system

Abstract

An object of a system according to an embodiment is to generate personalized VR content according to a preference of a user and to enhance a sense of immersion.SOLUTION: A system according to an embodiment includes a preference analysis unit, a scene generation unit, and a content optimization unit. The preference analysis unit analyzes a user's preference. The scene generation unit generates a scene or a scenario on the basis of the user's preference analyzed by the preference analysis unit. The content optimizer optimizes the scene or scenario generated by the scene generator for the VR goggles.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem of making it difficult to generate personalized content tailored to the user's preferences, making it difficult to maximize the immersive feeling of VR content.

[0005] The system according to the embodiment aims to generate personalized VR content tailored to the user's preferences and enhance the sense of immersion. [Means for solving the problem]

[0006] The system according to the embodiment includes a preference analysis unit, a scene generation unit, and a content optimization unit. The preference analysis unit analyzes user preferences. The scene generation unit generates a scene or scenario based on the user preferences analyzed by the preference analysis unit. The content optimization unit optimizes the scene or scenario generated by the scene generation unit for VR goggles. [Effects of the Invention]

[0007] The system according to the embodiment can generate personalized VR content tailored to the user's preferences, enhancing the sense of immersion. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple 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), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A personalized VR content generation system according to an embodiment of the present invention is a system in which a generation AI generates scenes and scenarios based on a user's preferences and provides immersive content optimized for VR goggles, allowing users to experience content that can be enjoyed endlessly.

[0029] A personalized VR content generation system according to an embodiment includes a preference analysis unit, a scene generation unit, and a content optimization unit. The preference analysis unit analyzes a user's preferences. For example, the generation AI collects the user's past viewing history and survey results to analyze the user's preferences. The generation AI can also analyze the user's social media activities and online reviews to understand preference trends. The generation AI can also collect the user's biometric information (e.g., heart rate, brain waves) and use it to analyze the user's preferences. The scene generation unit generates scenes and scenarios based on the user's preferences analyzed by the preference analysis unit. For example, the generation AI generates scenes featuring the user's favorite characters or scenarios based on a specific genre. The generation AI can also learn the user's past choices and actions to predict and generate the next scene or scenario. The generation AI can also generate scenes and scenarios incorporating elements from different cultures and regions. The content optimization unit optimizes the scenes and scenarios generated by the scene generation unit for VR goggles. For example, the generation AI adds 3D models and sound effects to enhance the user's immersive experience. The generation AI can also analyze the user's gaze tracking data and dynamically change content according to gaze movements. Furthermore, the generation AI can collect user motion data and generate interactive content according to the user's motions. As a result, the personalized VR content generation system according to the embodiment can generate personalized scenes and scenarios based on the user's preferences and provide content optimized for VR goggles. For example, the user can experience personalized scenes and scenarios by wearing VR goggles. This allows the user to experience immersive content that can be enjoyed endlessly.

[0030] The preference analysis unit analyzes the user's social media activity or online reviews to understand preference trends in more detail. The preference analysis unit, for example, analyzes the user's social media account and extracts preference trends from the content of posts and the history of "likes." For example, it analyzes posts related to a specific genre or character. The preference analysis unit can also analyze online reviews to understand the user's preference trends. For example, it analyzes review ratings and the content of comments to identify preference trends. In this way, by analyzing the user's social media activity and online reviews, it is possible to understand preference trends in more detail.

[0031] The preference analysis unit can collect biometric information from the user and use it to analyze preferences. For example, while the user is watching content, the preference analysis unit uses a wearable device to collect heart rate data and analyze changes in emotions. For example, preferences are identified based on increases and decreases in heart rate. The preference analysis unit can also collect brain wave data and use it to analyze the user's preferences. For example, brain wave patterns are analyzed to identify the user's preferences. This allows preferences to be analyzed based on the user's biometric information, enabling more accurate personalization.

[0032] When analyzing the user's preferences, the preference analysis unit can also take into account the preferences of family or friends to generate content that can be enjoyed by a group. The preference analysis unit, for example, collects viewing histories and preference data of the user's family and friends to identify common interests. For example, it generates content based on genres or characters that the whole family can enjoy. The preference analysis unit can also collect survey results of family and friends to understand preference trends. For example, it can identify common preferences based on the survey results. This allows content that can be enjoyed by a group to be generated by taking into account the preferences of family and friends.

[0033] The preference analysis unit can also integrate data from different devices. For example, the preference analysis unit collects the history of content viewed by a user on a smartphone or tablet and analyzes preference trends. For example, the viewing history on different devices is integrated and analyzed. The preference analysis unit can also collect data from a wearable device and use it for preference analysis. For example, preferences can be identified based on data from a wearable device. In this way, by integrating data from different devices, more detailed preference analysis becomes possible.

[0034] The scene generation unit can detect changes in a user's preferences and dynamically change scenes and scenarios accordingly. For example, the scene generation unit analyzes behavioral data (eye movement, click history, etc.) of the user while viewing in real time to detect changes in preferences. For example, it changes scenes according to eye movement. The scene generation unit can also detect changes in a user's preferences in real time and dynamically change scenes and scenarios accordingly. This makes it possible to always provide optimal content by dynamically changing scenes and scenarios according to changes in the user's preferences.

[0035] The scene generation unit can learn the user's past choices or actions and predict and generate the next scene or scenario. The scene generation unit, for example, analyzes the user's past viewing history and choices and predicts the scene or scenario that the user will likely prefer next. For example, the scene generation unit generates the next scene based on the past choices. The scene generation unit can also learn the user's past choices and actions and predict and generate the next scene or scenario. In this way, a more personalized experience can be provided by learning the user's past choices and actions and predicting and generating the next scene or scenario.

[0036] The scene generation unit can incorporate elements of different cultures or regions. For example, the scene generation unit collects the user's cultural background and regional information and customizes scenes and scenarios based on that information. For example, elements related to a specific culture or region can be incorporated. The scene generation unit can also generate scenes and scenarios that incorporate elements of different cultures or regions. This allows the user to have a new experience by incorporating elements of different cultures and regions.

[0037] The scene generation unit adds a function that allows a user to share scenes and scenarios with other users, allowing them to enjoy the experience together. The scene generation unit adds a function that allows a user to share scenes and scenarios that the user has generated with other users, for example, by generating a shared link and viewing the content together with other users. The scene generation unit also adds a function that allows a user to share scenes and scenarios with other users, allowing them to enjoy the experience together. This allows a user to share scenes and scenarios with other users, allowing them to enjoy the experience together.

[0038] The content optimization unit can analyze the user's gaze tracking data and dynamically change the content according to the gaze movement. For example, the content optimization unit collects the user's gaze tracking data in real time and dynamically changes the VR content according to the gaze movement. For example, it can focus on an object the user is looking at. The content optimization unit can also dynamically change the content based on the gaze tracking data. This makes it possible to provide a better experience by dynamically changing the content based on the user's gaze tracking data.

[0039] The content optimization unit can collect user motion data and generate interactive content according to the motion. For example, the content optimization unit collects user motion data (hand motions, body motions, etc.) in real time and generates interactive VR content according to the motion. For example, it can operate objects according to hand motions. The content optimization unit can also generate interactive content based on the motion data. This makes it possible to provide a better experience by generating interactive content based on the user motion data.

[0040] The content optimization unit can also generate content for devices other than VR goggles. For example, the content optimization unit optimizes content generated for VR goggles for AR glasses or smart mirrors. For example, it makes 3D models and interactive elements compatible with AR glasses. The content optimization unit can also generate content for devices other than VR goggles. This makes it possible to provide a wider variety of experiences by generating content for devices other than VR goggles.

[0041] The content optimization unit can add a function that allows users to customize content themselves, thereby providing a more personalized experience. The content optimization unit, for example, provides an interface that allows users to edit scenes and scenarios themselves. For example, the content optimization unit can customize the character placement and the progression of the scenario. The content optimization unit can also add a function that allows users to customize content themselves, thereby providing a more personalized experience. This allows users to customize content themselves, thereby providing a more personalized experience.

[0042] The system can collect user feedback and instantly reflect it in the content. For example, the system can collect real-time user feedback (comments, ratings, etc.) and instantly reflect it in the content. For example, the system can dynamically change scenes based on comments made during viewing. The system can also collect user feedback in real time and instantly reflect it in the content. This allows for a better experience by collecting user feedback in real time and instantly reflecting it in the content.

[0043] The system can analyze user behavior data and provide content at the optimal timing. For example, the system can analyze user behavior data (eye movement, click history, etc.) while the user is viewing in real time and provide content at the optimal timing. For example, the system can change the scene according to eye movement. The system can also analyze user behavior data and provide content at the optimal timing. This allows the system to provide a better experience by analyzing user behavior data and providing content at the optimal timing.

[0044] The system can make the content experienceable on different platforms. For example, the system optimizes content generated for VR goggles for PCs and smartphones. For example, the system makes 3D models and interactive elements compatible with PCs and smartphones. The system can also make the content experienceable on different platforms. This allows the content to be experienced on different platforms, thereby providing a more diverse experience.

[0045] The system may add a multi-user feature that allows a user to experience content simultaneously with other users. The system may, for example, add a multi-user feature that allows a user to experience content simultaneously with other users. For example, the system may provide an interface that allows multiple users to view content simultaneously. The system may also add a multi-user feature that allows a user to experience content simultaneously with other users. This may provide a better experience by allowing a user to experience content simultaneously with other users.

[0046] The system can analyze user feedback and automatically extract areas for content improvement. For example, the system collects user feedback (comments, ratings, etc.) and automatically extracts areas for content improvement using text mining technology. For example, areas for improvement can be identified based on negative comments. The system can also analyze user feedback and automatically extract areas for content improvement. This makes it possible to provide a better experience by analyzing user feedback and automatically extracting areas for content improvement.

[0047] The system can detect changes in a user's preferences and dynamically update content accordingly. For example, the system analyzes user behavior data (eye movement, click history, etc.) in real time while the user is watching to detect changes in preferences. For example, the system changes scenes according to eye movement. The system can also detect changes in a user's preferences in real time and dynamically update content accordingly. This allows the system to provide a better experience by detecting changes in a user's preferences in real time and dynamically updating content accordingly.

[0048] The system can automate content updates, allowing users to always experience the latest content. For example, the system uses generative AI to build a system that automates content updates based on user feedback and new data. For example, the system can automatically modify scenes based on user ratings. The system can also automate content updates, allowing users to always experience the latest content. This makes it possible to provide a better experience by automating content updates and allowing users to always experience the latest content.

[0049] The system can provide a more personalized experience by adding a function that allows users to update content themselves. For example, the system provides an interface that allows users to edit scenes and scenarios themselves. For example, the system can customize the placement of characters and the progression of a scenario. The system can also provide a more personalized experience by adding a function that allows users to update content themselves. This allows users to update content themselves, thereby providing a more personalized experience.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The preference analysis unit can also take the user's purchase history into account when analyzing the user's preferences. For example, it can collect data on products and services the user has purchased in the past and identify preference trends. The preference analysis unit can also suggest related content based on the user's purchase history. This allows for more detailed preference analysis by taking the user's purchase history into account.

[0052] The preference analysis unit can analyze a user's social media activity or online reviews to understand preference trends in more detail. For example, it can analyze a user's social media account and extract preference trends from the content of posts and "like" history. For example, it can analyze posts related to a specific genre or character. The preference analysis unit can also analyze online reviews to understand a user's preference trends. For example, it can analyze review ratings and comment content to identify preference trends. In this way, by analyzing a user's social media activity and online reviews, it is possible to understand preference trends in more detail.

[0053] The preference analysis unit can collect biometric information from the user and use it to analyze preferences. For example, while the user is watching content, the wearable device can collect heart rate data and analyze changes in emotions. For example, preferences can be identified based on increases and decreases in heart rate. The preference analysis unit can also collect brain wave data and use it to analyze the user's preferences. For example, brain wave patterns can be analyzed to identify the user's preferences. This allows preferences to be analyzed based on the user's biometric information, enabling more accurate personalization.

[0054] When analyzing the user's preferences, the preference analysis unit can also take into account the preferences of family or friends to generate content that can be enjoyed by a group. For example, it can collect viewing histories and preference data of the user's family and friends to identify common interests. For example, it can generate content based on genres or characters that the whole family can enjoy. The preference analysis unit can also collect survey results of family and friends to understand preference trends. For example, it can identify common preferences based on the survey results. This allows it to generate content that can be enjoyed by a group by taking into account the preferences of family and friends.

[0055] The preference analysis unit can also integrate data from different devices. For example, it can collect the history of content a user has viewed on a smartphone or tablet and analyze preference trends. For example, it can integrate and analyze viewing histories from different devices. The preference analysis unit can also collect data from wearable devices and use it for preference analysis. For example, it can identify preferences based on data from wearable devices. In this way, integrating data from different devices enables more detailed preference analysis.

[0056] The scene generation unit can detect changes in a user's preferences and dynamically change scenes and scenarios accordingly. For example, it can analyze user behavior data (eye movement, click history, etc.) while the user is viewing in real time to detect changes in preferences. For example, it can change scenes according to eye movement. The scene generation unit can also detect changes in a user's preferences in real time and dynamically change scenes and scenarios accordingly. This allows the system to always provide optimal content by dynamically changing scenes and scenarios according to changes in the user's preferences.

[0057] The scene generation unit can learn the user's past choices or actions and predict and generate the next scene or scenario. For example, it can analyze the user's past viewing history and choices and predict the scene or scenario that the user will likely prefer next. For example, it can generate the next scene based on the past choices. The scene generation unit can also learn the user's past choices and actions and predict and generate the next scene or scenario. This makes it possible to provide a more personalized experience by learning the user's past choices and actions and predicting and generating the next scene or scenario.

[0058] The scene generation unit can incorporate elements from different cultures or regions. For example, it collects information about the user's cultural background and region and customizes scenes and scenarios based on that information. For example, it can incorporate elements related to a specific culture or region. The scene generation unit can also generate scenes and scenarios that incorporate elements from different cultures and regions. This allows the user to have a new experience by incorporating elements from different cultures and regions.

[0059] The scene generation unit adds a function that allows a user to share scenes and scenarios with other users, allowing them to enjoy them together. For example, a function is added that allows a user to share scenes and scenarios that they have generated with other users. For example, a shared link is generated and the user can watch the scenes and scenarios together. The scene generation unit also adds a function that allows a user to share scenes and scenarios with other users, allowing them to enjoy them together. This allows a user to share scenes and scenarios with other users, allowing them to enjoy them together.

[0060] The content optimization unit can analyze the user's gaze tracking data and dynamically change the content according to the gaze movement. For example, the content optimization unit can collect the user's gaze tracking data in real time and dynamically change the VR content according to the gaze movement. For example, the content optimization unit can focus on an object the user is looking at. The content optimization unit can also dynamically change the content based on the gaze tracking data. This allows the user to provide a better experience by dynamically changing the content based on the user's gaze tracking data.

[0061] The content optimization unit can collect user motion data and generate interactive content according to the motion. For example, it can collect user motion data (hand motions, body motions, etc.) in real time and generate interactive VR content according to the motion. For example, it can operate objects according to hand motions. The content optimization unit can also generate interactive content based on the motion data. This makes it possible to provide a better experience by generating interactive content based on the user motion data.

[0062] The content optimization unit can also generate content for devices other than VR goggles. For example, content generated for VR goggles can be optimized for AR glasses or smart mirrors. For example, 3D models and interactive elements can be made compatible with AR glasses. The content optimization unit can also generate content for devices other than VR goggles. This allows for a more diverse range of experiences to be provided by generating content for devices other than VR goggles.

[0063] The content optimization unit can add a function that allows users to customize content themselves, thereby providing a more personalized experience. For example, it can provide an interface that allows users to edit scenes and scenarios themselves, for example, customizing character placement and scenario progression. The content optimization unit can also add a function that allows users to customize content themselves, thereby providing a more personalized experience. This allows users to customize content themselves, thereby providing a more personalized experience.

[0064] The system can collect user feedback and instantly reflect it in the content. For example, it can collect real-time user feedback (comments, ratings, etc.) and instantly reflect it in the content. For example, it can dynamically change scenes based on comments made while watching. The system can also collect user feedback in real time and instantly reflect it in the content. This allows for a better experience by collecting user feedback in real time and instantly reflecting it in the content.

[0065] The system can analyze user behavior data and provide content at the optimal timing. For example, it can analyze user behavior data (eye movement, click history, etc.) while the user is viewing in real time and provide content at the optimal timing. For example, it can change the scene according to eye movement. The system can also analyze user behavior data and provide content at the optimal timing. This allows for a better experience by analyzing user behavior data and providing content at the optimal timing.

[0066] The system can make content experienceable on different platforms. For example, content generated for VR goggles can be optimized for PCs and smartphones. For example, 3D models and interactive elements can be made compatible with PCs and smartphones. The system can also make content experienceable on different platforms. This allows content to be experienced on different platforms, providing a more diverse experience.

[0067] The system may add a multi-user feature that allows a user to experience content simultaneously with other users. For example, the system may add a multi-user feature that allows a user to experience content simultaneously with other users. For example, the system may provide an interface that allows multiple users to watch at the same time. The system may also add a multi-user feature that allows a user to experience content simultaneously with other users. This may provide a better experience by allowing a user to experience content simultaneously with other users.

[0068] The system can analyze user feedback and automatically extract areas for content improvement. For example, it collects user feedback (comments, ratings, etc.) and uses text mining technology to automatically extract areas for content improvement. For example, it identifies areas for improvement based on negative comments. The system can also analyze user feedback and automatically extract areas for content improvement. This makes it possible to provide a better experience by analyzing user feedback and automatically extracting areas for content improvement.

[0069] The system can detect changes in a user's preferences and dynamically update content accordingly. For example, it can analyze user behavior data (such as eye movements and click history) in real time while the user is watching to detect changes in preferences. For example, it can change scenes according to eye movements. The system can also detect changes in a user's preferences in real time and dynamically update content accordingly. This allows the system to provide a better experience by detecting changes in a user's preferences in real time and dynamically updating content accordingly.

[0070] The system can automate content updates, allowing users to always experience the latest content. For example, generative AI can be used to build a system that automates content updates based on user feedback and new data. For example, a scene can be automatically modified based on user ratings. The system can also automate content updates, allowing users to always experience the latest content. This can provide a better experience by automating content updates and allowing users to always experience the latest content.

[0071] The system can provide a more personalized experience by adding a function that allows users to update content themselves. For example, an interface is provided that allows users to edit scenes and scenarios themselves. For example, the system can customize the placement of characters or the progression of a scenario. The system can also provide a more personalized experience by adding a function that allows users to update content themselves. This allows users to update content themselves, thereby providing a more personalized experience.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The preference analysis unit analyzes the user's preferences. For example, the generation AI collects the user's past viewing history and survey results to analyze preferences. The generation AI can also analyze the user's social media activity and online reviews to understand preference trends. Furthermore, the generation AI can collect the user's biometric information (heart rate, brain waves, etc.) and use it to analyze preferences. Step 2: The scene generation unit generates scenes and scenarios based on the user's preferences analyzed by the preference analysis unit. For example, the generation AI generates scenes featuring the user's favorite characters or scenarios based on a specific genre. The generation AI can also learn the user's past choices and actions and predict and generate the next scene or scenario. Furthermore, the generation AI can generate scenes and scenarios that incorporate elements from different cultures and regions. Step 3: The content optimization unit optimizes the scenes and scenarios generated by the scene generation unit for VR goggles. For example, the generation AI adds 3D models and sound effects to create an immersive experience for the user. The generation AI can also analyze the user's eye-tracking data and dynamically change the content according to their eye movements. Furthermore, the generation AI can collect user movement data and generate interactive content according to their movements.

[0074] (Example 2) A personalized VR content generation system according to an embodiment of the present invention is a system in which a generation AI generates scenes and scenarios based on a user's preferences and provides immersive content optimized for VR goggles, allowing users to experience content that can be enjoyed endlessly.

[0075] A personalized VR content generation system according to an embodiment includes a preference analysis unit, a scene generation unit, and a content optimization unit. The preference analysis unit analyzes a user's preferences. For example, the generation AI collects the user's past viewing history and survey results to analyze the user's preferences. The generation AI can also analyze the user's social media activities and online reviews to understand preference trends. The generation AI can also collect the user's biometric information (e.g., heart rate, brain waves) and use it to analyze the user's preferences. The scene generation unit generates scenes and scenarios based on the user's preferences analyzed by the preference analysis unit. For example, the generation AI generates scenes featuring the user's favorite characters or scenarios based on a specific genre. The generation AI can also learn the user's past choices and actions to predict and generate the next scene or scenario. The generation AI can also generate scenes and scenarios incorporating elements from different cultures and regions. The content optimization unit optimizes the scenes and scenarios generated by the scene generation unit for VR goggles. For example, the generation AI adds 3D models and sound effects to enhance the user's immersive experience. The generation AI can also analyze the user's gaze tracking data and dynamically change content according to gaze movements. Furthermore, the generation AI can collect user motion data and generate interactive content according to the user's motions. As a result, the personalized VR content generation system according to the embodiment can generate personalized scenes and scenarios based on the user's preferences and provide content optimized for VR goggles. For example, the user can experience personalized scenes and scenarios by wearing VR goggles. This allows the user to experience immersive content that can be enjoyed endlessly.

[0076] The preference analysis unit can use the user's emotion estimation function to collect emotion data while the user is watching content and reflect it in the preference analysis. The preference analysis unit, for example, uses a camera or microphone to collect emotion data in real time while the user is watching content. For example, it analyzes facial expressions and vocal tone to identify emotions such as joy or surprise. The preference analysis unit can also use the emotion estimation function to collect emotion data while the user is watching content and reflect it in the preference analysis. This allows for analysis of preferences based on the user's real-time emotion data, enabling more accurate personalization.

[0077] The preference analysis unit analyzes the user's social media activity or online reviews to understand preference trends in more detail. The preference analysis unit, for example, analyzes the user's social media account and extracts preference trends from the content of posts and the history of "likes." For example, it analyzes posts related to a specific genre or character. The preference analysis unit can also analyze online reviews to understand the user's preference trends. For example, it analyzes review ratings and the content of comments to identify preference trends. In this way, by analyzing the user's social media activity and online reviews, it is possible to understand preference trends in more detail.

[0078] The preference analysis unit can collect biometric information from the user and use it to analyze preferences. For example, while the user is watching content, the preference analysis unit uses a wearable device to collect heart rate data and analyze changes in emotions. For example, preferences are identified based on increases and decreases in heart rate. The preference analysis unit can also collect brain wave data and use it to analyze the user's preferences. For example, brain wave patterns are analyzed to identify the user's preferences. This allows preferences to be analyzed based on the user's biometric information, enabling more accurate personalization.

[0079] When analyzing the user's preferences, the preference analysis unit can also take into account the preferences of family or friends to generate content that can be enjoyed by a group. The preference analysis unit, for example, collects viewing histories and preference data of the user's family and friends to identify common interests. For example, it generates content based on genres or characters that the whole family can enjoy. The preference analysis unit can also collect survey results of family and friends to understand preference trends. For example, it can identify common preferences based on the survey results. This allows content that can be enjoyed by a group to be generated by taking into account the preferences of family and friends.

[0080] The preference analysis unit can also integrate data from different devices. For example, the preference analysis unit collects the history of content viewed by a user on a smartphone or tablet and analyzes preference trends. For example, the viewing history on different devices is integrated and analyzed. The preference analysis unit can also collect data from a wearable device and use it for preference analysis. For example, preferences can be identified based on data from a wearable device. In this way, by integrating data from different devices, more detailed preference analysis becomes possible.

[0081] The preference analysis unit can use the emotion estimation function to estimate the emotion a user is feeling when entering their preferences and make suggestions that elicit positive emotions. The preference analysis unit, for example, uses a camera or microphone to estimate the emotion in real time when a user is entering their preferences. For example, it analyzes facial expressions and voice tone to make suggestions that elicit positive emotions. The preference analysis unit can also use the emotion estimation function to estimate the emotion a user is feeling when entering their preferences in real time and make suggestions that elicit positive emotions. This enables better personalization by eliciting positive emotions when a user enters their preferences.

[0082] The scene generation unit can use the emotion estimation function to generate a scene or scenario to which the user responds most emotionally. For example, the scene generation unit can use the emotion estimation function to identify a scene to which the user responded most emotionally from content that the user has previously viewed, and generate a new scene or scenario based on the scene. The scene generation unit can also use the emotion estimation function to generate a scene or scenario to which the user responds most emotionally. This can provide a more immersive experience by generating a scene or scenario to which the user responds most emotionally.

[0083] The scene generation unit can detect changes in a user's preferences and dynamically change scenes and scenarios accordingly. For example, the scene generation unit analyzes behavioral data (eye movement, click history, etc.) of the user while viewing in real time to detect changes in preferences. For example, it changes scenes according to eye movement. The scene generation unit can also detect changes in a user's preferences in real time and dynamically change scenes and scenarios accordingly. This makes it possible to always provide optimal content by dynamically changing scenes and scenarios according to changes in the user's preferences.

[0084] The scene generation unit can learn the user's past choices or actions and predict and generate the next scene or scenario. The scene generation unit, for example, analyzes the user's past viewing history and choices and predicts the scene or scenario that the user will likely prefer next. For example, the scene generation unit generates the next scene based on the past choices. The scene generation unit can also learn the user's past choices and actions and predict and generate the next scene or scenario. In this way, a more personalized experience can be provided by learning the user's past choices and actions and predicting and generating the next scene or scenario.

[0085] The scene generation unit can incorporate elements of different cultures or regions. For example, the scene generation unit collects the user's cultural background and regional information and customizes scenes and scenarios based on that information. For example, elements related to a specific culture or region can be incorporated. The scene generation unit can also generate scenes and scenarios that incorporate elements of different cultures or regions. This allows the user to have a new experience by incorporating elements of different cultures and regions.

[0086] The scene generation unit adds a function that allows a user to share scenes and scenarios with other users, allowing them to enjoy the experience together. The scene generation unit adds a function that allows a user to share scenes and scenarios that the user has generated with other users, for example, by generating a shared link and viewing the content together with other users. The scene generation unit also adds a function that allows a user to share scenes and scenarios with other users, allowing them to enjoy the experience together. This allows a user to share scenes and scenarios with other users, allowing them to enjoy the experience together.

[0087] The scene generation unit can use the emotion estimation function to estimate the emotion a user is feeling when selecting a scene or scenario and suggest the optimal option. The scene generation unit estimates the emotion in real time using a camera or microphone when the user is selecting a scene or scenario. For example, the scene generation unit analyzes facial expressions and voice tone to suggest the optimal option. The scene generation unit can also use the emotion estimation function to estimate the emotion a user is feeling when selecting a scene or scenario in real time and suggest the optimal option. This can provide a better experience by suggesting the optimal option when the user is selecting a scene or scenario.

[0088] The content optimization unit can use the emotion estimation function to generate content that the user feels most immersed in. For example, the content optimization unit can use the emotion estimation function to identify the most immersive scene from VR content that the user has previously viewed, and generate new content based on that scene. The content optimization unit can also use the emotion estimation function to generate content that the user feels most immersed in. This can provide a better experience by generating content that the user feels most immersed in.

[0089] The content optimization unit can analyze the user's gaze tracking data and dynamically change the content according to the gaze movement. For example, the content optimization unit collects the user's gaze tracking data in real time and dynamically changes the VR content according to the gaze movement. For example, it can focus on an object the user is looking at. The content optimization unit can also dynamically change the content based on the gaze tracking data. This makes it possible to provide a better experience by dynamically changing the content based on the user's gaze tracking data.

[0090] The content optimization unit can collect user motion data and generate interactive content according to the motion. For example, the content optimization unit collects user motion data (hand motions, body motions, etc.) in real time and generates interactive VR content according to the motion. For example, it can operate objects according to hand motions. The content optimization unit can also generate interactive content based on the motion data. This makes it possible to provide a better experience by generating interactive content based on the user motion data.

[0091] The content optimization unit can also generate content for devices other than VR goggles. For example, the content optimization unit optimizes content generated for VR goggles for AR glasses or smart mirrors. For example, it makes 3D models and interactive elements compatible with AR glasses. The content optimization unit can also generate content for devices other than VR goggles. This makes it possible to provide a wider variety of experiences by generating content for devices other than VR goggles.

[0092] The content optimization unit can add a function that allows users to customize content themselves, thereby providing a more personalized experience. The content optimization unit, for example, provides an interface that allows users to edit scenes and scenarios themselves. For example, the content optimization unit can customize the character placement and the progression of the scenario. The content optimization unit can also add a function that allows users to customize content themselves, thereby providing a more personalized experience. This allows users to customize content themselves, thereby providing a more personalized experience.

[0093] The content optimization unit can use the emotion estimation function to monitor the emotions of the user when experiencing content and provide optimal content. For example, the content optimization unit monitors emotions in real time using a camera or microphone while the user is experiencing VR content. For example, it analyzes facial expressions and voice tone to provide optimal content. The content optimization unit can also use the emotion estimation function to monitor the emotions of the user when experiencing content in real time and provide optimal content. In this way, a better experience can be provided by monitoring the user's emotions in real time and providing optimal content.

[0094] The system can use the emotion estimation function to provide a specific scene at the timing to which the user has the most emotional reaction. For example, the system can use the emotion estimation function to identify the timing to which the user had the most emotional reaction in content that the user has previously viewed, and provide a new scene based on that timing. The system can also use the emotion estimation function to provide a specific scene at the timing to which the user has the most emotional reaction. This can provide a better experience by providing a specific scene at the timing to which the user has the most emotional reaction.

[0095] The system can collect user feedback and instantly reflect it in the content. For example, the system can collect real-time user feedback (comments, ratings, etc.) and instantly reflect it in the content. For example, the system can dynamically change scenes based on comments made during viewing. The system can also collect user feedback in real time and instantly reflect it in the content. This allows for a better experience by collecting user feedback in real time and instantly reflecting it in the content.

[0096] The system can analyze user behavior data and provide content at the optimal timing. For example, the system can analyze user behavior data (eye movement, click history, etc.) while the user is viewing in real time and provide content at the optimal timing. For example, the system can change the scene according to eye movement. The system can also analyze user behavior data and provide content at the optimal timing. This allows the system to provide a better experience by analyzing user behavior data and providing content at the optimal timing.

[0097] The system can make the content experienceable on different platforms. For example, the system optimizes content generated for VR goggles for PCs and smartphones. For example, the system makes 3D models and interactive elements compatible with PCs and smartphones. The system can also make the content experienceable on different platforms. This allows the content to be experienced on different platforms, thereby providing a more diverse experience.

[0098] The system may add a multi-user feature that allows a user to experience content simultaneously with other users. The system may, for example, add a multi-user feature that allows a user to experience content simultaneously with other users. For example, the system may provide an interface that allows multiple users to view content simultaneously. The system may also add a multi-user feature that allows a user to experience content simultaneously with other users. This may provide a better experience by allowing a user to experience content simultaneously with other users.

[0099] The system can use the emotion estimation function to monitor the emotions of a user as they experience content and provide an optimal experience. For example, the system monitors emotions in real time using a camera or microphone while the user is experiencing content. For example, the system analyzes facial expressions and tone of voice to provide an optimal experience. The system can also use the emotion estimation function to monitor the emotions of a user as they experience content in real time and provide an optimal experience. This makes it possible to provide a better experience by monitoring the user's emotions in real time and providing an optimal experience.

[0100] The system can use the emotion estimation function to generate a new scenario based on a scene to which the user has the most emotional reaction. For example, the system can use the emotion estimation function to identify a scene to which the user has the most emotional reaction from content that the user has previously viewed, and generate a new scenario based on that scene. The system can also use the emotion estimation function to generate a new scenario based on a scene to which the user has the most emotional reaction. This can provide a better experience by generating a new scenario based on a scene to which the user has the most emotional reaction.

[0101] The system can analyze user feedback and automatically extract areas for content improvement. For example, the system collects user feedback (comments, ratings, etc.) and automatically extracts areas for content improvement using text mining technology. For example, areas for improvement can be identified based on negative comments. The system can also analyze user feedback and automatically extract areas for content improvement. This makes it possible to provide a better experience by analyzing user feedback and automatically extracting areas for content improvement.

[0102] The system can detect changes in a user's preferences and dynamically update content accordingly. For example, the system analyzes user behavior data (eye movement, click history, etc.) in real time while the user is watching to detect changes in preferences. For example, the system changes scenes according to eye movement. The system can also detect changes in a user's preferences in real time and dynamically update content accordingly. This allows the system to provide a better experience by detecting changes in a user's preferences in real time and dynamically updating content accordingly.

[0103] The system can automate content updates, allowing users to always experience the latest content. For example, the system uses generative AI to build a system that automates content updates based on user feedback and new data. For example, the system can automatically modify scenes based on user ratings. The system can also automate content updates, allowing users to always experience the latest content. This makes it possible to provide a better experience by automating content updates and allowing users to always experience the latest content.

[0104] The system can provide a more personalized experience by adding a function that allows users to update content themselves. For example, the system provides an interface that allows users to edit scenes and scenarios themselves. For example, the system can customize the placement of characters and the progression of a scenario. The system can also provide a more personalized experience by adding a function that allows users to update content themselves. This allows users to update content themselves, thereby providing a more personalized experience.

[0105] The system can use the emotion estimation function to monitor the emotions of a user as they experience content and provide optimal updates. For example, the system monitors emotions in real time using a camera or microphone while the user is experiencing content. For example, the system analyzes facial expressions and tone of voice and provides optimal updates. The system can also use the emotion estimation function to monitor the emotions of a user as they experience content in real time and provide optimal updates. This makes it possible to provide a better experience by monitoring the user's emotions in real time and providing optimal updates.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The preference analysis unit can also take the user's purchase history into account when analyzing the user's preferences. For example, it can collect data on products and services the user has purchased in the past and identify preference trends. The preference analysis unit can also suggest related content based on the user's purchase history. This allows for more detailed preference analysis by taking the user's purchase history into account.

[0108] The preference analysis unit can use the user's emotion estimation function to collect emotion data while the user is watching content and reflect it in the preference analysis. For example, emotion data can be collected in real time using a camera or microphone while the user is watching content. For example, facial expressions and tone of voice can be analyzed to identify emotions such as joy or surprise. The preference analysis unit can also use the emotion estimation function to collect emotion data while the user is watching content and reflect it in the preference analysis. This allows for analysis of preferences based on the user's real-time emotion data, enabling more accurate personalization.

[0109] The preference analysis unit can analyze a user's social media activity or online reviews to understand preference trends in more detail. For example, it can analyze a user's social media account and extract preference trends from the content of posts and "like" history. For example, it can analyze posts related to a specific genre or character. The preference analysis unit can also analyze online reviews to understand a user's preference trends. For example, it can analyze review ratings and comment content to identify preference trends. In this way, by analyzing a user's social media activity and online reviews, it is possible to understand preference trends in more detail.

[0110] The preference analysis unit can collect biometric information from the user and use it to analyze preferences. For example, while the user is watching content, the wearable device can collect heart rate data and analyze changes in emotions. For example, preferences can be identified based on increases and decreases in heart rate. The preference analysis unit can also collect brain wave data and use it to analyze the user's preferences. For example, brain wave patterns can be analyzed to identify the user's preferences. This allows preferences to be analyzed based on the user's biometric information, enabling more accurate personalization.

[0111] When analyzing the user's preferences, the preference analysis unit can also take into account the preferences of family or friends to generate content that can be enjoyed by a group. For example, it can collect viewing histories and preference data of the user's family and friends to identify common interests. For example, it can generate content based on genres or characters that the whole family can enjoy. The preference analysis unit can also collect survey results of family and friends to understand preference trends. For example, it can identify common preferences based on the survey results. This allows it to generate content that can be enjoyed by a group by taking into account the preferences of family and friends.

[0112] The preference analysis unit can also integrate data from different devices. For example, it can collect the history of content a user has viewed on a smartphone or tablet and analyze preference trends. For example, it can integrate and analyze viewing histories from different devices. The preference analysis unit can also collect data from wearable devices and use it for preference analysis. For example, it can identify preferences based on data from wearable devices. In this way, integrating data from different devices enables more detailed preference analysis.

[0113] The preference analysis unit can use the emotion estimation function to estimate the emotion a user is feeling when entering their preferences and make suggestions that elicit positive emotions. For example, when a user is entering their preferences, the emotion is estimated in real time using a camera or microphone. For example, facial expressions and tone of voice are analyzed to make suggestions that elicit positive emotions. The preference analysis unit can also use the emotion estimation function to estimate the emotion a user is feeling when entering their preferences in real time and make suggestions that elicit positive emotions. This enables better personalization by eliciting positive emotions when the user enters their preferences.

[0114] The scene generation unit can use the emotion estimation function to generate a scene or scenario to which the user responds most emotionally. For example, the emotion estimation function can be used to identify a scene to which the user responded most emotionally in content that the user has previously viewed, and generate a new scene or scenario based on that scene. The scene generation unit can also use the emotion estimation function to generate a scene or scenario to which the user responds most emotionally. This can provide a more immersive experience by generating a scene or scenario to which the user responds most emotionally.

[0115] The scene generation unit can detect changes in a user's preferences and dynamically change scenes and scenarios accordingly. For example, it can analyze user behavior data (eye movement, click history, etc.) while the user is viewing in real time to detect changes in preferences. For example, it can change scenes according to eye movement. The scene generation unit can also detect changes in a user's preferences in real time and dynamically change scenes and scenarios accordingly. This allows the system to always provide optimal content by dynamically changing scenes and scenarios according to changes in the user's preferences.

[0116] The scene generation unit can learn the user's past choices or actions and predict and generate the next scene or scenario. For example, it can analyze the user's past viewing history and choices and predict the scene or scenario that the user will likely prefer next. For example, it can generate the next scene based on the past choices. The scene generation unit can also learn the user's past choices and actions and predict and generate the next scene or scenario. This makes it possible to provide a more personalized experience by learning the user's past choices and actions and predicting and generating the next scene or scenario.

[0117] The scene generation unit can incorporate elements from different cultures or regions. For example, it collects information about the user's cultural background and region and customizes scenes and scenarios based on that information. For example, it can incorporate elements related to a specific culture or region. The scene generation unit can also generate scenes and scenarios that incorporate elements from different cultures and regions. This allows the user to have a new experience by incorporating elements from different cultures and regions.

[0118] The scene generation unit adds a function that allows a user to share scenes and scenarios with other users, allowing them to enjoy them together. For example, a function is added that allows a user to share scenes and scenarios that they have generated with other users. For example, a shared link is generated and the user can watch the scenes and scenarios together. The scene generation unit also adds a function that allows a user to share scenes and scenarios with other users, allowing them to enjoy them together. This allows a user to share scenes and scenarios with other users, allowing them to enjoy them together.

[0119] The scene generation unit can use the emotion estimation function to estimate the emotion a user is feeling when selecting a scene or scenario and suggest the optimal option. For example, when a user is selecting a scene or scenario, the emotion is estimated in real time using a camera or microphone. For example, facial expressions and tone of voice are analyzed to suggest the optimal option. The scene generation unit can also use the emotion estimation function to estimate the emotion a user is feeling when selecting a scene or scenario in real time and suggest the optimal option. This can provide a better experience by suggesting the optimal option when the user is selecting a scene or scenario.

[0120] The content optimization unit can use the emotion estimation function to generate content that the user finds most immersive. For example, the emotion estimation function can be used to identify the most immersive scene from VR content the user has previously viewed, and generate new content based on that scene. The content optimization unit can also use the emotion estimation function to generate content that the user finds most immersive. This can provide a better experience by generating content that the user finds most immersive.

[0121] The content optimization unit can analyze the user's gaze tracking data and dynamically change the content according to the gaze movement. For example, the content optimization unit can collect the user's gaze tracking data in real time and dynamically change the VR content according to the gaze movement. For example, the content optimization unit can focus on an object the user is looking at. The content optimization unit can also dynamically change the content based on the gaze tracking data. This allows the user to provide a better experience by dynamically changing the content based on the user's gaze tracking data.

[0122] The content optimization unit can collect user motion data and generate interactive content according to the motion. For example, it can collect user motion data (hand motions, body motions, etc.) in real time and generate interactive VR content according to the motion. For example, it can operate objects according to hand motions. The content optimization unit can also generate interactive content based on the motion data. This makes it possible to provide a better experience by generating interactive content based on the user motion data.

[0123] The content optimization unit can also generate content for devices other than VR goggles. For example, content generated for VR goggles can be optimized for AR glasses or smart mirrors. For example, 3D models and interactive elements can be made compatible with AR glasses. The content optimization unit can also generate content for devices other than VR goggles. This allows for a more diverse range of experiences to be provided by generating content for devices other than VR goggles.

[0124] The content optimization unit can add a function that allows users to customize content themselves, thereby providing a more personalized experience. For example, it can provide an interface that allows users to edit scenes and scenarios themselves, for example, customizing character placement and scenario progression. The content optimization unit can also add a function that allows users to customize content themselves, thereby providing a more personalized experience. This allows users to customize content themselves, thereby providing a more personalized experience.

[0125] The content optimization unit can use the emotion estimation function to monitor the emotions of the user when experiencing content and provide optimal content. For example, when the user is experiencing VR content, the camera and microphone can be used to monitor emotions in real time. For example, facial expressions and tone of voice can be analyzed to provide optimal content. The content optimization unit can also use the emotion estimation function to monitor the emotions of the user when experiencing content in real time and provide optimal content. This makes it possible to provide a better experience by monitoring the user's emotions in real time and providing optimal content.

[0126] The system can use the emotion estimation function to provide a specific scene at the timing to which the user has the most emotional reaction. For example, the emotion estimation function can be used to identify the timing to which the user had the most emotional reaction in content that the user has previously viewed, and provide a new scene based on that timing. The system can also use the emotion estimation function to provide a specific scene at the timing to which the user has the most emotional reaction. This can provide a better experience by providing a specific scene at the timing to which the user has the most emotional reaction.

[0127] The system can collect user feedback and instantly reflect it in the content. For example, it can collect real-time user feedback (comments, ratings, etc.) and instantly reflect it in the content. For example, it can dynamically change scenes based on comments made while watching. The system can also collect user feedback in real time and instantly reflect it in the content. This allows for a better experience by collecting user feedback in real time and instantly reflecting it in the content.

[0128] The system can analyze user behavior data and provide content at the optimal timing. For example, it can analyze user behavior data (eye movement, click history, etc.) while the user is viewing in real time and provide content at the optimal timing. For example, it can change the scene according to eye movement. The system can also analyze user behavior data and provide content at the optimal timing. This allows for a better experience by analyzing user behavior data and providing content at the optimal timing.

[0129] The system can make content experienceable on different platforms. For example, content generated for VR goggles can be optimized for PCs and smartphones. For example, 3D models and interactive elements can be made compatible with PCs and smartphones. The system can also make content experienceable on different platforms. This allows content to be experienced on different platforms, providing a more diverse experience.

[0130] The system may add a multi-user feature that allows a user to experience content simultaneously with other users. For example, the system may add a multi-user feature that allows a user to experience content simultaneously with other users. For example, the system may provide an interface that allows multiple users to watch at the same time. The system may also add a multi-user feature that allows a user to experience content simultaneously with other users. This may provide a better experience by allowing a user to experience content simultaneously with other users.

[0131] The system can use the emotion estimation function to monitor the emotions of a user as they experience content and provide an optimal experience. For example, the system can monitor emotions in real time using a camera or microphone while the user is experiencing content. For example, the system can analyze facial expressions and tone of voice to provide an optimal experience. The system can also use the emotion estimation function to monitor the emotions of a user as they experience content in real time and provide an optimal experience. This makes it possible to provide a better experience by monitoring the user's emotions in real time and providing an optimal experience.

[0132] The system can use the emotion estimation function to generate a new scenario based on the scene to which the user responds most emotionally. For example, the emotion estimation function can be used to identify the scene to which the user responded most emotionally in content that the user has previously viewed, and generate a new scenario based on that scene. The system can also use the emotion estimation function to generate a new scenario based on the scene to which the user responds most emotionally. This allows for a better experience by generating a new scenario based on the scene to which the user responds most emotionally.

[0133] The system can analyze user feedback and automatically extract areas for content improvement. For example, it collects user feedback (comments, ratings, etc.) and uses text mining technology to automatically extract areas for content improvement. For example, it identifies areas for improvement based on negative comments. The system can also analyze user feedback and automatically extract areas for content improvement. This makes it possible to provide a better experience by analyzing user feedback and automatically extracting areas for content improvement.

[0134] The system can detect changes in a user's preferences and dynamically update content accordingly. For example, it can analyze user behavior data (such as eye movements and click history) in real time while the user is watching to detect changes in preferences. For example, it can change scenes according to eye movements. The system can also detect changes in a user's preferences in real time and dynamically update content accordingly. This allows the system to provide a better experience by detecting changes in a user's preferences in real time and dynamically updating content accordingly.

[0135] The system can automate content updates, allowing users to always experience the latest content. For example, generative AI can be used to build a system that automates content updates based on user feedback and new data. For example, a scene can be automatically modified based on user ratings. The system can also automate content updates, allowing users to always experience the latest content. This can provide a better experience by automating content updates and allowing users to always experience the latest content.

[0136] The system can provide a more personalized experience by adding a function that allows users to update content themselves. For example, an interface is provided that allows users to edit scenes and scenarios themselves. For example, the system can customize the placement of characters or the progression of a scenario. The system can also provide a more personalized experience by adding a function that allows users to update content themselves. This allows users to update content themselves, thereby providing a more personalized experience.

[0137] The system can use the emotion estimation function to monitor the emotions of a user as they experience content and provide optimal updates. For example, the system can monitor emotions in real time using a camera or microphone while the user is experiencing content. For example, the system can analyze facial expressions and tone of voice to provide optimal updates. The system can also use the emotion estimation function to monitor the emotions of a user as they experience content in real time and provide optimal updates. This makes it possible to provide a better experience by monitoring the user's emotions in real time and providing optimal updates.

[0138] The processing flow of the second embodiment will be briefly explained below.

[0139] Step 1: The preference analysis unit analyzes the user's preferences. For example, the generation AI collects the user's past viewing history and survey results to analyze preferences. The generation AI can also analyze the user's social media activity and online reviews to understand preference trends. Furthermore, the generation AI can collect the user's biometric information (heart rate, brain waves, etc.) and use it to analyze preferences. Step 2: The scene generation unit generates scenes and scenarios based on the user's preferences analyzed by the preference analysis unit. For example, the generation AI generates scenes featuring the user's favorite characters or scenarios based on a specific genre. The generation AI can also learn the user's past choices and actions and predict and generate the next scene or scenario. Furthermore, the generation AI can generate scenes and scenarios that incorporate elements from different cultures and regions. Step 3: The content optimization unit optimizes the scenes and scenarios generated by the scene generation unit for VR goggles. For example, the generation AI adds 3D models and sound effects to create an immersive experience for the user. The generation AI can also analyze the user's eye-tracking data and dynamically change the content according to their eye movements. Furthermore, the generation AI can collect user movement data and generate interactive content according to their movements.

[0140] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0141] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0143] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0144] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0145] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0146] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0147] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0150] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0153] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0154] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0159] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0160] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0161] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0162] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0164] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0165] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0166] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0167] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0168] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0169] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0170] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0172] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0173] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0174] 7, a 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.

[0175] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0176] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0177] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0178] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0179] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0180] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0181] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0182] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0183] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0184] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0185] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0186] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0187] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0188] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0189] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0190] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0191] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0192] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0193] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0194] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0195] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0196] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0197] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0198] 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.

[0199] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0200] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0201] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0202] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0203] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0204] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0205] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0206] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0207] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a preference analysis unit that analyzes user preferences; a scene generation unit that generates a scene or a scenario based on the user's preferences analyzed by the preference analysis unit; a content optimization unit that optimizes the scene or the scenario generated by the scene generation unit for VR goggles. A system characterized by:

2. The preference analysis unit Collecting emotional data of the user while watching and reflecting it in the analysis of the user's preferences 2. The system of claim 1.

3. The scene generation unit Generate the scene or scenario to which the user has the most emotional response.

2. The system of claim 1.

4. The content optimization unit Generate content that the user finds most immersive 2. The system of claim 1.

5. The content optimization unit Monitor the emotions of the user when experiencing content and provide the most appropriate content 2. The system of claim 1.

6. The system comprises: Presenting specific scenes at the moment when the user has the most emotional response 2. The system of claim 1.

7. The system comprises: Generate a new scenario based on the scene to which the user has the most emotional reaction.

2. The system of claim 1.

8. The system comprises: Monitor the emotions of said users as they experience content and provide optimal updates 2. The system of claim 1.

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