system

Abstract

The system according to this embodiment aims to provide information about exhibited organisms and personalized entertainment in order to enhance the aquarium experience. [Solution] The system according to the embodiment comprises an analysis unit, a provision unit, a suggestion unit, and an execution unit. The analysis unit analyzes photographs taken by the user in the aquarium and identifies the types of exhibited organisms. The provision unit provides information on the ecology and interesting facts about the exhibited organisms identified by the analysis unit. The suggestion unit proposes personalized challenges and quizzes to the user based on the information provided by the provision unit. The execution unit executes the challenges and quizzes proposed by the suggestion unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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, sufficient information provision for enjoying the experience in an aquarium more deeply and provision of personalized entertainment have not been carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to provide information about the exhibited organisms and personalized entertainment in order to enjoy the experience in the aquarium more deeply.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a provision unit, a suggestion unit, and an execution unit. The analysis unit analyzes photographs taken by the user in the aquarium and identifies the types of exhibited organisms. The provision unit provides information on the ecology and interesting facts about the exhibited organisms identified by the analysis unit. The suggestion unit proposes personalized challenges and quizzes to the user based on the information provided by the provision unit. The execution unit executes the challenges and quizzes proposed by the suggestion unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide information about exhibited organisms and personalized entertainment to enhance the aquarium experience. [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 a plurality of 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) An entertainment system according to an embodiment of the present invention is an AI-powered entertainment service that revolutionizes the aquarium experience. This entertainment system instantly analyzes photos taken by users within the aquarium, accurately identifying the types of exhibited creatures and providing information about their ecology and interesting facts. For example, if a rare deep-sea fish is photographed, an explanation of its habitat and special abilities will be displayed. Furthermore, the entertainment system's AI learns each user's interests and behavioral patterns, suggesting personalized challenges and quizzes. For example, through educational games such as "Sea Creature Photo Hunt" and "Ecosystem Puzzle," users can deepen their understanding of marine life while having fun. The entertainment system also utilizes advanced AI image processing technology to add fantastical underwater effects to photographs or create composite photos that make it appear as if the user is swimming with the photographed fish. This allows users to enjoy a new experience that goes beyond simple photography. For example, when a user photographs exhibits or creatures within the aquarium, all they need to do is take a picture using their smartphone. For example, if a user photographs a rare deep-sea fish, that photo is input into the entertainment system. Next, the AI ​​in the entertainment system analyzes the input photo. The AI ​​uses image recognition technology to identify the types of exhibited creatures and provides information about their ecology and interesting facts. For example, if a photo of a deep-sea fish is analyzed, an explanation of the fish's habitat and special abilities will be displayed. Furthermore, the entertainment system allows the AI ​​to learn each user's interests and behavioral patterns, which in turn suggests personalized challenges and quizzes. For instance, through games such as "Sea Creature Photo Hunt" and "Ecosystem Puzzle," users can deepen their understanding of marine life while having fun. The entertainment system also utilizes advanced AI image processing technology to add fantastical underwater effects to photos or create composite images that make it appear as if you are swimming with the fish you photographed. This allows users to enjoy a new experience that goes beyond simple photography.In this way, the entertainment system functions as a comprehensive learning and entertainment platform that enhances the aquarium experience and promotes interest in and understanding of marine life. Users can not only have a fun experience through photography but also deepen their knowledge of marine life. For example, based on the photos taken by the user, AI can provide detailed information about the organism, satisfying the user's intellectual curiosity. In this way, the entertainment system can transform the user's aquarium experience and deepen their interest in and understanding of marine life.

[0029] The entertainment system according to this embodiment comprises an analysis unit, a provision unit, a proposal unit, and an execution unit. The analysis unit analyzes photographs taken by the user in the aquarium and identifies the types of exhibited organisms. The analysis unit identifies the types of exhibited organisms using, for example, image recognition technology. For example, the analysis unit extracts the characteristics of the exhibited organisms and identifies their types using deep learning technology. The analysis unit can also identify the types of exhibited organisms by analyzing their shape and color using computer vision technology. Furthermore, the analysis unit can also identify the types of exhibited organisms using AI. For example, the analysis unit inputs images of exhibited organisms into an AI model and identifies their types. The provision unit provides information on the ecology and interesting facts about the exhibited organisms identified by the analysis unit. The provision unit provides, for example, explanations about the habitat and special abilities of the identified exhibited organisms. For example, the provision unit provides information about the habitat of the exhibited organisms, such as water temperature, salinity, and symbiotic organisms. The provision unit can also provide information about the special abilities of the exhibited organisms, such as bioluminescence and camouflage. Furthermore, the provisioning unit can use AI to provide information about the ecology of exhibited organisms and interesting facts. For example, the provisioning unit inputs information about the exhibited organisms into an AI model to provide information about their ecology and interesting facts. The suggestion unit proposes personalized challenges and quizzes to the user based on the information provided by the provisioning unit. For example, the suggestion unit learns the user's interests and behavioral patterns to propose personalized challenges and quizzes. For example, the suggestion unit proposes the most suitable challenges and quizzes based on the user's past behavioral history and survey results. The suggestion unit can also use AI to propose personalized challenges and quizzes to the user. For example, the suggestion unit inputs user information into an AI model to propose the most suitable challenges and quizzes. The execution unit executes the challenges and quizzes proposed by the suggestion unit. For example, the execution unit executes the proposed challenges and quizzes and provides feedback to the user. For example, the execution unit evaluates the results of the challenges and quizzes and provides feedback to the user. The execution unit can also use AI to execute the proposed challenges and quizzes. For example, the execution unit inputs information about the challenges and quizzes into an AI model and outputs the execution results.As a result, the entertainment system according to this embodiment can revolutionize the user's aquarium experience and deepen their interest in and understanding of marine life.

[0030] The analysis unit analyzes photos taken by users within the aquarium to identify the types of exhibited organisms. For example, the analysis unit uses image recognition technology to identify the types of exhibited organisms. Specifically, it utilizes deep learning technology to extract the characteristics of the exhibited organisms and identify their species. Deep learning technology uses a large number of biological images as training data, enabling it to recognize features such as shape, color, and pattern of exhibited organisms with high accuracy. Furthermore, computer vision technology can also be used to analyze the shape and color of exhibited organisms and identify their species. Computer vision technology detects objects in images and analyzes their shape and color patterns to identify the types of exhibited organisms. For example, it can accurately identify organisms with specific characteristics, such as the scale patterns of fish or the transparent bodies of jellyfish. Additionally, the analysis unit can also use AI to identify the types of exhibited organisms. The process of inputting images of exhibited organisms into an AI model and identifying their species is based on a pre-trained database. Because the AI ​​model has learned from millions of biological images and understands the characteristics of each organism, it can quickly and accurately identify species from newly input images. This allows the analysis unit to analyze photos taken by users with high accuracy and identify the types of exhibited organisms.

[0031] The information provider will provide information on the ecology and interesting facts about the exhibited organisms identified by the analysis department. For example, the information provider will provide explanations about the habitat and special abilities of the identified exhibited organisms. Specifically, regarding the habitat of the exhibited organisms, it will provide information such as water temperature, salinity, and symbiotic organisms. For example, for fish that live in coral reefs, it can provide detailed explanations about the structure of the coral reef, its relationship with other organisms living there, and the effects of water temperature and salinity. The information provider can also provide information on the special abilities of the exhibited organisms, such as bioluminescence and camouflage. For example, regarding the bioluminescence of organisms that live in the deep sea, it can explain the mechanism and ecological role, providing users with interesting facts. Furthermore, the information provider can also provide information on the ecology and interesting facts of the exhibited organisms using AI. The process of inputting information on the exhibited organisms into the AI ​​model and providing information on their ecology and interesting facts is carried out based on a pre-trained database. The AI ​​model has learned from a vast amount of biological data and can provide users with detailed and accurate information. In this way, the information provider can provide users with a wealth of information on the ecology and interesting facts of the exhibited organisms, deepening their knowledge.

[0032] The Proposal Department suggests personalized challenges and quizzes to users based on information provided by the Provision Department. For example, the Proposal Department learns the user's interests and behavioral patterns to suggest personalized challenges and quizzes. Specifically, it suggests the most suitable challenges and quizzes based on the user's past behavioral history and survey results. For example, if a user shows interest in a particular exhibited animal, it can suggest quizzes and challenges related to that animal. The Proposal Department can also use AI to suggest personalized challenges and quizzes to users. The process of inputting user information into the AI ​​model and suggesting the most suitable challenges and quizzes is based on a pre-trained database. The AI ​​model can analyze the user's interests and behavioral patterns to suggest the most appropriate challenges and quizzes. For example, it can suggest quizzes with adjusted difficulty and content based on the results of quizzes the user has answered in the past and their level of interest in exhibited animals. This allows the Proposal Department to provide users with personalized challenges and quizzes and continue to engage their interest.

[0033] The execution unit executes the challenges and quizzes proposed by the proposal unit. For example, the execution unit executes the proposed challenges and quizzes and provides feedback to the user. Specifically, it evaluates the results of the challenges and quizzes and provides feedback to the user. For example, after the user answers a quiz, it can provide the result of whether the answer was correct or incorrect, as well as an explanation of the answer. The execution unit can also execute the proposed challenges and quizzes using AI. The process of inputting challenge and quiz information into the AI ​​model and outputting the execution results is performed based on a pre-trained database. The AI ​​model can analyze the user's answers and provide accurate feedback. For example, based on the content of the quiz answered by the user, it can suggest relevant additional information or the next challenge. Furthermore, the execution unit can collect user feedback and use it to improve the overall system. For example, based on the feedback provided by the user, it can adjust the content and difficulty of the challenges and quizzes to provide a better experience. In this way, the execution unit can provide effective feedback to the user and improve the quality of the entire entertainment system.

[0034] The analysis unit can identify the species of exhibited organisms using image recognition technology. For example, the analysis unit can extract features of exhibited organisms and identify their species using deep learning technology. For example, the analysis unit inputs images of exhibited organisms into a deep learning model, extracts features, and identifies the species. The analysis unit can also analyze the shape and color of exhibited organisms and identify their species using computer vision technology. For example, the analysis unit uses a computer vision algorithm to analyze the shape and color of exhibited organisms and identify their species. Furthermore, the analysis unit can also identify exhibited organisms using AI. For example, the analysis unit inputs images of exhibited organisms into an AI model and identifies their species. This allows for accurate identification of exhibited organisms using image recognition technology. Image recognition technology includes, but is not limited to, deep learning and computer vision technology. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input images of exhibited organisms into an AI model and identify their species.

[0035] The information provider can provide explanations about the habitat and special abilities of the identified exhibited organisms. For example, regarding the habitat of the identified exhibited organisms, the information provider can provide information such as water temperature, salinity, and symbiotic organisms. For example, regarding the habitat of the exhibited organisms, the information provider can provide information such as water temperature, salinity, and symbiotic organisms. The information provider can also provide information about the special abilities of the identified exhibited organisms, such as bioluminescence and camouflage. For example, the information provider can provide information about the special abilities of the exhibited organisms, such as bioluminescence and camouflage. Furthermore, the information provider can use AI to provide information about the ecology and interesting facts of the exhibited organisms. For example, the information provider can input information about the exhibited organisms into an AI model to provide information about their ecology and interesting facts. This allows for a deeper understanding of the user's knowledge by providing explanations about the habitat and special abilities of the exhibited organisms. Habitat information includes, but is not limited to, water temperature, salinity, and symbiotic organisms. Special abilities include, but are not limited to, bioluminescence and camouflage. Some or all of the processing described above in the provisioning unit may be performed using AI, for example, or without AI. For example, the provisioning unit can input information about exhibited organisms into an AI model to provide information about their ecology and interesting facts.

[0036] The suggestion unit can learn the user's interests and behavioral patterns and propose personalized challenges and quizzes. For example, the suggestion unit can propose the most suitable challenges and quizzes based on the user's past behavioral history and survey results. For example, the suggestion unit can analyze the user's past behavioral history and propose challenges and quizzes that are likely to interest the user. The suggestion unit can also propose personalized challenges and quizzes based on the user's survey results. For example, the suggestion unit can analyze the user's survey results and propose the most suitable challenges and quizzes. Furthermore, the suggestion unit can use AI to propose personalized challenges and quizzes to the user. For example, the suggestion unit can input the user's information into an AI model and propose the most suitable challenges and quizzes. In this way, by learning the user's interests and behavioral patterns, it can propose personalized challenges and quizzes. Interests and behavioral patterns include, but are not limited to, past behavioral history and survey results. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can input the user's information into an AI model and propose the most suitable challenges and quizzes.

[0037] The execution unit can carry out the proposed challenges and quizzes. For example, the execution unit can carry out the proposed challenges and quizzes and provide feedback to the user. For example, the execution unit can evaluate the results of the challenges and quizzes and provide feedback to the user. The execution unit can also carry out the proposed challenges and quizzes using AI. For example, the execution unit can input information about the challenges and quizzes into an AI model and output the execution results. This allows users to learn while having fun by carrying out the proposed challenges and quizzes. Challenges and quizzes include, but are not limited to, question format, difficulty level, and feedback method. Some or all of the above processing in the execution unit may be carried out using, for example, AI, or not using AI. For example, the execution unit can input information about the challenges and quizzes into an AI model and output the execution results.

[0038] The software can add fantastical underwater effects to captured photographs. For example, it can add effects such as light refraction and color correction to captured photographs. For instance, it can add a light refraction effect to a photograph to create an underwater atmosphere. It can also correct the color tone of a photograph to add a fantastical underwater effect. Furthermore, the software can use AI to add fantastical underwater effects to captured photographs. For example, it can input a photograph into an AI model to generate a fantastical underwater effect. This allows the software to provide users with a new experience by adding fantastical underwater effects to their photographs. These fantastical underwater effects include, but are not limited to, light refraction and color correction. Some or all of the above-described processes in the software may be performed using AI, or not. For example, the software can input a photograph into an AI model to generate a fantastical underwater effect.

[0039] The service provider can create composite photographs that make it appear as if the user is swimming with the photographed fish. For example, the service provider can create composite photographs that make it appear as if the user is swimming with the photographed fish. For example, the service provider can combine an image of the photographed fish with an image of the user to create a photograph that makes it appear as if they are swimming together. The service provider can also use AI to create composite photographs that make it appear as if the user is swimming with the photographed fish. For example, the service provider can input an image of the photographed fish and an image of the user into an AI model and generate a composite photograph. This allows the service provider to offer users a new experience by creating composite photographs that make it appear as if they are swimming with the photographed fish. Composite photographs include, but are not limited to, the use of image editing software and the accuracy of the composite. Some or all of the above-described processes in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input an image of the photographed fish and an image of the user into an AI model and generate a composite photograph.

[0040] The analysis unit can identify exhibited organisms based on background information from the photographs taken. For example, if aquatic plants are visible in the background, the analysis unit increases the likelihood that the organism is a freshwater fish. For example, the analysis unit analyzes a photograph with aquatic plants in the background and increases the likelihood that the organism is a freshwater fish. The analysis unit can also increase the likelihood that the organism is a marine organism if a coral reef is visible in the background. For example, the analysis unit analyzes a photograph with a coral reef in the background and increases the likelihood that the organism is a marine organism. Furthermore, the analysis unit can increase the likelihood that the organism is a benthic organism if a sandy area is visible in the background. For example, the analysis unit analyzes a photograph with a sandy area in the background and increases the likelihood that the organism is a benthic organism. In this way, considering the background information of the photographs improves the accuracy of identifying exhibited organisms. Background information includes, but is not limited to, the location, time, and environmental conditions of the photograph. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input background information from a photograph into an AI model to identify the exhibited organisms.

[0041] The analysis unit can identify the species of exhibited organisms by combining multiple photographs. For example, the analysis unit can combine photographs taken from multiple angles to grasp the overall appearance of the exhibited organism. For example, the analysis unit can analyze photographs taken from multiple angles to grasp the overall appearance of the exhibited organism. The analysis unit can also combine photographs taken at different times to analyze the behavioral patterns of the organism. For example, the analysis unit can analyze photographs taken at different times to grasp the behavioral patterns of the organism. Furthermore, the analysis unit can identify the species of exhibited organisms by combining photographs taken by multiple users. For example, the analysis unit can analyze photographs taken by multiple users to identify the species of exhibited organism. This improves the accuracy of identifying exhibited organisms by combining multiple photographs. Combining multiple photographs may include, but is not limited to, image matching technology and consideration of temporal continuity. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input multiple photographs into an AI model to identify the species of exhibited organisms.

[0042] The analysis unit can improve the accuracy of its analysis by referring to the user's past shooting history. For example, the analysis unit can improve the accuracy of its analysis based on data of organisms that the user has photographed in the past. For example, the analysis unit can improve the accuracy of its analysis by obtaining the user's past shooting history from a database. The analysis unit can also reflect the frequency of appearance of specific organisms in the analysis based on the user's past shooting history. For example, the analysis unit can analyze the user's past shooting history and reflect the frequency of appearance of specific organisms in the analysis. Furthermore, the analysis unit can also identify similar organisms by analyzing the user's past shooting history. For example, the analysis unit can analyze the user's past shooting history and identify similar organisms. This improves the accuracy of the analysis by referring to the user's past shooting history. Past shooting history includes, but is not limited to, the use of a database and methods for filtering the history. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can improve the accuracy of its analysis by inputting the user's past shooting history into an AI model.

[0043] The analysis unit can identify exhibited organisms based on the user's geographical location information. For example, the analysis unit can identify organisms inhabiting a region based on the geographical information of the location where the user took a photograph. For example, the analysis unit can acquire the user's location information and identify organisms inhabiting that region. The analysis unit can also identify organisms exhibited within a specific aquarium based on the user's location information. For example, the analysis unit can identify organisms exhibited within a specific aquarium based on the user's location information. Furthermore, the analysis unit can identify organisms specific to a region based on the user's location information. For example, the analysis unit can identify organisms specific to a region based on the user's location information. This improves the accuracy of exhibited organism identification by considering the user's geographical location information. Geographical location information includes, but is not limited to, the use of GPS data and the accuracy of location information. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the user's geographical location information into an AI model and identify exhibited organisms.

[0044] The information provider can update information by referring to the latest research data on the ecology of exhibited organisms. For example, the information provider can update information on the ecology of exhibited organisms by referring to the latest research papers. For example, the information provider can obtain the latest research papers from a database and update information on the ecology of exhibited organisms. The information provider can also provide information on the ecology of exhibited organisms by referring to the latest academic databases. For example, the information provider can provide information on the ecology of exhibited organisms by referring to the latest academic databases. Furthermore, the information provider can provide information on the ecology of exhibited organisms based on the latest research results. For example, the information provider can provide information on the ecology of exhibited organisms based on the latest research results. This improves the accuracy of the information provided by referring to the latest research data. The latest research data includes, but is not limited to, databases of academic papers and real-time updates. Some or all of the above processing in the information provider may be performed using, for example, AI, or not using AI. For example, the information provider can input the latest research data into an AI model and update the information.

[0045] The information provider can provide information by adding related videos and audio of the exhibited organisms. For example, the provider can provide videos showing the ecology of the exhibited organisms. For example, the provider can retrieve videos showing the ecology of the exhibited organisms from a database and provide the information. The provider can also provide the sounds and voices of the exhibited organisms. For example, the provider can retrieve the sounds and voices of the exhibited organisms from a database and provide the information. Furthermore, the provider can provide videos showing the habitat of the exhibited organisms. For example, the provider can retrieve videos showing the habitat of the exhibited organisms from a database and provide the information. This deepens the understanding of the information by adding related videos and audio. Related videos and audio include, but are not limited to, the length of the video, the content of the audio, and the format in which they are provided. Some or all of the processing described above in the information provider may be performed using AI, for example, or not using AI. For example, the provider can input related videos and audio into an AI model and provide the information.

[0046] The information provider can prioritize information based on the user's past interests. For example, the provider might prioritize information about organisms the user has shown interest in in the past. For example, the provider could retrieve the user's past interests from a database and prioritize the information. The provider could also prioritize relevant information based on the user's past interests. For example, the provider could analyze the user's past interests and prioritize relevant information. Furthermore, the provider could prioritize information based on the user's past behavior patterns. For example, the provider could analyze the user's past behavior patterns and prioritize the information. This allows for the provision of more appropriate information by prioritizing information based on the user's past interests. Past interests include, but are not limited to, past browsing history and survey results. Some or all of the above processing in the information provider may be performed using, for example, AI, or not. For example, the provider could input the user's past interests into an AI model and prioritize the information.

[0047] The service provider can analyze a user's social media activity and provide relevant information. For example, the service provider can analyze the content of a user's social media posts and provide relevant information. For example, the service provider can retrieve the content of a user's social media posts from a database and provide relevant information. The service provider can also provide relevant information based on a user's interests on social media. For example, the service provider can analyze a user's interests on social media and provide relevant information. Furthermore, the service provider can provide relevant information based on a user's social media activity history. For example, the service provider can analyze a user's social media activity history and provide relevant information. In this way, relevant information can be provided by analyzing a user's social media activity. Social media activity includes, but is not limited to, analyzing post content and analyzing followers. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input a user's social media activity into an AI model and provide relevant information.

[0048] The suggestion unit can make optimal suggestions by referring to the user's past challenge history. For example, the suggestion unit can make optimal suggestions based on challenges the user has successfully completed in the past. For example, the suggestion unit can retrieve the user's past challenge history from a database and make optimal suggestions. The suggestion unit can also suggest challenges that the user might be interested in based on their past challenge history. For example, the suggestion unit can analyze the user's past challenge history and suggest challenges that might be of interest. Furthermore, the suggestion unit can analyze the user's past challenge history and suggest challenges of the optimal difficulty level. For example, the suggestion unit can analyze the user's past challenge history and suggest challenges of the optimal difficulty level. In this way, the suggestion unit can make optimal suggestions by referring to the user's past challenge history. Past challenge history includes, but is not limited to, the use of a database and methods for filtering history. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can input the user's past challenge history into an AI model and make optimal suggestions.

[0049] The suggestion unit can customize the content of challenges and quizzes based on the user's current interests when making suggestions. For example, the suggestion unit can suggest a challenge related to an organism the user is currently interested in. For example, the suggestion unit can retrieve the user's current interests from a database and customize the content of challenges and quizzes. The suggestion unit can also suggest relevant quizzes based on the user's current interests. For example, the suggestion unit can analyze the user's current interests and suggest relevant quizzes. Furthermore, the suggestion unit can also customize the content of challenges and quizzes based on the user's current behavior patterns. For example, the suggestion unit can analyze the user's current behavior patterns and customize the content of challenges and quizzes. This allows for more appropriate suggestions by customizing the content of challenges and quizzes based on the user's current interests. Current interests include, but are not limited to, real-time behavior analysis and survey results. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can input the user's current interests into an AI model and customize the content of challenges and quizzes.

[0050] The suggestion unit can propose optimal challenges and quizzes based on the user's geographical location information when making a suggestion. For example, the suggestion unit can propose challenges related to the user's current location. For example, the suggestion unit can obtain the user's geographical location information and propose challenges related to the user's current location. The suggestion unit can also propose region-specific quizzes based on the user's location information. For example, the suggestion unit can propose region-specific quizzes based on the user's location information. Furthermore, the suggestion unit can propose optimal challenges based on the user's location information. For example, the suggestion unit can propose optimal challenges based on the user's location information. In this way, by considering the user's geographical location information, it is possible to propose optimal challenges and quizzes. Geographical location information includes, but is not limited to, the use of GPS data and the accuracy of location information. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can input the user's geographical location information into an AI model and propose optimal challenges and quizzes.

[0051] The suggestion unit can analyze the user's social media activity and suggest relevant challenges and quizzes when making suggestions. For example, the suggestion unit can analyze the content of the user's social media posts and suggest relevant challenges. For example, the suggestion unit can retrieve the content of the user's social media posts from a database and suggest relevant challenges. The suggestion unit can also suggest relevant quizzes based on the user's interests on social media. For example, the suggestion unit can analyze the user's interests on social media and suggest relevant quizzes. Furthermore, the suggestion unit can suggest relevant challenges based on the user's social media activity history. For example, the suggestion unit can analyze the user's social media activity history and suggest relevant challenges. In this way, by analyzing the user's social media activity, it is possible to suggest relevant challenges and quizzes. Social media activity includes, but is not limited to, analyzing content of posts and analyzing followers. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can input the user's social media activity into an AI model and suggest relevant challenges and quizzes.

[0052] The execution unit can select the optimal execution method by referring to the user's past execution history during execution. For example, the execution unit can select the optimal execution method based on the user's past successful execution methods. For example, the execution unit can retrieve the user's past execution history from a database and select the optimal execution method. The execution unit can also select execution methods that are likely to be of interest to the user from their past execution history. For example, the execution unit can analyze the user's past execution history and select execution methods that are likely to be of interest. Furthermore, the execution unit can analyze the user's past execution history and select an execution method of the optimal difficulty level. For example, the execution unit can analyze the user's past execution history and select an execution method of the optimal difficulty level. In this way, the optimal execution method can be selected by referring to the user's past execution history. Past execution history includes, but is not limited to, database usage and history filtering methods. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the user's past execution history into an AI model and select the optimal execution method.

[0053] The execution unit can customize the means of executing challenges and quizzes based on the user's current situation during execution. For example, the execution unit can select the optimal means of execution according to the user's current situation. For example, the execution unit can analyze the user's current situation in real time and select the optimal means of execution. The execution unit can also customize the means of executing challenges and quizzes based on the user's current situation. For example, the execution unit can analyze the user's current situation and customize the means of executing challenges and quizzes. Furthermore, the execution unit can analyze the user's current situation and suggest the optimal means of execution. For example, the execution unit can analyze the user's current situation in real time and suggest the optimal means of execution. This allows for more appropriate execution by customizing the means of execution based on the user's current situation. Current situation includes, but is not limited to, real-time behavioral analysis and environmental conditions. Some or all of the above processing in the execution unit may be performed using, for example, AI, or not using AI. For example, the execution unit can input the user's current situation into an AI model and customize the means of executing challenges and quizzes.

[0054] The execution unit can select the optimal execution method based on the user's geographical location information at runtime. For example, the execution unit can select an execution method related to the user's current location. For example, the execution unit can acquire the user's geographical location information and select an execution method related to the user's current location. The execution unit can also select a region-specific execution method based on the user's location information. For example, the execution unit can select a region-specific execution method based on the user's location information. Furthermore, the execution unit can select the optimal execution method based on the user's location information. For example, the execution unit can select the optimal execution method based on the user's location information. This allows the optimal execution method to be selected by considering the user's geographical location information. Geographical location information includes, but is not limited to, the use of GPS data and the accuracy of location information. Some or all of the above processing in the execution unit may be performed using, for example, AI, or without AI. For example, the execution unit can input the user's geographical location information into an AI model and select the optimal execution method.

[0055] The execution unit can analyze the user's social media activity at runtime and propose execution methods. For example, the execution unit can analyze the content of the user's social media posts and propose relevant execution methods. For example, the execution unit can retrieve the content of the user's social media posts from a database and propose relevant execution methods. The execution unit can also propose relevant execution methods based on the user's interests on social media. For example, the execution unit can analyze the user's interests on social media and propose relevant execution methods. Furthermore, the execution unit can propose relevant execution methods based on the user's social media activity history. For example, the execution unit can analyze the user's social media activity history and propose relevant execution methods. In this way, relevant execution methods can be proposed by analyzing the user's social media activity. Social media activity includes, but is not limited to, analysis of post content and analysis of followers. Some or all of the above processing in the execution unit may be performed using, for example, AI, or not using AI. For example, the execution unit can input the user's social media activity into an AI model and propose relevant execution methods.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The analysis unit can analyze metadata from photos taken by users and use it to identify exhibited organisms. For example, it can analyze the date, time, and location information of a photo to identify organisms that are likely to be present at that time and place. The analysis unit can also consider the shooting conditions of the photo (e.g., light intensity and angle) to identify organisms more accurately. Furthermore, the analysis unit can analyze the user's camera settings (e.g., ISO sensitivity and shutter speed) to evaluate the quality of the captured image and improve the accuracy of organism identification. In this way, the accuracy of identifying exhibited organisms is improved by utilizing the metadata of the photos.

[0058] The service provider can generate and provide 3D models of exhibited organisms based on photographs taken by the user. For example, the service provider can analyze photographs taken from multiple angles to generate a 3D model of the exhibited organism. The service provider can also display the generated 3D model on the user's smartphone or tablet, allowing the user to freely rotate and zoom in and out. Furthermore, the service provider can add interactive explanations to the 3D model, enabling the user to obtain detailed information about each part of the organism. In this way, utilizing the 3D model can deepen the user's understanding and provide a more engaging experience.

[0059] The analysis unit can analyze the continuity of photos taken by users to identify the behavioral patterns of exhibited organisms. For example, it can analyze consecutively taken photos to identify the organism's movement routes and behavioral patterns. The analysis unit can also combine photos taken at different times to analyze the organism's activity periods and changes in its behavior. Furthermore, the analysis unit can integrate photos taken by multiple users to identify the behavioral patterns of exhibited organisms in more detail. In this way, by utilizing the continuity of photos, it is possible to identify the behavioral patterns of exhibited organisms and improve the accuracy of the information provided to users.

[0060] The exhibiting department can provide interactive simulations about the ecology of exhibited organisms. For example, users can simulate the habitat of a specific organism and observe how it behaves. The department can also allow users to simulate food chains and ecosystem balances, enabling them to learn about the impact of environmental changes on organisms. Furthermore, the department can allow users to simulate the reproduction and growth processes of organisms, enabling them to understand the organism's life cycle. This allows for a deeper understanding and more engaging experience for users through interactive simulations.

[0061] The suggestion function can refer to a user's past challenge history and suggest new challenges the user hasn't yet attempted. For example, it can suggest a new challenge the user should try next based on challenges the user has successfully completed in the past. The suggestion function can also suggest new challenges that the user might be interested in, based on their past challenge history. Furthermore, it can analyze a user's past challenge history and suggest new challenges tailored to their skill level. This allows for the suggestion of more appropriate new challenges by leveraging the user's past challenge history.

[0062] The execution unit can customize the means of executing challenges and quizzes based on the user's current situation. For example, the execution unit can select the optimal means of execution according to the user's current situation. For example, the execution unit can analyze the user's current situation in real time and select the optimal means of execution. The execution unit can also customize the means of executing challenges and quizzes based on the user's current situation. For example, the execution unit can analyze the user's current situation and customize the means of executing challenges and quizzes. Furthermore, the execution unit can analyze the user's current situation and suggest the optimal means of execution. For example, the execution unit can analyze the user's current situation in real time and suggest the optimal means of execution. This allows for more appropriate execution by customizing the means of execution based on the user's current situation.

[0063] The information provider can update information by referring to the latest research data on the ecology of exhibited organisms. For example, they can update information on the ecology of exhibited organisms by referring to the latest research papers. The information provider can also provide information on the ecology of exhibited organisms by referring to the latest academic databases. Furthermore, the information provider can provide information on the ecology of exhibited organisms based on the latest research findings. This improves the accuracy of the information provided by referring to the latest research data.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The analysis unit analyzes photos taken by the user inside the aquarium to identify the types of exhibited organisms. The analysis unit uses image recognition technology, deep learning technology, computer vision technology, and AI to extract characteristics of the exhibited organisms and identify their species. Step 2: The provision department provides information on the ecology and interesting facts about the exhibited organisms identified by the analysis department. The provision department can also provide explanations about the habitat and special abilities of the identified exhibited organisms, and can use AI to provide information on the ecology and interesting facts about the exhibited organisms. Step 3: The suggestion department proposes personalized challenges and quizzes to the user based on the information provided by the delivery department. The suggestion department learns the user's interests and behavioral patterns and uses AI to propose the most suitable challenges and quizzes. Step 4: The execution unit executes the challenges and quizzes proposed by the proposal unit. The execution unit executes the proposed challenges and quizzes and provides feedback to the user. The execution results can also be output using AI.

[0066] (Example of form 2) An entertainment system according to an embodiment of the present invention is an AI-powered entertainment service that revolutionizes the aquarium experience. This entertainment system instantly analyzes photos taken by users within the aquarium, accurately identifying the types of exhibited creatures and providing information about their ecology and interesting facts. For example, if a rare deep-sea fish is photographed, an explanation of its habitat and special abilities will be displayed. Furthermore, the entertainment system's AI learns each user's interests and behavioral patterns, suggesting personalized challenges and quizzes. For example, through educational games such as "Sea Creature Photo Hunt" and "Ecosystem Puzzle," users can deepen their understanding of marine life while having fun. The entertainment system also utilizes advanced AI image processing technology to add fantastical underwater effects to photographs or create composite photos that make it appear as if the user is swimming with the photographed fish. This allows users to enjoy a new experience that goes beyond simple photography. For example, when a user photographs exhibits or creatures within the aquarium, all they need to do is take a picture using their smartphone. For example, if a user photographs a rare deep-sea fish, that photo is input into the entertainment system. Next, the AI ​​in the entertainment system analyzes the input photo. The AI ​​uses image recognition technology to identify the types of exhibited creatures and provides information about their ecology and interesting facts. For example, if a photo of a deep-sea fish is analyzed, an explanation of the fish's habitat and special abilities will be displayed. Furthermore, the entertainment system allows the AI ​​to learn each user's interests and behavioral patterns, which in turn suggests personalized challenges and quizzes. For instance, through games such as "Sea Creature Photo Hunt" and "Ecosystem Puzzle," users can deepen their understanding of marine life while having fun. The entertainment system also utilizes advanced AI image processing technology to add fantastical underwater effects to photos or create composite images that make it appear as if you are swimming with the fish you photographed. This allows users to enjoy a new experience that goes beyond simple photography.In this way, the entertainment system functions as a comprehensive learning and entertainment platform that enhances the aquarium experience and promotes interest in and understanding of marine life. Users can not only have a fun experience through photography but also deepen their knowledge of marine life. For example, based on the photos taken by the user, AI can provide detailed information about the organism, satisfying the user's intellectual curiosity. In this way, the entertainment system can transform the user's aquarium experience and deepen their interest in and understanding of marine life.

[0067] The entertainment system according to this embodiment comprises an analysis unit, a provision unit, a proposal unit, and an execution unit. The analysis unit analyzes photographs taken by the user in the aquarium and identifies the types of exhibited organisms. The analysis unit identifies the types of exhibited organisms using, for example, image recognition technology. For example, the analysis unit extracts the characteristics of the exhibited organisms and identifies their types using deep learning technology. The analysis unit can also identify the types of exhibited organisms by analyzing their shape and color using computer vision technology. Furthermore, the analysis unit can also identify the types of exhibited organisms using AI. For example, the analysis unit inputs images of exhibited organisms into an AI model and identifies their types. The provision unit provides information on the ecology and interesting facts about the exhibited organisms identified by the analysis unit. The provision unit provides, for example, explanations about the habitat and special abilities of the identified exhibited organisms. For example, the provision unit provides information about the habitat of the exhibited organisms, such as water temperature, salinity, and symbiotic organisms. The provision unit can also provide information about the special abilities of the exhibited organisms, such as bioluminescence and camouflage. Furthermore, the provisioning unit can use AI to provide information about the ecology of exhibited organisms and interesting facts. For example, the provisioning unit inputs information about the exhibited organisms into an AI model to provide information about their ecology and interesting facts. The suggestion unit proposes personalized challenges and quizzes to the user based on the information provided by the provisioning unit. For example, the suggestion unit learns the user's interests and behavioral patterns to propose personalized challenges and quizzes. For example, the suggestion unit proposes the most suitable challenges and quizzes based on the user's past behavioral history and survey results. The suggestion unit can also use AI to propose personalized challenges and quizzes to the user. For example, the suggestion unit inputs user information into an AI model to propose the most suitable challenges and quizzes. The execution unit executes the challenges and quizzes proposed by the suggestion unit. For example, the execution unit executes the proposed challenges and quizzes and provides feedback to the user. For example, the execution unit evaluates the results of the challenges and quizzes and provides feedback to the user. The execution unit can also use AI to execute the proposed challenges and quizzes. For example, the execution unit inputs information about the challenges and quizzes into an AI model and outputs the execution results.As a result, the entertainment system according to this embodiment can revolutionize the user's aquarium experience and deepen their interest in and understanding of marine life.

[0068] The analysis unit analyzes photos taken by users within the aquarium to identify the types of exhibited organisms. For example, the analysis unit uses image recognition technology to identify the types of exhibited organisms. Specifically, it utilizes deep learning technology to extract the characteristics of the exhibited organisms and identify their species. Deep learning technology uses a large number of biological images as training data, enabling it to recognize features such as shape, color, and pattern of exhibited organisms with high accuracy. Furthermore, computer vision technology can also be used to analyze the shape and color of exhibited organisms and identify their species. Computer vision technology detects objects in images and analyzes their shape and color patterns to identify the types of exhibited organisms. For example, it can accurately identify organisms with specific characteristics, such as the scale patterns of fish or the transparent bodies of jellyfish. Additionally, the analysis unit can also use AI to identify the types of exhibited organisms. The process of inputting images of exhibited organisms into an AI model and identifying their species is based on a pre-trained database. Because the AI ​​model has learned from millions of biological images and understands the characteristics of each organism, it can quickly and accurately identify species from newly input images. This allows the analysis unit to analyze photos taken by users with high accuracy and identify the types of exhibited organisms.

[0069] The information provider will provide information on the ecology and interesting facts about the exhibited organisms identified by the analysis department. For example, the information provider will provide explanations about the habitat and special abilities of the identified exhibited organisms. Specifically, regarding the habitat of the exhibited organisms, it will provide information such as water temperature, salinity, and symbiotic organisms. For example, for fish that live in coral reefs, it can provide detailed explanations about the structure of the coral reef, its relationship with other organisms living there, and the effects of water temperature and salinity. The information provider can also provide information on the special abilities of the exhibited organisms, such as bioluminescence and camouflage. For example, regarding the bioluminescence of organisms that live in the deep sea, it can explain the mechanism and ecological role, providing users with interesting facts. Furthermore, the information provider can also provide information on the ecology and interesting facts of the exhibited organisms using AI. The process of inputting information on the exhibited organisms into the AI ​​model and providing information on their ecology and interesting facts is carried out based on a pre-trained database. The AI ​​model has learned from a vast amount of biological data and can provide users with detailed and accurate information. In this way, the information provider can provide users with a wealth of information on the ecology and interesting facts of the exhibited organisms, deepening their knowledge.

[0070] The Proposal Department suggests personalized challenges and quizzes to users based on information provided by the Provision Department. For example, the Proposal Department learns the user's interests and behavioral patterns to suggest personalized challenges and quizzes. Specifically, it suggests the most suitable challenges and quizzes based on the user's past behavioral history and survey results. For example, if a user shows interest in a particular exhibited animal, it can suggest quizzes and challenges related to that animal. The Proposal Department can also use AI to suggest personalized challenges and quizzes to users. The process of inputting user information into the AI ​​model and suggesting the most suitable challenges and quizzes is based on a pre-trained database. The AI ​​model can analyze the user's interests and behavioral patterns to suggest the most appropriate challenges and quizzes. For example, it can suggest quizzes with adjusted difficulty and content based on the results of quizzes the user has answered in the past and their level of interest in exhibited animals. This allows the Proposal Department to provide users with personalized challenges and quizzes and continue to engage their interest.

[0071] The execution unit executes the challenges and quizzes proposed by the proposal unit. For example, the execution unit executes the proposed challenges and quizzes and provides feedback to the user. Specifically, it evaluates the results of the challenges and quizzes and provides feedback to the user. For example, after the user answers a quiz, it can provide the result of whether the answer was correct or incorrect, as well as an explanation of the answer. The execution unit can also execute the proposed challenges and quizzes using AI. The process of inputting challenge and quiz information into the AI ​​model and outputting the execution results is performed based on a pre-trained database. The AI ​​model can analyze the user's answers and provide accurate feedback. For example, based on the content of the quiz answered by the user, it can suggest relevant additional information or the next challenge. Furthermore, the execution unit can collect user feedback and use it to improve the overall system. For example, based on the feedback provided by the user, it can adjust the content and difficulty of the challenges and quizzes to provide a better experience. In this way, the execution unit can provide effective feedback to the user and improve the quality of the entire entertainment system.

[0072] The analysis unit can identify the species of exhibited organisms using image recognition technology. For example, the analysis unit can extract features of exhibited organisms and identify their species using deep learning technology. For example, the analysis unit inputs images of exhibited organisms into a deep learning model, extracts features, and identifies the species. The analysis unit can also analyze the shape and color of exhibited organisms and identify their species using computer vision technology. For example, the analysis unit uses a computer vision algorithm to analyze the shape and color of exhibited organisms and identify their species. Furthermore, the analysis unit can also identify exhibited organisms using AI. For example, the analysis unit inputs images of exhibited organisms into an AI model and identifies their species. This allows for accurate identification of exhibited organisms using image recognition technology. Image recognition technology includes, but is not limited to, deep learning and computer vision technology. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input images of exhibited organisms into an AI model and identify their species.

[0073] The information provider can provide explanations about the habitat and special abilities of the identified exhibited organisms. For example, regarding the habitat of the identified exhibited organisms, the information provider can provide information such as water temperature, salinity, and symbiotic organisms. For example, regarding the habitat of the exhibited organisms, the information provider can provide information such as water temperature, salinity, and symbiotic organisms. The information provider can also provide information about the special abilities of the identified exhibited organisms, such as bioluminescence and camouflage. For example, the information provider can provide information about the special abilities of the exhibited organisms, such as bioluminescence and camouflage. Furthermore, the information provider can use AI to provide information about the ecology and interesting facts of the exhibited organisms. For example, the information provider can input information about the exhibited organisms into an AI model to provide information about their ecology and interesting facts. This allows for a deeper understanding of the user's knowledge by providing explanations about the habitat and special abilities of the exhibited organisms. Habitat information includes, but is not limited to, water temperature, salinity, and symbiotic organisms. Special abilities include, but are not limited to, bioluminescence and camouflage. Some or all of the processing described above in the provisioning unit may be performed using AI, for example, or without AI. For example, the provisioning unit can input information about exhibited organisms into an AI model to provide information about their ecology and interesting facts.

[0074] The suggestion unit can learn the user's interests and behavioral patterns and propose personalized challenges and quizzes. For example, the suggestion unit can propose the most suitable challenges and quizzes based on the user's past behavioral history and survey results. For example, the suggestion unit can analyze the user's past behavioral history and propose challenges and quizzes that are likely to interest the user. The suggestion unit can also propose personalized challenges and quizzes based on the user's survey results. For example, the suggestion unit can analyze the user's survey results and propose the most suitable challenges and quizzes. Furthermore, the suggestion unit can use AI to propose personalized challenges and quizzes to the user. For example, the suggestion unit can input the user's information into an AI model and propose the most suitable challenges and quizzes. In this way, by learning the user's interests and behavioral patterns, it can propose personalized challenges and quizzes. Interests and behavioral patterns include, but are not limited to, past behavioral history and survey results. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can input the user's information into an AI model and propose the most suitable challenges and quizzes.

[0075] The execution unit can carry out the proposed challenges and quizzes. For example, the execution unit can carry out the proposed challenges and quizzes and provide feedback to the user. For example, the execution unit can evaluate the results of the challenges and quizzes and provide feedback to the user. The execution unit can also carry out the proposed challenges and quizzes using AI. For example, the execution unit can input information about the challenges and quizzes into an AI model and output the execution results. This allows users to learn while having fun by carrying out the proposed challenges and quizzes. Challenges and quizzes include, but are not limited to, question format, difficulty level, and feedback method. Some or all of the above processing in the execution unit may be carried out using, for example, AI, or not using AI. For example, the execution unit can input information about the challenges and quizzes into an AI model and output the execution results.

[0076] The software can add fantastical underwater effects to captured photographs. For example, it can add effects such as light refraction and color correction to captured photographs. For instance, it can add a light refraction effect to a photograph to create an underwater atmosphere. It can also correct the color tone of a photograph to add a fantastical underwater effect. Furthermore, the software can use AI to add fantastical underwater effects to captured photographs. For example, it can input a photograph into an AI model to generate a fantastical underwater effect. This allows the software to provide users with a new experience by adding fantastical underwater effects to their photographs. These fantastical underwater effects include, but are not limited to, light refraction and color correction. Some or all of the above-described processes in the software may be performed using AI, or not. For example, the software can input a photograph into an AI model to generate a fantastical underwater effect.

[0077] The service provider can create composite photographs that make it appear as if the user is swimming with the photographed fish. For example, the service provider can create composite photographs that make it appear as if the user is swimming with the photographed fish. For example, the service provider can combine an image of the photographed fish with an image of the user to create a photograph that makes it appear as if they are swimming together. The service provider can also use AI to create composite photographs that make it appear as if the user is swimming with the photographed fish. For example, the service provider can input an image of the photographed fish and an image of the user into an AI model and generate a composite photograph. This allows the service provider to offer users a new experience by creating composite photographs that make it appear as if they are swimming with the photographed fish. Composite photographs include, but are not limited to, the use of image editing software and the accuracy of the composite. Some or all of the above-described processes in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input an image of the photographed fish and an image of the user into an AI model and generate a composite photograph.

[0078] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is excited, the analysis unit can increase the accuracy of the analysis to provide more detailed information. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can also maintain normal accuracy and provide standard information if the user is relaxed. For example, the analysis unit can record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is tired, the analysis unit can reduce the accuracy of the analysis to provide concise information. For example, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for the provision of more appropriate information by adjusting the accuracy of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into an AI model to adjust the accuracy of the analysis.

[0079] The analysis unit can identify exhibited organisms based on background information from the photographs taken. For example, if aquatic plants are visible in the background, the analysis unit increases the likelihood that the organism is a freshwater fish. For example, the analysis unit analyzes a photograph with aquatic plants in the background and increases the likelihood that the organism is a freshwater fish. The analysis unit can also increase the likelihood that the organism is a marine organism if a coral reef is visible in the background. For example, the analysis unit analyzes a photograph with a coral reef in the background and increases the likelihood that the organism is a marine organism. Furthermore, the analysis unit can increase the likelihood that the organism is a benthic organism if a sandy area is visible in the background. For example, the analysis unit analyzes a photograph with a sandy area in the background and increases the likelihood that the organism is a benthic organism. In this way, considering the background information of the photographs improves the accuracy of identifying exhibited organisms. Background information includes, but is not limited to, the location, time, and environmental conditions of the photograph. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input background information from a photograph into an AI model to identify the exhibited organisms.

[0080] The analysis unit can identify the species of exhibited organisms by combining multiple photographs. For example, the analysis unit can combine photographs taken from multiple angles to grasp the overall appearance of the exhibited organism. For example, the analysis unit can analyze photographs taken from multiple angles to grasp the overall appearance of the exhibited organism. The analysis unit can also combine photographs taken at different times to analyze the behavioral patterns of the organism. For example, the analysis unit can analyze photographs taken at different times to grasp the behavioral patterns of the organism. Furthermore, the analysis unit can identify the species of exhibited organisms by combining photographs taken by multiple users. For example, the analysis unit can analyze photographs taken by multiple users to identify the species of exhibited organism. This improves the accuracy of identifying exhibited organisms by combining multiple photographs. Combining multiple photographs may include, but is not limited to, image matching technology and consideration of temporal continuity. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input multiple photographs into an AI model to identify the species of exhibited organisms.

[0081] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is excited, the analysis unit can provide a colorful and visually stimulating display method. For instance, the analysis unit can capture the user's facial expression with a camera, estimate the emotion using an emotion estimation algorithm, and provide a colorful and visually stimulating display method. The analysis unit can also provide a display method with calming colors if the user is relaxed. For example, the analysis unit can record the user's voice, estimate the emotion using voice analysis technology, and provide a display method with calming colors. Furthermore, if the user is tired, the analysis unit can provide a simple and highly visible display method. For example, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate the emotion using an emotion estimation algorithm, and provide a simple and highly visible display method. This allows for the provision of more appropriate information by adjusting the display method of the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generating AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user sentiment data into an AI model and adjust how the analysis results are displayed.

[0082] The analysis unit can improve the accuracy of its analysis by referring to the user's past shooting history. For example, the analysis unit can improve the accuracy of its analysis based on data of organisms that the user has photographed in the past. For example, the analysis unit can improve the accuracy of its analysis by obtaining the user's past shooting history from a database. The analysis unit can also reflect the frequency of appearance of specific organisms in the analysis based on the user's past shooting history. For example, the analysis unit can analyze the user's past shooting history and reflect the frequency of appearance of specific organisms in the analysis. Furthermore, the analysis unit can also identify similar organisms by analyzing the user's past shooting history. For example, the analysis unit can analyze the user's past shooting history and identify similar organisms. This improves the accuracy of the analysis by referring to the user's past shooting history. Past shooting history includes, but is not limited to, the use of a database and methods for filtering the history. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can improve the accuracy of its analysis by inputting the user's past shooting history into an AI model.

[0083] The analysis unit can identify exhibited organisms based on the user's geographical location information. For example, the analysis unit can identify organisms inhabiting a region based on the geographical information of the location where the user took a photograph. For example, the analysis unit can acquire the user's location information and identify organisms inhabiting that region. The analysis unit can also identify organisms exhibited within a specific aquarium based on the user's location information. For example, the analysis unit can identify organisms exhibited within a specific aquarium based on the user's location information. Furthermore, the analysis unit can identify organisms specific to a region based on the user's location information. For example, the analysis unit can identify organisms specific to a region based on the user's location information. This improves the accuracy of exhibited organism identification by considering the user's geographical location information. Geographical location information includes, but is not limited to, the use of GPS data and the accuracy of location information. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the user's geographical location information into an AI model and identify exhibited organisms.

[0084] The service provider can estimate the user's emotions and adjust the level of detail of the information provided based on the estimated emotions. For example, if the user is excited, the service provider can provide detailed information. For instance, the service provider can capture the user's facial expressions with a camera, estimate their emotions using an emotion estimation algorithm, and provide detailed information. The service provider can also provide standard information if the user is relaxed. For example, the service provider can record the user's voice, estimate their emotions using voice analysis technology, and provide standard information. Furthermore, if the user is tired, the service provider can provide concise information. For example, the service provider can collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate their emotions using an emotion estimation algorithm, and provide concise information. This allows the service provider to provide more appropriate information by adjusting the level of detail of the information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service delivery unit may be performed using AI, for example, or without AI. For example, the service delivery unit can input user emotion data into an AI model and adjust the level of detail of the information.

[0085] The information provider can update information by referring to the latest research data on the ecology of exhibited organisms. For example, the information provider can update information on the ecology of exhibited organisms by referring to the latest research papers. For example, the information provider can obtain the latest research papers from a database and update information on the ecology of exhibited organisms. The information provider can also provide information on the ecology of exhibited organisms by referring to the latest academic databases. For example, the information provider can provide information on the ecology of exhibited organisms by referring to the latest academic databases. Furthermore, the information provider can provide information on the ecology of exhibited organisms based on the latest research results. For example, the information provider can provide information on the ecology of exhibited organisms based on the latest research results. This improves the accuracy of the information provided by referring to the latest research data. The latest research data includes, but is not limited to, databases of academic papers and real-time updates. Some or all of the above processing in the information provider may be performed using, for example, AI, or not using AI. For example, the information provider can input the latest research data into an AI model and update the information.

[0086] The information provider can provide information by adding related videos and audio of the exhibited organisms. For example, the provider can provide videos showing the ecology of the exhibited organisms. For example, the provider can retrieve videos showing the ecology of the exhibited organisms from a database and provide the information. The provider can also provide the sounds and voices of the exhibited organisms. For example, the provider can retrieve the sounds and voices of the exhibited organisms from a database and provide the information. Furthermore, the provider can provide videos showing the habitat of the exhibited organisms. For example, the provider can retrieve videos showing the habitat of the exhibited organisms from a database and provide the information. This deepens the understanding of the information by adding related videos and audio. Related videos and audio include, but are not limited to, the length of the video, the content of the audio, and the format in which they are provided. Some or all of the processing described above in the information provider may be performed using AI, for example, or not using AI. For example, the provider can input related videos and audio into an AI model and provide the information.

[0087] The information provider can estimate the user's emotions and adjust the display order of the information based on the estimated emotions. For example, if the user is excited, the provider can display important information first. For example, the provider can capture the user's facial expression with a camera, estimate the emotion using an emotion estimation algorithm, and then display important information first. The provider can also display information in a sequential manner if the user is relaxed. For example, the provider can record the user's voice, estimate the emotion using voice analysis technology, and then display the information in a sequential manner. Furthermore, if the user is tired, the provider can display concise information first. For example, the provider can collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate the emotion using an emotion estimation algorithm, and then display concise information first. By adjusting the display order of information based on the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service delivery unit may be performed using AI, for example, or without AI. For example, the service delivery unit can input user emotion data into an AI model and adjust the display order of the information.

[0088] The information provider can prioritize information based on the user's past interests. For example, the provider might prioritize information about organisms the user has shown interest in in the past. For example, the provider could retrieve the user's past interests from a database and prioritize the information. The provider could also prioritize relevant information based on the user's past interests. For example, the provider could analyze the user's past interests and prioritize relevant information. Furthermore, the provider could prioritize information based on the user's past behavior patterns. For example, the provider could analyze the user's past behavior patterns and prioritize the information. This allows for the provision of more appropriate information by prioritizing information based on the user's past interests. Past interests include, but are not limited to, past browsing history and survey results. Some or all of the above processing in the information provider may be performed using, for example, AI, or not. For example, the provider could input the user's past interests into an AI model and prioritize the information.

[0089] The service provider can analyze a user's social media activity and provide relevant information. For example, the service provider can analyze the content of a user's social media posts and provide relevant information. For example, the service provider can retrieve the content of a user's social media posts from a database and provide relevant information. The service provider can also provide relevant information based on a user's interests on social media. For example, the service provider can analyze a user's interests on social media and provide relevant information. Furthermore, the service provider can provide relevant information based on a user's social media activity history. For example, the service provider can analyze a user's social media activity history and provide relevant information. In this way, relevant information can be provided by analyzing a user's social media activity. Social media activity includes, but is not limited to, analyzing post content and analyzing followers. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input a user's social media activity into an AI model and provide relevant information.

[0090] The suggestion unit can estimate the user's emotions and adjust the difficulty of the challenges and quizzes it suggests based on those emotions. For example, if the user is excited, the suggestion unit can suggest more difficult challenges and quizzes. For instance, it could capture the user's facial expressions with a camera, estimate their emotions using an emotion estimation algorithm, and then suggest more difficult challenges and quizzes. It can also suggest challenges and quizzes of standard difficulty if the user is relaxed. For example, it could record the user's voice, estimate their emotions using voice analysis technology, and then suggest challenges and quizzes of standard difficulty. Furthermore, if the user is tired, the suggestion unit can suggest challenges and quizzes of lower difficulty. For example, it could collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate their emotions using an emotion estimation algorithm, and then suggest challenges and quizzes of lower difficulty. This allows for more appropriate suggestions by adjusting the difficulty of challenges and quizzes based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. Some or all of the processing described above in the proposed section may be performed using AI, or not using AI. For example, the proposed section can input user sentiment data into an AI model to adjust the difficulty level of challenges and quizzes.

[0091] The suggestion unit can make optimal suggestions by referring to the user's past challenge history. For example, the suggestion unit can make optimal suggestions based on challenges the user has successfully completed in the past. For example, the suggestion unit can retrieve the user's past challenge history from a database and make optimal suggestions. The suggestion unit can also suggest challenges that the user might be interested in based on their past challenge history. For example, the suggestion unit can analyze the user's past challenge history and suggest challenges that might be of interest. Furthermore, the suggestion unit can analyze the user's past challenge history and suggest challenges of the optimal difficulty level. For example, the suggestion unit can analyze the user's past challenge history and suggest challenges of the optimal difficulty level. In this way, the suggestion unit can make optimal suggestions by referring to the user's past challenge history. Past challenge history includes, but is not limited to, the use of a database and methods for filtering history. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can input the user's past challenge history into an AI model and make optimal suggestions.

[0092] The suggestion unit can customize the content of challenges and quizzes based on the user's current interests when making suggestions. For example, the suggestion unit can suggest a challenge related to an organism the user is currently interested in. For example, the suggestion unit can retrieve the user's current interests from a database and customize the content of challenges and quizzes. The suggestion unit can also suggest relevant quizzes based on the user's current interests. For example, the suggestion unit can analyze the user's current interests and suggest relevant quizzes. Furthermore, the suggestion unit can also customize the content of challenges and quizzes based on the user's current behavior patterns. For example, the suggestion unit can analyze the user's current behavior patterns and customize the content of challenges and quizzes. This allows for more appropriate suggestions by customizing the content of challenges and quizzes based on the user's current interests. Current interests include, but are not limited to, real-time behavior analysis and survey results. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can input the user's current interests into an AI model and customize the content of challenges and quizzes.

[0093] The suggestion unit can estimate the user's emotions and adjust the order of suggested challenges and quizzes based on those emotions. For example, if the user is excited, the suggestion unit will suggest more difficult challenges first. For instance, it might capture the user's facial expressions with a camera, estimate their emotions using an emotion estimation algorithm, and then suggest more difficult challenges first. Furthermore, if the user is relaxed, the suggestion unit can suggest challenges in a logical order. For example, it might record the user's voice, estimate their emotions using voice analysis technology, and then suggest challenges in a logical order. Additionally, if the user is tired, the suggestion unit can suggest easier challenges first. For example, it might collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate their emotions using an emotion estimation algorithm, and then suggest easier challenges first. This allows for more appropriate suggestions by adjusting the order of challenges and quizzes based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. Some or all of the processing described above in the proposed section may be performed using AI, or not using AI. For example, the proposed section may input user sentiment data into an AI model to adjust the order of challenges and quizzes.

[0094] The suggestion unit can propose optimal challenges and quizzes based on the user's geographical location information when making a suggestion. For example, the suggestion unit can propose challenges related to the user's current location. For example, the suggestion unit can obtain the user's geographical location information and propose challenges related to the user's current location. The suggestion unit can also propose region-specific quizzes based on the user's location information. For example, the suggestion unit can propose region-specific quizzes based on the user's location information. Furthermore, the suggestion unit can propose optimal challenges based on the user's location information. For example, the suggestion unit can propose optimal challenges based on the user's location information. In this way, by considering the user's geographical location information, it is possible to propose optimal challenges and quizzes. Geographical location information includes, but is not limited to, the use of GPS data and the accuracy of location information. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can input the user's geographical location information into an AI model and propose optimal challenges and quizzes.

[0095] The suggestion unit can analyze the user's social media activity and suggest relevant challenges and quizzes when making suggestions. For example, the suggestion unit can analyze the content of the user's social media posts and suggest relevant challenges. For example, the suggestion unit can retrieve the content of the user's social media posts from a database and suggest relevant challenges. The suggestion unit can also suggest relevant quizzes based on the user's interests on social media. For example, the suggestion unit can analyze the user's interests on social media and suggest relevant quizzes. Furthermore, the suggestion unit can suggest relevant challenges based on the user's social media activity history. For example, the suggestion unit can analyze the user's social media activity history and suggest relevant challenges. In this way, by analyzing the user's social media activity, it is possible to suggest relevant challenges and quizzes. Social media activity includes, but is not limited to, analyzing content of posts and analyzing followers. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can input the user's social media activity into an AI model and suggest relevant challenges and quizzes.

[0096] The execution unit can estimate the user's emotions and adjust how challenges and quizzes are executed based on the estimated emotions. For example, if the user is excited, the execution unit can execute a high-difficulty challenge. For example, the execution unit can capture the user's facial expression with a camera, estimate the emotion using an emotion estimation algorithm, and execute a high-difficulty challenge. The execution unit can also execute a standard-difficulty challenge if the user is relaxed. For example, the execution unit can record the user's voice, estimate the emotion using voice analysis technology, and execute a standard-difficulty challenge. Furthermore, if the user is tired, the execution unit can execute an easy challenge. For example, the execution unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate the emotion using an emotion estimation algorithm, and execute an easy challenge. This allows for more appropriate execution by adjusting how challenges and quizzes are executed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input user emotion data into an AI model and adjust how challenges and quizzes are executed.

[0097] The execution unit can select the optimal execution method by referring to the user's past execution history during execution. For example, the execution unit can select the optimal execution method based on the user's past successful execution methods. For example, the execution unit can retrieve the user's past execution history from a database and select the optimal execution method. The execution unit can also select execution methods that are likely to be of interest to the user from their past execution history. For example, the execution unit can analyze the user's past execution history and select execution methods that are likely to be of interest. Furthermore, the execution unit can analyze the user's past execution history and select an execution method of the optimal difficulty level. For example, the execution unit can analyze the user's past execution history and select an execution method of the optimal difficulty level. In this way, the optimal execution method can be selected by referring to the user's past execution history. Past execution history includes, but is not limited to, database usage and history filtering methods. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the user's past execution history into an AI model and select the optimal execution method.

[0098] The execution unit can customize the means of executing challenges and quizzes based on the user's current situation during execution. For example, the execution unit can select the optimal means of execution according to the user's current situation. For example, the execution unit can analyze the user's current situation in real time and select the optimal means of execution. The execution unit can also customize the means of executing challenges and quizzes based on the user's current situation. For example, the execution unit can analyze the user's current situation and customize the means of executing challenges and quizzes. Furthermore, the execution unit can analyze the user's current situation and suggest the optimal means of execution. For example, the execution unit can analyze the user's current situation in real time and suggest the optimal means of execution. This allows for more appropriate execution by customizing the means of execution based on the user's current situation. Current situation includes, but is not limited to, real-time behavioral analysis and environmental conditions. Some or all of the above processing in the execution unit may be performed using, for example, AI, or not using AI. For example, the execution unit can input the user's current situation into an AI model and customize the means of executing challenges and quizzes.

[0099] The execution unit can estimate the user's emotions and adjust the order in which challenges and quizzes are executed based on the estimated emotions. For example, if the user is excited, the execution unit will execute the more difficult challenges first. For example, the execution unit may capture the user's facial expressions with a camera, estimate their emotions using an emotion estimation algorithm, and execute the more difficult challenges first. The execution unit can also execute challenges in a specific order if the user is relaxed. For example, the execution unit may record the user's voice, estimate their emotions using voice analysis technology, and execute the challenges in a specific order. Furthermore, if the user is tired, the execution unit may execute the easier challenges first. For example, the execution unit may collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate their emotions using an emotion estimation algorithm, and execute the easier challenges first. This allows for more appropriate execution by adjusting the order in which challenges and quizzes are executed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. Some or all of the processing described above in the execution unit may be performed using AI, or not using AI. For example, the execution unit may input user sentiment data into an AI model to adjust the execution order of challenges and quizzes.

[0100] The execution unit can select the optimal execution method based on the user's geographical location information at runtime. For example, the execution unit can select an execution method related to the user's current location. For example, the execution unit can acquire the user's geographical location information and select an execution method related to the user's current location. The execution unit can also select a region-specific execution method based on the user's location information. For example, the execution unit can select a region-specific execution method based on the user's location information. Furthermore, the execution unit can select the optimal execution method based on the user's location information. For example, the execution unit can select the optimal execution method based on the user's location information. This allows the optimal execution method to be selected by considering the user's geographical location information. Geographical location information includes, but is not limited to, the use of GPS data and the accuracy of location information. Some or all of the above processing in the execution unit may be performed using, for example, AI, or without AI. For example, the execution unit can input the user's geographical location information into an AI model and select the optimal execution method.

[0101] The execution unit can analyze the user's social media activity at runtime and propose execution methods. For example, the execution unit can analyze the content of the user's social media posts and propose relevant execution methods. For example, the execution unit can retrieve the content of the user's social media posts from a database and propose relevant execution methods. The execution unit can also propose relevant execution methods based on the user's interests on social media. For example, the execution unit can analyze the user's interests on social media and propose relevant execution methods. Furthermore, the execution unit can propose relevant execution methods based on the user's social media activity history. For example, the execution unit can analyze the user's social media activity history and propose relevant execution methods. In this way, relevant execution methods can be proposed by analyzing the user's social media activity. Social media activity includes, but is not limited to, analysis of post content and analysis of followers. Some or all of the above processing in the execution unit may be performed using, for example, AI, or not using AI. For example, the execution unit can input the user's social media activity into an AI model and propose relevant execution methods.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The analysis unit can analyze metadata from photos taken by users and use it to identify exhibited organisms. For example, it can analyze the date, time, and location information of a photo to identify organisms that are likely to be present at that time and place. The analysis unit can also consider the shooting conditions of the photo (e.g., light intensity and angle) to identify organisms more accurately. Furthermore, the analysis unit can analyze the user's camera settings (e.g., ISO sensitivity and shutter speed) to evaluate the quality of the captured image and improve the accuracy of organism identification. In this way, the accuracy of identifying exhibited organisms is improved by utilizing the metadata of the photos.

[0104] The service provider can generate and provide 3D models of exhibited organisms based on photographs taken by the user. For example, the service provider can analyze photographs taken from multiple angles to generate a 3D model of the exhibited organism. The service provider can also display the generated 3D model on the user's smartphone or tablet, allowing the user to freely rotate and zoom in and out. Furthermore, the service provider can add interactive explanations to the 3D model, enabling the user to obtain detailed information about each part of the organism. In this way, utilizing the 3D model can deepen the user's understanding and provide a more engaging experience.

[0105] The suggestion unit can estimate the user's emotions and adjust the themes of the challenges and quizzes it suggests based on those emotions. For example, if the user is excited, it can suggest challenges with active and exciting themes. For instance, the suggestion unit can capture the user's facial expressions with a camera, estimate their emotions using an emotion estimation algorithm, and suggest challenges with active themes. If the user is relaxed, it can also suggest quizzes with relaxing themes. For example, the suggestion unit can record the user's voice, estimate their emotions using voice analysis technology, and suggest quizzes with relaxing themes. Furthermore, if the user is tired, it can suggest challenges with easy and refreshing themes. For example, the suggestion unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate their emotions using an emotion estimation algorithm, and suggest challenges with refreshing themes. By adjusting themes based on the user's emotions, it can provide more appropriate suggestions.

[0106] The execution unit can estimate the user's emotions and adjust the feedback method for challenges and quizzes based on the estimated emotions. For example, if the user is excited, it can provide positive and energetic feedback. For instance, the execution unit can capture the user's facial expressions with a camera, estimate their emotions using an emotion estimation algorithm, and provide positive feedback. It can also provide calm and soothing feedback if the user is relaxed. For example, the execution unit can record the user's voice, estimate their emotions using voice analysis technology, and provide calm feedback. Furthermore, if the user is tired, it can provide concise and refreshing feedback. For example, the execution unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate their emotions using an emotion estimation algorithm, and provide refreshing feedback. This allows for more appropriate feedback to be provided by adjusting the feedback method based on the user's emotions.

[0107] The information provider can estimate the user's emotions and adjust the format of the information provided based on those emotions. For example, if the user is excited, the information can be provided in a visually stimulating infographic format. For instance, the provider could capture the user's facial expressions with a camera, estimate their emotions using an emotion estimation algorithm, and then provide the information in an infographic format. If the user is relaxed, detailed text-based explanations can also be provided. For example, the provider could record the user's voice, estimate their emotions using voice analysis technology, and then provide text-based explanations. Furthermore, if the user is tired, information can be provided in a concise, bullet-point format. For example, the provider could collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate their emotions using an emotion estimation algorithm, and then provide the information in a bullet-point format. This allows for the provision of more appropriate information by adjusting the format of the information based on the user's emotions.

[0108] The analysis unit can analyze the continuity of photos taken by users to identify the behavioral patterns of exhibited organisms. For example, it can analyze consecutively taken photos to identify the organism's movement routes and behavioral patterns. The analysis unit can also combine photos taken at different times to analyze the organism's activity periods and changes in its behavior. Furthermore, the analysis unit can integrate photos taken by multiple users to identify the behavioral patterns of exhibited organisms in more detail. In this way, by utilizing the continuity of photos, it is possible to identify the behavioral patterns of exhibited organisms and improve the accuracy of the information provided to users.

[0109] The exhibiting department can provide interactive simulations about the ecology of exhibited organisms. For example, users can simulate the habitat of a specific organism and observe how it behaves. The department can also allow users to simulate food chains and ecosystem balances, enabling them to learn about the impact of environmental changes on organisms. Furthermore, the department can allow users to simulate the reproduction and growth processes of organisms, enabling them to understand the organism's life cycle. This allows for a deeper understanding and more engaging experience for users through interactive simulations.

[0110] The suggestion function can refer to a user's past challenge history and suggest new challenges the user hasn't yet attempted. For example, it can suggest a new challenge the user should try next based on challenges the user has successfully completed in the past. The suggestion function can also suggest new challenges that the user might be interested in, based on their past challenge history. Furthermore, it can analyze a user's past challenge history and suggest new challenges tailored to their skill level. This allows for the suggestion of more appropriate new challenges by leveraging the user's past challenge history.

[0111] The execution unit can customize the means of executing challenges and quizzes based on the user's current situation. For example, the execution unit can select the optimal means of execution according to the user's current situation. For example, the execution unit can analyze the user's current situation in real time and select the optimal means of execution. The execution unit can also customize the means of executing challenges and quizzes based on the user's current situation. For example, the execution unit can analyze the user's current situation and customize the means of executing challenges and quizzes. Furthermore, the execution unit can analyze the user's current situation and suggest the optimal means of execution. For example, the execution unit can analyze the user's current situation in real time and suggest the optimal means of execution. This allows for more appropriate execution by customizing the means of execution based on the user's current situation.

[0112] The information provider can update information by referring to the latest research data on the ecology of exhibited organisms. For example, they can update information on the ecology of exhibited organisms by referring to the latest research papers. The information provider can also provide information on the ecology of exhibited organisms by referring to the latest academic databases. Furthermore, the information provider can provide information on the ecology of exhibited organisms based on the latest research findings. This improves the accuracy of the information provided by referring to the latest research data.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The analysis unit analyzes photos taken by the user inside the aquarium to identify the types of exhibited organisms. The analysis unit uses image recognition technology, deep learning technology, computer vision technology, and AI to extract characteristics of the exhibited organisms and identify their species. Step 2: The provision department provides information on the ecology and interesting facts about the exhibited organisms identified by the analysis department. The provision department can also provide explanations about the habitat and special abilities of the identified exhibited organisms, and can use AI to provide information on the ecology and interesting facts about the exhibited organisms. Step 3: The suggestion department proposes personalized challenges and quizzes to the user based on the information provided by the delivery department. The suggestion department learns the user's interests and behavioral patterns and uses AI to propose the most suitable challenges and quizzes. Step 4: The execution unit executes the challenges and quizzes proposed by the proposal unit. The execution unit executes the proposed challenges and quizzes and provides feedback to the user. The execution results can also be output using AI.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the analysis unit, provision unit, proposal unit, and execution unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit acquires a photograph taken by the user using the camera 42 of the smart device 14 and identifies the type of exhibited organism using image recognition technology by the identification processing unit 290 of the data processing unit 12. The provision unit provides, for example, information on the ecology and interesting facts of the exhibited organism identified by the identification processing unit 290 of the data processing unit 12. The proposal unit proposes a personalized challenge or quiz to the user, for example, by the identification processing unit 290 of the data processing unit 12. The execution unit executes the proposed challenge or quiz by, for example, the control unit 46A of the smart device 14 and provides feedback to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the analysis unit, provision unit, proposal unit, and execution unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit acquires a photograph taken by the user using the camera 42 of the smart glasses 214 and identifies the type of exhibited organism using image recognition technology by the identification processing unit 290 of the data processing unit 12. The provision unit provides, for example, information on the ecology and interesting facts of the exhibited organism identified by the identification processing unit 290 of the data processing unit 12. The proposal unit proposes a personalized challenge or quiz to the user, for example, by the identification processing unit 290 of the data processing unit 12. The execution unit executes the proposed challenge or quiz by, for example, the control unit 46A of the smart glasses 214 and provides feedback to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the analysis unit, provision unit, proposal unit, and execution unit, is implemented in, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit acquires a photograph taken by the user using the camera 42 of the headset terminal 314 and identifies the type of exhibited organism using image recognition technology by the identification processing unit 290 of the data processing unit 12. The provision unit provides, for example, information on the ecology and interesting facts of the exhibited organism identified by the identification processing unit 290 of the data processing unit 12. The proposal unit proposes a personalized challenge or quiz to the user, for example, by the identification processing unit 290 of the data processing unit 12. The execution unit executes the proposed challenge or quiz by, for example, the control unit 46A of the headset terminal 314 and provides feedback to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] Each of the multiple elements described above, including the analysis unit, provision unit, proposal unit, and execution unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit acquires photographs taken by the user using the camera 42 of the robot 414 and identifies the type of exhibited organism using image recognition technology by the identification processing unit 290 of the data processing unit 12. The provision unit provides, for example, information on the ecology and interesting facts of the exhibited organism identified by the identification processing unit 290 of the data processing unit 12. The proposal unit proposes personalized challenges and quizzes to the user, for example, by the identification processing unit 290 of the data processing unit 12. The execution unit executes the proposed challenges and quizzes by, for example, the control unit 46A of the robot 414 and provides feedback to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] (Note 1) An analysis unit analyzes photos taken by users inside the aquarium to identify the types of exhibited creatures, The providing unit provides information on the ecology and interesting facts of the exhibited organisms identified by the aforementioned analysis unit, Based on the information provided by the aforementioned provisioning unit, the proposal unit proposes personalized challenges and quizzes to the user, The system comprises an execution unit that executes challenges and quizzes proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Identifying the types of exhibited organisms using image recognition technology The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Provides explanations about the habitat and special abilities of the identified exhibited creatures. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, It learns the user's interests and behavioral patterns to suggest personalized challenges and quizzes. The system described in Appendix 1, characterized by the features described herein. (Note 5) The execution unit is, Complete the proposed challenges and quizzes. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Add a fantastical underwater effect to your photos. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, Create a composite image that makes it look like you're swimming with the fish you photographed. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, Identifying exhibited organisms based on background information from photographs taken. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, Identifying the species of exhibited organisms by combining multiple photographs The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system improves the accuracy of analysis by referencing the user's past shooting history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The exhibited organisms are identified based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, It estimates the user's emotions and adjusts the level of detail of the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, We update information by referring to the latest research data on the ecology of exhibited organisms. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, We provide information by adding related videos and audio of the exhibited organisms. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, It estimates the user's emotions and adjusts the display order of the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, Prioritize information based on the user's past interests. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, Analyze users' social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, It estimates the user's emotions and adjusts the difficulty of suggested challenges and quizzes based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, we refer to the user's past challenge history to make the most suitable suggestion. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making suggestions, customize the content of challenges and quizzes based on the user's current interests. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, It estimates the user's emotions and adjusts the order of suggested challenges and quizzes based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making suggestions, the system will propose the most suitable challenges and quizzes based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making suggestions, we analyze the user's social media activity and propose relevant challenges and quizzes. The system described in Appendix 1, characterized by the features described herein. (Note 26) The execution unit is, It estimates the user's emotions and adjusts how challenges and quizzes are delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The execution unit is, During execution, the system selects the optimal execution method by referring to the user's past execution history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The execution unit is, At runtime, the way challenges and quizzes are executed is customized based on the user's current status. The system described in Appendix 1, characterized by the features described herein. (Note 29) The execution unit is, It estimates the user's emotions and adjusts the order in which challenges and quizzes are executed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The execution unit is, At runtime, the optimal execution method is selected based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The execution unit is, During execution, the system analyzes the user's social media activity and proposes actionable steps. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0187] 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. An analysis unit analyzes photos taken by users inside the aquarium to identify the types of exhibited creatures, The providing unit provides information on the ecology and interesting facts of the exhibited organisms identified by the aforementioned analysis unit, Based on the information provided by the aforementioned provisioning unit, the proposal unit proposes personalized challenges and quizzes to the user, The system comprises an execution unit that executes challenges and quizzes proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned analysis unit, Identifying the types of exhibited organisms using image recognition technology The system according to feature 1.

3. The aforementioned supply unit is, Provides explanations about the habitat and special abilities of the identified exhibited creatures. The system according to feature 1.

4. The aforementioned proposal section is, It learns the user's interests and behavioral patterns to suggest personalized challenges and quizzes. The system according to feature 1.

5. The execution unit is, Complete the proposed challenges and quizzes. The system according to feature 1.

6. The aforementioned supply unit is, Add a fantastical underwater effect to your photos. The system according to feature 1.

7. The aforementioned supply unit is, Create a composite image that makes it look like you're swimming with the fish you photographed. The system according to feature 1.

8. The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system according to feature 1.

Images