system

Abstract

The system according to this embodiment aims to improve the entertainment experience by utilizing generative AI. [Solution] The system according to the embodiment comprises an experience unit, a generation unit, a collection unit, and an improvement unit. The experience unit experiences the attractions in each area. The generation unit generates attraction content using generation AI based on the content experienced by the experience unit. The collection unit collects visitor feedback. The improvement unit improves the attractions based on the feedback collected by the collection unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, 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 the conventional technology, the utilization of generative AI in the entertainment field has not been fully carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to improve the entertainment experience by utilizing generative AI.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an experience unit, a generation unit, a collection unit, and an improvement unit. The experience unit experiences the attractions in each area. The generation unit generates attraction content using generation AI based on the content experienced by the experience unit. The collection unit collects visitor feedback. The improvement unit improves the attractions based on the feedback collected by the collection unit. [Effects of the Invention]

[0007] The system according to this embodiment can enhance the entertainment experience by utilizing generative AI. [Brief explanation of the drawing]

[0008] [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. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI ​​Experience Park System according to an embodiment of the present invention is an idea for utilizing generative AI in the entertainment field, and proposes the establishment of an AI Experience Park (tentative name), a theme park themed on "the use of generative AI," where visitors can experience "AI for enjoyment" as various forms of attractions. The AI ​​Experience Park System is a theme park that utilizes generative AI to provide visitors with various entertainment experiences, and aims to constantly provide visitors with new experiences. For example, the AI ​​Experience Park is an indoor facility composed of multiple areas (experience zones). Each area has a theme, and visitors can experience various attractions (places for experiencing generative AI) based on each theme. For example, in the entrance zone, visitors can enjoy facility introductions and admission registration using generative AI. In the learning & creative experience zone, visitors can learn about generative AI while experiencing content generation. In the art & emotional experience zone, there is an exhibition space and photo opportunity for art works generated using generative AI. In the entertainment experience zone, visitors can experience games and quizzes using generative AI, and watch videos and animations created using generative AI. In the drone experience zone, visitors can enjoy drone photography and games while experiencing drone operation using generative AI. In the food and shopping zone, generative AI will be used to sell food and beverages and suggest souvenirs. Each attraction will have the objective of "entertaining" and "impressing" visitors, and will always utilize generative AI. In addition, each area and attraction will be able to accept votes, feedback, new ideas for using generative AI, and suggestions for new attractions from visitors after their experience. This will also be handled by generative AI. Based on the voting results and feedback from visitors, popularity rankings for each attraction will be created, and improvements to each attraction and replacements with new ones will be carried out regularly to ensure that visitors always have a new experience. For example, in the entrance zone, visitors will be offered experiences using generative AI, such as a map of the facility, a screen introducing the entire facility, and projection mapping.The park also offers screens displaying introductory videos for each attraction and experiences utilizing projection mapping. Furthermore, visitors can learn about entry procedures and payment methods using generative AI, experience entry procedures using facial recognition, and make reservations for attractions in each zone. Post-experience voting and feedback are also conducted by generative AI, and opinions and impressions from past visitors are displayed. In the Learning & Creative Experience Zone, visitors can learn the basics of generative AI, create avatars, generate content, and propose ideas. For example, visitors can create their own avatars using their own facial recognition photos, and easily create artwork and music using generative AI. In the Art & Emotional Experience Zone, there are exhibitions of artwork created by generative AI, and visitors can submit their own generated artwork, which will then be displayed by generative AI. There are also photo booths and experiences utilizing projection mapping. In the Entertainment Experience Zone, visitors can play games and quizzes using generative AI, and watch videos and animations generated by generative AI. In the drone experience zone, demonstrations of drone operation methods using generative AI are offered, and visitors can experience piloting drones using simple methods provided by the generative AI. The park also offers drone races powered by generative AI, and visitors can participate in drone games using the generative AI. In the food and shopping zone, generative AI provides souvenir suggestions and selection assistance, as well as menu advice and recommended dishes. Thus, the AI ​​Experience Park is a theme park that utilizes generative AI to provide visitors with a variety of entertainment experiences, aiming to constantly offer visitors new experiences. As a result, the AI ​​Experience Park system can continuously provide visitors with new entertainment experiences.

[0029] The AI ​​experience park system according to this embodiment comprises an experience unit, a generation unit, a collection unit, and an improvement unit. The experience unit experiences the attractions in each area. For example, in the entrance zone, visitors can enjoy facility introductions and admission registration using generation AI. The experience unit can also experience content generation while learning about generation AI in the learning and creative experience zone. Furthermore, in the art and emotional experience zone, the experience unit can provide exhibitions and photo spaces for artworks generated using generation AI. In the entertainment experience zone, the experience unit can experience games and quizzes using generation AI, and watch videos and animations created using generation AI. In the drone experience zone, the experience unit can enjoy drone photography and games while experiencing drone operation using generation AI. In the food and shopping zone, the experience unit can use generation AI to sell food and beverages and suggest souvenir products. The generation unit generates attraction content using generation AI based on the content experienced by the experience unit. For example, the generation unit generates artwork using generation AI. The generation unit can also generate game and quiz content using generation AI. Furthermore, the generation unit can generate drone operation methods using generation AI. The collection unit collects visitor feedback. The collection unit can, for example, collect visitor voting results and feedback. The improvement unit improves the attractions based on the feedback collected by the collection unit. The improvement unit can, for example, improve the attractions based on the collected feedback. Thus, the AI ​​experience park system according to the embodiment can experience the attractions in each area, generate attraction content using generation AI, collect feedback, and improve the attractions.

[0030] The Experience Section allows visitors to experience the attractions in each area. For example, in the entrance zone, visitors can enjoy facility introductions and admission check-in using generative AI. Specifically, the generative AI responds to visitors' questions in real time, guiding them through the facility's overview and recommended attractions. It can also scan visitors' faces using facial recognition technology to expedite the admission process. In the Learning & Creative Experience Zone, visitors can learn about generative AI and experience content creation. Here, visitors can learn about the mechanisms and algorithms of generative AI and generate their own content such as text, images, and music. For example, visitors can input their own ideas as prompts, and the generative AI will generate poems or paintings based on them. Furthermore, in the Art & Emotional Experience Zone, the Experience Section can provide exhibitions and photo spaces for artwork generated using generative AI. Here, digital art and interactive installations created by generative AI are displayed, and visitors can view them or collaborate with the generative AI to create their own artwork. The Experience Section offers visitors the opportunity to experience games and quizzes powered by generative AI in the entertainment zone, and to watch videos and animations created using generative AI. For example, visitors can deepen their knowledge by playing interactive games based on scenarios created by generative AI and taking quizzes. They can also watch short animations and videos created by generative AI and learn about the production process. In the Drone Experience Zone, visitors can enjoy drone photography and games while experiencing drone operation using generative AI. Here, they can learn the basic operation of a drone through simulations provided by generative AI and actually pilot a drone to take aerial photos. Furthermore, they can enjoy drone races and obstacle courses created by generative AI. In the Food & Shopping Zone, the Experience Section can utilize generative AI to sell food and beverages and suggest souvenirs. For example, the generative AI suggests the most suitable menu and souvenirs based on the visitor's preferences and allergy information, and takes orders. In this way, the Experience Section can provide visitors with diverse experiences and let them experience the potential of generative AI.

[0031] The generation unit generates attraction content using generative AI based on the experiences provided by the experience unit. For example, the generation unit can generate artwork using generative AI. Specifically, the generative AI generates unique artwork based on themes and styles entered by visitors. For example, if a visitor requests a painting with the theme of "nature," the generative AI will generate digital art depicting various natural landscapes, plants, and animals. The generation unit can also generate game and quiz content using generative AI. For example, the generative AI generates customized quiz questions and game scenarios according to the visitor's interests and knowledge level. This allows visitors to enjoy games with difficulty levels and themes that suit them. Furthermore, the generation unit can generate drone operation instructions using generative AI. Specifically, the generative AI generates optimal drone operation tutorials and missions according to the visitor's skill level and purpose. For example, it explains basic operation methods for beginners and introduces advanced flight techniques and photography techniques for advanced users. In this way, the generation unit can provide visitors with personalized experiences and demonstrate diverse applications of generative AI. In addition, the generation unit can also generate new attraction ideas and concepts using generative AI. For example, the generation AI analyzes visitor feedback and trend data to suggest the next attraction to introduce. This allows the generation department to constantly provide attractions that incorporate the latest technologies and ideas, keeping visitors interested.

[0032] The data collection unit collects visitor feedback. For example, it can collect visitor voting results and feedback. Specifically, the data collection unit conducts surveys with visitors after each attraction to ask about their satisfaction with the experience and areas for improvement. It can also provide an interface that allows visitors to provide real-time feedback while experiencing an attraction. For example, visitors can instantly input their feelings and opinions during the experience via a smartphone app or tablet device. Furthermore, the data collection unit can collect visitor behavioral and biometric data. For example, it can record visitors' movement routes, dwell time, heart rate, and facial expression changes using sensors and cameras to quantitatively evaluate the quality of the experience. This allows the data collection unit to collect visitor feedback from multiple perspectives and use it to improve attractions. In addition, the data collection unit centrally manages the collected feedback and stores it in a database. This allows the data collection unit to refer to past feedback data and analyze long-term trends and patterns. For example, it can track how evaluations of a particular attraction have changed over time and evaluate the effectiveness of improvements. This allows the collection department to support the provision of attractions that meet the needs and expectations of visitors, thereby improving the quality of the experience.

[0033] The Improvement Department improves attractions based on feedback collected by the Data Collection Department. For example, the Improvement Department can improve attractions based on the collected feedback. Specifically, the Improvement Department analyzes the feedback data to identify problems and requests pointed out by visitors. For example, if many visitors provide feedback such as "long wait times" or "difficult to operate" for a particular attraction, the Improvement Department will consider measures to solve these problems. To reduce wait times, they may consider introducing a reservation system or improving the operational efficiency of the attraction. For visitors who find the operation difficult, the Improvement Department can improve the quality of the experience by enhancing tutorials on how to operate the attraction or improving the interface. Furthermore, the Improvement Department can also develop new attractions or renovate existing ones based on the feedback. For example, if many visitors request "more interactive experiences," they may develop a new interactive attraction using generative AI. They can also keep visitors interested by adding new elements to existing attractions. In this way, the Improvement Department can improve attractions by reflecting visitor feedback and continuously improve the quality of the experience. Furthermore, the Improvement Department builds a feedback loop to evaluate the effectiveness of improvements and reflect them in the next improvement cycle. This allows the improvement department to constantly make improvements based on the latest information, thereby increasing visitor satisfaction.

[0034] The generation unit can generate artwork using a generation AI. For example, the generation unit can adjust the style of the artwork based on the visitor's emotions using the generation AI. If the visitor is relaxed, the generation unit can generate artwork with calm colors. If the visitor is excited, the generation unit can generate artwork with vibrant colors. Furthermore, if the visitor is sad, the generation unit can generate artwork with subdued colors. In this way, it becomes possible to generate artwork using a generation AI.

[0035] The generation unit can generate game and quiz content using generative AI. For example, the generation unit can use generative AI to adjust the difficulty level of games and quizzes based on the emotions of visitors. If visitors are relaxed, the generation unit can provide games and quizzes of an easy difficulty level. If visitors are excited, the generation unit can provide games and quizzes of a higher difficulty level. Furthermore, if visitors are feeling anxious, the generation unit can adjust the difficulty level to provide games and quizzes that provide a sense of security. In this way, game and quiz content can be generated using generative AI.

[0036] The generation unit can generate drone operation methods using generative AI. For example, the generation unit can adjust the drone operation methods based on the emotions of visitors using generative AI. If a visitor is relaxed, the generation unit can provide simple operation methods. If a visitor is excited, the generation unit can provide more difficult operation methods. Furthermore, if a visitor is feeling anxious, the generation unit can provide operation methods that provide a sense of security. In this way, drone operation methods can be generated using generative AI.

[0037] The data collection unit can collect visitor voting results and feedback. For example, the data collection unit can allow visitors to vote and provide feedback after experiencing an attraction. The data collection unit can collect visitor voting results and feedback to create a popularity ranking of attractions. The data collection unit can also identify areas for improvement of attractions based on visitor feedback. Furthermore, the data collection unit can accept suggestions for new attractions based on visitor feedback. This allows for the collection of visitor voting results and feedback. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input visitor feedback into AI and have the AI ​​perform the analysis of the feedback.

[0038] The Improvement Department can improve attractions based on the collected feedback. For example, the Improvement Department can change the content of attractions based on the collected feedback. The Improvement Department can create a popularity ranking of attractions based on visitor feedback and prioritize improvements to the most popular attractions. The Improvement Department can also add new attractions based on visitor feedback. Furthermore, the Improvement Department can review the operation methods of attractions based on visitor feedback. This allows for improvements to attractions based on the collected feedback. Some or all of the above processes in the Improvement Department may be performed using AI, for example, or not using AI. For example, the Improvement Department can input visitor feedback into AI and have the AI ​​perform the analysis of the feedback.

[0039] The experience department can analyze a visitor's past experience history during an experience and provide the most suitable experience. For example, the experience department can prioritize suggesting attractions that the visitor has enjoyed in the past. It can also exclude attractions that the visitor has avoided in the past. Furthermore, the experience department can suggest similar experiences based on the visitor's past experience history. In this way, it can analyze the visitor's past experience history and provide the most suitable experience. Some or all of the above processing in the experience department may be performed using AI, for example, or not. For example, the experience department can input the visitor's past experience history data into an AI and have the AI ​​suggest the most suitable experience.

[0040] The experience department can customize the experience content based on the visitor's age and interests. For example, the experience department can offer attractions for children. It can also offer attractions for adults. Furthermore, the experience department can provide attractions tailored to visitors with specific interests. This allows for the customization of the experience content based on the visitor's age and interests. Some or all of the above processes in the experience department may be performed using AI, for example, or not. For example, the experience department can input visitor age and interest data into AI and have the AI ​​customize the experience content.

[0041] The experience department can provide highly relevant experiences by taking into account the visitor's geographical location during the experience. For example, if a visitor is in a specific area, the experience department can provide attractions related to that area. Also, if a visitor is from a specific region, the experience department can provide attractions related to that region. Furthermore, if a visitor is interested in a specific place, the experience department can provide attractions related to that place. In this way, highly relevant experiences can be provided by taking into account the visitor's geographical location. Some or all of the above processing in the experience department may be performed using AI, for example, or not using AI. For example, the experience department can input the visitor's geographical location data into AI and have the AI ​​suggest highly relevant experiences.

[0042] The Experience Department can analyze visitors' social media activity during their experience and provide relevant experiences. For example, the Experience Department can offer attractions that visitors have shown interest in on social media. It can also prioritize attractions that visitors have shared on social media. Furthermore, the Experience Department can suggest relevant experiences based on visitors' social media activity. This allows for the analysis of visitors' social media activity and the provision of relevant experiences. Some or all of the above processing in the Experience Department may be performed using AI, for example, or not. For example, the Experience Department can input visitors' social media activity data into an AI and have the AI ​​suggest relevant experiences.

[0043] The generation unit can optimize its generation algorithm by referring to past visitor feedback during the generation process. For example, the generation unit can prioritize generating styles that visitors have previously given high ratings to. It can also exclude styles that visitors have previously given low ratings to. Furthermore, the generation unit can analyze past visitor feedback and generate the optimal style. This allows the generation algorithm to be optimized by referring to past visitor feedback. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input past visitor feedback data into AI and have the AI ​​perform the optimization of the generation algorithm.

[0044] The generation unit can customize the generated content based on the visitor's interests and preferences during the generation process. For example, the generation unit can generate artwork based on themes that the visitor is interested in. It can also generate artwork based on styles that the visitor is interested in. Furthermore, the generation unit can generate artwork that reflects the visitor's interests and preferences. This allows for customization of the generated content based on the visitor's interests and preferences. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input visitor interest data into AI and have the AI ​​perform the customization of the generated content.

[0045] The generation unit can generate highly relevant content by considering the visitor's geographical location information during the generation process. For example, if a visitor is in a specific area, the generation unit can generate games or quizzes related to that area. Furthermore, if a visitor is from a specific region, the generation unit can generate games or quizzes related to that region. Additionally, if a visitor is interested in a specific location, the generation unit can generate games or quizzes related to that location. This allows the generation unit to generate highly relevant content by considering the visitor's geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input visitor geographical location data into AI and have the AI ​​generate highly relevant content.

[0046] The generation unit can analyze visitors' social media activity during generation and generate relevant content. For example, the generation unit can generate games and quizzes based on themes visitors have shown interest in on social media. It can also generate games and quizzes based on themes visitors have shared on social media. Furthermore, the generation unit can generate relevant content from visitors' social media activity. This allows the generation unit to analyze visitors' social media activity and generate relevant content. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input visitors' social media activity data into AI and have the AI ​​generate relevant content.

[0047] The data collection unit can select the optimal collection method by referring to the visitor's past feedback history when collecting feedback. For example, if the visitor has provided detailed feedback in the past, the data collection unit can suggest a similar method. Similarly, if the visitor has provided brief feedback in the past, the data collection unit can suggest a similar method. Furthermore, the data collection unit can analyze the visitor's past feedback history and select the optimal collection method. This allows the data collection unit to select the optimal collection method by referring to the visitor's past feedback history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the visitor's past feedback history data into AI and have the AI ​​select the optimal collection method.

[0048] The data collection unit can prioritize collecting highly relevant feedback by considering the visitor's geographical location information during feedback collection. For example, if a visitor is in a specific area, the data collection unit can prioritize collecting feedback related to that area. Furthermore, if a visitor is from a specific region, the data collection unit can prioritize collecting feedback related to that region. Additionally, if a visitor is interested in a specific location, the data collection unit can prioritize collecting feedback related to that location. This allows for the priority collection of highly relevant feedback by considering the visitor's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input visitor geographical location data into an AI and have the AI ​​collect highly relevant feedback.

[0049] The improvement unit can select the optimal improvement method by referring to past visitor feedback during the improvement process. For example, the improvement unit can prioritize the adoption of improvement methods that visitors have previously given high ratings to. It can also exclude improvement methods that visitors have previously given low ratings to. Furthermore, the improvement unit can analyze past visitor feedback to select the optimal improvement method. This allows the improvement unit to select the optimal improvement method by referring to past visitor feedback. Some or all of the above processes in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input past visitor feedback data into AI and have the AI ​​select the optimal improvement method.

[0050] The improvement unit can customize improvements based on visitors' interests and preferences. For example, the improvement unit can improve attractions based on themes that visitors are interested in. It can also improve attractions based on styles that visitors are interested in. Furthermore, the improvement unit can improve attractions that reflect visitors' interests and preferences. This allows for customization of improvements based on visitors' interests and preferences. Some or all of the above processes in the improvement unit may be performed using AI, for example, or not. For example, the improvement unit can input visitor interest data into AI and have the AI ​​perform the customization of improvements.

[0051] The improvement unit can select the optimal improvement method when making improvements, taking into account the visitor's geographical location information. For example, if a visitor is in a specific area, the improvement unit can suggest improvement methods related to that area. Also, if a visitor is from a specific region, the improvement unit can suggest improvement methods related to that region. Furthermore, if a visitor is interested in a specific place, the improvement unit can suggest improvement methods related to that place. This allows the improvement unit to select the optimal improvement method while taking into account the visitor's geographical location information. Some or all of the above processing in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input visitor geographical location data into AI and have the AI ​​select the optimal improvement method.

[0052] The improvement department can analyze visitors' social media activity and propose relevant improvement methods during the improvement process. For example, the improvement department can improve attractions based on themes visitors have shown interest in on social media. It can also improve attractions based on themes visitors have shared on social media. Furthermore, the improvement department can propose relevant improvement methods based on visitors' social media activity. This allows the improvement department to analyze visitors' social media activity and propose relevant improvement methods. Some or all of the above processes in the improvement department may be performed using AI, for example, or not. For example, the improvement department can input visitors' social media activity data into AI and have the AI ​​propose relevant improvement methods.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The AI-powered park experience system can also be equipped with a "personalized guide function." This function can suggest the most suitable attractions and experiences based on each visitor's individual interests and past experience history. For example, if a visitor has enjoyed a particular art experience in the past, it can prioritize suggesting similar art experiences. Similarly, if a visitor has shown interest in a particular theme, it can suggest attractions related to that theme. Furthermore, the personalized guide function can suggest appropriate attractions based on the visitor's age and family structure. This allows visitors to efficiently enjoy the most suitable experiences for them.

[0055] The AI-powered park experience system can also be equipped with a "real-time feedback unit." This unit can collect feedback in real time while visitors are experiencing an attraction and instantly adjust the attraction's content. For example, if a visitor feels the difficulty level is too high while playing a game, the difficulty can be lowered in real time. Also, if a visitor prefers a particular color or style while experiencing art, the art style can be changed to match their preference. Furthermore, if a visitor finds the drone experience difficult to operate, the controls can be simplified. This ensures that visitors always enjoy the optimal experience.

[0056] The AI ​​Experience Park system can also be equipped with an "Ecosystem Collaboration Department." This department can collaborate with other theme parks and entertainment facilities to provide visitors with a consistent experience. For example, it can customize the AI ​​Experience Park experience based on data from attractions visitors enjoyed at other theme parks. It can also allow visitors to use points and rewards earned at other facilities within the AI ​​Experience Park. Furthermore, the Ecosystem Collaboration Department can plan collaborative events with other facilities to offer visitors new experiences. This allows visitors to enjoy a consistent entertainment experience across multiple facilities.

[0057] The AI-powered park experience system can also be equipped with a "health monitoring unit." This unit can monitor visitors' health status in real time and adjust the content of attractions as needed. For example, it can monitor visitors' heart rate and stress levels and adjust the difficulty of attractions to avoid excessive stress. It can also suggest relaxing attractions if visitors are tired. Furthermore, the health monitoring unit can suggest appropriate rest times based on visitors' health data. This ensures that visitors can enjoy the experience safely and comfortably.

[0058] The AI-powered park experience system can also include an "edutainment section." This section can offer experiences that combine entertainment and education. For example, visitors can enjoy fun attractions while learning about science and history. Visitors can also create learning content using generative AI and share it with other visitors. Furthermore, the edutainment section can provide an optimal learning experience based on the visitor's learning progress. This allows visitors to learn while having fun.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The Experience Section allows visitors to experience the attractions in each area. For example, visitors can enjoy facility introductions and admission check-in using generative AI in the entrance zone. They can also learn about generative AI and experience content creation in the learning & creative experience zone. Furthermore, the art & emotional experience zone can provide exhibitions and photo spaces for art works generated using generative AI. In the entertainment experience zone, visitors can experience games and quizzes using generative AI, and watch videos and animations created using generative AI. In the drone experience zone, visitors can enjoy drone photography and games while experiencing drone operation using generative AI. In the food & shopping zone, generative AI can be used to sell food and beverages and suggest souvenir products. Step 2: The generation unit uses a generation AI to generate attraction content based on the experience provided by the experience unit. For example, it can generate artwork using the generation AI. It can also generate game and quiz content using the generation AI. Furthermore, it can generate instructions on how to operate a drone using the generation AI. Step 3: The data collection unit collects visitor feedback. For example, it can collect visitor voting results and feedback. Step 4: The improvement unit improves the attraction based on the feedback collected by the data collection unit. For example, the attraction can be improved based on the collected feedback.

[0061] (Example of form 2) The AI ​​Experience Park System according to an embodiment of the present invention is an idea for utilizing generative AI in the entertainment field, and proposes the establishment of an AI Experience Park (tentative name), a theme park themed on "the use of generative AI," where visitors can experience "AI for enjoyment" as various forms of attractions. The AI ​​Experience Park System is a theme park that utilizes generative AI to provide visitors with various entertainment experiences, and aims to constantly provide visitors with new experiences. For example, the AI ​​Experience Park is an indoor facility composed of multiple areas (experience zones). Each area has a theme, and visitors can experience various attractions (places for experiencing generative AI) based on each theme. For example, in the entrance zone, visitors can enjoy facility introductions and admission registration using generative AI. In the learning & creative experience zone, visitors can learn about generative AI while experiencing content generation. In the art & emotional experience zone, there is an exhibition space and photo opportunity for art works generated using generative AI. In the entertainment experience zone, visitors can experience games and quizzes using generative AI, and watch videos and animations created using generative AI. In the drone experience zone, visitors can enjoy drone photography and games while experiencing drone operation using generative AI. In the food and shopping zone, generative AI will be used to sell food and beverages and suggest souvenirs. Each attraction will have the objective of "entertaining" and "impressing" visitors, and will always utilize generative AI. In addition, each area and attraction will be able to accept votes, feedback, new ideas for using generative AI, and suggestions for new attractions from visitors after their experience. This will also be handled by generative AI. Based on the voting results and feedback from visitors, popularity rankings for each attraction will be created, and improvements to each attraction and replacements with new ones will be carried out regularly to ensure that visitors always have a new experience. For example, in the entrance zone, visitors will be offered experiences using generative AI, such as a map of the facility, a screen introducing the entire facility, and projection mapping.The park also offers screens displaying introductory videos for each attraction and experiences utilizing projection mapping. Furthermore, visitors can learn about entry procedures and payment methods using generative AI, experience entry procedures using facial recognition, and make reservations for attractions in each zone. Post-experience voting and feedback are also conducted by generative AI, and opinions and impressions from past visitors are displayed. In the Learning & Creative Experience Zone, visitors can learn the basics of generative AI, create avatars, generate content, and propose ideas. For example, visitors can create their own avatars using their own facial recognition photos, and easily create artwork and music using generative AI. In the Art & Emotional Experience Zone, there are exhibitions of artwork created by generative AI, and visitors can submit their own generated artwork, which will then be displayed by generative AI. There are also photo booths and experiences utilizing projection mapping. In the Entertainment Experience Zone, visitors can play games and quizzes using generative AI, and watch videos and animations generated by generative AI. In the drone experience zone, demonstrations of drone operation methods using generative AI are offered, and visitors can experience piloting drones using simple methods provided by the generative AI. The park also offers drone races powered by generative AI, and visitors can participate in drone games using the generative AI. In the food and shopping zone, generative AI provides souvenir suggestions and selection assistance, as well as menu advice and recommended dishes. Thus, the AI ​​Experience Park is a theme park that utilizes generative AI to provide visitors with a variety of entertainment experiences, aiming to constantly offer visitors new experiences. As a result, the AI ​​Experience Park system can continuously provide visitors with new entertainment experiences.

[0062] The AI ​​experience park system according to this embodiment comprises an experience unit, a generation unit, a collection unit, and an improvement unit. The experience unit experiences the attractions in each area. For example, in the entrance zone, visitors can enjoy facility introductions and admission registration using generation AI. The experience unit can also experience content generation while learning about generation AI in the learning and creative experience zone. Furthermore, in the art and emotional experience zone, the experience unit can provide exhibitions and photo spaces for artworks generated using generation AI. In the entertainment experience zone, the experience unit can experience games and quizzes using generation AI, and watch videos and animations created using generation AI. In the drone experience zone, the experience unit can enjoy drone photography and games while experiencing drone operation using generation AI. In the food and shopping zone, the experience unit can use generation AI to sell food and beverages and suggest souvenir products. The generation unit generates attraction content using generation AI based on the content experienced by the experience unit. For example, the generation unit generates artwork using generation AI. The generation unit can also generate game and quiz content using generation AI. Furthermore, the generation unit can generate drone operation methods using generation AI. The collection unit collects visitor feedback. The collection unit can, for example, collect visitor voting results and feedback. The improvement unit improves the attractions based on the feedback collected by the collection unit. The improvement unit can, for example, improve the attractions based on the collected feedback. Thus, the AI ​​experience park system according to the embodiment can experience the attractions in each area, generate attraction content using generation AI, collect feedback, and improve the attractions.

[0063] The Experience Section allows visitors to experience the attractions in each area. For example, in the entrance zone, visitors can enjoy facility introductions and admission check-in using generative AI. Specifically, the generative AI responds to visitors' questions in real time, guiding them through the facility's overview and recommended attractions. It can also scan visitors' faces using facial recognition technology to expedite the admission process. In the Learning & Creative Experience Zone, visitors can learn about generative AI and experience content creation. Here, visitors can learn about the mechanisms and algorithms of generative AI and generate their own content such as text, images, and music. For example, visitors can input their own ideas as prompts, and the generative AI will generate poems or paintings based on them. Furthermore, in the Art & Emotional Experience Zone, the Experience Section can provide exhibitions and photo spaces for artwork generated using generative AI. Here, digital art and interactive installations created by generative AI are displayed, and visitors can view them or collaborate with the generative AI to create their own artwork. The Experience Section offers visitors the opportunity to experience games and quizzes powered by generative AI in the entertainment zone, and to watch videos and animations created using generative AI. For example, visitors can deepen their knowledge by playing interactive games based on scenarios created by generative AI and taking quizzes. They can also watch short animations and videos created by generative AI and learn about the production process. In the Drone Experience Zone, visitors can enjoy drone photography and games while experiencing drone operation using generative AI. Here, they can learn the basic operation of a drone through simulations provided by generative AI and actually pilot a drone to take aerial photos. Furthermore, they can enjoy drone races and obstacle courses created by generative AI. In the Food & Shopping Zone, the Experience Section can utilize generative AI to sell food and beverages and suggest souvenirs. For example, the generative AI suggests the most suitable menu and souvenirs based on the visitor's preferences and allergy information, and takes orders. In this way, the Experience Section can provide visitors with diverse experiences and let them experience the potential of generative AI.

[0064] The generation unit generates attraction content using generative AI based on the experiences provided by the experience unit. For example, the generation unit can generate artwork using generative AI. Specifically, the generative AI generates unique artwork based on themes and styles entered by visitors. For example, if a visitor requests a painting with the theme of "nature," the generative AI will generate digital art depicting various natural landscapes, plants, and animals. The generation unit can also generate game and quiz content using generative AI. For example, the generative AI generates customized quiz questions and game scenarios according to the visitor's interests and knowledge level. This allows visitors to enjoy games with difficulty levels and themes that suit them. Furthermore, the generation unit can generate drone operation instructions using generative AI. Specifically, the generative AI generates optimal drone operation tutorials and missions according to the visitor's skill level and purpose. For example, it explains basic operation methods for beginners and introduces advanced flight techniques and photography techniques for advanced users. In this way, the generation unit can provide visitors with personalized experiences and demonstrate diverse applications of generative AI. In addition, the generation unit can also generate new attraction ideas and concepts using generative AI. For example, the generation AI analyzes visitor feedback and trend data to suggest the next attraction to introduce. This allows the generation department to constantly provide attractions that incorporate the latest technologies and ideas, keeping visitors interested.

[0065] The data collection unit collects visitor feedback. For example, it can collect visitor voting results and feedback. Specifically, the data collection unit conducts surveys with visitors after each attraction to ask about their satisfaction with the experience and areas for improvement. It can also provide an interface that allows visitors to provide real-time feedback while experiencing an attraction. For example, visitors can instantly input their feelings and opinions during the experience via a smartphone app or tablet device. Furthermore, the data collection unit can collect visitor behavioral and biometric data. For example, it can record visitors' movement routes, dwell time, heart rate, and facial expression changes using sensors and cameras to quantitatively evaluate the quality of the experience. This allows the data collection unit to collect visitor feedback from multiple perspectives and use it to improve attractions. In addition, the data collection unit centrally manages the collected feedback and stores it in a database. This allows the data collection unit to refer to past feedback data and analyze long-term trends and patterns. For example, it can track how evaluations of a particular attraction have changed over time and evaluate the effectiveness of improvements. This allows the collection department to support the provision of attractions that meet the needs and expectations of visitors, thereby improving the quality of the experience.

[0066] The Improvement Department improves attractions based on feedback collected by the Data Collection Department. For example, the Improvement Department can improve attractions based on the collected feedback. Specifically, the Improvement Department analyzes the feedback data to identify problems and requests pointed out by visitors. For example, if many visitors provide feedback such as "long wait times" or "difficult to operate" for a particular attraction, the Improvement Department will consider measures to solve these problems. To reduce wait times, they may consider introducing a reservation system or improving the operational efficiency of the attraction. For visitors who find the operation difficult, the Improvement Department can improve the quality of the experience by enhancing tutorials on how to operate the attraction or improving the interface. Furthermore, the Improvement Department can also develop new attractions or renovate existing ones based on the feedback. For example, if many visitors request "more interactive experiences," they may develop a new interactive attraction using generative AI. They can also keep visitors interested by adding new elements to existing attractions. In this way, the Improvement Department can improve attractions by reflecting visitor feedback and continuously improve the quality of the experience. Furthermore, the Improvement Department builds a feedback loop to evaluate the effectiveness of improvements and reflect them in the next improvement cycle. This allows the improvement department to constantly make improvements based on the latest information, thereby increasing visitor satisfaction.

[0067] The generation unit can generate artwork using a generation AI. For example, the generation unit can adjust the style of the artwork based on the visitor's emotions using the generation AI. If the visitor is relaxed, the generation unit can generate artwork with calm colors. If the visitor is excited, the generation unit can generate artwork with vibrant colors. Furthermore, if the visitor is sad, the generation unit can generate artwork with subdued colors. In this way, it becomes possible to generate artwork using a generation AI.

[0068] The generation unit can generate game and quiz content using generative AI. For example, the generation unit can use generative AI to adjust the difficulty level of games and quizzes based on the emotions of visitors. If visitors are relaxed, the generation unit can provide games and quizzes of an easy difficulty level. If visitors are excited, the generation unit can provide games and quizzes of a higher difficulty level. Furthermore, if visitors are feeling anxious, the generation unit can adjust the difficulty level to provide games and quizzes that provide a sense of security. In this way, game and quiz content can be generated using generative AI.

[0069] The generation unit can generate drone operation methods using generative AI. For example, the generation unit can adjust the drone operation methods based on the emotions of visitors using generative AI. If a visitor is relaxed, the generation unit can provide simple operation methods. If a visitor is excited, the generation unit can provide more difficult operation methods. Furthermore, if a visitor is feeling anxious, the generation unit can provide operation methods that provide a sense of security. In this way, drone operation methods can be generated using generative AI.

[0070] The data collection unit can collect visitor voting results and feedback. For example, the data collection unit can allow visitors to vote and provide feedback after experiencing an attraction. The data collection unit can collect visitor voting results and feedback to create a popularity ranking of attractions. The data collection unit can also identify areas for improvement of attractions based on visitor feedback. Furthermore, the data collection unit can accept suggestions for new attractions based on visitor feedback. This allows for the collection of visitor voting results and feedback. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input visitor feedback into AI and have the AI ​​perform the analysis of the feedback.

[0071] The Improvement Department can improve attractions based on the collected feedback. For example, the Improvement Department can change the content of attractions based on the collected feedback. The Improvement Department can create a popularity ranking of attractions based on visitor feedback and prioritize improvements to the most popular attractions. The Improvement Department can also add new attractions based on visitor feedback. Furthermore, the Improvement Department can review the operation methods of attractions based on visitor feedback. This allows for improvements to attractions based on the collected feedback. Some or all of the above processes in the Improvement Department may be performed using AI, for example, or not using AI. For example, the Improvement Department can input visitor feedback into AI and have the AI ​​perform the analysis of the feedback.

[0072] The experience section can estimate visitors' emotions and adjust the experience content in real time based on the estimated emotions. For example, if a visitor is excited, the experience section can provide a more stimulating attraction. If a visitor is relaxed, the experience section can provide a calming experience. Furthermore, if a visitor is feeling anxious, the experience section can provide a reassuring experience. This allows the experience content to be adjusted in real time based on the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the experience section may be performed using AI, or not using AI. For example, the experience section can input visitor facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0073] The experience department can analyze a visitor's past experience history during an experience and provide the most suitable experience. For example, the experience department can prioritize suggesting attractions that the visitor has enjoyed in the past. It can also exclude attractions that the visitor has avoided in the past. Furthermore, the experience department can suggest similar experiences based on the visitor's past experience history. In this way, it can analyze the visitor's past experience history and provide the most suitable experience. Some or all of the above processing in the experience department may be performed using AI, for example, or not. For example, the experience department can input the visitor's past experience history data into an AI and have the AI ​​suggest the most suitable experience.

[0074] The experience department can customize the experience content based on the visitor's age and interests. For example, the experience department can offer attractions for children. It can also offer attractions for adults. Furthermore, the experience department can provide attractions tailored to visitors with specific interests. This allows for the customization of the experience content based on the visitor's age and interests. Some or all of the above processes in the experience department may be performed using AI, for example, or not. For example, the experience department can input visitor age and interest data into AI and have the AI ​​customize the experience content.

[0075] The experience section can estimate the emotions of visitors and adjust the order of experiences based on those estimated emotions. For example, if a visitor is excited, the experience section can offer stimulating attractions first. If a visitor is relaxed, the experience section can offer calming attractions first. Furthermore, if a visitor is feeling anxious, the experience section can offer reassuring attractions first. This allows the order of experiences to be adjusted based on the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the experience section may be performed using AI, or not using AI. For example, the experience section can input visitor facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0076] The experience department can provide highly relevant experiences by taking into account the visitor's geographical location during the experience. For example, if a visitor is in a specific area, the experience department can provide attractions related to that area. Also, if a visitor is from a specific region, the experience department can provide attractions related to that region. Furthermore, if a visitor is interested in a specific place, the experience department can provide attractions related to that place. In this way, highly relevant experiences can be provided by taking into account the visitor's geographical location. Some or all of the above processing in the experience department may be performed using AI, for example, or not using AI. For example, the experience department can input the visitor's geographical location data into AI and have the AI ​​suggest highly relevant experiences.

[0077] The Experience Department can analyze visitors' social media activity during their experience and provide relevant experiences. For example, the Experience Department can offer attractions that visitors have shown interest in on social media. It can also prioritize attractions that visitors have shared on social media. Furthermore, the Experience Department can suggest relevant experiences based on visitors' social media activity. This allows for the analysis of visitors' social media activity and the provision of relevant experiences. Some or all of the above processing in the Experience Department may be performed using AI, for example, or not. For example, the Experience Department can input visitors' social media activity data into an AI and have the AI ​​suggest relevant experiences.

[0078] The generation unit can estimate the visitor's emotions and adjust the style of the generated artwork based on the estimated visitor's emotions. For example, if the visitor is relaxed, the generation unit can generate artwork with calm colors. If the visitor is excited, the generation unit can generate artwork with vibrant colors. Furthermore, if the visitor is sad, the generation unit can generate artwork with subdued colors. This allows the style of the generated artwork to be adjusted based on the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input visitor emotion data into the generation AI and have the generation AI adjust the style of the artwork.

[0079] The generation unit can optimize its generation algorithm by referring to past visitor feedback during the generation process. For example, the generation unit can prioritize generating styles that visitors have previously given high ratings to. It can also exclude styles that visitors have previously given low ratings to. Furthermore, the generation unit can analyze past visitor feedback and generate the optimal style. This allows the generation algorithm to be optimized by referring to past visitor feedback. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input past visitor feedback data into AI and have the AI ​​perform the optimization of the generation algorithm.

[0080] The generation unit can customize the generated content based on the visitor's interests and preferences during the generation process. For example, the generation unit can generate artwork based on themes that the visitor is interested in. It can also generate artwork based on styles that the visitor is interested in. Furthermore, the generation unit can generate artwork that reflects the visitor's interests and preferences. This allows for customization of the generated content based on the visitor's interests and preferences. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input visitor interest data into AI and have the AI ​​perform the customization of the generated content.

[0081] The generation unit can estimate the emotions of visitors and adjust the difficulty level of the games and quizzes it generates based on the estimated emotions. For example, if a visitor is relaxed, the generation unit can provide games and quizzes of an easy difficulty level. If a visitor is excited, the generation unit can provide games and quizzes of a higher difficulty level. Furthermore, if a visitor is feeling anxious, the generation unit can adjust the difficulty level to provide games and quizzes that provide a sense of security. In this way, the difficulty level of the games and quizzes generated can be adjusted based on the emotions of visitors. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input visitor emotion data into the generation AI and have the generation AI adjust the difficulty level of the games and quizzes.

[0082] The generation unit can generate highly relevant content by considering the visitor's geographical location information during the generation process. For example, if a visitor is in a specific area, the generation unit can generate games or quizzes related to that area. Furthermore, if a visitor is from a specific region, the generation unit can generate games or quizzes related to that region. Additionally, if a visitor is interested in a specific location, the generation unit can generate games or quizzes related to that location. This allows the generation unit to generate highly relevant content by considering the visitor's geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input visitor geographical location data into AI and have the AI ​​generate highly relevant content.

[0083] The generation unit can analyze visitors' social media activity during generation and generate relevant content. For example, the generation unit can generate games and quizzes based on themes visitors have shown interest in on social media. It can also generate games and quizzes based on themes visitors have shared on social media. Furthermore, the generation unit can generate relevant content from visitors' social media activity. This allows the generation unit to analyze visitors' social media activity and generate relevant content. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input visitors' social media activity data into AI and have the AI ​​generate relevant content.

[0084] The data collection unit can estimate the visitor's emotions and adjust the feedback collection method based on the estimated visitor's emotions. For example, if a visitor is relaxed, the data collection unit can request detailed feedback. If a visitor is in a hurry, the data collection unit can request brief feedback. Furthermore, if a visitor is feeling anxious, the data collection unit can provide reassuring feedback. This allows the feedback collection method to be adjusted based on the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input visitor emotion data into a generative AI and have the generative AI adjust the feedback collection method.

[0085] The data collection unit can select the optimal collection method by referring to the visitor's past feedback history when collecting feedback. For example, if the visitor has provided detailed feedback in the past, the data collection unit can suggest a similar method. Similarly, if the visitor has provided brief feedback in the past, the data collection unit can suggest a similar method. Furthermore, the data collection unit can analyze the visitor's past feedback history and select the optimal collection method. This allows the data collection unit to select the optimal collection method by referring to the visitor's past feedback history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the visitor's past feedback history data into AI and have the AI ​​select the optimal collection method.

[0086] The data collection unit can estimate the emotions of visitors and determine the priority of feedback based on the estimated emotions. For example, if a visitor is excited, the data collection unit can prioritize collecting positive feedback. Similarly, if a visitor is feeling anxious, the data collection unit can prioritize collecting negative feedback. Furthermore, the data collection unit can adjust the priority of feedback based on the emotions of visitors. This allows for the prioritization of feedback based on the emotions of visitors. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input visitor emotion data into a generative AI and have the generative AI determine the priority of feedback.

[0087] The data collection unit can prioritize collecting highly relevant feedback by considering the visitor's geographical location information during feedback collection. For example, if a visitor is in a specific area, the data collection unit can prioritize collecting feedback related to that area. Furthermore, if a visitor is from a specific region, the data collection unit can prioritize collecting feedback related to that region. Additionally, if a visitor is interested in a specific location, the data collection unit can prioritize collecting feedback related to that location. This allows for the priority collection of highly relevant feedback by considering the visitor's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input visitor geographical location data into an AI and have the AI ​​collect highly relevant feedback.

[0088] The improvement unit can estimate visitors' emotions and adjust how the attraction is improved based on those estimated emotions. For example, if a visitor is relaxed, the improvement unit can suggest a gentle improvement. If a visitor is excited, the improvement unit can suggest a stimulating improvement. Furthermore, if a visitor is feeling anxious, the improvement unit can suggest a reassuring improvement. This allows the improvement of the attraction to be adjusted based on the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the improvement unit may be performed using AI, for example, or not using AI. For example, the improvement unit can input visitor emotion data into a generative AI and have the generative AI adjust how the attraction is improved.

[0089] The improvement unit can select the optimal improvement method by referring to past visitor feedback during the improvement process. For example, the improvement unit can prioritize the adoption of improvement methods that visitors have previously given high ratings to. It can also exclude improvement methods that visitors have previously given low ratings to. Furthermore, the improvement unit can analyze past visitor feedback to select the optimal improvement method. This allows the improvement unit to select the optimal improvement method by referring to past visitor feedback. Some or all of the above processes in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input past visitor feedback data into AI and have the AI ​​select the optimal improvement method.

[0090] The improvement unit can customize improvements based on visitors' interests and preferences. For example, the improvement unit can improve attractions based on themes that visitors are interested in. It can also improve attractions based on styles that visitors are interested in. Furthermore, the improvement unit can improve attractions that reflect visitors' interests and preferences. This allows for customization of improvements based on visitors' interests and preferences. Some or all of the above processes in the improvement unit may be performed using AI, for example, or not. For example, the improvement unit can input visitor interest data into AI and have the AI ​​perform the customization of improvements.

[0091] The improvement unit can estimate the emotions of visitors and determine the priority of improvements based on the estimated emotions. For example, if a visitor is excited, the improvement unit can prioritize positive improvements. Also, if a visitor is feeling anxious, the improvement unit can prioritize negative improvements. Furthermore, the improvement unit can adjust the priority of improvements based on the emotions of visitors. This allows the priority of improvements to be determined based on the emotions of visitors. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the improvement unit may be performed using AI, for example, or not using AI. For example, the improvement unit can input visitor emotion data into a generative AI and have the generative AI determine the priority of improvements.

[0092] The improvement unit can select the optimal improvement method when making improvements, taking into account the visitor's geographical location information. For example, if a visitor is in a specific area, the improvement unit can suggest improvement methods related to that area. Also, if a visitor is from a specific region, the improvement unit can suggest improvement methods related to that region. Furthermore, if a visitor is interested in a specific place, the improvement unit can suggest improvement methods related to that place. This allows the improvement unit to select the optimal improvement method while taking into account the visitor's geographical location information. Some or all of the above processing in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input visitor geographical location data into AI and have the AI ​​select the optimal improvement method.

[0093] The improvement department can analyze visitors' social media activity and propose relevant improvement methods during the improvement process. For example, the improvement department can improve attractions based on themes visitors have shown interest in on social media. It can also improve attractions based on themes visitors have shared on social media. Furthermore, the improvement department can propose relevant improvement methods based on visitors' social media activity. This allows the improvement department to analyze visitors' social media activity and propose relevant improvement methods. Some or all of the above processes in the improvement department may be performed using AI, for example, or not. For example, the improvement department can input visitors' social media activity data into AI and have the AI ​​propose relevant improvement methods.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The AI-powered park experience system can also be equipped with a "personalized guide function." This function can suggest the most suitable attractions and experiences based on each visitor's individual interests and past experience history. For example, if a visitor has enjoyed a particular art experience in the past, it can prioritize suggesting similar art experiences. Similarly, if a visitor has shown interest in a particular theme, it can suggest attractions related to that theme. Furthermore, the personalized guide function can suggest appropriate attractions based on the visitor's age and family structure. This allows visitors to efficiently enjoy the most suitable experiences for them.

[0096] The AI-powered park experience system can also be equipped with a "real-time feedback unit." This unit can collect feedback in real time while visitors are experiencing an attraction and instantly adjust the attraction's content. For example, if a visitor feels the difficulty level is too high while playing a game, the difficulty can be lowered in real time. Also, if a visitor prefers a particular color or style while experiencing art, the art style can be changed to match their preference. Furthermore, if a visitor finds the drone experience difficult to operate, the controls can be simplified. This ensures that visitors always enjoy the optimal experience.

[0097] The AI-powered park experience system can also be equipped with an "emotion analysis unit." This unit analyzes visitors' facial expressions and tone of voice to estimate their emotions in real time. For example, if a visitor is smiling, it can estimate that they are enjoying themselves and suggest more fun attractions. If a visitor is tired, it can suggest relaxing attractions. Furthermore, if a visitor is excited, it can suggest stimulating attractions. This allows the system to provide the optimal experience based on the visitor's emotions.

[0098] The AI-powered experience park system can also be equipped with an "interactive storytelling section." This section can change the story's progression in real time based on the visitor's choices and actions. For example, if a visitor makes a specific choice, the story's ending can change accordingly. Furthermore, if a visitor takes a specific action, new characters or events can appear based on that action. The interactive storytelling section can also adjust the tone and theme of the story based on the visitor's emotions. This allows visitors to enjoy a unique story experience tailored to their individual needs.

[0099] The AI ​​experience park system can also be equipped with a "virtual reality section." This section can provide visitors with experiences in a virtual reality world. For example, visitors can wear a virtual reality headset and explore different eras and places. They can also create art or play games in the virtual reality world. Furthermore, the virtual reality section can adjust the virtual reality environment based on the visitor's emotions. This allows visitors to enjoy unique experiences that cannot be experienced in reality.

[0100] The AI-powered park experience system can also be equipped with a "voice interaction unit." This unit can adjust the content of attractions based on the visitor's voice commands. For example, if a visitor requests a specific art style, the system can generate artwork based on that request. Similarly, if a visitor wants to change the difficulty level of a particular game, the system can adjust the difficulty level based on that request. Furthermore, the voice interaction unit can estimate the visitor's emotions from their voice and adjust the content of the attractions based on those emotions. This allows visitors to intuitively operate attractions using their voice.

[0101] The AI ​​Experience Park system can also be equipped with an "Ecosystem Collaboration Department." This department can collaborate with other theme parks and entertainment facilities to provide visitors with a consistent experience. For example, it can customize the AI ​​Experience Park experience based on data from attractions visitors enjoyed at other theme parks. It can also allow visitors to use points and rewards earned at other facilities within the AI ​​Experience Park. Furthermore, the Ecosystem Collaboration Department can plan collaborative events with other facilities to offer visitors new experiences. This allows visitors to enjoy a consistent entertainment experience across multiple facilities.

[0102] The AI-powered park experience system can also be equipped with a "health monitoring unit." This unit can monitor visitors' health status in real time and adjust the content of attractions as needed. For example, it can monitor visitors' heart rate and stress levels and adjust the difficulty of attractions to avoid excessive stress. It can also suggest relaxing attractions if visitors are tired. Furthermore, the health monitoring unit can suggest appropriate rest times based on visitors' health data. This ensures that visitors can enjoy the experience safely and comfortably.

[0103] The AI-powered park experience system can also be equipped with a "Community Collaboration Section." This section facilitates interaction among visitors, allowing them to enjoy the experience together. For example, visitors can chat or video call in real time with other visitors experiencing the same attraction. Visitors can also collaboratively create artwork or play games. Furthermore, the Community Collaboration Section can suggest the most suitable interaction partners based on the visitor's emotions. This allows visitors to enjoy the experience together with other visitors.

[0104] The AI-powered park experience system can also include an "edutainment section." This section can offer experiences that combine entertainment and education. For example, visitors can enjoy fun attractions while learning about science and history. Visitors can also create learning content using generative AI and share it with other visitors. Furthermore, the edutainment section can provide an optimal learning experience based on the visitor's learning progress. This allows visitors to learn while having fun.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The Experience Section allows visitors to experience the attractions in each area. For example, visitors can enjoy facility introductions and admission check-in using generative AI in the entrance zone. They can also learn about generative AI and experience content creation in the learning & creative experience zone. Furthermore, the art & emotional experience zone can provide exhibitions and photo spaces for art works generated using generative AI. In the entertainment experience zone, visitors can experience games and quizzes using generative AI, and watch videos and animations created using generative AI. In the drone experience zone, visitors can enjoy drone photography and games while experiencing drone operation using generative AI. In the food & shopping zone, generative AI can be used to sell food and beverages and suggest souvenir products. Step 2: The generation unit uses a generation AI to generate attraction content based on the experience provided by the experience unit. For example, it can generate artwork using the generation AI. It can also generate game and quiz content using the generation AI. Furthermore, it can generate instructions on how to operate a drone using the generation AI. Step 3: The data collection unit collects visitor feedback. For example, it can collect visitor voting results and feedback. Step 4: The improvement unit improves the attraction based on the feedback collected by the data collection unit. For example, the attraction can be improved based on the collected feedback.

[0107] 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.

[0108] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0109] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0110] Each of the multiple elements described above, including the experience unit, generation unit, collection unit, and improvement unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the experience unit is implemented by the smart device 14, and visitors can experience the attractions in each area using the control unit 46A of the smart device 14. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and generates attraction content using generation AI. The collection unit is implemented by, for example, the control unit 46A of the smart device 14, and collects visitor feedback. The improvement unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and can improve the attractions based on the collected feedback. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] 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.

[0113] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0114] 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.

[0115] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0116] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0117] 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.

[0118] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0119] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0120] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0121] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0123] 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.

[0124] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0125] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the experience unit, generation unit, collection unit, and improvement unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the experience unit is implemented by the smart glasses 214, allowing visitors to experience the attractions in each area using the control unit 46A of the smart glasses 214. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates attraction content using generation AI. The collection unit is implemented by, for example, the control unit 46A of the smart glasses 214, which collects visitor feedback. The improvement unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which can improve the attractions based on the collected feedback. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] 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.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0130] 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.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0132] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] 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.

[0134] 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.

[0135] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the experience unit, generation unit, collection unit, and improvement unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the experience unit is implemented by the headset terminal 314, allowing visitors to experience the attractions in each area using the control unit 46A of the headset terminal 314. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates attraction content using generation AI. The collection unit is implemented by, for example, the control unit 46A of the headset terminal 314, which collects visitor feedback. The improvement unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which can improve the attractions based on the collected feedback. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0146] 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.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0148] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] 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.

[0150] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0151] 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.

[0152] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] 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.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the experience unit, generation unit, collection unit, and improvement unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the experience unit is implemented by the robot 414, and visitors can experience the attractions in each area using the control unit 46A of the robot 414. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and generates attraction content using a generation AI. The collection unit is implemented by, for example, the control unit 46A of the robot 414, and collects visitor feedback. The improvement unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and can improve the attractions based on the collected feedback. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0160] 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.

[0161] Figure 9 shows the 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.

[0162] 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.

[0163] 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.

[0164] 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, and motorcycles, 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 based, for example, 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.

[0165] 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."

[0166] 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.

[0167] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0176] 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 other things 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.

[0177] 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.

[0178] (Note 1) The experience section allows you to experience the attractions in each area, A generation unit that generates attraction content using a generation AI based on the content experienced by the aforementioned experience unit, A collection department that gathers visitor feedback, The system includes an improvement unit that improves the attraction based on the feedback collected by the collection unit. A system characterized by the following features. (Note 2) The generating unit is Generating artwork using generative AI The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Generative AI is used to generate game and quiz content. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate drone operation methods using generational AI. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Collect visitor voting results and feedback. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned improvement unit is, We will improve the attraction based on the feedback we collect. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned experience section is, It estimates the emotions of visitors and adjusts the experience in real time based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned experience section is, During the experience, we analyze the visitor's past experience history to provide the most suitable experience. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned experience section is, During the experience, the content will be customized based on the visitor's age and interests. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned experience section is, The system estimates the emotions of visitors and adjusts the order of experiences based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned experience section is, During the experience, we take into account the visitor's geographical location to provide a highly relevant experience. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned experience section is, During the experience, we analyze visitors' social media activity and provide relevant experiences. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the emotions of visitors and adjusts the style of the artwork generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During generation, the generation algorithm is optimized by referring to past feedback from visitors. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During generation, the generated content is customized based on the interests and concerns of the visitors. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is The system estimates the emotions of visitors and adjusts the difficulty level of games and quizzes generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, the system considers the visitor's geographical location to generate highly relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During generation, the social media activity of visitors is analyzed, and relevant content is generated. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned collection unit is We estimate the emotions of visitors and adjust the feedback collection method based on the estimated emotions of visitors. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned collection unit is When collecting feedback, the optimal collection method is selected by referring to the visitor's past feedback history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned collection unit is The system estimates the emotions of visitors and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned collection unit is When collecting feedback, the geographical location of visitors will be taken into consideration to prioritize the collection of highly relevant feedback. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned improvement unit is, We estimate the emotions of visitors and adjust how we improve the attractions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned improvement unit is, When making improvements, we will refer to past feedback from visitors to select the most suitable improvement method. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned improvement unit is, When making improvements, customize the improvements based on the interests and concerns of visitors. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned improvement unit is, We estimate the emotions of visitors and determine the priority of improvements based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned improvement unit is, When making improvements, the optimal improvement method will be selected by considering the geographical location information of visitors. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned improvement unit is, During the improvement process, we analyze visitors' social media activity and propose relevant improvement methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The experience section allows you to experience the attractions in each area, A generation unit that generates attraction content using generation AI based on the content experienced by the aforementioned experience unit, A collection department that gathers visitor feedback, The system includes an improvement unit that improves the attraction based on the feedback collected by the collection unit. A system characterized by the following features.

2. The generating unit is Generating artwork using generative AI. The system according to feature 1.

3. The generating unit is Generative AI is used to generate game and quiz content. The system according to feature 1.

4. The generating unit is Generating drone operation methods using generative AI. The system according to feature 1.

5. The aforementioned collection unit is Collect visitor voting results and feedback. The system according to feature 1.

6. The aforementioned improvement unit is, We will improve the attraction based on the feedback we collect. The system according to feature 1.

7. The aforementioned experience section is, It estimates the emotions of visitors and adjusts the experience in real time based on those estimated emotions. The system according to feature 1.

8. The aforementioned experience section is, During the experience, we analyze the visitor's past experience history to provide the most suitable experience. The system according to feature 1.

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