system

Abstract

The system according to this embodiment aims to analyze electroencephalogram (EEG) data and generate text and images. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, a generation unit, and a provision unit. The acquisition unit acquires electroencephalogram (EEG) data. The analysis unit analyzes the EEG data acquired by the acquisition unit. The generation unit generates text and images based on the EEG data analyzed by the analysis unit. The provision unit provides the text and images generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, electroencephalogram data has not been sufficiently utilized to effectively generate text or images, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze electroencephalogram data and generate text or images.

Means for Solving the Problems

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, a generation unit, and a provision unit. The acquisition unit acquires electroencephalogram data. The analysis unit analyzes the electroencephalogram data acquired by the acquisition unit. The generation unit generates text or images based on the electroencephalogram data analyzed by the analysis unit. The provision unit provides the text or images generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze electroencephalogram (EEG) data and generate text and images. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 Brain Communication system according to an embodiment of the present invention is a system that utilizes brainwaves and generative AI to produce technologies such as generative AI that converts thoughts into text and DreamDiffusion that converts mental images into diagrams. The Brain Communication system aims to contribute to solving social issues in a wide range of fields, including medicine, education, and industry, by fusing brain science and AI technology. For example, the Brain Communication system involves attaching a device to measure brainwaves and acquiring brainwave data. Technologies such as fMRI and EEG are used to measure brainwaves. fMRI is a method that observes changes in the blood volume of the brain, and EEG is a method that measures the electrical activity of the brain by attaching a brainwave measurement device. This makes it possible to understand the state of brain activity in detail. Next, the Brain Communication system analyzes the acquired brainwave data. The brainwave data is analyzed to calculate a time-frequency spectrum using methods such as Fourier transform, and changes in concentration and emotion are measured. In addition, by averaging multiple brainwaves, the brainwave response pattern can be analyzed, and it is possible to observe when a person felt discomfort and what they perceived as abnormal. Furthermore, the Brain Communication system inputs analyzed brainwave data into a generating AI, which then uses a generating AI to transcribe thoughts into text and DreamDiffusion to create images from mental images, thereby generating text and images. This makes it possible to visualize an individual's thoughts and emotions from brainwave data. For example, in the medical field, the Brain Communication system can optimize patients' brain function and improve their quality of life. For instance, by analyzing brainwave data, it can understand changes in a patient's concentration and emotions and propose appropriate treatment methods. In the education field, the Brain Communication system can analyze students' brainwave data and provide advice to improve learning effectiveness. Moreover, in the industrial field, the Brain Communication system can analyze employees' brainwave data and propose measures to improve productivity.In this way, the Brain Communication system, by utilizing brainwaves and AI generation, can optimize an individual's brain function, improve their quality of life, and contribute to increased productivity and the creation of new business opportunities for companies. Thus, the Brain Communication system can optimize an individual's brain function and improve their quality of life.

[0029] The Brain Communication system according to this embodiment comprises an acquisition unit, an analysis unit, a generation unit, and a provision unit. The acquisition unit acquires electroencephalogram (EEG) data. The acquisition unit acquires EEG data using, for example, technologies such as fMRI or EEG. fMRI is a method for observing changes in blood volume in the brain, and EEG is a method for measuring the electrical activity of the brain by attaching an EEG measurement device. The acquisition unit can, for example, observe the activity of a specific region of the brain in detail using fMRI. EEG can, for example, measure real-time changes in EEG with high accuracy. The acquisition unit can also, for example, continuously acquire EEG data over a long period of time. The analysis unit analyzes the EEG data acquired by the acquisition unit. The analysis unit analyzes the EEG data using, for example, a method such as Fourier transform and calculates a time-frequency spectrum. The analysis unit can, for example, analyze the frequency components of the EEG data in detail and measure changes in concentration and emotion. The analysis unit can also analyze the response pattern of the EEG by, for example, averaging multiple EEG readings. The analysis unit can, for example, analyze the fluctuation patterns of electroencephalogram (EEG) data to observe when a person felt discomfort and what they perceived as abnormal. The generation unit generates text and images based on the EEG data analyzed by the analysis unit. The generation unit generates text from EEG data using, for example, a generation AI. The generation AI is, for example, a generation AI that takes EEG data as input and generates text. The generation unit generates images from EEG data using, for example, DreamDiffusion. DreamDiffusion is, for example, a generation AI that takes EEG data as input and generates images. The generation unit provides the user with the text and images generated from the EEG data. The provision unit provides the text and images generated by the generation unit. The provision unit displays the generated text and images to the user. The provision unit can utilize the generated text and images in fields such as medicine, education, and industry. The provision unit can provide the generated text and images to the user through digital devices.As a result, the Brain Communication system according to this embodiment can visualize an individual's thoughts and emotions by acquiring, analyzing, generating, and providing brainwave data.

[0030] The acquisition unit acquires electroencephalogram (EEG) data. The acquisition unit acquires EEG data using technologies such as fMRI and EEG. fMRI is a method for observing changes in blood volume in the brain, while EEG is a method for measuring the electrical activity of the brain by attaching an EEG measurement device. Specifically, fMRI can capture changes in blood flow within the brain with high resolution using a strong magnetic field and radio waves. This makes it possible to observe in detail how specific areas of the brain are active. On the other hand, EEG measures the electrical activity of the brain in real time with high precision by placing electrodes on the scalp. EEG has particularly high temporal resolution and can capture changes in EEG in milliseconds, making it suitable for observing instantaneous reactions and short-term changes. By combining these technologies, the acquisition unit can capture brain activity from multiple perspectives. For example, by using fMRI to observe the activity of specific areas of the brain in detail and simultaneously using EEG to measure real-time changes in EEG, more comprehensive data can be obtained. Furthermore, the acquisition unit can continuously acquire EEG data over long periods, allowing for the tracking of changes and trends in brain activity over time. For example, by monitoring brain responses in daily life and brain activity while performing specific tasks over a long period, it is possible to gain a detailed understanding of an individual's brain characteristics and patterns. This allows the acquisition unit to build a foundation for collecting EEG data from multiple perspectives and continuously, and providing it to the analysis and generation units.

[0031] The analysis unit analyzes the electroencephalogram (EEG) data acquired by the acquisition unit. The analysis unit analyzes the EEG data using methods such as Fourier transform and calculates a time-frequency spectrum. Specifically, by using the Fourier transform, the time-domain signals of the EEG data can be converted to the frequency domain, allowing for the analysis of the intensity of each frequency component. This enables detailed analysis of the frequency components of the EEG, making it possible to measure changes in concentration and emotion. For example, by observing changes in the intensity of specific frequency bands (alpha waves, beta waves, gamma waves, etc.), an individual's state of concentration and relaxation can be evaluated. The analysis unit can also reduce noise and more clearly analyze EEG response patterns by averaging multiple EEG readings. This allows for highly accurate capture of the brain's response to specific stimuli. Furthermore, the analysis unit can analyze the fluctuation patterns of the EEG data to observe when a person felt discomfort and what they perceived as abnormal. For example, if a specific EEG pattern appears while performing a particular task, the meaning of that pattern can be analyzed to evaluate the individual's emotions and cognitive state. Based on these analysis results, the analysis unit can generate data to provide to the generation unit, gaining a detailed understanding of the individual's brain activity state. This allows the analysis unit to comprehensively analyze the acquired electroencephalogram (EEG) data and evaluate the individual's brain activity state in detail.

[0032] The generation unit generates text and images based on the electroencephalogram (EEG) data analyzed by the analysis unit. For example, the generation unit uses a generation AI to generate text from EEG data. The generation AI, for example, takes EEG data as input and generates text. Specifically, the generation AI extracts features from the EEG data and generates natural language text based on them. For example, it can generate text that expresses thoughts and emotions at the time based on the concentration state and emotional changes obtained from the EEG data. The generation unit also uses DreamDiffusion to generate images from EEG data. DreamDiffusion is a generation AI that, for example, takes EEG data as input and generates images. Specifically, DreamDiffusion analyzes patterns in the EEG data and generates visual images based on them. For example, it can generate images that visually represent the mental landscape or image at the time based on emotional and thought patterns obtained from the EEG data. By using these generation AIs, the generation unit can generate diverse forms of content from EEG data. Furthermore, the generation unit is equipped with an interface for providing the generated text and images to the user, allowing the user to easily view the generated content. This allows the generation unit to represent the information obtained from electroencephalogram data in a concrete form and provide it to the user.

[0033] The provider unit provides the text and images generated by the generator unit. The provider unit, for example, displays the generated text and images to the user. Specifically, the provider unit displays the generated text and images on the screen of a digital device, making them intuitively understandable to the user. For example, it displays the generated text and images on the screen of a smartphone, tablet, or computer, allowing the user to view them. The provider unit can utilize the generated text and images in fields such as medicine, education, and industry. For example, in the medical field, information obtained from patient electroencephalogram (EEG) data can be used as a reference for diagnosis and treatment. In the education field, it can be used to understand students' concentration levels and emotional changes, improving learning effectiveness. In the industrial field, it can be used to monitor employees' stress levels and concentration, contributing to improved work efficiency and safety management. The provider unit can provide the generated text and images to users through digital devices. For example, it can allow users to access the generated content through smartphone apps or web applications. Furthermore, the provider unit can collect user feedback and continuously improve the accuracy and quality of the generated content. This allows the service provider to deliver generated text and images to users in a variety of ways, making individual thoughts and emotions visible.

[0034] The acquisition unit can acquire electroencephalogram (EEG) data using technologies such as fMRI and EEG. For example, the acquisition unit can use fMRI to observe the activity of specific brain regions in detail. For example, the acquisition unit can use EEG to measure real-time changes in EEG with high accuracy. For example, the acquisition unit can continuously acquire EEG data over a long period of time. This improves the accuracy of EEG data acquisition by using technologies such as fMRI and EEG. Some or all of the above-described processes in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input fMRI or EEG data into a generating AI and have the generating AI perform the acquisition of EEG data.

[0035] The analysis unit can analyze electroencephalogram (EEG) data using methods such as Fourier transforms and calculate a time-frequency spectrum. The analysis unit can, for example, use Fourier transforms to analyze the frequency components of EEG data in detail. The analysis unit can, for example, calculate a time-frequency spectrum and analyze the fluctuation patterns of EEG data. The analysis unit can, for example, analyze the frequency components of EEG data and measure changes in concentration and emotion. This improves the accuracy of EEG data analysis by using methods such as Fourier transforms. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input EEG data into a generating AI and have the generating AI perform Fourier transforms and calculate time-frequency spectrums.

[0036] The analysis unit can analyze brainwave response patterns by averaging multiple brainwave data. For example, the analysis unit can average multiple brainwave data to analyze brainwave response patterns in detail. For example, the analysis unit can analyze the fluctuation patterns of brainwave data to observe when a person felt discomfort and what they perceived as abnormal. This allows for a detailed analysis of brainwave response patterns by averaging multiple brainwave data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input multiple brainwave data into a generating AI and have the generating AI perform the analysis of brainwave response patterns.

[0037] The generation unit can generate text from electroencephalogram (EEG) data using a generation AI. For example, the generation unit receives EEG data as input and generates text using the generation AI. The generation AI is, for example, a generation AI that analyzes EEG data and generates text based on its content. The generation unit can, for example, provide the user with the text generated from the EEG data. This makes it possible to generate highly accurate text from EEG data by using the generation AI. Some or all of the above-described processes in the generation unit are performed using the generation AI. For example, the generation unit can input EEG data into the generation AI and have it perform text generation.

[0038] The generation unit can generate images from electroencephalogram (EEG) data using DreamDiffusion. For example, the generation unit receives EEG data as input using DreamDiffusion and generates an image. DreamDiffusion is a generation AI that analyzes EEG data and generates an image based on its contents. The generation unit can provide the user with the image generated from the EEG data. This allows for the generation of highly accurate images from EEG data using DreamDiffusion. Some or all of the above-described processes in the generation unit are performed using the generation AI. For example, the generation unit can input EEG data into DreamDiffusion and have it perform image generation.

[0039] The service provider can provide the generated text and images to the user. The service provider can, for example, display the generated text and images to the user. The service provider can, for example, provide the generated text and images to the user through a digital device. The service provider can, for example, utilize the generated text and images in fields such as medicine, education, and industry. By providing the generated text and images to the user, the user can verify the results. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the generated text and images into an AI model and have the AI ​​execute the optimal method for providing them to the user.

[0040] The service provider can utilize the generated text and images in fields such as medicine, education, and industry. For example, the service provider can use the generated text and images to support diagnosis in the medical field. For example, the service provider can use the generated text and images to support learning in the education field. For example, the service provider can use the generated text and images to improve productivity in the industrial field. In this way, by utilizing the generated text and images in fields such as medicine, education, and industry, it can contribute to solving a wide range of social issues. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the generated text and images into an AI model and have the AI ​​execute the optimal utilization method for each field.

[0041] The acquisition unit can analyze the user's past EEG data and select the optimal acquisition method. For example, the acquisition unit can identify the time period in which the most stable data can be acquired from the user's past EEG data. For example, the acquisition unit can select the optimal acquisition device (fMRI, EEG, etc.) based on the user's past EEG data. For example, the acquisition unit can analyze the user's past EEG data and propose an acquisition method in a low-noise environment. In this way, the optimal acquisition method can be selected by analyzing the user's past EEG data. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's past EEG data into a generating AI and have the generating AI select the optimal acquisition method.

[0042] The acquisition unit can filter brainwave data based on the user's current activity status and areas of interest when acquiring brainwave data. For example, if the user is reading, the acquisition unit can prioritize acquiring brainwave data related to reading. For example, if the user is exercising, the acquisition unit can prioritize acquiring brainwave data related to exercise. For example, if the user is relaxed, the acquisition unit can prioritize acquiring brainwave data related to relaxation. By filtering based on the user's activity status and areas of interest, highly relevant data can be acquired. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data on the user's activity status and areas of interest into a generating AI and have the generating AI perform the filtering.

[0043] The acquisition unit can measure the user's stress level when acquiring electroencephalogram (EEG) data and prioritize acquiring data when the user is under low stress. For example, the acquisition unit can acquire EEG data when the user's stress level is low. For example, if the user's stress level is high, the acquisition unit can acquire EEG data after stress reduction. For example, the acquisition unit can monitor the user's stress level in real time and acquire data at times when stress is low. By measuring the user's stress level, the acquisition unit can prioritize acquiring data when the user is under low stress. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's stress level data into a generating AI and have the generating AI execute the data acquisition timing.

[0044] The 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 at home, the acquisition unit can prioritize the acquisition of EEG data related to activities at home. For example, if the user is at work, the acquisition unit can prioritize the acquisition of EEG data related to activities at work. For example, if the user is out, the acquisition unit can prioritize the acquisition of EEG data related to activities at the location where they are out. This allows for the priority acquisition of highly relevant data by considering the user's geographical location. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's geographical location information into a generating AI and have the generating AI prioritize data acquisition.

[0045] The acquisition unit can analyze the user's social media activity and acquire relevant data when acquiring electroencephalogram (EEG) data. For example, the acquisition unit can acquire relevant EEG data while the user is active on social media. For example, the acquisition unit can analyze the content of the user's social media posts and acquire relevant EEG data. For example, the acquisition unit can analyze the user's social media usage patterns and acquire EEG data at the optimal timing. This allows for the acquisition of relevant data by analyzing the user's social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data on the user's social media activity into a generating AI and have the generating AI execute the data acquisition timing.

[0046] The acquisition unit can select the optimal acquisition timing for brainwave data acquisition by considering the user's sleep pattern. For example, the acquisition unit can acquire brainwave data when the user enters deep sleep. For example, the acquisition unit can acquire brainwave data when the user enters light sleep. For example, the acquisition unit can analyze the user's sleep cycle and acquire brainwave data at the optimal timing. This allows the acquisition unit to select the optimal acquisition timing by considering the user's sleep pattern. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data on the user's sleep pattern into a generating AI and have the generating AI execute the data acquisition timing.

[0047] 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 perform a detailed analysis on important EEG data. For example, the analysis unit can perform a simplified analysis on less important EEG data. For example, the analysis unit can determine the priority of the analysis according to its importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the EEG data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the EEG data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0048] 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 a concentration analysis algorithm to EEG data related to concentration. For example, the analysis unit can apply an emotion analysis algorithm to EEG data related to emotion. For example, the analysis unit can apply a stress analysis algorithm to EEG data related to stress. By applying different analysis algorithms depending on the category of EEG data, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of the EEG data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0049] The analysis unit can apply filtering techniques to remove noise from EEG data during analysis. For example, the analysis unit can apply a bandpass filter to remove noise from EEG data. For example, the analysis unit can apply a moving average filter to remove noise from EEG data. For example, the analysis unit can apply a wavelet transform to remove noise from EEG data. By removing noise from the EEG data, the accuracy of the analysis results is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have a generating AI perform noise reduction on the EEG data.

[0050] The analysis unit can determine the priority of analysis based on the timing of EEG data acquisition during analysis. For example, the analysis unit may prioritize the analysis of the most recent EEG data. For example, the analysis unit may prioritize the analysis of EEG data from the time when an important event occurred. For example, the analysis unit may determine the priority of analysis based on the user's activity pattern. This enables efficient analysis by determining the priority of analysis based on the timing of EEG data acquisition. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of EEG data acquisition into a generating AI and have the generating AI determine the priority of analysis.

[0051] The analysis unit can adjust the order of analysis based on the relevance of the electroencephalogram (EEG) data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant EEG data. For example, the analysis unit can postpone the analysis of less relevant EEG data. The analysis unit can dynamically adjust the order of analysis based on the relevance of the EEG data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the EEG data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the EEG data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0052] The analysis unit can detect abnormal values ​​in the electroencephalogram (EEG) data during analysis and correct the analysis results based on these abnormal values. For example, the analysis unit can detect abnormal values ​​and exclude them from the analysis results. For example, the analysis unit can detect abnormal values ​​and apply a correction algorithm to modify the analysis results. For example, the analysis unit can detect abnormal values ​​and recalculate the analysis results based on these abnormal values. This improves the accuracy of the analysis results by detecting and correcting abnormal values ​​in the EEG data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input abnormal values ​​in the EEG data to a generating AI and have the generating AI perform the correction of the analysis results.

[0053] The generation unit can adjust the level of detail of the generated data based on the importance of the EEG data during generation. For example, the generation unit can generate detailed text or images based on important EEG data. For example, the generation unit can generate simplified text or images based on less important EEG data. For example, the generation unit can determine the generation priority according to importance. This allows for efficient generation by adjusting the level of detail of the generated data based on the importance of the EEG data. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the importance of the EEG data into the generation AI and have the generation AI adjust the level of detail of the generated data.

[0054] The generation unit can apply different generation algorithms depending on the category of the EEG data during generation. For example, the generation unit can apply a generation algorithm specialized for concentration to EEG data related to concentration. For example, the generation unit can apply a generation algorithm specialized for emotion to EEG data related to emotion. For example, the generation unit can apply a generation algorithm specialized for stress to EEG data related to stress. By applying different generation algorithms depending on the category of EEG data, the generation accuracy is improved. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the category of the EEG data into the generation AI and have the generation AI execute the application of the generation algorithm.

[0055] The generation unit can customize the generated content by considering the fluctuation patterns of the electroencephalogram (EEG) data during generation. For example, the generation unit can adjust the tone of the text based on the fluctuation patterns of the EEG data. For example, the generation unit can adjust the color tone of an image based on the fluctuation patterns of the EEG data. For example, the generation unit can adjust the level of detail of the generated content based on the fluctuation patterns of the EEG data. This allows for more appropriate customization of the generated content by considering the fluctuation patterns of the EEG data. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the fluctuation patterns of the EEG data into the generation AI and have the generation AI perform the customization of the generated content.

[0056] The generation unit can determine the generation priority based on the timing of EEG data acquisition during generation. For example, the generation unit can preferentially generate text and images based on the most recent EEG data. For example, the generation unit can preferentially generate text and images based on EEG data from the time an important event occurred. For example, the generation unit can determine the generation priority based on the user's activity pattern. This enables efficient generation by determining the generation priority based on the timing of EEG data acquisition. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the timing of EEG data acquisition to the generation AI and have the generation AI determine the generation priority.

[0057] The generation unit can adjust the generation order based on the relevance of the EEG data during generation. For example, the generation unit can preferentially generate text and images based on highly relevant EEG data. For example, the generation unit can postpone the generation of text and images based on less relevant EEG data. The generation unit can dynamically adjust the generation order based on the relevance of the EEG data. This enables efficient generation by adjusting the generation order based on the relevance of the EEG data. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the relevance of the EEG data into the generation AI and have the generation AI perform the adjustment of the generation order.

[0058] The generation unit can detect abnormal values ​​in the electroencephalogram (EEG) data during generation and correct the generated content based on these abnormal values. For example, the generation unit can detect abnormal values ​​and exclude them from the generated content. For example, the generation unit can detect abnormal values ​​and apply a correction algorithm to modify the generated content. For example, the generation unit can detect abnormal values ​​and recalculate the generated content based on these abnormal values. This improves the accuracy of the generated content by detecting and correcting abnormal values ​​in the EEG data. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input abnormal values ​​in the EEG data into the generation AI and have the generation AI perform the correction of the generated content.

[0059] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider can select the most frequently used display method from the user's past operation history. For example, the service provider can analyze the user's past operation history and propose the optimal display method. For example, the service provider can provide a customized display method based on the user's past operation history. This allows the optimal display method to be selected by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's past operation history into a generating AI and have the generating AI perform the selection of the display method.

[0060] The service provider can customize the content offered based on the user's current activity at the time of delivery. For example, if the user is reading, the service provider can provide text and images related to reading. For example, if the user is exercising, the service provider can provide text and images related to exercise. For example, if the user is relaxing, the service provider can provide text and images related to relaxation. By customizing the content based on the user's current activity, more appropriate content can be provided. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's current activity into a generating AI and have the generating AI perform the customization of the content offered.

[0061] The service provider can collect user feedback at the time of delivery and improve the service provided. For example, the service provider can collect user feedback and improve the service provided. For example, the service provider can analyze user feedback and propose the optimal delivery method. For example, the service provider can customize the service provided based on user feedback. This allows for continuous improvement of the service provided by collecting user feedback. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input user feedback into a generating AI and have the generating AI perform improvements to the service provided.

[0062] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. For example, if the user is using a tablet, the service provider can provide a display method optimized for a large screen. For example, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This allows the service provider to select the optimal display method by taking into account the user's device information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's device information into a generating AI and have the generating AI perform the selection of the display method.

[0063] The service provider can provide highly relevant content by considering the user's geographical location information at the time of delivery. For example, if the user is at home, the service provider can provide text and images related to activities at home. For example, if the user is at work, the service provider can provide text and images related to activities at work. For example, if the user is out, the service provider can provide text and images related to activities while out. This allows the service provider to provide highly relevant content by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the content to be delivered.

[0064] The service provider can analyze the user's social media activity and provide relevant content at the time of delivery. For example, the service provider can provide relevant text and images while the user is active on social media. For example, the service provider can analyze the content of the user's social media posts and provide relevant text and images. For example, the service provider can analyze the user's social media usage patterns and provide text and images at the optimal time. In this way, relevant content can be provided by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's social media activity into a generating AI and have the generating AI select the content to be provided.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The Brain Communication system can also be equipped with a function to monitor the user's brainwave data in real time and issue an alert if an anomaly is detected. For example, the acquisition unit continuously acquires brainwave data, and the analysis unit analyzes the data in real time to detect abnormal patterns. If an anomaly is detected, the provision unit can issue an alert to the user and prompt them to take appropriate action. This allows for real-time monitoring of the user's health status and early detection of abnormalities.

[0067] The Brain Communication system can also be equipped with a function to generate individualized learning plans based on the user's brainwave data. For example, the acquisition unit acquires the user's brainwave data during learning, and the analysis unit analyzes concentration and comprehension. The generation unit generates an optimal learning plan for the user based on the analysis results, and the provision unit provides that plan to the user. This maximizes the user's learning effectiveness.

[0068] The Brain Communication system can also be equipped with a function to generate an exercise plan based on the user's brainwave data. For example, the acquisition unit acquires the user's brainwave data during exercise, and the analysis unit analyzes the effects of the exercise. The generation unit generates an optimal exercise plan for the user based on the analysis results, and the provision unit provides that plan to the user. This maximizes the effectiveness of the user's exercise.

[0069] The Brain Communication system can also be equipped with a function to provide advice to improve sleep quality based on the user's brainwave data. For example, the acquisition unit acquires the user's brainwave data during sleep, and the analysis unit analyzes the quality of sleep. The generation unit generates advice to improve sleep quality based on the analysis results, and the provision unit provides it to the user. This can improve the user's sleep quality.

[0070] The Brain Communication system can also be equipped with a function to provide a training plan to improve concentration based on the user's brainwave data. For example, the acquisition unit acquires the user's brainwave data while they are concentrating, and the analysis unit analyzes the level of concentration. The generation unit generates a training plan to improve concentration based on the analysis results, and the provision unit provides it to the user. This can improve the user's concentration.

[0071] The following briefly describes the processing flow for example form 1.

[0072] Step 1: The acquisition unit acquires electroencephalogram (EEG) data. The acquisition unit acquires EEG data using techniques such as fMRI or EEG. fMRI is a method for observing changes in blood volume in the brain, and EEG is a method for measuring the electrical activity of the brain by attaching an EEG measurement device. The acquisition unit can, for example, use fMRI to observe the activity of specific areas of the brain in detail. EEG can, for example, measure real-time changes in EEG with high accuracy. The acquisition unit can also, for example, continuously acquire EEG data over a long period of time. Step 2: The analysis unit analyzes the electroencephalogram (EEG) data acquired by the acquisition unit. The analysis unit analyzes the EEG data using methods such as Fourier transform and calculates a time-frequency spectrum. The analysis unit can, for example, analyze the frequency components of the EEG data in detail to measure changes in concentration and emotion. The analysis unit can also analyze the EEG response pattern by, for example, averaging multiple EEG readings. The analysis unit can, for example, analyze the fluctuation pattern of the EEG data to observe when the person felt discomfort and what they perceived as abnormal. Step 3: The generation unit generates text and images based on the electroencephalogram (EEG) data analyzed by the analysis unit. The generation unit generates text from the EEG data using, for example, a generation AI. The generation AI is, for example, a generation AI that takes EEG data as input and generates text. The generation unit generates images from the EEG data using, for example, DreamDiffusion. DreamDiffusion is, for example, a generation AI that takes EEG data as input and generates images. The generation unit provides the user with, for example, the text and images generated from the EEG data. Step 4: The provider unit provides the text and images generated by the generator unit. The provider unit, for example, displays the generated text and images to the user. The provider unit can utilize the generated text and images in fields such as medicine, education, and industry. The provider unit can provide the generated text and images to the user through a digital device, for example.

[0073] (Example of form 2) The Brain Communication system according to an embodiment of the present invention is a system that utilizes brainwaves and generative AI to produce technologies such as generative AI that converts thoughts into text and DreamDiffusion that converts mental images into diagrams. The Brain Communication system aims to contribute to solving social issues in a wide range of fields, including medicine, education, and industry, by fusing brain science and AI technology. For example, the Brain Communication system involves attaching a device to measure brainwaves and acquiring brainwave data. Technologies such as fMRI and EEG are used to measure brainwaves. fMRI is a method that observes changes in the blood volume of the brain, and EEG is a method that measures the electrical activity of the brain by attaching a brainwave measurement device. This makes it possible to understand the state of brain activity in detail. Next, the Brain Communication system analyzes the acquired brainwave data. The brainwave data is analyzed to calculate a time-frequency spectrum using methods such as Fourier transform, and changes in concentration and emotion are measured. In addition, by averaging multiple brainwaves, the brainwave response pattern can be analyzed, and it is possible to observe when a person felt discomfort and what they perceived as abnormal. Furthermore, the Brain Communication system inputs analyzed brainwave data into a generating AI, which then uses a generating AI to transcribe thoughts into text and DreamDiffusion to create images from mental images, thereby generating text and images. This makes it possible to visualize an individual's thoughts and emotions from brainwave data. For example, in the medical field, the Brain Communication system can optimize patients' brain function and improve their quality of life. For instance, by analyzing brainwave data, it can understand changes in a patient's concentration and emotions and propose appropriate treatment methods. In the education field, the Brain Communication system can analyze students' brainwave data and provide advice to improve learning effectiveness. Moreover, in the industrial field, the Brain Communication system can analyze employees' brainwave data and propose measures to improve productivity.In this way, the Brain Communication system, by utilizing brainwaves and AI generation, can optimize an individual's brain function, improve their quality of life, and contribute to increased productivity and the creation of new business opportunities for companies. Thus, the Brain Communication system can optimize an individual's brain function and improve their quality of life.

[0074] The Brain Communication system according to this embodiment comprises an acquisition unit, an analysis unit, a generation unit, and a provision unit. The acquisition unit acquires electroencephalogram (EEG) data. The acquisition unit acquires EEG data using, for example, technologies such as fMRI or EEG. fMRI is a method for observing changes in blood volume in the brain, and EEG is a method for measuring the electrical activity of the brain by attaching an EEG measurement device. The acquisition unit can, for example, observe the activity of a specific region of the brain in detail using fMRI. EEG can, for example, measure real-time changes in EEG with high accuracy. The acquisition unit can also, for example, continuously acquire EEG data over a long period of time. The analysis unit analyzes the EEG data acquired by the acquisition unit. The analysis unit analyzes the EEG data using, for example, a method such as Fourier transform and calculates a time-frequency spectrum. The analysis unit can, for example, analyze the frequency components of the EEG data in detail and measure changes in concentration and emotion. The analysis unit can also analyze the response pattern of the EEG by, for example, averaging multiple EEG readings. The analysis unit can, for example, analyze the fluctuation patterns of electroencephalogram (EEG) data to observe when a person felt discomfort and what they perceived as abnormal. The generation unit generates text and images based on the EEG data analyzed by the analysis unit. The generation unit generates text from EEG data using, for example, a generation AI. The generation AI is, for example, a generation AI that takes EEG data as input and generates text. The generation unit generates images from EEG data using, for example, DreamDiffusion. DreamDiffusion is, for example, a generation AI that takes EEG data as input and generates images. The generation unit provides the user with the text and images generated from the EEG data. The provision unit provides the text and images generated by the generation unit. The provision unit displays the generated text and images to the user. The provision unit can utilize the generated text and images in fields such as medicine, education, and industry. The provision unit can provide the generated text and images to the user through digital devices.As a result, the Brain Communication system according to this embodiment can visualize an individual's thoughts and emotions by acquiring, analyzing, generating, and providing brainwave data.

[0075] The acquisition unit acquires electroencephalogram (EEG) data. The acquisition unit acquires EEG data using technologies such as fMRI and EEG. fMRI is a method for observing changes in blood volume in the brain, while EEG is a method for measuring the electrical activity of the brain by attaching an EEG measurement device. Specifically, fMRI can capture changes in blood flow within the brain with high resolution using a strong magnetic field and radio waves. This makes it possible to observe in detail how specific areas of the brain are active. On the other hand, EEG measures the electrical activity of the brain in real time with high precision by placing electrodes on the scalp. EEG has particularly high temporal resolution and can capture changes in EEG in milliseconds, making it suitable for observing instantaneous reactions and short-term changes. By combining these technologies, the acquisition unit can capture brain activity from multiple perspectives. For example, by using fMRI to observe the activity of specific areas of the brain in detail and simultaneously using EEG to measure real-time changes in EEG, more comprehensive data can be obtained. Furthermore, the acquisition unit can continuously acquire EEG data over long periods, allowing for the tracking of changes and trends in brain activity over time. For example, by monitoring brain responses in daily life and brain activity while performing specific tasks over a long period, it is possible to gain a detailed understanding of an individual's brain characteristics and patterns. This allows the acquisition unit to build a foundation for collecting EEG data from multiple perspectives and continuously, and providing it to the analysis and generation units.

[0076] The analysis unit analyzes the electroencephalogram (EEG) data acquired by the acquisition unit. The analysis unit analyzes the EEG data using methods such as Fourier transform and calculates a time-frequency spectrum. Specifically, by using the Fourier transform, the time-domain signals of the EEG data can be converted to the frequency domain, allowing for the analysis of the intensity of each frequency component. This enables detailed analysis of the frequency components of the EEG, making it possible to measure changes in concentration and emotion. For example, by observing changes in the intensity of specific frequency bands (alpha waves, beta waves, gamma waves, etc.), an individual's state of concentration and relaxation can be evaluated. The analysis unit can also reduce noise and more clearly analyze EEG response patterns by averaging multiple EEG readings. This allows for highly accurate capture of the brain's response to specific stimuli. Furthermore, the analysis unit can analyze the fluctuation patterns of the EEG data to observe when a person felt discomfort and what they perceived as abnormal. For example, if a specific EEG pattern appears while performing a particular task, the meaning of that pattern can be analyzed to evaluate the individual's emotions and cognitive state. Based on these analysis results, the analysis unit can generate data to provide to the generation unit, gaining a detailed understanding of the individual's brain activity state. This allows the analysis unit to comprehensively analyze the acquired electroencephalogram (EEG) data and evaluate the individual's brain activity state in detail.

[0077] The generation unit generates text and images based on the electroencephalogram (EEG) data analyzed by the analysis unit. For example, the generation unit uses a generation AI to generate text from EEG data. The generation AI, for example, takes EEG data as input and generates text. Specifically, the generation AI extracts features from the EEG data and generates natural language text based on them. For example, it can generate text that expresses thoughts and emotions at the time based on the concentration state and emotional changes obtained from the EEG data. The generation unit also uses DreamDiffusion to generate images from EEG data. DreamDiffusion is a generation AI that, for example, takes EEG data as input and generates images. Specifically, DreamDiffusion analyzes patterns in the EEG data and generates visual images based on them. For example, it can generate images that visually represent the mental landscape or image at the time based on emotional and thought patterns obtained from the EEG data. By using these generation AIs, the generation unit can generate diverse forms of content from EEG data. Furthermore, the generation unit is equipped with an interface for providing the generated text and images to the user, allowing the user to easily view the generated content. This allows the generation unit to represent the information obtained from electroencephalogram data in a concrete form and provide it to the user.

[0078] The provider unit provides the text and images generated by the generator unit. The provider unit, for example, displays the generated text and images to the user. Specifically, the provider unit displays the generated text and images on the screen of a digital device, making them intuitively understandable to the user. For example, it displays the generated text and images on the screen of a smartphone, tablet, or computer, allowing the user to view them. The provider unit can utilize the generated text and images in fields such as medicine, education, and industry. For example, in the medical field, information obtained from patient electroencephalogram (EEG) data can be used as a reference for diagnosis and treatment. In the education field, it can be used to understand students' concentration levels and emotional changes, improving learning effectiveness. In the industrial field, it can be used to monitor employees' stress levels and concentration, contributing to improved work efficiency and safety management. The provider unit can provide the generated text and images to users through digital devices. For example, it can allow users to access the generated content through smartphone apps or web applications. Furthermore, the provider unit can collect user feedback and continuously improve the accuracy and quality of the generated content. This allows the service provider to deliver generated text and images to users in a variety of ways, making individual thoughts and emotions visible.

[0079] The acquisition unit can acquire electroencephalogram (EEG) data using technologies such as fMRI and EEG. For example, the acquisition unit can use fMRI to observe the activity of specific brain regions in detail. For example, the acquisition unit can use EEG to measure real-time changes in EEG with high accuracy. For example, the acquisition unit can continuously acquire EEG data over a long period of time. This improves the accuracy of EEG data acquisition by using technologies such as fMRI and EEG. Some or all of the above-described processes in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input fMRI or EEG data into a generating AI and have the generating AI perform the acquisition of EEG data.

[0080] The analysis unit can analyze electroencephalogram (EEG) data using methods such as Fourier transforms and calculate a time-frequency spectrum. The analysis unit can, for example, use Fourier transforms to analyze the frequency components of EEG data in detail. The analysis unit can, for example, calculate a time-frequency spectrum and analyze the fluctuation patterns of EEG data. The analysis unit can, for example, analyze the frequency components of EEG data and measure changes in concentration and emotion. This improves the accuracy of EEG data analysis by using methods such as Fourier transforms. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input EEG data into a generating AI and have the generating AI perform Fourier transforms and calculate time-frequency spectrums.

[0081] The analysis unit can analyze brainwave response patterns by averaging multiple brainwave data. For example, the analysis unit can average multiple brainwave data to analyze brainwave response patterns in detail. For example, the analysis unit can analyze the fluctuation patterns of brainwave data to observe when a person felt discomfort and what they perceived as abnormal. This allows for a detailed analysis of brainwave response patterns by averaging multiple brainwave data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input multiple brainwave data into a generating AI and have the generating AI perform the analysis of brainwave response patterns.

[0082] The generation unit can generate text from electroencephalogram (EEG) data using a generation AI. For example, the generation unit receives EEG data as input and generates text using the generation AI. The generation AI is, for example, a generation AI that analyzes EEG data and generates text based on its content. The generation unit can, for example, provide the user with the text generated from the EEG data. This makes it possible to generate highly accurate text from EEG data by using the generation AI. Some or all of the above-described processes in the generation unit are performed using the generation AI. For example, the generation unit can input EEG data into the generation AI and have it perform text generation.

[0083] The generation unit can generate images from electroencephalogram (EEG) data using DreamDiffusion. For example, the generation unit receives EEG data as input using DreamDiffusion and generates an image. DreamDiffusion is a generation AI that analyzes EEG data and generates an image based on its contents. The generation unit can provide the user with the image generated from the EEG data. This allows for the generation of highly accurate images from EEG data using DreamDiffusion. Some or all of the above-described processes in the generation unit are performed using the generation AI. For example, the generation unit can input EEG data into DreamDiffusion and have it perform image generation.

[0084] The service provider can provide the generated text and images to the user. The service provider can, for example, display the generated text and images to the user. The service provider can, for example, provide the generated text and images to the user through a digital device. The service provider can, for example, utilize the generated text and images in fields such as medicine, education, and industry. By providing the generated text and images to the user, the user can verify the results. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the generated text and images into an AI model and have the AI ​​execute the optimal method for providing them to the user.

[0085] The service provider can utilize the generated text and images in fields such as medicine, education, and industry. For example, the service provider can use the generated text and images to support diagnosis in the medical field. For example, the service provider can use the generated text and images to support learning in the education field. For example, the service provider can use the generated text and images to improve productivity in the industrial field. In this way, by utilizing the generated text and images in fields such as medicine, education, and industry, it can contribute to solving a wide range of social issues. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the generated text and images into an AI model and have the AI ​​execute the optimal utilization method for each field.

[0086] 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 periodically acquire EEG data to collect stable data. For example, if the user is stressed, the acquisition unit can acquire EEG data when the stress level decreases. For example, if the user is focused, the acquisition unit can acquire EEG data at the moment when their concentration level increases. By adjusting the timing of EEG data acquisition based on the user's emotions, more appropriate data can be obtained. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input user emotion data into the generative AI and have the generative AI adjust the timing of EEG data acquisition.

[0087] The acquisition unit can analyze the user's past EEG data and select the optimal acquisition method. For example, the acquisition unit can identify the time period in which the most stable data can be acquired from the user's past EEG data. For example, the acquisition unit can select the optimal acquisition device (fMRI, EEG, etc.) based on the user's past EEG data. For example, the acquisition unit can analyze the user's past EEG data and propose an acquisition method in a low-noise environment. In this way, the optimal acquisition method can be selected by analyzing the user's past EEG data. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's past EEG data into a generating AI and have the generating AI select the optimal acquisition method.

[0088] The acquisition unit can filter brainwave data based on the user's current activity status and areas of interest when acquiring brainwave data. For example, if the user is reading, the acquisition unit can prioritize acquiring brainwave data related to reading. For example, if the user is exercising, the acquisition unit can prioritize acquiring brainwave data related to exercise. For example, if the user is relaxed, the acquisition unit can prioritize acquiring brainwave data related to relaxation. By filtering based on the user's activity status and areas of interest, highly relevant data can be acquired. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data on the user's activity status and areas of interest into a generating AI and have the generating AI perform the filtering.

[0089] The acquisition unit can measure the user's stress level when acquiring electroencephalogram (EEG) data and prioritize acquiring data when the user is under low stress. For example, the acquisition unit can acquire EEG data when the user's stress level is low. For example, if the user's stress level is high, the acquisition unit can acquire EEG data after stress reduction. For example, the acquisition unit can monitor the user's stress level in real time and acquire data at times when stress is low. By measuring the user's stress level, the acquisition unit can prioritize acquiring data when the user is under low stress. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's stress level data into a generating AI and have the generating AI execute the data acquisition timing.

[0090] The acquisition unit can estimate the user's emotions and determine the priority of brainwave data to acquire based on the estimated user emotions. For example, if the user is relaxed, the acquisition unit can prioritize acquiring brainwave data related to relaxation. For example, if the user is concentrating, the acquisition unit can prioritize acquiring brainwave data related to concentration. For example, if the user is stressed, the acquisition unit can prioritize acquiring brainwave data related to stress. In this way, important data can be acquired preferentially by determining the priority of brainwave data based on the user's emotions. Emotion estimation is implemented using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's emotion data into a generative AI and have the generative AI perform the determination of the priority of brainwave data.

[0091] The 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 at home, the acquisition unit can prioritize the acquisition of EEG data related to activities at home. For example, if the user is at work, the acquisition unit can prioritize the acquisition of EEG data related to activities at work. For example, if the user is out, the acquisition unit can prioritize the acquisition of EEG data related to activities at the location where they are out. This allows for the priority acquisition of highly relevant data by considering the user's geographical location. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's geographical location information into a generating AI and have the generating AI prioritize data acquisition.

[0092] The acquisition unit can analyze the user's social media activity and acquire relevant data when acquiring electroencephalogram (EEG) data. For example, the acquisition unit can acquire relevant EEG data while the user is active on social media. For example, the acquisition unit can analyze the content of the user's social media posts and acquire relevant EEG data. For example, the acquisition unit can analyze the user's social media usage patterns and acquire EEG data at the optimal timing. This allows for the acquisition of relevant data by analyzing the user's social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data on the user's social media activity into a generating AI and have the generating AI execute the data acquisition timing.

[0093] The acquisition unit can select the optimal acquisition timing for brainwave data acquisition by considering the user's sleep pattern. For example, the acquisition unit can acquire brainwave data when the user enters deep sleep. For example, the acquisition unit can acquire brainwave data when the user enters light sleep. For example, the acquisition unit can analyze the user's sleep cycle and acquire brainwave data at the optimal timing. This allows the acquisition unit to select the optimal acquisition timing by considering the user's sleep pattern. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data on the user's sleep pattern into a generating AI and have the generating AI execute the data acquisition timing.

[0094] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can display the analysis results in a visually easy-to-understand manner. For example, if the user is stressed, the analysis unit can display the analysis results concisely. For example, if the user is focused, the analysis unit can provide detailed analysis results. This allows for the provision of more appropriate analysis results by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0095] 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 perform a detailed analysis on important EEG data. For example, the analysis unit can perform a simplified analysis on less important EEG data. For example, the analysis unit can determine the priority of the analysis according to its importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the EEG data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the EEG data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0096] 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 a concentration analysis algorithm to EEG data related to concentration. For example, the analysis unit can apply an emotion analysis algorithm to EEG data related to emotion. For example, the analysis unit can apply a stress analysis algorithm to EEG data related to stress. By applying different analysis algorithms depending on the category of EEG data, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of the EEG data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0097] The analysis unit can apply filtering techniques to remove noise from EEG data during analysis. For example, the analysis unit can apply a bandpass filter to remove noise from EEG data. For example, the analysis unit can apply a moving average filter to remove noise from EEG data. For example, the analysis unit can apply a wavelet transform to remove noise from EEG data. By removing noise from the EEG data, the accuracy of the analysis results is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have a generating AI perform noise reduction on the EEG data.

[0098] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis. For example, if the user is stressed, the analysis unit can perform a concise analysis. For example, if the user is focused, the analysis unit can perform a detailed analysis related to concentration. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the length of the analysis.

[0099] The analysis unit can determine the priority of analysis based on the timing of EEG data acquisition during analysis. For example, the analysis unit may prioritize the analysis of the most recent EEG data. For example, the analysis unit may prioritize the analysis of EEG data from the time when an important event occurred. For example, the analysis unit may determine the priority of analysis based on the user's activity pattern. This enables efficient analysis by determining the priority of analysis based on the timing of EEG data acquisition. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of EEG data acquisition into a generating AI and have the generating AI determine the priority of analysis.

[0100] The analysis unit can adjust the order of analysis based on the relevance of the electroencephalogram (EEG) data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant EEG data. For example, the analysis unit can postpone the analysis of less relevant EEG data. The analysis unit can dynamically adjust the order of analysis based on the relevance of the EEG data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the EEG data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the EEG data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0101] The analysis unit can detect abnormal values ​​in the electroencephalogram (EEG) data during analysis and correct the analysis results based on these abnormal values. For example, the analysis unit can detect abnormal values ​​and exclude them from the analysis results. For example, the analysis unit can detect abnormal values ​​and apply a correction algorithm to modify the analysis results. For example, the analysis unit can detect abnormal values ​​and recalculate the analysis results based on these abnormal values. This improves the accuracy of the analysis results by detecting and correcting abnormal values ​​in the EEG data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input abnormal values ​​in the EEG data to a generating AI and have the generating AI perform the correction of the analysis results.

[0102] The generation unit can estimate the user's emotions and adjust the way it expresses the generated text and images based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate text and images with calm expressions. For example, if the user is stressed, the generation unit can generate text and images with simple and easy-to-understand expressions. For example, if the user is focused, the generation unit can generate text and images with detailed and specific expressions. By adjusting the way it expresses the generated text and images based on the user's emotions, it is possible to provide more appropriate generation results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the way it expresses the text and images.

[0103] The generation unit can adjust the level of detail of the generated data based on the importance of the EEG data during generation. For example, the generation unit can generate detailed text or images based on important EEG data. For example, the generation unit can generate simplified text or images based on less important EEG data. For example, the generation unit can determine the generation priority according to importance. This allows for efficient generation by adjusting the level of detail of the generated data based on the importance of the EEG data. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the importance of the EEG data into the generation AI and have the generation AI adjust the level of detail of the generated data.

[0104] The generation unit can apply different generation algorithms depending on the category of the EEG data during generation. For example, the generation unit can apply a generation algorithm specialized for concentration to EEG data related to concentration. For example, the generation unit can apply a generation algorithm specialized for emotion to EEG data related to emotion. For example, the generation unit can apply a generation algorithm specialized for stress to EEG data related to stress. By applying different generation algorithms depending on the category of EEG data, the generation accuracy is improved. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the category of the EEG data into the generation AI and have the generation AI execute the application of the generation algorithm.

[0105] The generation unit can customize the generated content by considering the fluctuation patterns of the electroencephalogram (EEG) data during generation. For example, the generation unit can adjust the tone of the text based on the fluctuation patterns of the EEG data. For example, the generation unit can adjust the color tone of an image based on the fluctuation patterns of the EEG data. For example, the generation unit can adjust the level of detail of the generated content based on the fluctuation patterns of the EEG data. This allows for more appropriate customization of the generated content by considering the fluctuation patterns of the EEG data. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the fluctuation patterns of the EEG data into the generation AI and have the generation AI perform the customization of the generated content.

[0106] The generation unit can estimate the user's emotions and adjust the length of the text and images it generates based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate longer text and more detailed images. If the user is stressed, for example, the generation unit can generate shorter, more concise text and simpler images. If the user is focused, for example, the generation unit can generate more detailed and specific text and images. By adjusting the length of the text and images generated based on the user's emotions, more appropriate generation results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the length of the text and images.

[0107] The generation unit can determine the generation priority based on the timing of EEG data acquisition during generation. For example, the generation unit can preferentially generate text and images based on the most recent EEG data. For example, the generation unit can preferentially generate text and images based on EEG data from the time an important event occurred. For example, the generation unit can determine the generation priority based on the user's activity pattern. This enables efficient generation by determining the generation priority based on the timing of EEG data acquisition. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the timing of EEG data acquisition to the generation AI and have the generation AI determine the generation priority.

[0108] The generation unit can adjust the generation order based on the relevance of the EEG data during generation. For example, the generation unit can preferentially generate text and images based on highly relevant EEG data. For example, the generation unit can postpone the generation of text and images based on less relevant EEG data. The generation unit can dynamically adjust the generation order based on the relevance of the EEG data. This enables efficient generation by adjusting the generation order based on the relevance of the EEG data. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the relevance of the EEG data into the generation AI and have the generation AI perform the adjustment of the generation order.

[0109] The generation unit can detect abnormal values ​​in the electroencephalogram (EEG) data during generation and correct the generated content based on these abnormal values. For example, the generation unit can detect abnormal values ​​and exclude them from the generated content. For example, the generation unit can detect abnormal values ​​and apply a correction algorithm to modify the generated content. For example, the generation unit can detect abnormal values ​​and recalculate the generated content based on these abnormal values. This improves the accuracy of the generated content by detecting and correcting abnormal values ​​in the EEG data. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input abnormal values ​​in the EEG data into the generation AI and have the generation AI perform the correction of the generated content.

[0110] The service provider can estimate the user's emotions and adjust the display method of the text and images based on the estimated emotions. For example, if the user is relaxed, the service provider can provide a calm display method. For example, if the user is stressed, the service provider can provide a simple and easy-to-understand display method. For example, if the user is focused, the service provider can provide a detailed and specific display method. By adjusting the display method of the text and images based on the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider is performed using generative AI. For example, the service provider can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0111] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider can select the most frequently used display method from the user's past operation history. For example, the service provider can analyze the user's past operation history and propose the optimal display method. For example, the service provider can provide a customized display method based on the user's past operation history. This allows the optimal display method to be selected by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's past operation history into a generating AI and have the generating AI perform the selection of the display method.

[0112] The service provider can customize the content offered based on the user's current activity at the time of delivery. For example, if the user is reading, the service provider can provide text and images related to reading. For example, if the user is exercising, the service provider can provide text and images related to exercise. For example, if the user is relaxing, the service provider can provide text and images related to relaxation. By customizing the content based on the user's current activity, more appropriate content can be provided. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's current activity into a generating AI and have the generating AI perform the customization of the content offered.

[0113] The service provider can collect user feedback at the time of delivery and improve the service provided. For example, the service provider can collect user feedback and improve the service provided. For example, the service provider can analyze user feedback and propose the optimal delivery method. For example, the service provider can customize the service provided based on user feedback. This allows for continuous improvement of the service provided by collecting user feedback. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input user feedback into a generating AI and have the generating AI perform improvements to the service provided.

[0114] The service provider can estimate the user's emotions and adjust the instructions for operating the text and images it provides based on the estimated emotions. For example, if the user is relaxed, the service provider can provide gentle instructions. If the user is stressed, the service provider can provide simple and easy-to-understand instructions. If the user is focused, the service provider can provide detailed and specific instructions. By adjusting the instructions for operating the text and images provided based on the user's emotions, more appropriate operation becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider is performed using generative AI. For example, the service provider can input user emotion data into the generative AI and have the generative AI perform the adjustment of the operating instructions.

[0115] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. For example, if the user is using a tablet, the service provider can provide a display method optimized for a large screen. For example, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This allows the service provider to select the optimal display method by taking into account the user's device information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's device information into a generating AI and have the generating AI perform the selection of the display method.

[0116] The service provider can provide highly relevant content by considering the user's geographical location information at the time of delivery. For example, if the user is at home, the service provider can provide text and images related to activities at home. For example, if the user is at work, the service provider can provide text and images related to activities at work. For example, if the user is out, the service provider can provide text and images related to activities while out. This allows the service provider to provide highly relevant content by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the content to be delivered.

[0117] The service provider can analyze the user's social media activity and provide relevant content at the time of delivery. For example, the service provider can provide relevant text and images while the user is active on social media. For example, the service provider can analyze the content of the user's social media posts and provide relevant text and images. For example, the service provider can analyze the user's social media usage patterns and provide text and images at the optimal time. In this way, relevant content can be provided by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's social media activity into a generating AI and have the generating AI select the content to be provided.

[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] The Brain Communication system can also be equipped with a function to monitor the user's brainwave data in real time and issue an alert if an anomaly is detected. For example, the acquisition unit continuously acquires brainwave data, and the analysis unit analyzes the data in real time to detect abnormal patterns. If an anomaly is detected, the provision unit can issue an alert to the user and prompt them to take appropriate action. This allows for real-time monitoring of the user's health status and early detection of abnormalities.

[0120] The Brain Communication system can also be equipped with a function to generate individualized learning plans based on the user's brainwave data. For example, the acquisition unit acquires the user's brainwave data during learning, and the analysis unit analyzes concentration and comprehension. The generation unit generates an optimal learning plan for the user based on the analysis results, and the provision unit provides that plan to the user. This maximizes the user's learning effectiveness.

[0121] The Brain Communication system can also be equipped with a function to provide relaxation content based on the user's brainwave data. For example, the acquisition unit acquires brainwave data when the user is feeling stressed, and the analysis unit analyzes the stress level. The generation unit generates music or videos with relaxation effects based on the analysis results, and the provision unit provides them to the user. This can reduce the user's stress.

[0122] The Brain Communication system can also be equipped with a function to generate an exercise plan based on the user's brainwave data. For example, the acquisition unit acquires the user's brainwave data during exercise, and the analysis unit analyzes the effects of the exercise. The generation unit generates an optimal exercise plan for the user based on the analysis results, and the provision unit provides that plan to the user. This maximizes the effectiveness of the user's exercise.

[0123] The Brain Communication system can also be equipped with a function to provide advice to improve sleep quality based on the user's brainwave data. For example, the acquisition unit acquires the user's brainwave data during sleep, and the analysis unit analyzes the quality of sleep. The generation unit generates advice to improve sleep quality based on the analysis results, and the provision unit provides it to the user. This can improve the user's sleep quality.

[0124] The Brain Communication system can also be equipped with a function to monitor changes in emotions in real time based on the user's brainwave data and provide appropriate feedback. For example, the acquisition unit continuously acquires the user's brainwave data, the analysis unit analyzes changes in emotions in real time, and the provision unit provides appropriate feedback to the user based on the analysis results. This allows for a rapid response to changes in the user's emotions.

[0125] The Brain Communication system can also be equipped with a function to provide a training plan to improve concentration based on the user's brainwave data. For example, the acquisition unit acquires the user's brainwave data while they are concentrating, and the analysis unit analyzes the level of concentration. The generation unit generates a training plan to improve concentration based on the analysis results, and the provision unit provides it to the user. This can improve the user's concentration.

[0126] The Brain Communication system can also be equipped with a function to generate music that responds to the user's emotions based on their brainwave data. For example, the acquisition unit acquires brainwave data indicating the user's emotional state, and the analysis unit analyzes the emotions. The generation unit generates music that matches the user's emotions based on the analysis results, and the provision unit provides it to the user. This allows the user to enjoy music that responds to their emotions.

[0127] The Brain Communication system can also be equipped with a function to adjust lighting according to the user's emotions based on brainwave data. For example, the acquisition unit acquires brainwave data indicating the user's emotional state, and the analysis unit analyzes the emotions. The provision unit adjusts the color and brightness of the lighting to match the user's emotions based on the analysis results. This makes it possible to provide a comfortable environment that responds to the user's emotions.

[0128] The Brain Communication system can also be equipped with a function to provide aromatherapy tailored to the user's emotions based on their brainwave data. For example, the acquisition unit acquires brainwave data indicating the user's emotional state, and the analysis unit analyzes the emotions. The provision unit then provides aromatherapy that matches the user's emotions based on the analysis results. This allows the user to achieve a relaxation effect tailored to their emotions.

[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 acquires EEG data using techniques such as fMRI or EEG. fMRI is a method for observing changes in blood volume in the brain, and EEG is a method for measuring the electrical activity of the brain by attaching an EEG measurement device. The acquisition unit can, for example, use fMRI to observe the activity of specific areas of the brain in detail. EEG can, for example, measure real-time changes in EEG with high accuracy. The acquisition unit can also, for example, continuously acquire EEG data over a long period of time. Step 2: The analysis unit analyzes the electroencephalogram (EEG) data acquired by the acquisition unit. The analysis unit analyzes the EEG data using methods such as Fourier transform and calculates a time-frequency spectrum. The analysis unit can, for example, analyze the frequency components of the EEG data in detail to measure changes in concentration and emotion. The analysis unit can also analyze the EEG response pattern by, for example, averaging multiple EEG readings. The analysis unit can, for example, analyze the fluctuation pattern of the EEG data to observe when the person felt discomfort and what they perceived as abnormal. Step 3: The generation unit generates text and images based on the electroencephalogram (EEG) data analyzed by the analysis unit. The generation unit generates text from the EEG data using, for example, a generation AI. The generation AI is, for example, a generation AI that takes EEG data as input and generates text. The generation unit generates images from the EEG data using, for example, DreamDiffusion. DreamDiffusion is, for example, a generation AI that takes EEG data as input and generates images. The generation unit provides the user with, for example, the text and images generated from the EEG data. Step 4: The provider unit provides the text and images generated by the generator unit. The provider unit, for example, displays the generated text and images to the user. The provider unit can utilize the generated text and images in fields such as medicine, education, and industry. The provider unit can provide the generated text and images to the user through a digital device, for example.

[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, generation unit, and provision 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 and collects the EEG data using the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the EEG data using methods such as Fourier transform. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates text and images using generation AI and DreamDiffusion. The provision unit is implemented in the control unit 46A of the smart device 14 and displays the generated text and images to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed 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, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit acquires electroencephalogram (EEG) data using the camera 42 and microphone 238 of the smart glasses 214 and collects the EEG data using the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the EEG data using methods such as Fourier transform. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates text and images using generation AI or DreamDiffusion. The provision unit is implemented in the control unit 46A of the smart glasses 214 and displays the generated text and images to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0151] [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, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit acquires electroencephalogram (EEG) data using the camera 42 and microphone 238 of the headset terminal 314 and collects the EEG data using the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the EEG data using methods such as Fourier transform. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates text and images using generation AI and DreamDiffusion. The provision unit is implemented in the control unit 46A of the headset terminal 314 and displays the generated text and images to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[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, generation unit, and provision unit, is implemented in 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 and collects the EEG data using the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the EEG data using methods such as Fourier transform. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates text and images using generation AI and DreamDiffusion. The provision unit is implemented in the control unit 46A of the robot 414 and displays the generated text and images to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed 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 generation unit that generates text and images based on electroencephalogram data analyzed by the analysis unit, The system includes a providing unit that provides text and images generated by the generation unit. A system characterized by the following features. (Note 2) The acquisition unit is, EEG data is acquired using technologies such as fMRI and EEG. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, EEG data is analyzed using techniques such as Fourier transform, and a time-frequency spectrum is calculated. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, By averaging multiple brainwave data points, the brainwave response patterns are analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Generating text from brainwave data using generative AI The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Using DreamDiffusion to generate images from electroencephalogram (EEG) data The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, Provide the generated text and images to the user. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned supply unit is, Utilizing generated text and images in fields such as healthcare, education, and industry. The system described in Appendix 1, characterized by the features described herein. (Note 9) 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 10) 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 11) The acquisition unit is, When acquiring electroencephalogram (EEG) data, filtering is performed based on the user's current activity status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, When acquiring electroencephalogram (EEG) data, the user's stress level is measured, and data from low-stress states is prioritized for acquisition. The system described in Appendix 1, characterized by the features described herein. (Note 13) 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 14) 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 15) 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 16) The acquisition unit is, When acquiring electroencephalogram (EEG) data, the optimal acquisition timing is selected considering the user's sleep pattern. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) 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 19) 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 20) The aforementioned analysis unit, During analysis, a filtering technique is applied to remove noise from the electroencephalogram (EEG) data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) 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 23) 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 24) The aforementioned analysis unit, During analysis, abnormal values ​​in the electroencephalogram data are detected, and the analysis results are corrected based on these abnormal values. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is It estimates the user's emotions and adjusts the way text and images are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is During generation, the level of detail is adjusted based on the importance of the EEG data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is During generation, different generation algorithms are applied depending on the category of EEG data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is During generation, the generated content is customized by considering the fluctuation patterns of the electroencephalogram (EEG) data. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is It estimates the user's emotions and adjusts the length of the generated text and images based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The generating unit is During generation, the generation priority is determined based on the timing of EEG data acquisition. The system described in Appendix 1, characterized by the features described herein. (Note 31) The generating unit is During generation, the generation order is adjusted based on the relevance of the EEG data. The system described in Appendix 1, characterized by the features described herein. (Note 32) The generating unit is During generation, abnormal values ​​in the electroencephalogram data are detected, and the generated content is corrected based on these abnormal values. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, It estimates the user's emotions and adjusts how text and images are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, When providing the service, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned supply unit is, When providing the service, the content will be customized based on the user's current activity level. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned supply unit is, We collect user feedback at the time of release and improve the content of the service. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned supply unit is, It estimates the user's emotions and adjusts the text and image interaction instructions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned supply unit is, When providing the service, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned supply unit is, When providing content, we will consider the user's geographical location to deliver highly relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned supply unit is, When providing content, we analyze the user's social media activity and provide relevant information. The system described in Appendix 1, 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 generation unit that generates text and images based on electroencephalogram data analyzed by the analysis unit, The system includes a providing unit that provides text and images generated by the generation unit. A system characterized by the following features.

2. The acquisition unit is, EEG data is acquired using technologies such as fMRI and EEG. The system according to feature 1.

3. The aforementioned analysis unit, EEG data is analyzed using techniques such as Fourier transform, and a time-frequency spectrum is calculated. The system according to feature 1.

4. The aforementioned analysis unit, By averaging multiple brainwave data points, the brainwave response patterns are analyzed. The system according to feature 1.

5. The generating unit is Generating text from electroencephalogram (EEG) data using generative AI. The system according to feature 1.

6. The generating unit is Using DreamDiffusion to generate images from electroencephalogram (EEG) data The system according to feature 1.

7. The aforementioned supply unit is, Provide the generated text and images to the user. The system according to feature 1.

8. The aforementioned supply unit is, Utilizing generated text and images in fields such as healthcare, education, and industry. The system according to feature 1.

Images