plan including the generated explanatory information and providing it to the user through virtual reality technology. This makes it possible to provide an optimized learning experience for each individual user and effectively promote interest and learning.

[0705] "User interest information" refers to data related to specific themes or topics that users are interested in.

[0706] "Learning objective information" refers to data that indicates what a user wants to learn and what kind of knowledge they want to acquire.

[0707] "User information" refers to comprehensive data that combines user interest information and learning objectives information, and forms the basis for creating a user profile.

[0708] "Exhibits" refer to items, works, and materials displayed for educational purposes in museums and other educational institutions.

[0709] A "generative model" is an algorithm or system used to generate relevant information from given input information using machine learning techniques.

[0710] A "personalized visit plan" is an optimized schedule of exhibit visits built based on each user's individual interests and learning objectives.

[0711] "Virtual reality technology" is a technology that uses computer graphics to allow users to experience environments different from the real world.

[0712] "Evaluation information" refers to data collected from users, including their impressions and opinions on exhibits and visit plans.

[0713] The system for implementing this invention consists of a server, a terminal, and a user. First, the user uses the terminal to input their interests and learning objectives. The terminal consists of hardware devices such as tablets, smartphones, and personal computers, and automatically transmits the input information to the server.

[0714] The server generates or updates a user profile based on interest and learning objective information received from the user. A database system (e.g., MySQL) is used for this profile generation. The server then selects relevant exhibits based on the user profile. Database management and information retrieval technologies are used for this selection.

[0715] For selected exhibits, the server uses a generative AI model to generate detailed explanatory information. This generative AI model utilizes natural language processing techniques (e.g., GPT). The generative AI model takes a prompt as input and outputs relevant detailed explanatory text. For example, the prompt might be "Please provide detailed background information on Renaissance art."

[0716] Based on the generated explanatory information, the server creates a personalized visit plan and sends it to the terminal. This plan includes the order of exhibits, points of explanation, and suggested duration of stay. In addition, the terminal also uses a VR headset to present the visit plan to the user through virtual reality technology. This allows the user to enjoy information about the exhibits visually and aurally through an experience in a virtual space.

[0717] After the tour ends, users enter feedback using a device. This feedback is sent to the server as evaluation data. The server analyzes this information to optimize the content offered for subsequent visits. This results in a more enriching experience tailored to each user's individual interests and knowledge level.

[0718] This system provides a personalized learning experience and delivers meaningful information to visitors.

[0719] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0720] Step 1:

[0721] The user inputs information about their interests and learning objectives into the device. Using a touchscreen or keyboard, the user enters topics such as "ancient Egyptian pyramids" or "dinosaur evolution." This input is the output of this step and becomes the input data sent by the device to the next step.

[0722] Step 2:

[0723] The terminal converts the interest and learning objective information entered by the user into packet format and sends it to the server using a secure protocol (e.g., HTTPS). This data transfer is the specific operation of the terminal. The output of this step is the input data that the server will then process.

[0724] Step 3:

[0725] The server generates or updates a user profile based on the received user interest and learning objectives information. The server utilizes a database system to process the information and store it in storage. The output of this step is the generated or updated user profile.

[0726] Step 4:

[0727] The server selects relevant exhibits from the database based on the generated user profile. The server uses information retrieval techniques to retrieve the appropriate exhibits. The output of this step is a list of the selected exhibits.

[0728] Step 5:

[0729] The server utilizes a generative AI model to generate explanatory information related to the selected exhibits. The model receives a prompt and generates the explanatory text through natural language processing. An example prompt is, "Please provide detailed background information on Renaissance art." The output of this step is the generated explanatory information.

[0730] Step 6:

[0731] The server uses the generated explanatory information to create a personalized visit plan. The plan includes the order of exhibits and points of explanation. The server sends this plan to the terminal. The output obtained in this step is the visit plan, and displaying it on the terminal is the next step.

[0732] Step 7:

[0733] The terminal presents the received visit plan to the user via a VR headset or display. The terminal displays the contents of the visit plan in a virtual space and provides dynamic information display in response to the user's movements and choices. The output of this step leads to feedback as part of the user experience.

[0734] Step 8:

[0735] After completing their visit, users enter feedback information into a terminal. This feedback includes their impressions of the exhibits and their wishes for future visits, and this data is sent from the terminal to the server.

[0736] Step 9:

[0737] The server receives and analyzes user feedback to optimize content for the next visit. The server uses a learning algorithm to analyze the feedback. The output of this step is an optimized content plan for the next visit.

[0738] (Application Example 1)

[0739] 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".

[0740] Current exhibition facilities face challenges in providing exhibits tailored to the individual interests and learning objectives of visitors, and fixed exhibit content limits visitors' opportunities to deepen their knowledge. Furthermore, physical limitations restrict the amount of information that can be presented at one time, resulting in a lack of appeal, particularly to repeat visitors.

[0741] 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.

[0742] This invention includes a server that acquires user interest information and learning objective information and generates or updates a user profile based on this information; a server that uses a generative model to select multiple exhibits based on the user profile and generate explanatory information about the selected exhibits; and a server that uses virtual reality technology to provide the user with an experience of the tour plan. This makes it possible to provide a personalized virtual tour optimized for each individual user and to realize a deep museum experience tailored to each visitor.

[0743] A "user" is someone who uses the system to engage in virtual experiences, and the content provided is tailored to their interests and learning objectives.

[0744] "Interest information" refers to information about themes and fields that users are particularly interested in, and this information is used by the system to select content.

[0745] "Learning objectives information" refers to data that represents the educational goals and intentions of the user when using the system, and is used to determine the direction of the content that the system provides.

[0746] A "user profile" refers to a collection of data about an individual user that is generated or updated based on the user's interests and learning objectives.

[0747] "Exhibition content" refers to educational or exhibitional content selected based on user profiles and presented to users in a virtual space.

[0748] "Explanatory information" refers to detailed information about the selected exhibits, and is provided through a generative model.

[0749] A "generative model" refers to an algorithm or system used to generate explanatory information based on the user's interests.

[0750] A "tour plan" refers to a virtual itinerary or schedule that combines a series of exhibits and explanatory information presented to the user.

[0751] "Virtual reality technology" is a general term for technologies that enable users to experience a three-dimensional space different from the real world, and is used for experiencing exhibits in virtual spaces.

[0752] "Feedback information" refers to information about opinions and impressions that users provide after an experience, and is data that the system uses to improve the user experience and optimize subsequent visits.

[0753] The system for implementing this invention consists of a user, a terminal device, and a server. In this system, the user first inputs information about their interests and learning objectives into the terminal device. For example, they can input specific themes such as "contemporary art" or "dinosaur ecology."

[0754] The terminal device transmits the acquired information to a server. This server operates on a cloud platform (such as Google Cloud or AWS) and generates or updates a user profile based on the input information. Based on the user profile, the server uses a generative AI model to select exhibits related to the theme entered by the user. This model uses natural language processing technology and can select relevant exhibits even from complex interest information.

[0755] The server generates detailed explanatory information about the selected exhibits. This information includes details such as the history behind a particular artwork and the techniques used to create it. The generated information is then transmitted to a terminal device as a personalized tour plan using virtual reality technology. The user can then use the terminal device to experience VR and gain a sensory understanding of the selected exhibits.

[0756] After the tour experience, users input feedback information using a terminal device. This feedback information is sent to a server and used to optimize the content provided during future visits.

[0757] For example, if a user is interested in "Renaissance sculpture" and uses the system on that theme, the server will use an AI model to select works by Michelangelo and Donatello and generate detailed explanatory information about their historical background and technical methods. An example of a prompt to the generating AI model would be, "Generate a list of the most suitable exhibits related to 'Renaissance sculpture,' which the user is interested in, and provide detailed explanations for each exhibit."

[0758] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0759] Step 1:

[0760] Users input their interest information and learning objectives into the terminal device. The input information is text data indicating specific hobbies and areas of interest. When the terminal device receives this information, it forms the basis for sending data to the next server.

[0761] Step 2:

[0762] The terminal device transmits interest information and learning objective information obtained from the user to the server. The input here is the user's interest-related information registered as text, which is then passed to the server as a dataset to generate or update the user profile.

[0763] Step 3:

[0764] The server generates or updates user profiles based on the received information. It analyzes the received dataset as input, integrates it with existing user information, and constructs a new interest profile. This results in a profile tailored to the user's specific interests.

[0765] Step 4:

[0766] The server uses a generative AI model to select exhibits based on the user profile. In this processing step, the input profile data is provided to the AI ​​model, which selects highly relevant exhibits. The AI ​​model uses natural language processing techniques to output an optimized exhibit list based on complex interest information.

[0767] Step 5:

[0768] The server generates detailed explanatory information about the exhibits it has created. Based on the selected list of exhibits, the AI ​​model constructs detailed explanatory text. The input is the selected list of exhibits, and the output is explanatory text about those exhibits. You can use prompts to instruct the AI ​​and generate the explanations.

[0769] Step 6:

[0770] The server uses the generated explanatory information to create a personalized tour plan and sends it to the terminal device. The tour plan is constructed based on the generated explanations and sent to the terminal device, where it is output as a dataset to prepare for the VR experience.

[0771] Step 7:

[0772] The terminal device provides the user with a VR experience based on a tour plan received from the server. The terminal device utilizes virtual reality technology to visualize the exhibits, allowing the user to explore them viscerally. The input for this step is the tour plan from the server, and the output is the user's visual experience.

[0773] Step 8:

[0774] After the VR experience ends, users input feedback information into a terminal device. The user's impressions and requests for future visits are saved in text format on the terminal device. This input forms the basis for sending the next data to the server.

[0775] Step 9:

[0776] The terminal device sends feedback information to the server, and optimizations are made for the next visit based on the previous experience. This feedback information is recorded on the server as an evaluation of the user experience and output as preparation data for the next visit.

[0777] 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.

[0778] This invention provides a more personalized museum experience by recognizing the user's emotions in addition to their interests and learning objectives. This system works in cooperation with its server, terminal, and user components to generate content that accurately reflects the user's interests and emotions.

[0779] First, the user enters their desired field of study and areas of interest through their device. This information is sent from the device to the server in order to generate or update the user profile.

[0780] Based on the information received, the server selects relevant exhibits from the database and simultaneously generates explanations about the selected exhibits using a generative model. During this process, the emotion engine analyzes the user's emotions, evaluating their emotional state through user feedback and facial expression analysis during the experience.

[0781] Using the generated explanatory information and emotion evaluation results, the server creates a personalized tour plan based on the user's profile and sends it to the device. The device presents this tour plan to the user and provides explanations of the exhibits visually or audibly through the VR space or screen. By analyzing the user's emotions towards specific exhibits in real time, the content provided is tailored to the user's emotional state.

[0782] After the tour ends, users enter feedback about their experience and send it to the server via their device. The server then combines this feedback with the results of the emotion engine to optimize the content provided to the user on their next visit.

[0783] For example, if a user expresses interest in "Impressionist paintings" and a tour plan is generated, the server will select several exhibits related to Impressionism and create explanations for them using a generation model. If the user rates a particular artwork as "moved and delighted," the system will prioritize showing additional information related to that artwork or other works by the same artist on their next visit.

[0784] In this way, by combining information technology and emotion analysis technology, this system can provide users with a scientifically-based, personalized museum experience, supporting sustained learning and the stimulation of interest.

[0785] The following describes the processing flow.

[0786] Step 1:

[0787] Users input their areas of interest and learning objectives using their devices. This information is necessary for building a user profile and is therefore transmitted from the device to the server in real time.

[0788] Step 2:

[0789] The server creates or updates user profiles based on the received interest and learning objectives information. This profile also takes into account the user's past behavior history and feedback information.

[0790] Step 3:

[0791] The server references the user profile and selects relevant exhibits from the database. A generative model is used to automatically generate descriptions and background information for the selected exhibits.

[0792] Step 4:

[0793] To evaluate in real time the emotions a user expresses in response to the displayed exhibits, the device sends data of the user's facial expressions and voice to the emotion engine. The emotion engine analyzes this data and evaluates the user's emotional state.

[0794] Step 5:

[0795] The server uses emotion evaluation results obtained from the emotion engine to optimize a personalized tour plan. This tour plan includes the order in which exhibits are presented and the content of explanations tailored to the user's emotions.

[0796] Step 6:

[0797] The device displays an optimized tour plan to the user and provides explanations of the exhibits through the VR space or screen. Based on emotional feedback, users can have a more moving and interesting experience.

[0798] Step 7:

[0799] After the tour ends, users enter their impressions and evaluations of the experience into a feedback form and send it to the server via their device.

[0800] Step 8:

[0801] The server integrates feedback information and the sentiment engine's evaluation results to prepare for optimizing content for the next visit. This process maintains the user's deep learning and improves their interest.

[0802] (Example 2)

[0803] 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".

[0804] Traditional museum experiences provide users with uniform information, resulting in insufficient personalization tailored to individual interests and emotional states. This makes it difficult to improve user learning effectiveness and satisfaction, leading to challenges in sustained learning and interest stimulation.

[0805] 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.

[0806] In this invention, the server includes means for acquiring user interest information, learning objective information, and sentiment information, and generating or updating a user profile based on this information; means for selecting a plurality of exhibits based on the user profile and sentiment information, and using a generative model to generate explanatory information about the selected exhibits; and means for generating and presenting a personalized tour plan to the user, which includes the generated explanatory information and the user's sentiment evaluation results.

[0807] This makes it possible to provide a museum experience optimized for each individual user, improve learning effectiveness and satisfaction, and stimulate sustainable learning and interest.

[0808] A "user profile" is a dataset created based on information such as a user's interests, learning objectives, and emotional state, representing the individual characteristics and preferences of each user.

[0809] A "generative model" is an algorithm that utilizes natural language processing techniques to generate explanatory information about relevant exhibits based on user profiles and interest information.

[0810] "Emotional information" refers to data that analyzes the emotions and psychological states a user exhibits during an experience and expresses them as numerical values ​​or categories.

[0811] A "personalized tour plan" is an experience plan in which specific exhibits and explanations are selected and arranged based on each user's individual profile and emotional evaluation results.

[0812] "Natural language processing technology" is a field of information technology that enables computers to understand, generate, and analyze human language.

[0813] This invention is a system that utilizes user interest information, learning objectives, and emotional information to provide a personalized museum experience. This system involves the collaboration of a server, terminal, and user to provide customized information to the user.

[0814] First, the user enters their areas of interest and learning objectives into the device. The device then organizes this information and sends it to the server. The interface provided by this device is designed to be intuitive for the user.

[0815] The server uses a database to select exhibits that match the user's interests. A database management system like MySQL is used in this process. A generative AI model is then used to generate detailed explanations for the selected exhibits. The generative model implements natural language processing technology, allowing for flexible information generation based on input prompts. For example, a prompt might be, "Generate a detailed explanation of Impressionist paintings."

[0816] The server further analyzes the user's emotions from multiple perspectives using an emotion engine. It analyzes the user's facial expressions and voice data in real time and records the evaluation results. This information is used as a basis for making decisions to provide the user with a more personalized experience.

[0817] Using the generated explanatory information and sentiment evaluation results, the server creates a personalized tour plan and sends it to the device. When presenting this information to the user, the device can utilize VR technology to provide a more immersive museum experience.

[0818] As an example, if a user is interested in "Impressionist paintings," the server selects several relevant exhibits and generates explanations for each using an AI model. Additional information is provided for works that particularly impressed the user, and these are prioritized for subsequent visits, thereby continuously enhancing the user's interest and learning.

[0819] Thus, this invention combines information technology, generative AI models, and emotion analysis to provide users with an innovative museum experience.

[0820] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0821] Step 1:

[0822] Users input their interests and learning goals using the device's interface. Specific examples of input include selecting themes such as "Impressionist paintings" or "cutting-edge science and technology." The device then collects the user's interest and learning objectives and prepares to send this information to the server.

[0823] Step 2:

[0824] The terminal formats the information entered by the user and sends it to the server using a communication protocol. At this time, the user ID and session information are also sent to ensure the uniqueness of the user information on the server side. The output here is formatted data containing the user's interest information and learning objectives.

[0825] Step 3:

[0826] The server selects relevant exhibits from the database based on the user's interest and learning objectives information received from the terminal. This database search process filters exhibits to match keywords such as "Impressionism." The output at this stage is a list of selected exhibits.

[0827] Step 4:

[0828] The server uses a generative AI model to generate explanations for the selected exhibits. It takes prompts such as "Please create a detailed explanation for the selected exhibits" and uses natural language processing techniques to generate the explanations. As a result, explanations are generated for each exhibit, and these become the output.

[0829] Step 5:

[0830] The server uses an emotion engine to analyze the user's emotions. Specifically, it analyzes the user's facial expression data and voice data to quantify their emotional state. This results in the output of emotional information about the user's experience.

[0831] Step 6:

[0832] The server combines the generated commentary and sentiment information to create a personalized tour plan based on the user's profile information. This tour plan includes exhibits and commentary that are likely to keep the user interested. The output here is the customized tour plan.

[0833] Step 7:

[0834] The terminal presents the user with a personalized tour plan received from the server. The user can view the selected exhibits and their explanations through a VR device or display. At the same time, the terminal also collects data to analyze the user's reactions again for sentiment analysis.

[0835] Step 8:

[0836] Users enter feedback into a terminal after the experience ends. This feedback may include their satisfaction with the experience and requests for additional information. This information will be used to improve the service on their next visit.

[0837] Step 9:

[0838] The device sends feedback received from the user to the server. The server integrates the feedback information with the results of sentiment analysis and retains it as data to further optimize the experience for the next visit. This allows for a more personalized experience to be provided on subsequent visits.

[0839] (Application Example 2)

[0840] 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".

[0841] There is a growing need to improve the quality of the user experience by providing personalized content in real time that responds to the diverse preferences and emotional states of users. However, conventional systems rely on fixed content and have the challenge of not being able to fully address the individual needs of users. Against this backdrop, there is a demand for a more adaptive and personalized information delivery system that takes into account users' interest information and emotional data.

[0842] 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.

[0843] In this invention, the server includes means for acquiring user interest information and sentiment data and generating or updating a user profile based on this information; means for selecting a plurality of information elements based on the user profile and using a generative model to generate explanations about the selected information elements; and means for generating personalized suggestions including the generated explanations and presenting them to the user. This enables adaptive information provision that meets the individual needs of the user.

[0844] A "user" refers to an individual who shows interest in and experiences exhibits or informational elements.

[0845] "Interest information" refers to information related to the fields or subjects that the user is interested in.

[0846] "Emotional data" refers to information that indicates a user's emotional state, and includes data that is analyzed in real time.

[0847] A "user profile" is a record of personalized information generated based on a user's interests and emotional data.

[0848] "Information elements" refer to items such as exhibits and products offered to users, which are selected or suggested during the experience.

[0849] A "generative model" is a system based on artificial intelligence technology used to generate output for a specific task.

[0850] "Explanation" refers to descriptive text or audio information about specific information elements, provided to deepen the user's understanding.

[0851] "Personalized suggestions" refer to the provision of information that is individually tailored based on the user's interests and sentiment data.

[0852] This invention constructs a system that acquires user interest information and sentiment data, and generates or updates a personalized user profile based on this information. Users input information of interest using a smartphone or wearable device, and the device transmits the acquired data to a server. The server utilizes a generative AI model to select multiple information elements based on the user profile and generates explanations about them.

[0853] The core of this system is a generative model that utilizes natural language processing technology to create personalized explanations for each user. This generative model employs AI technology to provide information tailored to the user's preferences and emotions. The emotion engine analyzes the user's emotional state in real time and obtains feedback information. Using this analyzed information and the generated explanations, the server generates personalized suggestions and presents them to the user through their device.

[0854] As a concrete example, consider a case where a user shows strong interest in "the latest smartphone." The server selects relevant product information and creates a description of its features using a generative model. If the user expresses interest in this product, the system adjusts to prioritize showing additional accessory information on their next visit.

[0855] As an example of a prompt, input the following into the generative AI model:

[0856] Please generate product descriptions based on the following user information:

[0857] Product Category: Smartphones

[0858] Interests: Latest technology

[0859] User sentiment: Interesting

[0860] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0861] Step 1:

[0862] Users input their interest information through the device, and the device sends this information to the server. The input data includes the user's interest categories and keywords, and the user profile is updated based on this. The device plays a role in appropriately formatting the necessary data and transferring it to the server using a secure communication method.

[0863] Step 2:

[0864] The server analyzes the received interest information and updates the user profile. Using the entered interest information and past data, it defines the user's preferences in more detail and stores them in the database so that they can be used for future information provision. At this time, the database optimizes the profile based on the newly entered information.

[0865] Step 3:

[0866] The server uses a generative AI model to select information elements that fit the user profile. Based on the user profile and interest information as input, the generative model extracts relevant information elements and generates explanations using defined prompts.

[0867] Step 4:

[0868] The generated explanations are packaged by the server as personalized suggestions and sent to the device. The server receives the text data generated by the generative AI model and converts it into a user-friendly format. This output highlights informational elements that align with the user's emotions and interests.

[0869] Step 5:

[0870] The user reviews the suggestions sent from the device and enters feedback and emotional data about the experience. At this time, the user records their emotions regarding the suggested information in data format, and the device sends this data back to the server.

[0871] Step 6:

[0872] The server analyzes the feedback and sentiment data received and updates the user profile for further personalization. Based on the feedback information, the profile is adjusted to provide the most relevant information elements on the next visit and saved as new data.

[0873] 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.

[0874] 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.

[0875] 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.

[0876] 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.

[0877] 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.

[0878] 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.

[0879] 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.

[0880] 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.

[0881] 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."

[0882] 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.

[0883] 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.

[0884] 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.

[0885] 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.

[0886] 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.

[0887] 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.

[0888] 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.

[0889] 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.

[0890] 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.

[0891] 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.

[0892] 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.

[0893] 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.

[0894] The following is further disclosed regarding the embodiments described above.

[0895] (Claim 1)

[0896] A means for obtaining user interest information and learning objective information, and for generating or updating a user profile based on this information,

[0897] Means for selecting multiple exhibits based on the user profile and using a generative model to generate explanatory information about the selected exhibits,

[0898] A means of generating and presenting a personalized tour plan, including generated explanatory information, to the user.

[0899] A means for obtaining user feedback information and optimizing the content for the next visit based on said feedback information,

[0900] A system that includes this.

[0901] (Claim 2)

[0902] The system according to claim 1, characterized in that the generative model is a model using natural language processing technology.

[0903] (Claim 3)

[0904] The system according to claim 1, further comprising means for evaluating the user's learning progress and adjusting the exhibits and explanatory content provided on the next visit.

[0905] "Example 1"

[0906] (Claim 1)

[0907] A means for obtaining user interest information and learning objective information, and for generating or updating user information based on this information,

[0908] A means for selecting multiple exhibits based on the user information and using a generation model to generate explanatory information about the selected exhibits,

[0909] A means of generating and presenting a personalized visit plan, including the generated explanatory information, to the user.

[0910] A means for obtaining user evaluation information and optimizing information for the next visit based on said evaluation information,

[0911] A means for providing the aforementioned personalized visit plan through virtual reality technology,

[0912] A system that includes this.

[0913] (Claim 2)

[0914] The system according to claim 1, characterized in that the generative model is a model using natural language processing technology.

[0915] (Claim 3)

[0916] The system according to claim 1, further comprising means for evaluating the user's learning progress and adjusting the exhibits and explanatory content provided on the next visit, and further provided in combination with virtual reality technology.

[0917] "Application Example 1"

[0918] (Claim 1)

[0919] A means for obtaining user interest information and learning objective information, and for generating or updating a user profile based on this information,

[0920] A means for selecting multiple exhibits based on the user profile and using a generation model to generate explanatory information about the selected exhibits,

[0921] A means for generating a personalized tour plan that includes generated explanatory information and presenting it via the user's terminal device,

[0922] A means of providing users with the experience of the aforementioned tour plan using virtual reality technology,

[0923] A means for obtaining user feedback information and optimizing the content of the next visit based on said feedback information,

[0924] A system that includes this.

[0925] (Claim 2)

[0926] The system according to claim 1, characterized in that the generative model is a model using natural language processing technology and provides a visual experience using virtual reality technology.

[0927] (Claim 3)

[0928] The system according to claim 1, further comprising means for evaluating the user's learning progress and adjusting the exhibits and explanations provided on the next visit.

[0929] "Example 2 of combining an emotion engine"

[0930] (Claim 1)

[0931] Means for acquiring user interest information, learning objective information, and emotional information, and for generating or updating user profiles based on this information,

[0932] Means for selecting multiple exhibits based on the user profile and sentiment information, and using a generative model to generate explanatory information about the selected exhibits,

[0933] A means of generating and presenting a personalized tour plan to the user, including generated explanatory information and the user's sentiment evaluation results.

[0934] A means for obtaining user feedback information and optimizing the content for the next visit based on the said feedback information and sentiment evaluation results,

[0935] A system that includes this.

[0936] (Claim 2)

[0937] The system according to claim 1, characterized in that the generation model is a model using natural language processing technology and generates explanatory information using prompt sentences.

[0938] (Claim 3)

[0939] The system according to claim 1, further comprising means for evaluating the user's learning progress and emotional information, and adjusting the exhibits and explanatory content provided on the next visit.

[0940] "Application example 2 when combining with an emotional engine"

[0941] (Claim 1)

[0942] A means for acquiring user interest information and sentiment data, and for generating or updating user profiles based on this information,

[0943] Means for selecting multiple information elements based on the user profile and using a generative model to generate explanations about the selected information elements,

[0944] A means of generating personalized suggestions, including generated explanations, and presenting them to the user.

[0945] A means for acquiring emotional data and feedback from users and optimizing information elements for the next visit based on said data,

[0946] A system that includes this.

[0947] (Claim 2)

[0948] The system according to claim 1, characterized in that the generative model is a model using natural language processing technology.

[0949] (Claim 3)

[0950] The system according to claim 1, further comprising means for evaluating the user's emotional state in real time and adjusting the information elements and explanatory content to be provided on the next visit. [Explanation of Symbols]

[0951] 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 for obtaining user interest information and learning objective information, and for generating or updating a user profile based on this information, A means for selecting multiple exhibits based on the user profile and using a generation model to generate explanatory information about the selected exhibits, A means for generating a personalized tour plan that includes generated explanatory information and presenting it via the user's terminal device, A means of providing users with the experience of the aforementioned tour plan using virtual reality technology, A means for obtaining user feedback information and optimizing the content of the next visit based on said feedback information, A system that includes this.

2. The system according to claim 1, characterized in that the generative model is a model using natural language processing technology and provides a visual experience using virtual reality technology.

3. The system according to claim 1, further comprising means for evaluating the user's learning progress and adjusting the exhibits and explanations provided on the next visit.

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