system

Abstract

The system according to this embodiment aims to generate optical illusion art and combine education and entertainment. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects content generated by visual effects software. The analysis unit analyzes the content collected by the collection unit. The generation unit generates illusion art based on the data analyzed by the analysis unit. The provision unit provides the illusion art generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there are problems that the experience of illusion and learning opportunities are limited, and expertise is required for creating visual tricks.

[0005] The system according to the embodiment aims to generate illusion art and integrate education and entertainment.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects content generated by visual effects software. The analysis unit analyzes the content collected by the collection unit. The generation unit generates illusion art based on the data analyzed by the analysis unit. The provision unit provides the illusion art generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can generate optical illusion art and combine education and entertainment. [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 including 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 3, 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 reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) The illusion experience creation system according to an embodiment of the present invention is a new type of platform that uses AI to create illusion experiences and fuses education and entertainment. This illusion experience creation system uses AI to analyze content generated by visual effects software and create illusion experiences in real time. This illusion experience creation system integrates with an educational platform to support lessons using content themed on optical illusions. Furthermore, this illusion experience creation system allows AI to dynamically generate illusion art based on user interaction, which can be used in art galleries and exhibitions. For example, the AI ​​analyzes illusion content generated by visual effects software in real time and provides the user with an illusion experience. For example, the AI ​​analyzes an illusion video generated by visual effects software, allowing the user to experience the video in real time. Next, this illusion experience is integrated with an educational platform. Specifically, it works in conjunction with educational tools to support lessons using content themed on optical illusions. For example, when a teacher conducts a lesson on optical illusions on an educational platform, the AI-generated illusion video can be used to provide students with a visual experience. Furthermore, the AI ​​dynamically generates illusion art based on user interaction. For example, when a user performs a specific action, the AI ​​can generate optical illusion art accordingly, which can then be used in art galleries and exhibitions. In this way, users can enjoy an interactive optical illusion experience. This mechanism improves understanding of vision and cognition. Through optical illusions, students can directly experience the fascination of visual science and enhance educational effectiveness. For example, through lessons on optical illusions, students can gain a deeper understanding of the relationship between vision and cognition. It also promotes the creation of creative content. By generating and adjusting visual effects, AI offers new possibilities for promotion and art production. For example, the advertising industry can use AI-generated optical illusions to create visually appealing promotions. Furthermore, the fusion of education and entertainment can meet new market needs. By providing education and entertainment simultaneously, the edutainment market is expected to expand.For example, educational institutions and creators in the entertainment industry can leverage this platform to create new business opportunities. This allows the illusion creation system to improve understanding of vision and cognition, promote creative content production, and meet new market needs through the convergence of education and entertainment.

[0029] The illusion experience creation system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects content generated by visual effects software. The collection unit collects, for example, illusion content generated by visual effects software. The collection unit provides material for illusion experiences by collecting illusion content generated by visual effects software. The analysis unit analyzes the content collected by the collection unit. The analysis unit analyzes, for example, the collected illusion content in real time. The analysis unit provides an immediate illusion experience by analyzing the collected illusion content in real time. The generation unit generates illusion art based on the data analyzed by the analysis unit. The generation unit dynamically generates illusion art based on, for example, user interaction. The generation unit provides an interactive illusion experience by dynamically generating illusion art based on user interaction. The provision unit provides the illusion art generated by the generation unit. The provision unit provides, for example, the generated illusion art to an art gallery or exhibition. The provision unit provides visual entertainment by offering the generated illusion art to art galleries and exhibitions. Thus, the illusion experience creation system according to this embodiment can create illusion experiences by collecting, analyzing, generating, and providing content generated by visual effects software.

[0030] The collection unit collects content generated by visual effects software. Specifically, it collects illusion content generated by visual effects software, providing material for illusion experiences. Visual effects software uses computer graphics and animation techniques to generate content that causes visual illusions. For example, visual effects software creates videos and images that utilize optical illusions and paradoxes and sends them to the collection unit. The collection unit centrally manages this content and stores it in a database. The collected content is organized so that the analysis and generation units can access it, and tags and metadata are added as needed. Furthermore, the collection unit has the function to receive data from the visual effects software in real time and immediately send it to the analysis unit. In this way, the collection unit efficiently collects diverse illusion content generated by visual effects software and plays a role in building the foundation of the entire system. The collection unit is designed to respond to version upgrades of the visual effects software and the addition of new effects, so it can always incorporate the latest illusion content. In this way, the collection unit can continue to provide material for visual entertainment as the core of the illusion experience creation system.

[0031] The analysis unit analyzes the content collected by the collection unit in real time. Specifically, it analyzes the collected illusion content to understand its content and characteristics. The analysis unit uses AI technology to perform pattern recognition and feature extraction of illusion content. For example, it uses image recognition algorithms to detect visual tricks and paradoxes contained in the illusion content and evaluate their effects. Furthermore, the analysis unit analyzes the metadata of the collected content to classify the content type, theme, and intensity of visual effects. This allows the analysis unit to analyze the collected illusion content in detail and generate foundational data for providing illusion experiences immediately. To enable real-time analysis, the analysis unit utilizes servers with high processing power and cloud computing technology. This allows the analysis unit to process large amounts of data quickly and provide immediate feedback to the generation unit. In addition, the analysis unit can continuously improve the accuracy of its analysis algorithms based on past analysis results and user feedback. This allows the analysis unit to always perform highly accurate analysis incorporating the latest technologies, enhancing the reliability and effectiveness of the illusion experience creation system.

[0032] The generation unit generates illusion art based on data analyzed by the analysis unit. Specifically, it dynamically generates illusion art based on user interaction, providing an interactive illusion experience. The generation unit uses AI technology to generate illusion art in real time in response to user input and actions. For example, when a user operates the touchscreen, the illusion art on the screen changes, creating a visual trick. It also incorporates a mechanism that detects user movement with sensors and dynamically changes the illusion art in response to that movement. Through these interactions, the generation unit provides users with a new visual experience. Furthermore, the generation unit optimizes the illusion art generation process based on data provided by the analysis unit. For example, it utilizes visual tricks and paradoxes detected by the analysis unit to generate more effective illusion art. The generation unit can also collect user reactions and feedback in real time and adjust the generation algorithm based on that. This allows the generation unit to continuously provide the optimal illusion experience for the user. The generation unit utilizes hardware and software with advanced graphics processing capabilities, enabling it to generate complex visual effects in real time. This allows the generation unit to provide users with interactive and engaging illusion art, playing a central role in the illusion experience creation system.

[0033] The provider division provides the illusion art generated by the generator division. Specifically, it provides the generated illusion art to art galleries and exhibitions, offering visual entertainment. The provider division displays the generated illusion art using high-resolution displays and projectors, providing viewers with an immersive visual experience. For example, in art galleries, large screens and interactive displays are installed, allowing viewers to directly touch and interact with the illusion art. In exhibitions and events, projection mapping technology is used to project the illusion art onto buildings and objects, surprising and impressing the audience. The provider division utilizes these technologies to effectively deliver the generated illusion art and create visual entertainment. Furthermore, the provider division can also widely distribute the generated illusion art through online platforms. For example, it can enable users to enjoy the illusion art at home or on the go through websites and mobile apps. By leveraging these online platforms, the provider division can globally distribute the generated illusion art and deliver visual entertainment to a large number of people. This allows the service provider to deliver the generated illusion art in a variety of ways, maximizing the effectiveness of the illusion experience creation system.

[0034] The collection unit can collect illusion content generated by visual effects software. For example, the collection unit collects illusion content generated by visual effects software. By collecting illusion content generated by visual effects software, the collection unit provides material for illusion experiences. Illusion content includes, but is not limited to, images and videos that utilize visual illusions. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input illusion content generated by visual effects software into AI, and the AI ​​can select the types of content to collect.

[0035] The analysis unit can analyze the collected illusion content in real time. For example, the analysis unit analyzes the collected illusion content in real time. By analyzing the collected illusion content in real time, the analysis unit provides an immediate illusion experience. Real time includes, but is not limited to, analysis latency and processing speed. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected illusion content into an AI, which can then analyze it in real time.

[0036] The generation unit can dynamically generate illusion art based on user interaction. For example, the generation unit dynamically generates illusion art based on user interaction. By dynamically generating illusion art based on user interaction, the generation unit provides an interactive illusion experience. User interaction includes, but is not limited to, touch operations and gesture recognition. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input user interaction data into an AI, which can then dynamically generate illusion art.

[0037] The provider can provide the generated illusion art to art galleries and exhibitions. The provider can, for example, provide the generated illusion art to art galleries and exhibitions. The provider provides visual entertainment by providing the generated illusion art to art galleries and exhibitions. Art galleries and exhibitions include, but are not limited to, specific museums and online exhibitions. Some or all of the above processing in the provider may be performed using, for example, AI, or not using AI. For example, the provider can input the generated illusion art into an AI, which can then select the optimal method of provision.

[0038] The service provider can integrate with an educational platform to support lessons using content on the theme of optical illusions. For example, the service provider can integrate with an educational platform to support lessons using content on the theme of optical illusions. By integrating with an educational platform, the service provider provides educational content on the theme of optical illusions. The educational platform includes, but is not limited to, specific online educational services and their functions. Some or all of the processing described above in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input optical illusion art into an AI on the educational platform, and the AI ​​can generate optimal educational content.

[0039] The collection unit can select the optimal content based on the version and settings information of the visual effects software during collection. For example, the collection unit prioritizes collecting content generated with the latest version of the visual effects software. The collection unit can select the optimal illusion content based on specific settings information. The collection unit can select content to collect while considering the compatibility of the visual effects software. This improves the accuracy of collection by selecting the optimal content based on the version and settings information of the visual effects software. The version and settings information of the visual effects software includes, but is not limited to, the software version number and the contents of the settings file. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the version and settings information of the visual effects software into the AI, and the AI ​​can select the optimal content.

[0040] The data collection unit can analyze the user's past use history of visual effects software during data collection and select the optimal collection method. For example, the data collection unit can collect the most suitable illusion content based on the user's past use history of visual effects software. The data collection unit can prioritize the collection of preferred content based on the user's past use history. The data collection unit can analyze the user's usage history and select the most effective collection method. This allows the optimal collection method to be selected by analyzing the user's past use history of visual effects software. Usage history includes, but is not limited to, log data and user operation history. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's past use history of visual effects software into AI, which can then select the optimal collection method.

[0041] The data collection unit can prioritize collecting highly relevant content based on the user's geographical location information during data collection. For example, the data collection unit can collect region-related illusion content based on the user's current location. The data collection unit can collect illusion content that is appropriate to the cultural background based on the user's geographical location information. The data collection unit can select the most relevant illusion content considering the user's location information. This improves the accuracy of data collection by prioritizing the collection of highly relevant content based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's geographical location information into AI, which can then select highly relevant content.

[0042] The data collection unit can analyze the user's social media activity and collect relevant content during the collection process. For example, the data collection unit can analyze the user's interests on social media and collect relevant illusion content. The data collection unit can select the most relevant illusion content based on the user's social media activity history. The data collection unit can collect relevant illusion content considering the interests of the user's followers and friends on social media. In this way, relevant content can be collected by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's social media activity into AI, which can then select relevant content.

[0043] The analysis unit can apply different analysis algorithms depending on the type of content collected during analysis. For example, the analysis unit can apply a motion analysis algorithm to video content. For still image content, it can apply an image analysis algorithm. For text content, it can apply a natural language processing algorithm. By applying different analysis algorithms depending on the type of content collected, the accuracy of the analysis is improved. Analysis algorithms include, but are not limited to, image analysis algorithms and text analysis algorithms. 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 the type of content collected into the AI, which can then apply the most suitable analysis algorithm.

[0044] The analysis unit can improve the accuracy of its analysis based on the interrelationships of the collected content during the analysis process. For example, the analysis unit can integrate the analysis results by considering the relationships between videos and still images. The analysis unit can also integrate the analysis results by considering the relationships between text and images. The analysis unit can analyze the interrelationships of multiple content pieces and provide comprehensive analysis results. This allows for more accurate analysis results by improving the accuracy of the analysis based on the interrelationships of the collected content. These interrelationships include, but are not limited to, co-occurrence network analysis and correlation analysis. 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 the interrelationships of the collected content into the AI, which can then improve the accuracy of the analysis.

[0045] The analysis unit can determine the priority of analysis based on the submission date of the collected content during analysis. For example, the analysis unit may prioritize the analysis of the most recent content. The analysis unit may postpone the analysis of older content. The analysis unit can adjust the analysis schedule based on the submission date. This improves the efficiency of analysis by determining the priority of analysis based on the submission date of the collected content. The submission date includes, but is not limited to, the submission date and time, and the submission deadline. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the submission dates of the collected content into the AI, and the AI ​​can determine the priority of analysis.

[0046] The analysis unit can improve the accuracy of its analysis based on relevant literature for the collected content during the analysis process. For example, the analysis unit can supplement the analysis results by referring to relevant academic papers. The analysis unit can supplement the analysis results by referring to relevant patent documents. The analysis unit can supplement the analysis results by referring to relevant technical documents. This improves the accuracy of the analysis based on relevant literature for the collected content, thereby providing more accurate analysis results. Relevant literature includes, but is not limited to, academic papers, patent documents, and technical reports. 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 relevant literature for the collected content into the AI, which can then improve the accuracy of the analysis.

[0047] The generation unit can generate optimal illusion art by analyzing the user's interaction history during generation. For example, the generation unit can generate new illusion art based on patterns of illusion art that the user has previously liked. The generation unit can select the optimal style of illusion art from the user's interaction history. The generation unit can analyze the user's interaction history and generate the most effective illusion art. In this way, the optimal illusion art can be generated by analyzing the user's interaction history. Interaction history includes, but is not limited to, user operation logs and interaction frequency. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the user's interaction history into AI, and the AI ​​can generate the optimal illusion art.

[0048] The generation unit can optimize the generation algorithm based on the characteristics of the collected content during generation. For example, the generation unit can optimize the generation algorithm based on the characteristics of video content. The generation unit can optimize the generation algorithm based on the characteristics of still image content. The generation unit can optimize the generation algorithm based on the characteristics of text content. This improves the accuracy of generation by optimizing the generation algorithm based on the characteristics of the collected content. The generation algorithm includes, but is not limited to, image generation algorithms and 3D model generation algorithms. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the characteristics of the collected content into the AI, which can then apply the most suitable generation algorithm.

[0049] The generation unit can generate optimal illusion art based on the user's geographical location information during generation. For example, the generation unit can generate region-related illusion art based on the user's current location. The generation unit can generate illusion art that is appropriate to the cultural background based on the user's geographical location information. The generation unit can generate optimal illusion art considering the user's location information. This improves the accuracy of generation by generating optimal illusion art based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the user's geographical location information into AI, and the AI ​​can generate optimal illusion art.

[0050] The generation unit can customize the illusion art it generates by analyzing the user's social media activity during the generation process. For example, the generation unit can analyze the user's interests on social media and generate relevant illusion art. The generation unit can generate optimal illusion art based on the user's social media activity history. The generation unit can generate relevant illusion art considering the interests of the user's followers and friends on social media. In this way, the generated illusion art can be customized by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the user's social media activity into AI, and the AI ​​can generate optimal illusion art.

[0051] The service provider can select the optimal service delivery method based on the user's past usage history of visual effects software at the time of delivery. For example, the service provider can select the optimal optical illusion art delivery method based on the user's past usage history of visual effects software. The service provider can prioritize the user's preferred delivery method based on their past usage history. The service provider can analyze the user's usage history and select the most effective delivery method. This improves the accuracy of delivery by selecting the optimal delivery method based on the user's past usage history of visual effects software. Usage history includes, but is not limited to, log data and user operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past usage history of visual effects software into AI, and the AI ​​can select the optimal delivery method.

[0052] The service provider can filter the illusion art offered based on the user's current projects and areas of interest at the time of delivery. For example, the service provider may prioritize offering illusion art related to the user's current projects. The service provider can filter the most suitable illusion art based on the user's areas of interest. The service provider can select the illusion art to offer, taking into account the user's projects and areas of interest. This improves the accuracy of the service by filtering the illusion art offered based on the user's current projects and areas of interest. Current projects and areas of interest include, but are not limited to, the content of the project and keywords related to the area of ​​interest. 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 the user's current projects and areas of interest into an AI, which can then select the most suitable illusion art.

[0053] The service provider can provide the most suitable optical illusion art based on the user's geographical location information at the time of delivery. For example, the service provider can provide region-related optical illusion art based on the user's current location. The service provider can provide optical illusion art that is appropriate to the cultural background based on the user's geographical location information. The service provider can provide the most suitable optical illusion art considering the user's location information. This improves the accuracy of the delivery by providing the most suitable optical illusion art based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. 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 the user's geographical location information into AI, and the AI ​​can provide the most suitable optical illusion art.

[0054] The service provider can customize the illusion art offered by analyzing the user's social media activity at the time of delivery. For example, the service provider can analyze the user's interests on social media and provide relevant illusion art. The service provider can provide optimal illusion art based on the user's social media activity history. The service provider can provide relevant illusion art considering the interests of the user's followers and friends on social media. In this way, the illusion art offered can be customized by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and the number of 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 the user's social media activity into AI, and the AI ​​can provide optimal illusion art.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The illusion experience creation system can also be equipped with a voice recognition unit. The voice recognition unit can analyze the user's voice commands and dynamically change the content of the illusion experience. For example, if the user says "make it brighter," the voice recognition unit can analyze the command and adjust the brightness of the illusion experience. Also, if the user says "move to the next scene," the voice recognition unit can analyze the command and switch to the next illusion scene. Furthermore, if the user says "stop," the voice recognition unit can analyze the command and pause the illusion experience. This allows users to control the illusion experience more intuitively using voice commands.

[0057] The illusion experience creation system can also be equipped with a biometric recognition unit. This unit can analyze the user's biometric information and individually customize the content of the illusion experience. For example, it can measure the user's heart rate and, if the heart rate is high, provide illusion content with a relaxing effect. It can also analyze the user's pupil movement and provide visually stimulating content. Furthermore, it can measure the electrical resistance of the user's skin and provide illusion experiences tailored to their stress level. This allows for the provision of more personalized illusion experiences based on the user's biometric information.

[0058] The illusion experience creation system can also be equipped with an environment recognition unit. The environment recognition unit can analyze information about the user's surroundings and dynamically change the content of the illusion experience. For example, if the user is in a dark room, the environment recognition unit can analyze that information and adjust the brightness of the illusion experience. Also, if the user is in a noisy place, the environment recognition unit can analyze that information and adjust the volume of the illusion experience. Furthermore, if the user is outdoors, the environment recognition unit can analyze that information and change the content of the illusion experience to be suitable for the outdoors. This makes it possible to provide a more appropriate illusion experience according to the user's surrounding environment.

[0059] The illusion experience creation system can also be equipped with a feedback unit. This feedback unit can collect user feedback and use it to improve the content of the illusion experience. For example, if a user completes a survey after experiencing an illusion, the feedback unit can analyze the responses and incorporate them into the next illusion experience. Furthermore, if a user provides feedback in real time, the feedback unit can analyze that information and immediately adjust the content of the illusion experience. Even if a user does not provide feedback, the feedback unit can analyze user behavior data and use it as indirect feedback. This allows for the provision of better illusion experiences based on user feedback.

[0060] The illusion experience creation system can also be equipped with a prediction unit. This unit can analyze the user's past behavioral data and predict the content of the next illusion experience to be provided. For example, it can predict the next content to be provided based on the illusion content the user has previously enjoyed. It can also analyze the user's past interaction data and predict the actions they are most likely to perform next. Furthermore, it can predict the content of the next illusion experience to be provided based on the user's past feedback. This allows for the provision of more appropriate illusion experiences based on the user's past behavioral data.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The collection unit collects content generated by visual effects software. For example, it collects optical illusion content generated by visual effects software and provides material for optical illusion experiences. Step 2: The analysis unit analyzes the content collected by the collection unit. For example, it analyzes the collected illusion content in real time and provides an immediate illusion experience. Step 3: The generation unit generates illusion art based on the data analyzed by the analysis unit. For example, it dynamically generates illusion art based on user interaction, providing an interactive illusion experience. Step 4: The provider unit provides the illusion art generated by the generator unit. For example, the generated illusion art is provided to an art gallery or exhibition to provide visual entertainment.

[0063] (Example of form 2) The illusion experience creation system according to an embodiment of the present invention is a new type of platform that uses AI to create illusion experiences and fuses education and entertainment. This illusion experience creation system uses AI to analyze content generated by visual effects software and create illusion experiences in real time. This illusion experience creation system integrates with an educational platform to support lessons using content themed on optical illusions. Furthermore, this illusion experience creation system allows AI to dynamically generate illusion art based on user interaction, which can be used in art galleries and exhibitions. For example, the AI ​​analyzes illusion content generated by visual effects software in real time and provides the user with an illusion experience. For example, the AI ​​analyzes an illusion video generated by visual effects software, allowing the user to experience the video in real time. Next, this illusion experience is integrated with an educational platform. Specifically, it works in conjunction with educational tools to support lessons using content themed on optical illusions. For example, when a teacher conducts a lesson on optical illusions on an educational platform, the AI-generated illusion video can be used to provide students with a visual experience. Furthermore, the AI ​​dynamically generates illusion art based on user interaction. For example, when a user performs a specific action, the AI ​​can generate optical illusion art accordingly, which can then be used in art galleries and exhibitions. In this way, users can enjoy an interactive optical illusion experience. This mechanism improves understanding of vision and cognition. Through optical illusions, students can directly experience the fascination of visual science and enhance educational effectiveness. For example, through lessons on optical illusions, students can gain a deeper understanding of the relationship between vision and cognition. It also promotes the creation of creative content. By generating and adjusting visual effects, AI offers new possibilities for promotion and art production. For example, the advertising industry can use AI-generated optical illusions to create visually appealing promotions. Furthermore, the fusion of education and entertainment can meet new market needs. By providing education and entertainment simultaneously, the edutainment market is expected to expand.For example, educational institutions and creators in the entertainment industry can leverage this platform to create new business opportunities. This allows the illusion creation system to improve understanding of vision and cognition, promote creative content production, and meet new market needs through the convergence of education and entertainment.

[0064] The illusion experience creation system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects content generated by visual effects software. The collection unit collects, for example, illusion content generated by visual effects software. The collection unit provides material for illusion experiences by collecting illusion content generated by visual effects software. The analysis unit analyzes the content collected by the collection unit. The analysis unit analyzes, for example, the collected illusion content in real time. The analysis unit provides an immediate illusion experience by analyzing the collected illusion content in real time. The generation unit generates illusion art based on the data analyzed by the analysis unit. The generation unit dynamically generates illusion art based on, for example, user interaction. The generation unit provides an interactive illusion experience by dynamically generating illusion art based on user interaction. The provision unit provides the illusion art generated by the generation unit. The provision unit provides, for example, the generated illusion art to an art gallery or exhibition. The provision unit provides visual entertainment by offering the generated illusion art to art galleries and exhibitions. Thus, the illusion experience creation system according to this embodiment can create illusion experiences by collecting, analyzing, generating, and providing content generated by visual effects software.

[0065] The collection unit collects content generated by visual effects software. Specifically, it collects illusion content generated by visual effects software, providing material for illusion experiences. Visual effects software uses computer graphics and animation techniques to generate content that causes visual illusions. For example, visual effects software creates videos and images that utilize optical illusions and paradoxes and sends them to the collection unit. The collection unit centrally manages this content and stores it in a database. The collected content is organized so that the analysis and generation units can access it, and tags and metadata are added as needed. Furthermore, the collection unit has the function to receive data from the visual effects software in real time and immediately send it to the analysis unit. In this way, the collection unit efficiently collects diverse illusion content generated by visual effects software and plays a role in building the foundation of the entire system. The collection unit is designed to respond to version upgrades of the visual effects software and the addition of new effects, so it can always incorporate the latest illusion content. In this way, the collection unit can continue to provide material for visual entertainment as the core of the illusion experience creation system.

[0066] The analysis unit analyzes the content collected by the collection unit in real time. Specifically, it analyzes the collected illusion content to understand its content and characteristics. The analysis unit uses AI technology to perform pattern recognition and feature extraction of illusion content. For example, it uses image recognition algorithms to detect visual tricks and paradoxes contained in the illusion content and evaluate their effects. Furthermore, the analysis unit analyzes the metadata of the collected content to classify the content type, theme, and intensity of visual effects. This allows the analysis unit to analyze the collected illusion content in detail and generate foundational data for providing illusion experiences immediately. To enable real-time analysis, the analysis unit utilizes servers with high processing power and cloud computing technology. This allows the analysis unit to process large amounts of data quickly and provide immediate feedback to the generation unit. In addition, the analysis unit can continuously improve the accuracy of its analysis algorithms based on past analysis results and user feedback. This allows the analysis unit to always perform highly accurate analysis incorporating the latest technologies, enhancing the reliability and effectiveness of the illusion experience creation system.

[0067] The generation unit generates illusion art based on data analyzed by the analysis unit. Specifically, it dynamically generates illusion art based on user interaction, providing an interactive illusion experience. The generation unit uses AI technology to generate illusion art in real time in response to user input and actions. For example, when a user operates the touchscreen, the illusion art on the screen changes, creating a visual trick. It also incorporates a mechanism that detects user movement with sensors and dynamically changes the illusion art in response to that movement. Through these interactions, the generation unit provides users with a new visual experience. Furthermore, the generation unit optimizes the illusion art generation process based on data provided by the analysis unit. For example, it utilizes visual tricks and paradoxes detected by the analysis unit to generate more effective illusion art. The generation unit can also collect user reactions and feedback in real time and adjust the generation algorithm based on that. This allows the generation unit to continuously provide the optimal illusion experience for the user. The generation unit utilizes hardware and software with advanced graphics processing capabilities, enabling it to generate complex visual effects in real time. This allows the generation unit to provide users with interactive and engaging illusion art, playing a central role in the illusion experience creation system.

[0068] The provider division provides the illusion art generated by the generator division. Specifically, it provides the generated illusion art to art galleries and exhibitions, offering visual entertainment. The provider division displays the generated illusion art using high-resolution displays and projectors, providing viewers with an immersive visual experience. For example, in art galleries, large screens and interactive displays are installed, allowing viewers to directly touch and interact with the illusion art. In exhibitions and events, projection mapping technology is used to project the illusion art onto buildings and objects, surprising and impressing the audience. The provider division utilizes these technologies to effectively deliver the generated illusion art and create visual entertainment. Furthermore, the provider division can also widely distribute the generated illusion art through online platforms. For example, it can enable users to enjoy the illusion art at home or on the go through websites and mobile apps. By leveraging these online platforms, the provider division can globally distribute the generated illusion art and deliver visual entertainment to a large number of people. This allows the service provider to deliver the generated illusion art in a variety of ways, maximizing the effectiveness of the illusion experience creation system.

[0069] The collection unit can collect illusion content generated by visual effects software. For example, the collection unit collects illusion content generated by visual effects software. By collecting illusion content generated by visual effects software, the collection unit provides material for illusion experiences. Illusion content includes, but is not limited to, images and videos that utilize visual illusions. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input illusion content generated by visual effects software into AI, and the AI ​​can select the types of content to collect.

[0070] The analysis unit can analyze the collected illusion content in real time. For example, the analysis unit analyzes the collected illusion content in real time. By analyzing the collected illusion content in real time, the analysis unit provides an immediate illusion experience. Real time includes, but is not limited to, analysis latency and processing speed. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected illusion content into an AI, which can then analyze it in real time.

[0071] The generation unit can dynamically generate illusion art based on user interaction. For example, the generation unit dynamically generates illusion art based on user interaction. By dynamically generating illusion art based on user interaction, the generation unit provides an interactive illusion experience. User interaction includes, but is not limited to, touch operations and gesture recognition. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input user interaction data into an AI, which can then dynamically generate illusion art.

[0072] The provider can provide the generated illusion art to art galleries and exhibitions. The provider can, for example, provide the generated illusion art to art galleries and exhibitions. The provider provides visual entertainment by providing the generated illusion art to art galleries and exhibitions. Art galleries and exhibitions include, but are not limited to, specific museums and online exhibitions. Some or all of the above processing in the provider may be performed using, for example, AI, or not using AI. For example, the provider can input the generated illusion art into an AI, which can then select the optimal method of provision.

[0073] The service provider can integrate with an educational platform to support lessons using content on the theme of optical illusions. For example, the service provider can integrate with an educational platform to support lessons using content on the theme of optical illusions. By integrating with an educational platform, the service provider provides educational content on the theme of optical illusions. The educational platform includes, but is not limited to, specific online educational services and their functions. Some or all of the processing described above in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input optical illusion art into an AI on the educational platform, and the AI ​​can generate optimal educational content.

[0074] The data collection unit can estimate the user's emotions and adjust the type of content it collects based on the estimated emotions. For example, if the user is excited, the data collection unit may prioritize collecting visually stimulating illusion content. If the user is relaxed, the data collection unit may collect calming illusion content. If the user is stressed, the data collection unit may collect relaxing illusion content. By adjusting the type of content collected based on the user's emotions, a more appropriate illusion experience can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into an AI and adjust the type of content the AI ​​collects.

[0075] The collection unit can select the optimal content based on the version and settings information of the visual effects software during collection. For example, the collection unit prioritizes collecting content generated with the latest version of the visual effects software. The collection unit can select the optimal illusion content based on specific settings information. The collection unit can select content to collect while considering the compatibility of the visual effects software. This improves the accuracy of collection by selecting the optimal content based on the version and settings information of the visual effects software. The version and settings information of the visual effects software includes, but is not limited to, the software version number and the contents of the settings file. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the version and settings information of the visual effects software into the AI, and the AI ​​can select the optimal content.

[0076] The data collection unit can analyze the user's past use history of visual effects software during data collection and select the optimal collection method. For example, the data collection unit can collect the most suitable illusion content based on the user's past use history of visual effects software. The data collection unit can prioritize the collection of preferred content based on the user's past use history. The data collection unit can analyze the user's usage history and select the most effective collection method. This allows the optimal collection method to be selected by analyzing the user's past use history of visual effects software. Usage history includes, but is not limited to, log data and user operation history. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's past use history of visual effects software into AI, which can then select the optimal collection method.

[0077] The data collection unit can estimate the user's emotions and determine the priority of content to collect based on the estimated emotions. For example, if the user is excited, the data collection unit may prioritize collecting visually stimulating illusion content. If the user is relaxed, the data collection unit may prioritize collecting calming illusion content. If the user is stressed, the data collection unit may prioritize collecting relaxing illusion content. This allows for a more appropriate illusionary experience by prioritizing content collection 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 may be, 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 data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI, which can then determine the priority of content to collect.

[0078] The data collection unit can prioritize collecting highly relevant content based on the user's geographical location information during data collection. For example, the data collection unit can collect region-related illusion content based on the user's current location. The data collection unit can collect illusion content that is appropriate to the cultural background based on the user's geographical location information. The data collection unit can select the most relevant illusion content considering the user's location information. This improves the accuracy of data collection by prioritizing the collection of highly relevant content based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's geographical location information into AI, which can then select highly relevant content.

[0079] The data collection unit can analyze the user's social media activity and collect relevant content during the collection process. For example, the data collection unit can analyze the user's interests on social media and collect relevant illusion content. The data collection unit can select the most relevant illusion content based on the user's social media activity history. The data collection unit can collect relevant illusion content considering the interests of the user's followers and friends on social media. In this way, relevant content can be collected by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's social media activity into AI, which can then select relevant content.

[0080] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide deep insights. If the user is in a hurry, the analysis unit can perform a rapid analysis and provide concise results. If the user is excited, the analysis unit can provide visually stimulating analysis results. By adjusting the analysis method based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into AI, and the AI ​​can adjust the analysis method.

[0081] The analysis unit can apply different analysis algorithms depending on the type of content collected during analysis. For example, the analysis unit can apply a motion analysis algorithm to video content. For still image content, it can apply an image analysis algorithm. For text content, it can apply a natural language processing algorithm. By applying different analysis algorithms depending on the type of content collected, the accuracy of the analysis is improved. Analysis algorithms include, but are not limited to, image analysis algorithms and text analysis algorithms. 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 the type of content collected into the AI, which can then apply the most suitable analysis algorithm.

[0082] The analysis unit can improve the accuracy of its analysis based on the interrelationships of the collected content during the analysis process. For example, the analysis unit can integrate the analysis results by considering the relationships between videos and still images. The analysis unit can also integrate the analysis results by considering the relationships between text and images. The analysis unit can analyze the interrelationships of multiple content pieces and provide comprehensive analysis results. This allows for more accurate analysis results by improving the accuracy of the analysis based on the interrelationships of the collected content. These interrelationships include, but are not limited to, co-occurrence network analysis and correlation analysis. 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 the interrelationships of the collected content into the AI, which can then improve the accuracy of the analysis.

[0083] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results based on the user's emotions, a more appropriate display method can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the AI, and the AI ​​can adjust the display method of the analysis results.

[0084] The analysis unit can determine the priority of analysis based on the submission date of the collected content during analysis. For example, the analysis unit may prioritize the analysis of the most recent content. The analysis unit may postpone the analysis of older content. The analysis unit can adjust the analysis schedule based on the submission date. This improves the efficiency of analysis by determining the priority of analysis based on the submission date of the collected content. The submission date includes, but is not limited to, the submission date and time, and the submission deadline. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the submission dates of the collected content into the AI, and the AI ​​can determine the priority of analysis.

[0085] The analysis unit can improve the accuracy of its analysis based on relevant literature for the collected content during the analysis process. For example, the analysis unit can supplement the analysis results by referring to relevant academic papers. The analysis unit can supplement the analysis results by referring to relevant patent documents. The analysis unit can supplement the analysis results by referring to relevant technical documents. This improves the accuracy of the analysis based on relevant literature for the collected content, thereby providing more accurate analysis results. Relevant literature includes, but is not limited to, academic papers, patent documents, and technical reports. 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 relevant literature for the collected content into the AI, which can then improve the accuracy of the analysis.

[0086] The generation unit can estimate the user's emotions and adjust the type of illusion art it generates based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate calming illusion art. If the user is excited, the generation unit can generate visually stimulating illusion art. If the user is stressed, the generation unit can generate relaxing illusion art. By adjusting the type of illusion art generated based on the user's emotions, more appropriate illusion art can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into an AI and adjust the type of illusion art the AI ​​generates.

[0087] The generation unit can generate optimal illusion art by analyzing the user's interaction history during generation. For example, the generation unit can generate new illusion art based on patterns of illusion art that the user has previously liked. The generation unit can select the optimal style of illusion art from the user's interaction history. The generation unit can analyze the user's interaction history and generate the most effective illusion art. In this way, the optimal illusion art can be generated by analyzing the user's interaction history. Interaction history includes, but is not limited to, user operation logs and interaction frequency. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the user's interaction history into AI, and the AI ​​can generate the optimal illusion art.

[0088] The generation unit can optimize the generation algorithm based on the characteristics of the collected content during generation. For example, the generation unit can optimize the generation algorithm based on the characteristics of video content. The generation unit can optimize the generation algorithm based on the characteristics of still image content. The generation unit can optimize the generation algorithm based on the characteristics of text content. This improves the accuracy of generation by optimizing the generation algorithm based on the characteristics of the collected content. The generation algorithm includes, but is not limited to, image generation algorithms and 3D model generation algorithms. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the characteristics of the collected content into the AI, which can then apply the most suitable generation algorithm.

[0089] The generation unit can estimate the user's emotions and determine the priority of the illusion art to generate based on the estimated user emotions. For example, if the user is relaxed, the generation unit may prioritize generating calming illusion art. If the user is excited, the generation unit may prioritize generating visually stimulating illusion art. If the user is stressed, the generation unit may prioritize generating relaxing illusion art. By determining the priority of the illusion art to generate based on the user's emotions, it is possible to provide more appropriate illusion art. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into an AI and determine the priority of the illusion art to be generated by the AI.

[0090] The generation unit can generate optimal illusion art based on the user's geographical location information during generation. For example, the generation unit can generate region-related illusion art based on the user's current location. The generation unit can generate illusion art that is appropriate to the cultural background based on the user's geographical location information. The generation unit can generate optimal illusion art considering the user's location information. This improves the accuracy of generation by generating optimal illusion art based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the user's geographical location information into AI, and the AI ​​can generate optimal illusion art.

[0091] The generation unit can customize the illusion art it generates by analyzing the user's social media activity during the generation process. For example, the generation unit can analyze the user's interests on social media and generate relevant illusion art. The generation unit can generate optimal illusion art based on the user's social media activity history. The generation unit can generate relevant illusion art considering the interests of the user's followers and friends on social media. In this way, the generated illusion art can be customized by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the user's social media activity into AI, and the AI ​​can generate optimal illusion art.

[0092] The service provider can estimate the user's emotions and adjust the display method of the illusion art based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can provide a display method that includes detailed information. If the user is in a hurry, the service provider can provide a display method that gets straight to the point. By adjusting the display method of the illusion art based on the user's emotions, a more appropriate display method can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into AI and adjust the display method of the illusion art provided by the AI.

[0093] The service provider can select the optimal service delivery method based on the user's past usage history of visual effects software at the time of delivery. For example, the service provider can select the optimal optical illusion art delivery method based on the user's past usage history of visual effects software. The service provider can prioritize the user's preferred delivery method based on their past usage history. The service provider can analyze the user's usage history and select the most effective delivery method. This improves the accuracy of delivery by selecting the optimal delivery method based on the user's past usage history of visual effects software. Usage history includes, but is not limited to, log data and user operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past usage history of visual effects software into AI, and the AI ​​can select the optimal delivery method.

[0094] The service provider can filter the illusion art offered based on the user's current projects and areas of interest at the time of delivery. For example, the service provider may prioritize offering illusion art related to the user's current projects. The service provider can filter the most suitable illusion art based on the user's areas of interest. The service provider can select the illusion art to offer, taking into account the user's projects and areas of interest. This improves the accuracy of the service by filtering the illusion art offered based on the user's current projects and areas of interest. Current projects and areas of interest include, but are not limited to, the content of the project and keywords related to the area of ​​interest. 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 the user's current projects and areas of interest into an AI, which can then select the most suitable illusion art.

[0095] The service provider can estimate the user's emotions and determine the priority of the illusion art to be provided based on the estimated emotions. For example, if the user is relaxed, the service provider may prioritize providing calming illusion art. If the user is excited, the service provider may prioritize providing visually stimulating illusion art. If the user is stressed, the service provider may prioritize providing relaxing illusion art. In this way, by determining the priority of the illusion art to be provided based on the user's emotions, more appropriate illusion art can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into AI and the AI ​​can determine the priority of the illusion art to be provided.

[0096] The service provider can provide the most suitable optical illusion art based on the user's geographical location information at the time of delivery. For example, the service provider can provide region-related optical illusion art based on the user's current location. The service provider can provide optical illusion art that is appropriate to the cultural background based on the user's geographical location information. The service provider can provide the most suitable optical illusion art considering the user's location information. This improves the accuracy of the delivery by providing the most suitable optical illusion art based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. 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 the user's geographical location information into AI, and the AI ​​can provide the most suitable optical illusion art.

[0097] The service provider can customize the illusion art offered by analyzing the user's social media activity at the time of delivery. For example, the service provider can analyze the user's interests on social media and provide relevant illusion art. The service provider can provide optimal illusion art based on the user's social media activity history. The service provider can provide relevant illusion art considering the interests of the user's followers and friends on social media. In this way, the illusion art offered can be customized by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and the number of 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 the user's social media activity into AI, and the AI ​​can provide optimal illusion art.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The illusion experience creation system can also be equipped with a voice recognition unit. The voice recognition unit can analyze the user's voice commands and dynamically change the content of the illusion experience. For example, if the user says "make it brighter," the voice recognition unit can analyze the command and adjust the brightness of the illusion experience. Also, if the user says "move to the next scene," the voice recognition unit can analyze the command and switch to the next illusion scene. Furthermore, if the user says "stop," the voice recognition unit can analyze the command and pause the illusion experience. This allows users to control the illusion experience more intuitively using voice commands.

[0100] The illusion experience creation system can also be equipped with a biometric recognition unit. This unit can analyze the user's biometric information and individually customize the content of the illusion experience. For example, it can measure the user's heart rate and, if the heart rate is high, provide illusion content with a relaxing effect. It can also analyze the user's pupil movement and provide visually stimulating content. Furthermore, it can measure the electrical resistance of the user's skin and provide illusion experiences tailored to their stress level. This allows for the provision of more personalized illusion experiences based on the user's biometric information.

[0101] The illusion experience creation system can also be equipped with an environment recognition unit. The environment recognition unit can analyze information about the user's surroundings and dynamically change the content of the illusion experience. For example, if the user is in a dark room, the environment recognition unit can analyze that information and adjust the brightness of the illusion experience. Also, if the user is in a noisy place, the environment recognition unit can analyze that information and adjust the volume of the illusion experience. Furthermore, if the user is outdoors, the environment recognition unit can analyze that information and change the content of the illusion experience to be suitable for the outdoors. This makes it possible to provide a more appropriate illusion experience according to the user's surrounding environment.

[0102] The illusion experience creation system can also be equipped with a feedback unit. This feedback unit can collect user feedback and use it to improve the content of the illusion experience. For example, if a user completes a survey after experiencing an illusion, the feedback unit can analyze the responses and incorporate them into the next illusion experience. Furthermore, if a user provides feedback in real time, the feedback unit can analyze that information and immediately adjust the content of the illusion experience. Even if a user does not provide feedback, the feedback unit can analyze user behavior data and use it as indirect feedback. This allows for the provision of better illusion experiences based on user feedback.

[0103] The illusion experience creation system can also be equipped with a prediction unit. This unit can analyze the user's past behavioral data and predict the content of the next illusion experience to be provided. For example, it can predict the next content to be provided based on the illusion content the user has previously enjoyed. It can also analyze the user's past interaction data and predict the actions they are most likely to perform next. Furthermore, it can predict the content of the next illusion experience to be provided based on the user's past feedback. This allows for the provision of more appropriate illusion experiences based on the user's past behavioral data.

[0104] The illusion experience creation system can also be equipped with an emotion estimation unit. This unit can estimate the user's emotions from their facial expressions and voice, and dynamically change the content of the illusion experience. For example, if the user smiles, the emotion estimation unit can analyze this information and provide enjoyable illusion content. If the user shows a surprised expression, the emotion estimation unit can analyze this information and provide illusion content that includes elements of surprise. Furthermore, if the user shows a sad expression, the emotion estimation unit can analyze this information and provide illusion content that has a relaxing effect. This allows for the provision of more appropriate illusion experiences based on the user's emotions.

[0105] The illusion experience creation system can also be equipped with an emotional feedback unit. This unit can monitor the user's emotions in real time and adjust the content of the illusion experience accordingly. For example, if the user is excited, the emotional feedback unit can analyze this information and provide visually stimulating illusion content. If the user is relaxed, the emotional feedback unit can analyze this information and provide calming illusion content. Furthermore, if the user is stressed, the emotional feedback unit can analyze this information and provide relaxing illusion content. This allows for the provision of a more appropriate illusion experience based on the user's emotions.

[0106] The illusion experience creation system can also be equipped with an emotional history unit. This unit can analyze the user's past emotional data and customize the content of the illusion experience. For example, based on the user's past emotional data when they were relaxed, it can provide relaxing illusion content. Similarly, based on the user's past emotional data when they were excited, it can provide visually stimulating illusion content. Furthermore, based on the user's past emotional data when they were stressed, it can provide relaxing illusion content. This allows for the provision of more appropriate illusion experiences based on the user's past emotional data.

[0107] The illusion experience creation system can also be equipped with an emotion prediction unit. This unit analyzes the user's past emotional data and predicts the content of the next illusion experience to be provided. For example, based on the user's past emotional data when they were relaxed, it can predict the next illusion content to be provided that has a relaxing effect. Similarly, based on the user's past emotional data when they were excited, it can predict the next illusion content to be provided that is visually stimulating. Furthermore, based on the user's past emotional data when they were stressed, it can predict the next illusion content to be provided that has a relaxing effect. This allows the system to provide a more appropriate illusion experience based on the user's past emotional data.

[0108] The illusion experience creation system can also be equipped with an emotion learning unit. This unit can learn user emotion data and continuously improve the content of the illusion experience. For example, it can learn how users felt about previously provided illusion content and reflect that in the next illusion experience. It can also learn what kind of content is best suited to the user based on the user's emotion data and optimize the content provided. Furthermore, it can continuously collect user emotion data and improve the content of the illusion experience in real time. This allows for the provision of more appropriate illusion experiences based on user emotion data.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The collection unit collects content generated by visual effects software. For example, it collects optical illusion content generated by visual effects software and provides material for optical illusion experiences. Step 2: The analysis unit analyzes the content collected by the collection unit. For example, it analyzes the collected illusion content in real time and provides an immediate illusion experience. Step 3: The generation unit generates illusion art based on the data analyzed by the analysis unit. For example, it dynamically generates illusion art based on user interaction, providing an interactive illusion experience. Step 4: The provider unit provides the illusion art generated by the generator unit. For example, the generated illusion art is provided to an art gallery or exhibition to provide visual entertainment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects illusion content generated by the visual effects software. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected illusion content in real time. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates illusion art based on the analyzed data. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the generated illusion art to an art gallery or exhibition. 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.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects illusion content generated by the visual effects software. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected illusion content in real time. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates illusion art based on the analyzed data. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the generated illusion art to an art gallery or exhibition. 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.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects illusion content generated by the visual effects software. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected illusion content in real time. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates illusion art based on the analyzed data. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the generated illusion art to an art gallery or exhibition. 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.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects illusion content generated by the visual effects software. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected illusion content in real time. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates illusion art based on the analyzed data. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides the generated illusion art to an art gallery or exhibition. 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A collection unit that collects content generated by visual effects software, An analysis unit analyzes the content collected by the aforementioned collection unit, A generation unit that generates illusion art based on the data analyzed by the analysis unit, The system includes a providing unit that provides the illusion art generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect optical illusion content generated by visual effects software. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze collected illusion content in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Dynamically generates optical illusion art based on user interaction. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, We provide the generated illusion art to art galleries and exhibitions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Integrating with the educational platform to support lessons using content themed around optical illusions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the types of content collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During collection, the optimal content is selected based on the version and settings information of the visual effects software. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, the system analyzes the user's past usage history of visual effects software to select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates user sentiment and determines the priority of content to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant content based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes the user's social media activity and collects relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the type of content collected. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved based on the interrelationships of the collected content. The system described in Appendix 1, characterized by the features described herein. (Note 16) 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 17) The aforementioned analysis unit, During analysis, the priority of analysis is determined based on when the collected content was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved based on the relevant literature for the collected content. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts the type of illusion art generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the system analyzes the user's interaction history to generate the optimal optical illusion art. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, the generation algorithm is optimized based on the characteristics of the collected content. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and determines the priority of the illusion art to generate based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the system generates the optimal optical illusion art based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the system analyzes the user's social media activity to customize the generated optical illusion art. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the illusion art is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the software, the optimal delivery method will be selected based on the user's past usage history of visual effects software. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, the illusion art offered will be filtered based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the optical illusion art to be presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, the optimal optical illusion art will be delivered based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, At the time of delivery, the system analyzes the user's social media activity to customize the optical illusion art provided. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 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. A collection unit that collects content generated by visual effects software, An analysis unit analyzes the content collected by the aforementioned collection unit, A generation unit that generates illusion art based on the data analyzed by the analysis unit, The system includes a providing unit that provides the illusion art generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect optical illusion content generated by visual effects software. The system according to feature 1.

3. The aforementioned analysis unit, Analyze collected illusion content in real time. The system according to feature 1.

4. The generating unit is Dynamically generates optical illusion art based on user interaction. The system according to feature 1.

5. The aforementioned supply unit is, We provide the generated illusion art to art galleries and exhibitions. The system according to feature 1.

6. The aforementioned supply unit is, Integrating with the educational platform to support lessons using content themed around optical illusions. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the types of content collected based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is During collection, the optimal content is selected based on the version and settings information of the visual effects software. The system according to feature 1.

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