system

Abstract

The system according to this embodiment aims to enable people with physical limitations to move freely in a virtual space, thereby improving their sense of mental fulfillment and the effectiveness of rehabilitation. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, a control unit, and a feedback unit. The acquisition unit acquires electroencephalogram (EEG) data. The analysis unit analyzes the EEG data acquired by the acquisition unit. The control unit controls movement in the virtual space based on the data analyzed by the analysis unit. The feedback unit provides multisensory feedback based on the movement controlled by the control unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that people with physical constraints are likely to become mentally isolated and depressed in daily life, and the rehabilitation effect cannot be fully obtained.

[0005] The system according to the embodiment aims to enable people with physical constraints to move freely in a virtual space and improve the sense of mental fulfillment and the rehabilitation effect.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, an analysis unit, a control unit, and a feedback unit. The acquisition unit acquires electroencephalogram (EEG) data. The analysis unit analyzes the EEG data acquired by the acquisition unit. The control unit controls movement in the virtual space based on the data analyzed by the analysis unit. The feedback unit provides multisensory feedback based on the movement controlled by the control unit. [Effects of the Invention]

[0007] The system according to this embodiment allows people with physical limitations to move freely in a virtual space, thereby improving their sense of fulfillment and the effectiveness of their rehabilitation. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The virtual life system according to an embodiment of the present invention is a virtual reality mobility system that integrates AI and BCI (brainwave interface). This virtual life system aims to allow people with physical limitations to move freely in a virtual space. This system enhances immersion and provides a realistic experience through multisensory feedback such as sight, touch, and sound. In particular, it can highlight special moments by utilizing "VR Moments," adding enjoyment and motivation to rehabilitation and daily experiences. First, the user intends to move in the virtual space through the BCI. The BCI acquires the user's brainwave data, and the AI ​​analyzes this data to reflect the user's intentions in the movements in the virtual space. For example, if the user thinks "move forward," the AI ​​analyzes that intention and realizes the user's movement forward in the virtual space. Next, the AI ​​analyzes the user's emotions and state in real time and optimally adjusts feedback such as touch and sound. As a result, the user can get an experience as if they were moving in the real world. For example, if the user is walking through a flower field in the virtual space, the AI ​​will reproduce the scent of flowers and the sound of the wind, providing the user with a realistic experience. Furthermore, special moments can be highlighted by utilizing "VR Moments." For example, if a user sees a beautiful sunset in a virtual space, the AI ​​can highlight that moment, evoking a sense of awe in the user. This adds enjoyment and motivation to rehabilitation and daily experiences, improving user motivation. This system allows users to move freely beyond physical limitations, leading to a sense of mental fulfillment and increased motivation for rehabilitation. In addition to improving the quality of life, it energizes daily life through social participation and new experiences, enhancing the effectiveness of rehabilitation. For example, it is expected that elderly people enjoying travel in a virtual space will reduce feelings of isolation and increase their motivation for rehabilitation. Thus, the virtual life system allows users to move freely beyond physical limitations, leading to a sense of mental fulfillment and increased motivation for rehabilitation. In addition to improving the quality of life, it energizes daily life through social participation and new experiences, enhancing the effectiveness of rehabilitation.

[0029] The virtual life system according to this embodiment comprises an acquisition unit, an analysis unit, a control unit, and a feedback unit. The acquisition unit acquires electroencephalogram (EEG) data. The acquisition unit can acquire, for example, EEG data. The acquisition unit can also acquire specific EEG patterns. For example, the acquisition unit can acquire EEG data when the user is relaxed. The analysis unit analyzes the EEG data acquired by the acquisition unit. The analysis unit can analyze the EEG data using, for example, signal processing techniques. The analysis unit can also analyze the EEG data using machine learning algorithms. For example, the analysis unit can analyze the user's intentions from the EEG data. The control unit controls movement in the virtual space based on the data analyzed by the analysis unit. The control unit can control, for example, the movement of a character. The control unit can also control the movement of objects. For example, if the user thinks "move forward," the control unit can control the character's movement in the virtual space. The feedback unit provides multisensory feedback based on the movement controlled by the control unit. The feedback unit can provide, for example, visual feedback. The feedback unit can also provide tactile feedback. For example, the feedback unit can reproduce the scent of flowers and the sound of wind when the user is walking through a flower field in a virtual space. This allows the virtual life system to acquire and analyze brainwave data, control movement within the virtual space, and provide multi-sensory feedback, thereby offering the user a realistic experience.

[0030] The acquisition unit acquires electroencephalogram (EEG) data. For example, the acquisition unit can acquire EEG data. Specifically, the acquisition unit collects EEG signals through multiple electrodes attached to the user's scalp. These electrodes detect electrical activity from different parts of the brain, amplifying weak electrical signals and converting them into digital data. The acquisition unit can collect EEG data in real time, continuously monitoring the user's brain state. The acquisition unit can also acquire specific EEG patterns. For example, it can acquire EEG data when the user is relaxed. Relaxed brainwaves are dominated by alpha and theta waves, and identifying these waveforms can reveal the user's mental state. Furthermore, the acquisition unit can detect EEG patterns when the user is concentrating or stressed. This allows the acquisition unit to understand the user's diverse mental states in real time and reflect this in the overall system operation. Using noise reduction technology, the acquisition unit minimizes interference from the external environment and user movements, enabling accurate EEG data acquisition. This allows the acquisition unit to acquire user EEG data with high accuracy, improving the overall system performance.

[0031] The analysis unit analyzes the electroencephalogram (EEG) data acquired by the acquisition unit. The analysis unit can analyze EEG data using signal processing techniques, for example. Specifically, it uses methods such as Fourier transforms and wavelet transforms to decompose EEG data into frequency components and analyze the energy distribution of each frequency band. The analysis unit can also analyze EEG data using machine learning algorithms. For example, it can analyze user intent from EEG data. Machine learning algorithms learn from past EEG data and corresponding user behavior data to estimate user intent from new EEG data. Specifically, they use algorithms such as neural networks and support vector machines to extract features from EEG data and classify user intent. Furthermore, the analysis unit can monitor user EEG data over the long term and build models optimized for individual users. This allows the analysis unit to analyze user intent with high accuracy and improve the overall system responsiveness. The analysis unit can analyze EEG data in real time and quickly grasp user intent and mental state. This allows the analysis unit to efficiently and accurately analyze the user's electroencephalogram (EEG) data, thereby improving the overall system performance.

[0032] The control unit controls movement within the virtual space based on data analyzed by the analysis unit. For example, the control unit can control the movement of a character. Specifically, the control unit controls the movement and actions of the virtual character in real time based on the user's intentions. For example, if the user thinks "move forward," the control unit can control the character's movement in the virtual space. The control unit can also control the movement of objects. For example, if the user thinks "pick up an object," the control unit can control the object's movement in the virtual space. To accurately reflect the user's intentions, the control unit receives data from the analysis unit in real time and quickly controls the movement. Furthermore, the control unit can make corrections to improve the smoothness and realism of the movement according to the environment and situation in the virtual space. For example, it can use a physics engine to correct the movement so that the character's movement looks natural. In addition, the control unit can control multiple movements simultaneously based on the user's intentions. This allows the control unit to reflect the user's intentions in real time and make the experience in the virtual space more realistic.

[0033] The feedback unit provides multisensory feedback based on movements controlled by the control unit. For example, the feedback unit can provide visual feedback. Specifically, it provides the user with visual information within the virtual space, allowing the user to visually confirm their movements within the virtual space. The feedback unit can also provide haptic feedback. For example, if the user is walking through a flower field in the virtual space, the feedback unit can reproduce the scent of flowers and the sound of wind. Furthermore, the feedback unit can use haptic devices to reproduce the sensation of touching an object in the virtual space. This allows the user to experience the virtual space more realistically. The feedback unit integrates multisensory feedback, such as visual, auditory, tactile, and olfactory feedback, to provide the user with a comprehensive experience. For example, if the user is walking along a beach in the virtual space, the feedback unit can reproduce the sound of waves, the scent of the sea, and the feel of sand. This allows the feedback unit to provide the user with a realistic experience and enhance immersion in the virtual space. Furthermore, the feedback unit can collect user feedback and continuously improve the accuracy and effectiveness of the feedback. This allows the feedback system to provide users with a more realistic and satisfying experience.

[0034] The virtual life system includes an emphasis section that highlights special moments. For example, if a user sees a beautiful sunset in the virtual space, the emphasis section can highlight that moment. The emphasis section can, for example, visually enhance the colors of the sunset. Furthermore, the emphasis section can acoustically reproduce the sound of a setting sun. For instance, by realistically reproducing the sound of a setting sun, the emphasis section can evoke emotion in the user. In this way, the virtual life system can evoke emotion in the user by highlighting special moments.

[0035] The virtual life system is equipped with an emotion analysis unit that analyzes the user's emotions and state in real time. For example, the emotion analysis unit can estimate emotions by analyzing the user's brainwave data. It can, for instance, determine whether the user is relaxed or stressed. Furthermore, the emotion analysis unit can estimate emotions by analyzing the user's facial expression data. For example, it can analyze the user's smile or sadness. This allows the virtual life system to provide optimal feedback by analyzing the user's emotions and state in real time.

[0036] The feedback unit can provide multi-sensory feedback, including visual, tactile, and auditory feedback. For example, the feedback unit can provide visual feedback using a display. It can also provide tactile feedback using a vibration device. For instance, when a user is walking through a flower field in a virtual space, the feedback unit can reproduce the scent of flowers and the sound of wind. In this way, the feedback unit can provide a realistic experience to the user by offering multi-sensory feedback.

[0037] The analysis unit can analyze the user's brainwave data and reflect the user's intentions in the movements within the virtual space. For example, the analysis unit can analyze brainwave patterns to interpret the user's intentions. If the user thinks, for instance, "move forward," the analysis unit can analyze that intention and make the character move forward in the virtual space. The analysis unit can also predict movements based on the user's brainwave data. For example, if the user thinks, "jump," the analysis unit can analyze that intention and make the character jump in the virtual space. In this way, the analysis unit can reflect the user's intentions in the movements within the virtual space, providing the user with a realistic experience.

[0038] The control unit can control movement within the virtual space. For example, the control unit can control the movement of a character. For instance, if the user thinks "move forward," the control unit can control the character's movement in the virtual space. The control unit can also control the movement of objects. For example, if the user thinks "lift an object," the control unit can realize that action in the virtual space. In this way, the control unit can provide the user with a realistic experience by controlling movement within the virtual space.

[0039] The acquisition unit can analyze the user's past brainwave data and select the optimal acquisition method. For example, the acquisition unit can identify the time of day when the most stable data can be obtained from the user's past brainwave data. Furthermore, the acquisition unit can select a data acquisition method under specific environmental conditions based on the user's past brainwave data. For example, the acquisition unit can analyze the user's past brainwave data and determine the optimal sensor placement. Thus, the acquisition unit can select the optimal acquisition method by analyzing past brainwave data.

[0040] The acquisition unit can filter EEG data based on the user's current activity and environment. For example, if the user is in a quiet environment, the acquisition unit can set noise filtering to a minimum. Conversely, if the user is in a noisy environment, the acquisition unit can enhance noise filtering to maintain data accuracy. For instance, if the user is performing a specific activity, the acquisition unit can prioritize acquiring EEG data related to that activity. This allows the acquisition unit to maintain data accuracy by filtering based on the user's current activity and environment.

[0041] The data acquisition unit can prioritize the acquisition of highly relevant data by considering the user's geographical location when acquiring electroencephalogram (EEG) data. For example, if the user is in a specific location, the acquisition unit can prioritize the acquisition of EEG data related to that location. Furthermore, if the user is on the move, the acquisition unit can prioritize the acquisition of EEG data related to that movement. For example, if the user is in a specific environment, the acquisition unit can prioritize the acquisition of EEG data related to that environment. In this way, the acquisition unit can prioritize the acquisition of highly relevant data by considering geographical location information.

[0042] The data acquisition unit can analyze the user's social media activity when acquiring electroencephalogram (EEG) data and acquire relevant data. For example, if the user posts about a specific topic on social media, the data acquisition unit can prioritize acquiring EEG data related to that topic. Furthermore, if the user expresses a specific emotion on social media, the data acquisition unit can prioritize acquiring EEG data related to that emotion. For instance, if the user shares a specific activity on social media, the data acquisition unit can prioritize acquiring EEG data related to that activity. In this way, the data acquisition unit can prioritize acquiring relevant data by analyzing social media activity.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the electroencephalogram (EEG) data during the analysis. For example, the analysis unit can analyze highly important EEG data in detail and simplify less important data. Furthermore, the analysis unit can apply multiple analysis algorithms to highly important EEG data. For instance, the analysis unit can prioritize the analysis of highly important EEG data and postpone the analysis of less important data. This allows the analysis unit to perform efficient analysis by adjusting the level of detail based on importance.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the electroencephalogram (EEG) data during analysis. For example, the analysis unit can apply an analysis algorithm specialized for relaxation to EEG data in a relaxed state. Similarly, it can apply an analysis algorithm specialized for stress to EEG data in a stressed state. For example, the analysis unit can apply an analysis algorithm specialized for concentration to EEG data in a focused state. By applying different analysis algorithms depending on the category, the analysis unit can perform more accurate analysis.

[0045] The analysis unit can determine the priority of analysis based on when the electroencephalogram (EEG) data was acquired. For example, the analysis unit can prioritize the analysis of recently acquired EEG data. It can also prioritize the analysis of EEG data acquired during specific events. For instance, based on the user's activity history, the analysis unit can prioritize the analysis of EEG data acquired during specific time periods. This allows the analysis unit to prioritize the analysis of the most recent data by determining the priority of analysis based on the acquisition date.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the electroencephalogram (EEG) data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant EEG data. It can also postpone the analysis of less relevant EEG data. For instance, the analysis unit can evaluate the relevance of multiple EEG data sets and optimize the order of analysis. This allows the analysis unit to perform efficient analysis by adjusting the order of analysis based on relevance.

[0047] The control unit can analyze the user's past operation history during control to select the optimal control method. For example, the control unit can select the most effective control method from the user's past operation history. Furthermore, the control unit can prioritize control of specific operation patterns based on the user's past operation history. For example, the control unit can analyze the user's past operation history to select the optimal operation speed. Thus, the control unit can select the optimal control method by analyzing past operation history.

[0048] The control unit can customize movement control based on the user's current activity level during operation. For example, if the user is relaxed, the control unit can control movements appropriate for that relaxed state. Similarly, if the user is stressed, the control unit can control movements that reduce stress. For instance, if the user is focused, the control unit can control movements that maintain focus. This allows the control unit to achieve more appropriate movements by customizing movement control based on the user's current activity level.

[0049] The control unit can select the optimal movement control method by considering the user's geographical location information during control. For example, if the user is in a specific location, the control unit can control movement appropriate to that location. Furthermore, if the user is moving, the control unit can control movement appropriate to that movement. For example, if the user is in a specific environment, the control unit can control movement appropriate to that environment. In this way, the control unit can select the optimal movement control method by considering geographical location information.

[0050] The control unit can analyze the user's social media activity during control and adjust the control of movements accordingly. For example, if the user is posting about a specific topic on social media, the control unit can control movements related to that topic. It can also control movements related to specific emotions if the user is expressing those emotions on social media. For instance, if the user is sharing a specific activity on social media, the control unit can control movements related to that activity. This allows the control unit to prioritize and control relevant movements by analyzing social media activity.

[0051] The feedback unit can analyze the user's past feedback history to select the optimal feedback method during the feedback process. For example, it can select the most effective feedback method based on the user's past feedback history. Furthermore, the feedback unit can prioritize providing specific feedback patterns based on the user's past feedback history. For instance, it can analyze the user's past feedback history to select the optimal feedback timing. Thus, by analyzing past feedback history, the feedback unit can select the most appropriate feedback method.

[0052] The feedback unit can customize the means of feedback based on the user's current activity level. For example, if the user is relaxed, the feedback unit can provide feedback appropriate to that relaxed state. It can also provide stress-reducing feedback if the user is stressed. For instance, if the user is focused, the feedback unit can provide feedback to help maintain focus. This allows the feedback unit to provide more appropriate feedback by customizing the means of feedback based on the user's current activity level.

[0053] The feedback unit can select the optimal feedback method by considering the user's geographical location information when providing feedback. For example, if the user is in a specific location, the feedback unit can provide feedback appropriate to that location. Furthermore, if the user is on the move, the feedback unit can provide feedback appropriate to their movement. For instance, if the user is in a specific environment, the feedback unit can provide feedback appropriate to that environment. In this way, the feedback unit can select the optimal feedback method by considering geographical location information.

[0054] The feedback unit can analyze a user's social media activity and adjust the means of feedback when providing feedback. For example, if a user posts about a specific topic on social media, the feedback unit can provide feedback related to that topic. It can also provide feedback related to a user expressing a specific emotion on social media. For instance, if a user shares a specific activity on social media, the feedback unit can provide feedback related to that activity. This allows the feedback unit to prioritize providing relevant feedback by analyzing social media activity.

[0055] The emphasis function can analyze the user's past experience history to select the optimal emphasis method during emphasis. For example, the emphasis function can select the most effective emphasis method from the user's past experience history. Furthermore, the emphasis function can prioritize the emphasis of specific experience patterns based on the user's past experience history. For example, the emphasis function can analyze the user's past experience history to select the optimal emphasis timing. Thus, the emphasis function can select the optimal emphasis method by analyzing past experience history.

[0056] The emphasis function can customize the means of emphasis based on the user's current activity level. For example, if the user is relaxed, the emphasis function can provide emphasis appropriate to that relaxed state. It can also provide emphasis to reduce stress if the user is stressed. For example, if the user is focused, the emphasis function can provide emphasis to maintain focus. In this way, the emphasis function can provide more appropriate emphasis by customizing the means of emphasis based on the user's current activity level.

[0057] The emphasis function can select the optimal emphasis method by considering the user's geographical location information during emphasis. For example, if the user is in a specific location, the emphasis function can provide emphasis appropriate to that location. Furthermore, if the user is on the move, the emphasis function can provide emphasis appropriate to movement. For instance, if the user is in a specific environment, the emphasis function can provide emphasis appropriate to that environment. In this way, the emphasis function can select the optimal emphasis method by considering geographical location information.

[0058] The emphasis function can analyze the user's social media activity and adjust the means of emphasis during the emphasis process. For example, if the user posts about a specific topic on social media, the emphasis function can provide emphasis related to that topic. It can also provide emphasis related to a specific emotion if the user expresses that emotion on social media. For instance, if the emphasis function shares a specific activity on social media, it can provide emphasis related to that activity. This allows the emphasis function to prioritize relevant emphasis by analyzing social media activity.

[0059] The emotion analysis unit can analyze the user's past emotional data to select the optimal analysis method during emotion analysis. For example, the emotion analysis unit can select the most effective analysis method from the user's past emotional data. Furthermore, the emotion analysis unit can prioritize the analysis of specific emotional patterns based on the user's past emotional data. For example, the emotion analysis unit can analyze the user's past emotional data to select the optimal analysis timing. In this way, the emotion analysis unit can select the optimal analysis method by analyzing past emotional data.

[0060] The emotion analysis unit can customize its analysis methods based on the user's current activity level during emotion analysis. For example, if the user is relaxed, the emotion analysis unit can provide an analysis appropriate for that relaxed state. Similarly, if the user is stressed, the emotion analysis unit can provide an analysis that helps reduce stress. For instance, if the user is focused, the emotion analysis unit can provide an analysis that helps maintain focus. This allows the emotion analysis unit to perform more appropriate analysis by customizing its methods based on the user's current activity level.

[0061] The emotion analysis unit can select the optimal analysis method by considering the user's geographical location information during emotion analysis. For example, if the user is in a specific location, the emotion analysis unit can provide emotion analysis appropriate for that location. Furthermore, if the user is on the move, the emotion analysis unit can provide emotion analysis appropriate for that movement. For example, if the user is in a specific environment, the emotion analysis unit can provide emotion analysis appropriate for that environment. In this way, the emotion analysis unit can select the optimal analysis method by considering geographical location information.

[0062] The sentiment analysis unit can analyze a user's social media activity and adjust the analysis method during sentiment analysis. For example, if a user posts about a specific topic on social media, the sentiment analysis unit can provide sentiment analysis related to that topic. It can also provide sentiment analysis related to a specific emotion expressed by a user on social media. For instance, if a user shares a specific activity on social media, the sentiment analysis unit can provide sentiment analysis related to that activity. This allows the sentiment analysis unit to prioritize providing relevant sentiment analysis by analyzing social media activity.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] A virtual life system can include a biometric data acquisition unit that acquires biometric data such as the user's heart rate and skin electrical responses, in addition to an analysis unit that analyzes the user's brainwave data. The biometric data acquisition unit can, for example, acquire the heart rate and skin electrical responses when the user is relaxed. The analysis unit analyzes this biometric data in combination with the brainwave data to more accurately understand the user's state. For example, if the user is stressed, their heart rate may increase and their skin electrical responses may change. As a result, the virtual life system can utilize the user's biometric data to perform more accurate analysis and provide optimal feedback.

[0065] A virtual life system can include an environmental data acquisition unit that acquires user environmental data in addition to an acquisition unit that acquires user brainwave data. The environmental data acquisition unit can acquire data such as the temperature, humidity, and brightness of the room where the user is located. The analysis unit analyzes this environmental data in combination with the brainwave data to more accurately understand the user's state. For example, when a user is relaxed, the room temperature is often appropriate and the lighting is soft. As a result, the virtual life system can utilize the user's environmental data to perform more accurate analysis and provide optimal feedback.

[0066] A virtual life system can include a motion data acquisition unit in addition to an analysis unit that analyzes the user's brainwave data. The motion data acquisition unit can acquire data such as the user's hand and foot movements and posture. The analysis unit can analyze this motion data in combination with brainwave data to more accurately understand the user's intentions. For example, if a user intends to move forward, their hand and foot movements will often be directed forward. This allows the virtual life system to utilize the user's motion data to perform more accurate intention analysis and provide optimal feedback.

[0067] The virtual life system can include a food data acquisition unit in addition to an acquisition unit that acquires the user's brainwave data. The food data acquisition unit can, for example, acquire data on the type, quantity, and nutrients of the food consumed by the user. The analysis unit can then analyze this food data in combination with the brainwave data to gain a more accurate understanding of the user's state. For example, when a user is relaxed, they are often consuming a balanced diet. This allows the virtual life system to utilize the user's food data to perform more accurate analysis and provide optimal feedback.

[0068] The virtual life system can include a sleep data acquisition unit in addition to an acquisition unit that acquires the user's brainwave data. The sleep data acquisition unit can, for example, acquire the user's sleep quality, duration, and brainwave data during sleep. The analysis unit can then analyze this sleep data in combination with the brainwave data to gain a more accurate understanding of the user's state. For example, when a user is relaxed, they are often getting good quality sleep. This allows the virtual life system to utilize the user's sleep data to perform more accurate analysis and provide optimal feedback.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The acquisition unit acquires electroencephalogram (EEG) data. The acquisition unit can acquire EEG data, for example. The acquisition unit can also acquire specific EEG patterns. For example, the acquisition unit can acquire EEG data when the user is relaxed. Step 2: The analysis unit analyzes the electroencephalogram (EEG) data acquired by the acquisition unit. The analysis unit can analyze the EEG data using, for example, signal processing techniques. Alternatively, the analysis unit can analyze the EEG data using machine learning algorithms. For example, the analysis unit can analyze the user's intentions from the EEG data. Step 3: The control unit controls movement within the virtual space based on the data analyzed by the analysis unit. The control unit can, for example, control the movement of a character. It can also control the movement of objects. For example, if the user thinks "move forward," the control unit can control the character's movement in the virtual space. Step 4: The feedback unit provides multisensory feedback based on the movements controlled by the control unit. The feedback unit can provide, for example, visual feedback. It can also provide tactile feedback. For example, if the user is walking through a flower field in a virtual space, the feedback unit can reproduce the scent of flowers and the sound of wind.

[0071] (Example of form 2) The virtual life system according to an embodiment of the present invention is a virtual reality mobility system that integrates AI and BCI (brainwave interface). This virtual life system aims to allow people with physical limitations to move freely in a virtual space. This system enhances immersion and provides a realistic experience through multisensory feedback such as sight, touch, and sound. In particular, it can highlight special moments by utilizing "VR Moments," adding enjoyment and motivation to rehabilitation and daily experiences. First, the user intends to move in the virtual space through the BCI. The BCI acquires the user's brainwave data, and the AI ​​analyzes this data to reflect the user's intentions in the movements in the virtual space. For example, if the user thinks "move forward," the AI ​​analyzes that intention and realizes the user's movement forward in the virtual space. Next, the AI ​​analyzes the user's emotions and state in real time and optimally adjusts feedback such as touch and sound. As a result, the user can get an experience as if they were moving in the real world. For example, if the user is walking through a flower field in the virtual space, the AI ​​will reproduce the scent of flowers and the sound of the wind, providing the user with a realistic experience. Furthermore, special moments can be highlighted by utilizing "VR Moments." For example, if a user sees a beautiful sunset in a virtual space, the AI ​​can highlight that moment, evoking a sense of awe in the user. This adds enjoyment and motivation to rehabilitation and daily experiences, improving user motivation. This system allows users to move freely beyond physical limitations, leading to a sense of mental fulfillment and increased motivation for rehabilitation. In addition to improving the quality of life, it energizes daily life through social participation and new experiences, enhancing the effectiveness of rehabilitation. For example, it is expected that elderly people enjoying travel in a virtual space will reduce feelings of isolation and increase their motivation for rehabilitation. Thus, the virtual life system allows users to move freely beyond physical limitations, leading to a sense of mental fulfillment and increased motivation for rehabilitation. In addition to improving the quality of life, it energizes daily life through social participation and new experiences, enhancing the effectiveness of rehabilitation.

[0072] The virtual life system according to this embodiment comprises an acquisition unit, an analysis unit, a control unit, and a feedback unit. The acquisition unit acquires electroencephalogram (EEG) data. The acquisition unit can acquire, for example, EEG data. The acquisition unit can also acquire specific EEG patterns. For example, the acquisition unit can acquire EEG data when the user is relaxed. The analysis unit analyzes the EEG data acquired by the acquisition unit. The analysis unit can analyze the EEG data using, for example, signal processing techniques. The analysis unit can also analyze the EEG data using machine learning algorithms. For example, the analysis unit can analyze the user's intentions from the EEG data. The control unit controls movement in the virtual space based on the data analyzed by the analysis unit. The control unit can control, for example, the movement of a character. The control unit can also control the movement of objects. For example, if the user thinks "move forward," the control unit can control the character's movement in the virtual space. The feedback unit provides multisensory feedback based on the movement controlled by the control unit. The feedback unit can provide, for example, visual feedback. The feedback unit can also provide tactile feedback. For example, the feedback unit can reproduce the scent of flowers and the sound of wind when the user is walking through a flower field in a virtual space. This allows the virtual life system to acquire and analyze brainwave data, control movement within the virtual space, and provide multi-sensory feedback, thereby offering the user a realistic experience.

[0073] The acquisition unit acquires electroencephalogram (EEG) data. For example, the acquisition unit can acquire EEG data. Specifically, the acquisition unit collects EEG signals through multiple electrodes attached to the user's scalp. These electrodes detect electrical activity from different parts of the brain, amplifying weak electrical signals and converting them into digital data. The acquisition unit can collect EEG data in real time, continuously monitoring the user's brain state. The acquisition unit can also acquire specific EEG patterns. For example, it can acquire EEG data when the user is relaxed. Relaxed brainwaves are dominated by alpha and theta waves, and identifying these waveforms can reveal the user's mental state. Furthermore, the acquisition unit can detect EEG patterns when the user is concentrating or stressed. This allows the acquisition unit to understand the user's diverse mental states in real time and reflect this in the overall system operation. Using noise reduction technology, the acquisition unit minimizes interference from the external environment and user movements, enabling accurate EEG data acquisition. This allows the acquisition unit to acquire user EEG data with high accuracy, improving the overall system performance.

[0074] The analysis unit analyzes the electroencephalogram (EEG) data acquired by the acquisition unit. The analysis unit can analyze EEG data using signal processing techniques, for example. Specifically, it uses methods such as Fourier transforms and wavelet transforms to decompose EEG data into frequency components and analyze the energy distribution of each frequency band. The analysis unit can also analyze EEG data using machine learning algorithms. For example, it can analyze user intent from EEG data. Machine learning algorithms learn from past EEG data and corresponding user behavior data to estimate user intent from new EEG data. Specifically, they use algorithms such as neural networks and support vector machines to extract features from EEG data and classify user intent. Furthermore, the analysis unit can monitor user EEG data over the long term and build models optimized for individual users. This allows the analysis unit to analyze user intent with high accuracy and improve the overall system responsiveness. The analysis unit can analyze EEG data in real time and quickly grasp user intent and mental state. This allows the analysis unit to efficiently and accurately analyze the user's electroencephalogram (EEG) data, thereby improving the overall system performance.

[0075] The control unit controls movement within the virtual space based on data analyzed by the analysis unit. For example, the control unit can control the movement of a character. Specifically, the control unit controls the movement and actions of the virtual character in real time based on the user's intentions. For example, if the user thinks "move forward," the control unit can control the character's movement in the virtual space. The control unit can also control the movement of objects. For example, if the user thinks "pick up an object," the control unit can control the object's movement in the virtual space. To accurately reflect the user's intentions, the control unit receives data from the analysis unit in real time and quickly controls the movement. Furthermore, the control unit can make corrections to improve the smoothness and realism of the movement according to the environment and situation in the virtual space. For example, it can use a physics engine to correct the movement so that the character's movement looks natural. In addition, the control unit can control multiple movements simultaneously based on the user's intentions. This allows the control unit to reflect the user's intentions in real time and make the experience in the virtual space more realistic.

[0076] The feedback unit provides multisensory feedback based on movements controlled by the control unit. For example, the feedback unit can provide visual feedback. Specifically, it provides the user with visual information within the virtual space, allowing the user to visually confirm their movements within the virtual space. The feedback unit can also provide haptic feedback. For example, if the user is walking through a flower field in the virtual space, the feedback unit can reproduce the scent of flowers and the sound of wind. Furthermore, the feedback unit can use haptic devices to reproduce the sensation of touching an object in the virtual space. This allows the user to experience the virtual space more realistically. The feedback unit integrates multisensory feedback, such as visual, auditory, tactile, and olfactory feedback, to provide the user with a comprehensive experience. For example, if the user is walking along a beach in the virtual space, the feedback unit can reproduce the sound of waves, the scent of the sea, and the feel of sand. This allows the feedback unit to provide the user with a realistic experience and enhance immersion in the virtual space. Furthermore, the feedback unit can collect user feedback and continuously improve the accuracy and effectiveness of the feedback. This allows the feedback system to provide users with a more realistic and satisfying experience.

[0077] The virtual life system includes an emphasis section that highlights special moments. For example, if a user sees a beautiful sunset in the virtual space, the emphasis section can highlight that moment. The emphasis section can, for example, visually enhance the colors of the sunset. Furthermore, the emphasis section can acoustically reproduce the sound of a setting sun. For instance, by realistically reproducing the sound of a setting sun, the emphasis section can evoke emotion in the user. In this way, the virtual life system can evoke emotion in the user by highlighting special moments.

[0078] The virtual life system is equipped with an emotion analysis unit that analyzes the user's emotions and state in real time. For example, the emotion analysis unit can estimate emotions by analyzing the user's brainwave data. It can, for instance, determine whether the user is relaxed or stressed. Furthermore, the emotion analysis unit can estimate emotions by analyzing the user's facial expression data. For example, it can analyze the user's smile or sadness. This allows the virtual life system to provide optimal feedback by analyzing the user's emotions and state in real time.

[0079] The feedback unit can provide multi-sensory feedback, including visual, tactile, and auditory feedback. For example, the feedback unit can provide visual feedback using a display. It can also provide tactile feedback using a vibration device. For instance, when a user is walking through a flower field in a virtual space, the feedback unit can reproduce the scent of flowers and the sound of wind. In this way, the feedback unit can provide a realistic experience to the user by offering multi-sensory feedback.

[0080] The analysis unit can analyze the user's brainwave data and reflect the user's intentions in the movements within the virtual space. For example, the analysis unit can analyze brainwave patterns to interpret the user's intentions. If the user thinks, for instance, "move forward," the analysis unit can analyze that intention and make the character move forward in the virtual space. The analysis unit can also predict movements based on the user's brainwave data. For example, if the user thinks, "jump," the analysis unit can analyze that intention and make the character jump in the virtual space. In this way, the analysis unit can reflect the user's intentions in the movements within the virtual space, providing the user with a realistic experience.

[0081] The control unit can control movement within the virtual space. For example, the control unit can control the movement of a character. For instance, if the user thinks "move forward," the control unit can control the character's movement in the virtual space. The control unit can also control the movement of objects. For example, if the user thinks "lift an object," the control unit can realize that action in the virtual space. In this way, the control unit can provide the user with a realistic experience by controlling movement within the virtual space.

[0082] The acquisition unit can estimate the user's emotions and adjust the timing of EEG data acquisition based on the estimated emotions. For example, if the user is relaxed, the acquisition unit can set a lower frequency of EEG data acquisition to maintain a natural state. Conversely, if the user is stressed, the acquisition unit can set a higher frequency of EEG data acquisition to collect more detailed data. For example, if the user is focused, the acquisition unit can prioritize acquiring EEG data related to a specific task. In this way, the acquisition unit can acquire more accurate data by adjusting the timing of EEG data acquisition based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The acquisition unit can analyze the user's past brainwave data and select the optimal acquisition method. For example, the acquisition unit can identify the time of day when the most stable data can be obtained from the user's past brainwave data. Furthermore, the acquisition unit can select a data acquisition method under specific environmental conditions based on the user's past brainwave data. For example, the acquisition unit can analyze the user's past brainwave data and determine the optimal sensor placement. Thus, the acquisition unit can select the optimal acquisition method by analyzing past brainwave data.

[0084] The acquisition unit can filter EEG data based on the user's current activity and environment. For example, if the user is in a quiet environment, the acquisition unit can set noise filtering to a minimum. Conversely, if the user is in a noisy environment, the acquisition unit can enhance noise filtering to maintain data accuracy. For instance, if the user is performing a specific activity, the acquisition unit can prioritize acquiring EEG data related to that activity. This allows the acquisition unit to maintain data accuracy by filtering based on the user's current activity and environment.

[0085] The acquisition unit can estimate the user's emotions and determine the priority of brainwave data to acquire based on the estimated emotions. For example, if the user is relaxed, the acquisition unit can prioritize acquiring brainwave data related to relaxation. Similarly, if the user is stressed, the acquisition unit can prioritize acquiring brainwave data related to stress. For example, if the user is focused, the acquisition unit can prioritize acquiring brainwave data related to concentration. In this way, the acquisition unit can prioritize acquiring important data by determining the priority of brainwave data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The data acquisition unit can prioritize the acquisition of highly relevant data by considering the user's geographical location when acquiring electroencephalogram (EEG) data. For example, if the user is in a specific location, the acquisition unit can prioritize the acquisition of EEG data related to that location. Furthermore, if the user is on the move, the acquisition unit can prioritize the acquisition of EEG data related to that movement. For example, if the user is in a specific environment, the acquisition unit can prioritize the acquisition of EEG data related to that environment. In this way, the acquisition unit can prioritize the acquisition of highly relevant data by considering geographical location information.

[0087] The data acquisition unit can analyze the user's social media activity when acquiring electroencephalogram (EEG) data and acquire relevant data. For example, if the user posts about a specific topic on social media, the data acquisition unit can prioritize acquiring EEG data related to that topic. Furthermore, if the user expresses a specific emotion on social media, the data acquisition unit can prioritize acquiring EEG data related to that emotion. For instance, if the user shares a specific activity on social media, the data acquisition unit can prioritize acquiring EEG data related to that activity. In this way, the data acquisition unit can prioritize acquiring relevant data by analyzing social media activity.

[0088] The analysis unit can estimate the user's emotions and adjust the method of analyzing EEG data based on the estimated emotions. For example, if the user is relaxed, the analysis unit can analyze in detail the EEG data related to the relaxed state. Similarly, if the user is stressed, the analysis unit can analyze in detail the EEG data related to the stressed state. For example, if the user is focused, the analysis unit can analyze in detail the EEG data related to the focused state. This allows the analysis unit to perform more accurate analysis by adjusting the analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The analysis unit can adjust the level of detail of the analysis based on the importance of the electroencephalogram (EEG) data during the analysis. For example, the analysis unit can analyze highly important EEG data in detail and simplify less important data. Furthermore, the analysis unit can apply multiple analysis algorithms to highly important EEG data. For instance, the analysis unit can prioritize the analysis of highly important EEG data and postpone the analysis of less important data. This allows the analysis unit to perform efficient analysis by adjusting the level of detail based on importance.

[0090] The analysis unit can apply different analysis algorithms depending on the category of the electroencephalogram (EEG) data during analysis. For example, the analysis unit can apply an analysis algorithm specialized for relaxation to EEG data in a relaxed state. Similarly, it can apply an analysis algorithm specialized for stress to EEG data in a stressed state. For example, the analysis unit can apply an analysis algorithm specialized for concentration to EEG data in a focused state. By applying different analysis algorithms depending on the category, the analysis unit can perform more accurate analysis.

[0091] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can prioritize the analysis of brainwave data related to relaxation. Similarly, if the user is stressed, the analysis unit can prioritize the analysis of brainwave data related to stress. For example, if the user is focused, the analysis unit can prioritize the analysis of brainwave data related to concentration. In this way, the analysis unit can prioritize the analysis of important data by determining the priority of analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The analysis unit can determine the priority of analysis based on when the electroencephalogram (EEG) data was acquired. For example, the analysis unit can prioritize the analysis of recently acquired EEG data. It can also prioritize the analysis of EEG data acquired during specific events. For instance, based on the user's activity history, the analysis unit can prioritize the analysis of EEG data acquired during specific time periods. This allows the analysis unit to prioritize the analysis of the most recent data by determining the priority of analysis based on the acquisition date.

[0093] The analysis unit can adjust the order of analysis based on the relevance of the electroencephalogram (EEG) data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant EEG data. It can also postpone the analysis of less relevant EEG data. For instance, the analysis unit can evaluate the relevance of multiple EEG data sets and optimize the order of analysis. This allows the analysis unit to perform efficient analysis by adjusting the order of analysis based on relevance.

[0094] The control unit can estimate the user's emotions and adjust the control method of movement in the virtual space based on the estimated user emotions. For example, if the user is relaxed, the control unit can control slow, relaxed movements. It can also control movements that reduce stress if the user is stressed. For example, if the user is focused, the control unit can control movements that maintain focus. This allows the control unit to achieve more appropriate movements by adjusting the movement control method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The control unit can analyze the user's past operation history during control to select the optimal control method. For example, the control unit can select the most effective control method from the user's past operation history. Furthermore, the control unit can prioritize control of specific operation patterns based on the user's past operation history. For example, the control unit can analyze the user's past operation history to select the optimal operation speed. Thus, the control unit can select the optimal control method by analyzing past operation history.

[0096] The control unit can customize movement control based on the user's current activity level during operation. For example, if the user is relaxed, the control unit can control movements appropriate for that relaxed state. Similarly, if the user is stressed, the control unit can control movements that reduce stress. For instance, if the user is focused, the control unit can control movements that maintain focus. This allows the control unit to achieve more appropriate movements by customizing movement control based on the user's current activity level.

[0097] The control unit can estimate the user's emotions and determine the priority of movement control based on the estimated emotions. For example, if the user is relaxed, the control unit can prioritize movements related to relaxation. Similarly, if the user is stressed, the control unit can prioritize movements related to stress. For example, if the user is focused, the control unit can prioritize movements related to focus. This allows the control unit to prioritize important movements by determining the priority of movement control based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The control unit can select the optimal movement control method by considering the user's geographical location information during control. For example, if the user is in a specific location, the control unit can control movement appropriate to that location. Furthermore, if the user is moving, the control unit can control movement appropriate to that movement. For example, if the user is in a specific environment, the control unit can control movement appropriate to that environment. In this way, the control unit can select the optimal movement control method by considering geographical location information.

[0099] The control unit can analyze the user's social media activity during control and adjust the control of movements accordingly. For example, if the user is posting about a specific topic on social media, the control unit can control movements related to that topic. It can also control movements related to specific emotions if the user is expressing those emotions on social media. For instance, if the user is sharing a specific activity on social media, the control unit can control movements related to that activity. This allows the control unit to prioritize and control relevant movements by analyzing social media activity.

[0100] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. For example, if the user is relaxed, the feedback unit can provide acoustic feedback appropriate to that relaxed state. It can also provide haptic feedback to reduce stress if the user is stressed. For example, if the user is focused, the feedback unit can provide visual feedback to maintain focus. This allows the feedback unit to provide more appropriate feedback by adjusting the content of the feedback based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The feedback unit can analyze the user's past feedback history to select the optimal feedback method during the feedback process. For example, it can select the most effective feedback method based on the user's past feedback history. Furthermore, the feedback unit can prioritize providing specific feedback patterns based on the user's past feedback history. For instance, it can analyze the user's past feedback history to select the optimal feedback timing. Thus, by analyzing past feedback history, the feedback unit can select the most appropriate feedback method.

[0102] The feedback unit can customize the means of feedback based on the user's current activity level. For example, if the user is relaxed, the feedback unit can provide feedback appropriate to that relaxed state. It can also provide stress-reducing feedback if the user is stressed. For instance, if the user is focused, the feedback unit can provide feedback to help maintain focus. This allows the feedback unit to provide more appropriate feedback by customizing the means of feedback based on the user's current activity level.

[0103] The feedback unit can estimate the user's emotions and prioritize feedback based on the estimated emotions. For example, if the user is relaxed, the feedback unit can prioritize providing feedback related to relaxation. Similarly, if the user is stressed, the feedback unit can prioritize providing feedback related to stress. For example, if the user is focused, the feedback unit can prioritize providing feedback related to focus. In this way, the feedback unit can prioritize important feedback by prioritizing feedback based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The feedback unit can select the optimal feedback method by considering the user's geographical location information when providing feedback. For example, if the user is in a specific location, the feedback unit can provide feedback appropriate to that location. Furthermore, if the user is on the move, the feedback unit can provide feedback appropriate to their movement. For instance, if the user is in a specific environment, the feedback unit can provide feedback appropriate to that environment. In this way, the feedback unit can select the optimal feedback method by considering geographical location information.

[0105] The feedback unit can analyze a user's social media activity and adjust the means of feedback when providing feedback. For example, if a user posts about a specific topic on social media, the feedback unit can provide feedback related to that topic. It can also provide feedback related to a user expressing a specific emotion on social media. For instance, if a user shares a specific activity on social media, the feedback unit can provide feedback related to that activity. This allows the feedback unit to prioritize providing relevant feedback by analyzing social media activity.

[0106] The emphasis unit can estimate the user's emotions and adjust how special moments are emphasized based on the estimated emotions. For example, if the user is relaxed, the emphasis unit can provide an emphasis method appropriate for that relaxed state. It can also provide an emphasis method that reduces stress if the user is stressed. For example, if the user is focused, the emphasis unit can provide an emphasis method that maintains focus. In this way, the emphasis unit can provide a more emotionally impactful experience by adjusting how special moments are emphasized based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The emphasis function can analyze the user's past experience history to select the optimal emphasis method during emphasis. For example, the emphasis function can select the most effective emphasis method from the user's past experience history. Furthermore, the emphasis function can prioritize the emphasis of specific experience patterns based on the user's past experience history. For example, the emphasis function can analyze the user's past experience history to select the optimal emphasis timing. Thus, the emphasis function can select the optimal emphasis method by analyzing past experience history.

[0108] The emphasis function can customize the means of emphasis based on the user's current activity level. For example, if the user is relaxed, the emphasis function can provide emphasis appropriate to that relaxed state. It can also provide emphasis to reduce stress if the user is stressed. For example, if the user is focused, the emphasis function can provide emphasis to maintain focus. In this way, the emphasis function can provide more appropriate emphasis by customizing the means of emphasis based on the user's current activity level.

[0109] The emphasis unit can estimate the user's emotions and determine the priority of emphasis based on the estimated emotions. For example, if the user is relaxed, the emphasis unit can prioritize emphasis related to relaxation. Similarly, if the user is stressed, the emphasis unit can prioritize emphasis related to stress. For example, if the user is focused, the emphasis unit can prioritize emphasis related to concentration. In this way, the emphasis unit can prioritize important emphasis by determining the priority of emphasis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0110] The emphasis function can select the optimal emphasis method by considering the user's geographical location information during emphasis. For example, if the user is in a specific location, the emphasis function can provide emphasis appropriate to that location. Furthermore, if the user is on the move, the emphasis function can provide emphasis appropriate to movement. For instance, if the user is in a specific environment, the emphasis function can provide emphasis appropriate to that environment. In this way, the emphasis function can select the optimal emphasis method by considering geographical location information.

[0111] The emphasis function can analyze the user's social media activity and adjust the means of emphasis during the emphasis process. For example, if the user posts about a specific topic on social media, the emphasis function can provide emphasis related to that topic. It can also provide emphasis related to a specific emotion if the user expresses that emotion on social media. For instance, if the emphasis function shares a specific activity on social media, it can provide emphasis related to that activity. This allows the emphasis function to prioritize relevant emphasis by analyzing social media activity.

[0112] The emotion analysis unit can estimate the user's emotions and adjust the emotion analysis method based on the estimated user emotions. For example, if the user is relaxed, the emotion analysis unit can apply an emotion analysis method related to relaxation. Similarly, if the user is stressed, the emotion analysis unit can apply an emotion analysis method related to stress. For example, if the user is focused, the emotion analysis unit can apply an emotion analysis method related to focus. This allows the emotion analysis unit to perform more accurate analysis by adjusting the emotion analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0113] The emotion analysis unit can analyze the user's past emotional data to select the optimal analysis method during emotion analysis. For example, the emotion analysis unit can select the most effective analysis method from the user's past emotional data. Furthermore, the emotion analysis unit can prioritize the analysis of specific emotional patterns based on the user's past emotional data. For example, the emotion analysis unit can analyze the user's past emotional data to select the optimal analysis timing. In this way, the emotion analysis unit can select the optimal analysis method by analyzing past emotional data.

[0114] The emotion analysis unit can customize its analysis methods based on the user's current activity level during emotion analysis. For example, if the user is relaxed, the emotion analysis unit can provide an analysis appropriate for that relaxed state. Similarly, if the user is stressed, the emotion analysis unit can provide an analysis that helps reduce stress. For instance, if the user is focused, the emotion analysis unit can provide an analysis that helps maintain focus. This allows the emotion analysis unit to perform more appropriate analysis by customizing its methods based on the user's current activity level.

[0115] The emotion analysis unit can estimate the user's emotions and determine the priority of emotion analysis based on the estimated user emotions. For example, if the user is relaxed, the emotion analysis unit can prioritize providing emotion analysis related to relaxation. Similarly, if the user is stressed, the emotion analysis unit can prioritize providing emotion analysis related to stress. For example, if the user is focused, the emotion analysis unit can prioritize providing emotion analysis related to focus. In this way, the emotion analysis unit can prioritize important analyses by determining the priority of emotion analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0116] The emotion analysis unit can select the optimal analysis method by considering the user's geographical location information during emotion analysis. For example, if the user is in a specific location, the emotion analysis unit can provide emotion analysis appropriate for that location. Furthermore, if the user is on the move, the emotion analysis unit can provide emotion analysis appropriate for that movement. For example, if the user is in a specific environment, the emotion analysis unit can provide emotion analysis appropriate for that environment. In this way, the emotion analysis unit can select the optimal analysis method by considering geographical location information.

[0117] The sentiment analysis unit can analyze a user's social media activity and adjust the analysis method during sentiment analysis. For example, if a user posts about a specific topic on social media, the sentiment analysis unit can provide sentiment analysis related to that topic. It can also provide sentiment analysis related to a specific emotion expressed by a user on social media. For instance, if a user shares a specific activity on social media, the sentiment analysis unit can provide sentiment analysis related to that activity. This allows the sentiment analysis unit to prioritize providing relevant sentiment analysis by analyzing social media activity.

[0118] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0119] A virtual life system can include a biometric data acquisition unit that acquires biometric data such as the user's heart rate and skin electrical responses, in addition to an analysis unit that analyzes the user's brainwave data. The biometric data acquisition unit can, for example, acquire the heart rate and skin electrical responses when the user is relaxed. The analysis unit analyzes this biometric data in combination with the brainwave data to more accurately understand the user's state. For example, if the user is stressed, their heart rate may increase and their skin electrical responses may change. As a result, the virtual life system can utilize the user's biometric data to perform more accurate analysis and provide optimal feedback.

[0120] A virtual life system can include an emotion analysis unit that estimates the user's emotions, as well as an emotion learning unit that stores and learns from the user's past emotional data. The emotion learning unit can, for example, store and learn from brainwave data and facial expression data from when the user was relaxed in the past. This allows the emotion analysis unit to more accurately estimate the user's emotions based on past data. For instance, if the user was relaxed in a specific situation in the past, the emotion learning unit can learn that data and estimate that the user is likely to relax again in a similar situation. This enables the virtual life system to utilize the user's past emotional data to perform more accurate emotion estimation and provide optimal feedback.

[0121] A virtual life system can include an environmental data acquisition unit that acquires user environmental data in addition to an acquisition unit that acquires user brainwave data. The environmental data acquisition unit can acquire data such as the temperature, humidity, and brightness of the room where the user is located. The analysis unit analyzes this environmental data in combination with the brainwave data to more accurately understand the user's state. For example, when a user is relaxed, the room temperature is often appropriate and the lighting is soft. As a result, the virtual life system can utilize the user's environmental data to perform more accurate analysis and provide optimal feedback.

[0122] A virtual life system can be equipped with an emotion analysis unit that estimates the user's emotions, as well as a voice analysis unit that analyzes the user's voice data. The voice analysis unit can, for example, analyze the user's voice tone and speaking patterns to estimate emotions. The emotion analysis unit can analyze voice data in combination with brainwave data and facial expression data to estimate the user's emotions more accurately. For example, when a user is relaxed, their voice tone is often calm and they speak slowly. This allows the virtual life system to utilize the user's voice data to perform more accurate emotion estimation and provide optimal feedback.

[0123] A virtual life system can include a motion data acquisition unit in addition to an analysis unit that analyzes the user's brainwave data. The motion data acquisition unit can acquire data such as the user's hand and foot movements and posture. The analysis unit can analyze this motion data in combination with brainwave data to more accurately understand the user's intentions. For example, if a user intends to move forward, their hand and foot movements will often be directed forward. This allows the virtual life system to utilize the user's motion data to perform more accurate intention analysis and provide optimal feedback.

[0124] A virtual life system can be equipped with an olfactory analysis unit that analyzes the user's olfactory data, in addition to an emotion analysis unit that estimates the user's emotions. The olfactory analysis unit can, for example, analyze the type and intensity of scents the user perceives and estimate their emotions. The emotion analysis unit can analyze olfactory data in combination with electroencephalogram (EEG) data and facial expression data to more accurately estimate the user's emotions. For example, when a user is relaxed, they often perceive pleasant scents. As a result, the virtual life system can utilize the user's olfactory data to perform more accurate emotion estimation and provide optimal feedback.

[0125] The virtual life system can include a food data acquisition unit in addition to an acquisition unit that acquires the user's brainwave data. The food data acquisition unit can, for example, acquire data on the type, quantity, and nutrients of the food consumed by the user. The analysis unit can then analyze this food data in combination with the brainwave data to gain a more accurate understanding of the user's state. For example, when a user is relaxed, they are often consuming a balanced diet. This allows the virtual life system to utilize the user's food data to perform more accurate analysis and provide optimal feedback.

[0126] A virtual life system can be equipped with an emotion analysis unit that estimates the user's emotions, as well as an eye-tracking analysis unit that analyzes the user's gaze data. The eye-tracking analysis unit can, for example, analyze where the user is looking and the patterns of their eye movements to estimate their emotions. The emotion analysis unit can analyze the eye-tracking data in combination with brainwave data and facial expression data to estimate the user's emotions more accurately. For example, when a user is relaxed, their gaze often moves gently. As a result, the virtual life system can utilize the user's eye-tracking data to perform more accurate emotion estimation and provide optimal feedback.

[0127] The virtual life system can include a sleep data acquisition unit in addition to an acquisition unit that acquires the user's brainwave data. The sleep data acquisition unit can, for example, acquire the user's sleep quality, duration, and brainwave data during sleep. The analysis unit can then analyze this sleep data in combination with the brainwave data to gain a more accurate understanding of the user's state. For example, when a user is relaxed, they are often getting good quality sleep. This allows the virtual life system to utilize the user's sleep data to perform more accurate analysis and provide optimal feedback.

[0128] A virtual life system can be equipped with a social interaction analysis unit that analyzes the user's social interaction data, in addition to an emotion analysis unit that estimates the user's emotions. The social interaction analysis unit can, for example, analyze how the user interacts with others, including the frequency and content of those interactions, and estimate their emotions. The emotion analysis unit can analyze social interaction data in combination with electroencephalogram (EEG) data and facial expression data to more accurately estimate the user's emotions. For example, when a user is relaxed, they are often engaging in friendly interactions. This allows the virtual life system to utilize the user's social interaction data to perform more accurate emotion estimation and provide optimal feedback.

[0129] The following briefly describes the processing flow for example form 2.

[0130] Step 1: The acquisition unit acquires electroencephalogram (EEG) data. The acquisition unit can acquire EEG data, for example. The acquisition unit can also acquire specific EEG patterns. For example, the acquisition unit can acquire EEG data when the user is relaxed. Step 2: The analysis unit analyzes the electroencephalogram (EEG) data acquired by the acquisition unit. The analysis unit can analyze the EEG data using, for example, signal processing techniques. Alternatively, the analysis unit can analyze the EEG data using machine learning algorithms. For example, the analysis unit can analyze the user's intentions from the EEG data. Step 3: The control unit controls movement within the virtual space based on the data analyzed by the analysis unit. The control unit can, for example, control the movement of a character. It can also control the movement of objects. For example, if the user thinks "move forward," the control unit can control the character's movement in the virtual space. Step 4: The feedback unit provides multisensory feedback based on the movements controlled by the control unit. The feedback unit can provide, for example, visual feedback. It can also provide tactile feedback. For example, if the user is walking through a flower field in a virtual space, the feedback unit can reproduce the scent of flowers and the sound of wind.

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0132] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0133] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the acquisition unit, analysis unit, control unit, feedback unit, and enhancement unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit acquires electroencephalogram (EEG) data using the camera 42 and microphone 38B of the smart device 14. The analysis unit analyzes the EEG data using the specific processing unit 290 of the data processing unit 12. The control unit controls movement in the virtual space using the specific processing unit 290 of the data processing unit 12. The feedback unit provides multisensory feedback using the control unit 46A of the smart device 14. The enhancement unit highlights special moments using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0136] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the acquisition unit, analysis unit, control unit, feedback unit, and enhancement unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit acquires electroencephalogram (EEG) data using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit analyzes the EEG data using the specific processing unit 290 of the data processing unit 12. The control unit controls movement in the virtual space using the specific processing unit 290 of the data processing unit 12. The feedback unit provides multisensory feedback using the control unit 46A of the smart glasses 214. The enhancement unit highlights special moments using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0151] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0152] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0154] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0155] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0158] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0159] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0160] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0161] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0163] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0165] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] Each of the multiple elements described above, including the acquisition unit, analysis unit, control unit, feedback unit, and enhancement unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit acquires electroencephalogram (EEG) data using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit analyzes the EEG data using the specific processing unit 290 of the data processing unit 12. The control unit controls movement in the virtual space using the specific processing unit 290 of the data processing unit 12. The feedback unit provides multisensory feedback using the control unit 46A of the headset terminal 314. The enhancement unit highlights special moments using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0167] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0168] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0169] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0170] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0171] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0172] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0173] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0174] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0175] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0176] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0177] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0178] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0179] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0180] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0181] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0182] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0183] Each of the multiple elements described above, including the acquisition unit, analysis unit, control unit, feedback unit, and enhancement unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit acquires electroencephalogram (EEG) data using the camera 42 and microphone 238 of the robot 414. The analysis unit analyzes the EEG data using the specific processing unit 290 of the data processing unit 12. The control unit controls movement in the virtual space using the specific processing unit 290 of the data processing unit 12. The feedback unit provides multisensory feedback using the control unit 46A of the robot 414. The enhancement unit highlights special moments using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0184] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0185] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0186] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0187] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0188] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0189] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0190] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0191] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0192] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0193] 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.

[0194] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0195] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0196] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0197] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0198] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0199] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0200] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0201] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0202] (Note 1) An acquisition unit that acquires electroencephalogram data, An analysis unit analyzes the electroencephalogram data acquired by the acquisition unit, A control unit that controls movement in the virtual space based on the data analyzed by the analysis unit, A feedback unit that provides multi-sensory feedback based on the movement controlled by the control unit, Equipped with A system characterized by the following features. (Note 2) Features an emphasis section to highlight special moments. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features an emotion analysis unit that analyzes the user's emotions and state in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned feedback unit is It provides multisensory feedback such as visual, tactile, and auditory feedback. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, The system analyzes the user's brainwave data and reflects the user's intentions in their movements within the virtual space. The system described in Appendix 1, characterized by the features described herein. (Note 6) The control unit, Controlling movement within a virtual space The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of EEG data acquisition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, The system analyzes the user's past brainwave data and selects the optimal acquisition method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, When acquiring electroencephalogram (EEG) data, filtering is performed based on the user's current activity status and environment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, The system estimates the user's emotions and prioritizes the acquisition of brainwave data based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, When acquiring electroencephalogram (EEG) data, the system prioritizes the acquisition of highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, When acquiring electroencephalogram (EEG) data, the system analyzes the user's social media activity and obtains relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the method of analyzing EEG data based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the EEG data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of EEG data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of analysis is determined based on when the electroencephalogram (EEG) data was acquired. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the EEG data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The control unit, It estimates the user's emotions and adjusts the control method of movement in the virtual space based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The control unit, During control, the system analyzes the user's past behavior history to select the optimal control method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The control unit, During control, the movement control is customized based on the user's current activity status. The system described in Appendix 1, characterized by the features described herein. (Note 22) The control unit, It estimates the user's emotions and determines the priority of motion control based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The control unit, During control, the optimal motion control method is selected by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The control unit, During control, the system analyzes the user's social media activity and adjusts the control of movement accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback unit is It estimates the user's emotions and adjusts the content of the feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback unit is When providing feedback, the system analyzes the user's past feedback history to select the most suitable feedback method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback unit is When providing feedback, customize the feedback method based on the user's current activity level. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned feedback unit is When providing feedback, the optimal feedback method is selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned feedback unit is When providing feedback, we analyze users' social media activity and adjust the feedback methods accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned emphasis section is, It estimates the user's emotions and adjusts how special moments are highlighted based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned emphasis section is, During emphasis, the system analyzes the user's past experience history to select the optimal emphasis method. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned emphasis section is, When highlighting, customize the highlighting method based on the user's current activity. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned emphasis section is, It estimates the user's emotions and determines the emphasis priority based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned emphasis section is, When highlighting, the system selects the optimal highlighting method by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned emphasis section is, During emphasis, we analyze users' social media activity and adjust the means of emphasis accordingly. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned emotion analysis unit, The system estimates the user's emotions and adjusts the emotion analysis method based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned emotion analysis unit, During sentiment analysis, the system analyzes the user's past sentiment data to select the optimal analysis method. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned emotion analysis unit, During sentiment analysis, the analysis method is customized based on the user's current activity status. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned emotion analysis unit, It estimates the user's emotions and determines the priority of emotion analysis based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned emotion analysis unit, When performing sentiment analysis, the optimal analysis method is selected by considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 42) The aforementioned emotion analysis unit, During sentiment analysis, we analyze the user's social media activity and adjust the analysis method accordingly. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0203] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An acquisition unit that acquires electroencephalogram data, An analysis unit analyzes the electroencephalogram data acquired by the acquisition unit, A control unit that controls movement in the virtual space based on the data analyzed by the analysis unit, A feedback unit that provides multi-sensory feedback based on the movement controlled by the control unit, Equipped with A system characterized by the following features.

2. Features an emphasis section to highlight special moments. The system according to feature 1.

3. It features an emotion analysis unit that analyzes the user's emotions and state in real time. The system according to feature 1.

4. The aforementioned feedback unit is It provides multisensory feedback such as visual, tactile, and auditory feedback. The system according to feature 1.

5. The aforementioned analysis unit, The system analyzes the user's brainwave data and reflects the user's intentions in their movements within the virtual space. The system according to feature 1.

6. The control unit, Controlling movement within a virtual space The system according to feature 1.

7. The acquisition unit is, The system estimates the user's emotions and adjusts the timing of EEG data acquisition based on the estimated emotions. The system according to feature 1.

8. The acquisition unit is, The system analyzes the user's past brainwave data and selects the optimal acquisition method. The system according to feature 1.

9. The acquisition unit is, When acquiring electroencephalogram (EEG) data, filtering is performed based on the user's current activity status and environment. The system according to feature 1.

10. The acquisition unit is, The system estimates the user's emotions and prioritizes the acquisition of brainwave data based on the estimated emotions. The system according to feature 1.

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