system

Abstract

The system according to this embodiment aims to support communication by verbalizing and visualizing the patient's emotions and thoughts based on their brainwaves. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, a generation unit, and a display unit. The acquisition unit acquires the patient's brain waves. The analysis unit analyzes the brain wave data acquired by the acquisition unit and analyzes the brain waves and emotions. The generation unit verbalizes and visualizes the data analyzed by the analysis unit. The display unit displays the content generated by the generation unit on a monitor.

Description

Technical Field

[0006]

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to accurately grasp emotions and thoughts based on a patient's brain waves, and there is a problem that communication support is not sufficiently provided.

[0005] The system according to the embodiment aims to verbalize and visualize emotions and thoughts based on a patient's brain waves and support communication.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, an analysis unit, a generation unit, and a display unit. The acquisition unit acquires the patient's brain waves. The analysis unit analyzes the brain wave data acquired by the acquisition unit and analyzes the brain waves and emotions. The generation unit verbalizes and visualizes the data analyzed by the analysis unit. The display unit displays the content generated by the generation unit on a monitor. [Effects of the Invention]

[0007] The system according to this embodiment can verbalize and visualize emotions and thoughts based on a patient's brainwaves, thereby supporting communication. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The thought analysis system according to an embodiment of the present invention is a system that resolves thoughts that cannot be put into words using electroencephalography (EEG) technology. The thought analysis system acquires the patient's brainwaves and analyzes the brainwaves and emotions using deep learning. Based on the analyzed brainwave and emotion data, it uses generative AI to verbalize and visualize the thoughts. The generated content is displayed on a monitor, making it easier for doctors and caregivers to understand what the patient is thinking by reviewing it. This system is used as a communication support tool for caregivers and medical professionals. For example, the thought analysis system acquires the patient's brainwaves. In this case, it utilizes EEG, a highly real-time and versatile brainwave measurement device. EEG is one of the main technologies for measuring brainwaves and has excellent temporal and spatial resolution. For example, by analyzing the frequency of brainwaves, such as alpha waves when the patient is relaxed and beta waves when the patient is excited, it is possible to measure the patient's emotions and concentration. Next, the acquired brainwave data is analyzed using deep learning. Deep learning learns the relationship between brainwaves and emotions and can estimate emotions from brainwaves. For example, if a patient is experiencing stress, deep learning can be used to analyze their emotions and quantify the degree of stress. Based on the analyzed brainwave and emotional data, generative AI is used to verbalize and visualize the patient's thoughts. The generative AI generates text based on the brainwave data, verbalizing what the patient is thinking. For example, if a patient is thinking "I'm hungry," the generative AI can output that thought as text. The generative AI can also generate images based on the brainwave data, visualizing the patient's thoughts as images. For example, if a patient is thinking "cat," the generative AI can display that image as an image. The generated content is displayed on a monitor, making it easier for doctors and caregivers to understand what the patient is thinking. For example, if a patient is thinking "I can't see the clock," doctors and caregivers can understand the patient's needs and take appropriate action by checking the generated text and images on the monitor. This system is used as a communication support tool for caregivers and medical professionals.For example, it can support communication with patients who have difficulty communicating verbally, such as bedridden patients, patients with aphasia, and patients with dementia. This reduces the burden on caregivers and medical staff and allows for a more accurate understanding of patients' needs. The thought analysis system verbalizes and visualizes patients' thoughts, making it easier for doctors and caregivers to understand what patients are thinking.

[0029] The thought analysis system according to this embodiment comprises an acquisition unit, an analysis unit, a generation unit, and a display unit. The acquisition unit acquires the patient's brainwaves. The acquisition unit acquires brainwaves using, for example, an EEG, a highly real-time and versatile brainwave measurement device. EEG is one of the main technologies for measuring brainwaves and has excellent temporal and spatial resolution. For example, by analyzing the frequency of brainwaves, such as alpha waves when the patient is relaxed and beta waves when the patient is excited, the patient's emotions and concentration can be measured. The analysis unit uses deep learning to analyze the brainwave data acquired by the acquisition unit and analyze the relationship between brainwaves and emotions. Deep learning learns the relationship between brainwaves and emotions and can estimate emotions from brainwaves. For example, if the patient is feeling stressed, deep learning can analyze that emotion and quantify the degree of stress. The generation unit uses generation AI to verbalize and visualize the data analyzed by the analysis unit. The generation AI generates text based on the brainwave data and verbalizes what the patient is thinking. For example, if a patient is thinking "I'm hungry," the generating AI can output that thought as text. The generating AI can also generate images based on brainwave data, visualizing the patient's thoughts as images. For example, if a patient is thinking "cat," the generating AI can display that image as an image. The display unit displays the content generated by the generating unit on a monitor. By displaying the generated text and images on the monitor, the display unit makes it easier for doctors and caregivers to understand what the patient is thinking. For example, if a patient is thinking "I can't see the clock," doctors and caregivers can understand the patient's needs and take appropriate action by checking the generated text and images on the monitor. In this way, the thought analysis system according to this embodiment can verbalize and visualize the patient's thoughts, making it easier for doctors and caregivers to understand what the patient is thinking.

[0030] The acquisition unit acquires the patient's brainwaves (EEG). The acquisition unit uses, for example, an EEG (Electroencephalography) device, which is highly real-time and versatile. EEG is one of the main technologies for measuring brainwaves and boasts excellent temporal and spatial resolution. Specifically, the EEG device places multiple electrodes on the scalp to measure the brain's electrical activity with high precision. This allows for real-time acquisition of the patient's EEG patterns. For example, by analyzing the frequency of brainwaves, such as alpha waves when the patient is relaxed or beta waves when they are excited, the patient's emotions and concentration can be measured. Furthermore, the EEG device is equipped with noise reduction capabilities, minimizing interference from the external environment. This improves the accuracy of the acquired EEG data, leading to more precise processing in the analysis unit. Additionally, the EEG device is highly resistant to patient movement, enabling EEG measurement during daily activities. This allows for the acquisition of EEG data under various conditions, such as when the patient is relaxed or stressed. The acquisition unit collects this EEG data in real time and transmits it to the analysis unit. This ensures real-time performance throughout the entire system, enabling rapid analysis and response.

[0031] The analysis unit uses deep learning to analyze brainwave data acquired by the acquisition unit, and analyzes the relationship between brainwaves and emotions. Deep learning learns the relationship between brainwaves and emotions, and can estimate emotions from brainwaves. Specifically, the deep learning model is trained using a large amount of brainwave data and corresponding emotion data. As a result, the model learns the relationship between brainwave patterns and specific emotional states, and can estimate emotions with high accuracy even for new brainwave data. For example, if a patient is experiencing stress, deep learning can analyze their emotions and quantify the degree of stress. The analysis unit also preprocesses the brainwave data, performing noise reduction and feature extraction. This improves the quality of the data input to the deep learning model and increases the accuracy of the analysis results. Furthermore, the analysis unit can process data in real time and generate emotion analysis results quickly. This allows doctors and caregivers to immediately grasp the patient's current emotional state and take appropriate action. The analysis unit can also utilize historical data and statistical information to analyze long-term emotional fluctuations and trends. This allows for continuous monitoring of changes in the patient's emotional state and adjustment of treatment plans as needed.

[0032] The generation unit uses a generation AI to verbalize and visualize data analyzed by the analysis unit. The generation AI generates text based on electroencephalogram (EEG) data, verbalizing the patient's thoughts. Specifically, the generation AI uses natural language processing technology to convert features extracted from EEG data into text. For example, if a patient is thinking "I'm hungry," the generation AI can output that thought as text. The generation AI can also generate images based on EEG data, visualizing the patient's thoughts as images. For example, if a patient is thinking "cat," the generation AI can display that image as an image. The generation AI uses a deep learning model to learn the correspondence between EEG data and text or images. This allows it to generate text and images with high accuracy even for new EEG data. The generation unit outputs the generated text and images in an appropriate format and sends them to the display unit. This allows doctors and caregivers to visually confirm the patient's thoughts. Furthermore, the generation unit can evaluate the quality of the generated text and images and make corrections or improvements as needed. This improves the accuracy and reliability of the generated content, allowing doctors and caregivers to understand patients' thoughts more precisely.

[0033] The display unit displays the content generated by the generation unit on a monitor. By displaying the generated text and images on the monitor, the display unit makes it easier for doctors and caregivers to understand what the patient is thinking. Specifically, the display unit uses a high-resolution display to clearly show the generated text and images. For example, if a patient thinks, "I can't see the clock," doctors and caregivers can understand the patient's needs and take appropriate action by checking the generated text and images on the monitor. The display unit is designed to allow intuitive operation of the generated content through a user interface. This allows doctors and caregivers to quickly check the necessary information and take appropriate action. Furthermore, the display unit also has a function to save the generated content for later reference. This enables analysis and evaluation based on past data, which can be used to help with patient treatment and care. The display unit offers multiple display modes, allowing users to select the optimal display method depending on the situation. For example, there are modes that display only text, and modes that display images and text simultaneously. This allows doctors and caregivers to display information optimally according to the situation and understand the patient's thoughts more accurately.

[0034] The acquisition unit can acquire brainwaves using an EEG, a highly real-time and versatile electroencephalogram (EEG) measurement device. The acquisition unit can, for example, acquire brainwaves using an EEG, a highly real-time and versatile EEG measurement device. EEG is one of the main technologies for measuring brainwaves and excels in both temporal and spatial resolution. For example, by analyzing the frequency of brainwaves, such as alpha waves when the patient is relaxed and beta waves when the patient is excited, it is possible to measure the patient's emotions and concentration. This allows for real-time acquisition of brainwaves using an EEG. EEG, a highly real-time and versatile EEG measurement device, is one of the main technologies for measuring brainwaves and excels in both temporal and spatial resolution. For example, by analyzing the frequency of brainwaves, such as alpha waves when the patient is relaxed and beta waves when the patient is excited, EEG can measure the patient's emotions and concentration. Some or all of the above-described processing in the acquisition unit may be performed using AI, or without AI. For example, the acquisition unit can input the EEG data acquired by the EEG into a generating AI and have the generating AI perform the analysis of the EEG data.

[0035] The analysis unit can analyze brainwaves and emotions using a deep learning model. For example, the analysis unit can analyze brainwaves and emotions using a deep learning model. Deep learning learns the relationship between brainwaves and emotions and can estimate emotions from brainwaves. For example, if a patient is experiencing stress, deep learning can analyze their emotions and quantify the degree of stress. This improves the accuracy of brainwave and emotion analysis by using a deep learning model. The deep learning model learns the relationship between brainwaves and emotions and can estimate emotions from brainwaves. For example, if a patient is experiencing stress, the deep learning model can analyze their emotions and quantify the degree of stress. Some or all of the above processing in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input brainwave data into a generating AI using a deep learning model and have the generating AI perform the analysis of the brainwave data.

[0036] The generation unit can generate text based on electroencephalogram (EEG) data using a generation AI. For example, the generation unit can generate text based on EEG data using a generation AI. The generation AI generates text based on EEG data, verbalizing the patient's thoughts. For example, if the patient is thinking "I'm hungry," the generation AI can output that thought as text. Thus, by using a generation AI, text can be generated based on EEG data. The generation AI generates text based on EEG data, verbalizing the patient's thoughts. For example, if the patient is thinking "I'm hungry," the generation AI can output that thought as text. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or without using a generation AI. For example, the generation unit can input EEG data into a generation AI and have the generation AI perform text generation.

[0037] The generation unit can generate images based on electroencephalogram (EEG) data using a generation AI. For example, the generation unit can generate images based on EEG data using a generation AI. The generation AI generates images based on EEG data, visualizing the patient's thoughts as images. For example, if the patient is thinking of a "cat," the generation AI can display that image as an image. Thus, by using a generation AI, images can be generated based on EEG data. The generation AI generates images based on EEG data, visualizing the patient's thoughts as images. For example, if the patient is thinking of a "cat," the generation AI can display that image as an image. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or without using a generation AI. For example, the generation unit can input EEG data into a generation AI and have the generation AI perform image generation.

[0038] The display unit can display the generated text and images on a monitor. For example, the display unit can display the generated text and images on a monitor. By displaying the generated text and images on a monitor, the display unit makes it easier for doctors and caregivers to understand what the patient is thinking. For example, if a patient thinks, "I can't see the clock," by checking the generated text and images on the monitor, doctors and caregivers can understand the patient's needs and take appropriate action. Thus, displaying the generated text and images on a monitor makes it easier for doctors and caregivers to understand what the patient is thinking. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the generated text and images into a generating AI and have the generating AI perform the generation of the display content.

[0039] The acquisition unit can analyze the patient's past electroencephalogram (EEG) data and select the optimal acquisition method. For example, the acquisition unit can acquire EEG data at a specific time period based on past EEG data. It can also acquire EEG data during a specific activity based on past EEG data. Furthermore, it can acquire EEG data during a specific emotional state based on past EEG data. This allows for efficient EEG acquisition by selecting the optimal acquisition method based on 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 past EEG data into a generating AI and have the generating AI select the optimal acquisition method.

[0040] The acquisition unit can filter brainwave data based on the patient's current health and activity levels. For example, if the patient is exercising, the acquisition unit can remove noise caused by exercise before acquiring brainwave data. Furthermore, if the patient is eating, the acquisition unit can consider the effects of food when acquiring brainwave data. Additionally, if the patient is resting, the acquisition unit can prioritize acquiring brainwave data from a relaxed state. This allows for the acquisition of less noisy brainwave data by filtering based on health and activity levels. Some or all of the above processing in the acquisition unit may be performed using AI, or without AI. For example, the acquisition unit can input patient health and activity data into a generating AI and have the generating AI perform the filtering.

[0041] The acquisition unit can prioritize the acquisition of highly relevant data by considering the patient's geographical location information when acquiring electroencephalogram (EEG) data. For example, if the patient is at home, the acquisition unit will prioritize acquiring EEG data from home. It can also prioritize acquiring EEG data from the hospital if the patient is at the hospital. Furthermore, if the patient is out, the acquisition unit can prioritize acquiring EEG data from their location. This allows for the acquisition of highly relevant EEG data by considering geographical location information. 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 patient's geographical location information into a generating AI and have the generating AI prioritize the acquisition of highly relevant data.

[0042] The acquisition unit can analyze the patient's social media activity and acquire relevant data when acquiring electroencephalogram (EEG) data. For example, if the patient is experiencing stress on social media, the acquisition unit can acquire EEG data at that time. It can also acquire EEG data if the patient is relaxed on social media. Furthermore, it can acquire EEG data if the patient is excited on social media. In this way, relevant EEG data can be acquired by analyzing 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 the patient's social media activity data into a generating AI and have the generating AI acquire the relevant EEG data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the electroencephalogram (EEG) data during the analysis. For example, the analysis unit can perform a detailed analysis on important EEG data. It can also perform a simplified analysis on less important EEG data. Furthermore, it can perform an analysis with an appropriate level of detail on EEG data of moderate importance. This allows for efficient analysis by adjusting the level of detail based on importance. 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 based on importance.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the electroencephalogram (EEG) data during analysis. For example, the analysis unit can apply a relaxation-specific analysis algorithm to EEG data related to relaxation. It can also apply a stress-specific analysis algorithm to EEG data related to stress. Furthermore, it can apply a concentration-specific analysis algorithm to EEG data related to concentration. By applying different analysis algorithms depending on the category, the accuracy of the analysis 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 input the category of the EEG data into a generating AI and have the generating AI execute the application of an analysis algorithm according to the category.

[0045] The analysis unit can determine the priority of analysis based on the acquisition timing of electroencephalogram (EEG) data during analysis. For example, the analysis unit may prioritize the analysis of the most recent EEG data. The analysis unit can also perform analysis while referring to past EEG data. Furthermore, the analysis unit can prioritize the analysis of EEG data from a specific time period. This allows for efficient analysis by determining the priority of analysis based on the acquisition timing. 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 acquisition timing of the EEG data into a generating AI and have the generating AI determine the priority of analysis based on the acquisition timing.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the electroencephalogram (EEG) data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant EEG data. It can also postpone the analysis of less relevant EEG data. Furthermore, the analysis unit can appropriately analyze EEG data with a moderate degree of relevance. This allows for efficient analysis by adjusting the order of analysis based on relevance. 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 relevance of the EEG data into a generating AI and have the generating AI perform the adjustment of the analysis order based on relevance.

[0047] The generation unit can adjust the level of detail of the generated data based on the importance of the electroencephalogram (EEG) data. For example, the generation unit can generate detailed text and images for important EEG data. It can also generate simplified text and images for less important EEG data. Furthermore, it can generate text and images with an appropriate level of detail for EEG data of moderate importance. This allows for efficient generation by adjusting the level of detail based on importance. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without 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 based on importance.

[0048] The generation unit can apply different generation algorithms depending on the category of the electroencephalogram (EEG) data during generation. For example, the generation unit can apply a generation algorithm specifically for relaxation to EEG data related to relaxation. It can also apply a generation algorithm specifically for stress to EEG data related to stress. Furthermore, it can apply a generation algorithm specifically for concentration to EEG data related to concentration. By applying different generation algorithms depending on the category, the generation accuracy is improved. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the category of the EEG data into the generation AI and cause the generation AI to apply a generation algorithm according to the category.

[0049] The generation unit can determine the generation priority based on the acquisition timing of the electroencephalogram (EEG) data during generation. For example, the generation unit may prioritize generating the most recent EEG data. The generation unit can also generate data while referring to past EEG data. Furthermore, the generation unit can prioritize generating EEG data from a specific time period. This allows for efficient generation by determining the generation priority based on the acquisition timing. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the acquisition timing of the EEG data into the generation AI and have the generation AI determine the generation priority based on the acquisition timing.

[0050] The generation unit can adjust the generation order based on the relevance of the electroencephalogram (EEG) data during generation. For example, the generation unit can prioritize generating highly relevant EEG data. It can also postpone the generation of less relevant EEG data. Furthermore, the generation unit can appropriately generate EEG data with moderate relevance. This allows for efficient generation by adjusting the generation order based on relevance. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without 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 adjust the generation order based on relevance.

[0051] The display unit can select the optimal display method by referring to the patient's past data when displaying information. For example, the display unit can select the optimal display method based on the display method the patient has preferred in the past. The display unit can also select a display method with high visibility based on the patient's past data. Furthermore, the display unit can select a display method that includes detailed information based on the patient's past data. In this way, the optimal display method can be selected by referring to past data. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the patient's past data into a generating AI and have the generating AI perform the selection of the optimal display method.

[0052] The display unit can adjust its display method to take into account the patient's current health condition when displaying information. For example, if the patient is tired, the display unit can provide a simple and highly visible display method. If the patient is relaxed, the display unit can also provide a display method that includes detailed information. Furthermore, if the patient is agitated, the display unit can provide a visually stimulating display method. This allows for the provision of more appropriate information by adjusting the display method based on the patient's health condition. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input patient health condition data into a generating AI and have the generating AI perform adjustments to the display method based on the patient's health condition.

[0053] The display unit can select the optimal display method when displaying information, taking into account the patient's device information. For example, if the patient is using a smartphone, the display unit can provide a display method that matches the screen size. Furthermore, if the patient is using a tablet, the display unit can provide a display method optimized for a larger screen. Additionally, if the patient is using a smartwatch, the display unit can provide a concise and highly visible display method. This improves visibility by selecting the optimal display method based on device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the patient's device information into a generating AI and have the generating AI select the optimal display method based on the device information.

[0054] The display unit can provide multilingual displays according to the patient's language settings when displaying information. For example, the display unit can automatically set the display language based on the language settings of the patient's device. The display unit can also provide a language switching function if the patient uses multiple languages. Furthermore, if the patient selects a specific language, the display unit can provide the information in that language. This makes it easier for the patient to understand by providing multilingual displays according to their language settings. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the patient's language setting data into a generating AI and cause the generating AI to execute a multilingual display based on the language settings.

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

[0056] The thought analysis system may also include a voice output unit. The voice output unit can output the generated text and images as speech. For example, if a patient is thinking "I'm hungry," the generated text can be output as speech and communicated to a doctor or caregiver. Also, if a patient is thinking "cat," the generated image can be described aloud. This allows for understanding the patient's thoughts through speech, even when visual confirmation is difficult. The voice output unit can output the generated content using speech synthesis technology. For example, speech synthesis technology can be used to output the generated text in a natural-sounding voice. Furthermore, when describing a generated image aloud, the voice output unit can also describe the features of the image in detail. This can complement visual information. Some or all of the above-described processing in the voice output unit may be performed using AI, for example, or without AI. For example, the voice output unit can input the generated text and image data into a generating AI and have the generating AI perform the generation of speech output.

[0057] The thought analysis system may also include a feedback unit. The feedback unit allows doctors and caregivers to provide feedback on the patient's thoughts. For example, a doctor can reassure the patient by responding to their thoughts with "Understood." Similarly, a caregiver can reduce the patient's anxiety by responding to their requests with "I will address that immediately." This enables two-way communication and improves patient satisfaction. The feedback unit can output the feedback entered by doctors and caregivers as text or audio. For example, feedback entered by a doctor can be displayed as text, and feedback entered by a caregiver can be output as audio. This allows for the provision of both visual and auditory feedback. Some or all of the processing described above in the feedback unit may be performed using AI, or without AI. For example, the feedback unit can input feedback data entered by doctors and caregivers into a generating AI, which can then generate the feedback.

[0058] The thought analysis system may also include a data storage unit. The data storage unit can store acquired electroencephalogram (EEG) data and analysis results. For example, it can periodically store a patient's EEG data to track long-term changes. Furthermore, by storing analysis results, the patient's condition can be evaluated by comparing them with past data. This allows doctors and caregivers to more accurately understand the patient's condition. The data storage unit can search and reference stored data as needed. For example, it can search for EEG data for a specific period and display it along with the analysis results. It can also visualize changes in the patient's condition as graphs or charts based on the stored data. This facilitates data management and utilization. Some or all of the above-described processes in the data storage unit may be performed using AI, for example, or without AI. For example, the data storage unit can input acquired EEG data and analysis results into a generating AI, allowing the generating AI to perform data storage and management.

[0059] The thought analysis system may also include a notification unit. This notification unit can notify doctors and caregivers of important analysis results and changes in the patient's condition. For example, if a patient is experiencing sudden stress, the notification unit can send an alert to the doctor. Also, if the patient's condition is stable, the notification unit can notify caregivers to encourage appropriate action. This enables a quick response and ensures the patient's safety. The notification unit can output notification content as text or audio. For example, important analysis results can be displayed as text. Changes in the patient's condition can also be notified as audio. This allows for both visual and auditory notification. Some or all of the above-described processes in the notification unit may be performed using AI, or not. For example, the notification unit can input important analysis results and patient condition change data into a generating AI, and have the generating AI generate notifications.

[0060] The thought analysis system may further include a user configuration section. This section allows doctors and caregivers to customize the system's settings. For example, they can set the frequency and content of notifications. They can also customize the display method and audio output settings. This allows doctors and caregivers to optimize the system to their needs. The user configuration section can save settings and modify them as needed. For example, different settings can be applied to specific patients. Settings can also be backed up and restored. This enables flexible setting management. Some or all of the above-described processes in the user configuration section may be performed using AI, or not. For example, the user configuration section can input setting data entered by doctors and caregivers into a generating AI, which can then manage and apply the settings.

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

[0062] Step 1: The acquisition unit acquires the patient's brainwaves. The acquisition unit acquires brainwaves using, for example, an EEG, a highly real-time and versatile electroencephalogram (EEG) measurement device. EEG is one of the main technologies for measuring brainwaves and has excellent temporal and spatial resolution. For example, by analyzing the frequency of brainwaves, such as alpha waves when the patient is relaxed and beta waves when the patient is excited, it is possible to measure the patient's emotions and concentration level. Step 2: The analysis unit uses deep learning to analyze the electroencephalogram (EEG) data acquired by the acquisition unit, and analyzes the relationship between EEG and emotion. Deep learning learns the relationship between EEG and emotion, and can estimate emotion from EEG. For example, if a patient is experiencing stress, deep learning can be used to analyze that emotion and quantify the degree of stress. Step 3: The generation unit uses a generation AI to verbalize and visualize the data analyzed by the analysis unit. The generation AI generates text based on the electroencephalogram (EEG) data, verbalizing what the patient is thinking. For example, if the patient is thinking "I'm hungry," the generation AI can output that thought as text. The generation AI can also generate images based on the EEG data, visualizing the patient's thoughts as images. For example, if the patient is thinking "cat," the generation AI can display that image as an image. Step 4: The display unit displays the content generated by the generation unit on the monitor. By displaying the generated text and images on the monitor, the display unit makes it easier for doctors and caregivers to understand what the patient is thinking. For example, if a patient thinks, "I can't see the clock," doctors and caregivers can understand the patient's needs and take appropriate action by checking the generated text and images on the monitor.

[0063] (Example of form 2) The thought analysis system according to an embodiment of the present invention is a system that resolves thoughts that cannot be put into words using electroencephalography (EEG) technology. The thought analysis system acquires the patient's brainwaves and analyzes the brainwaves and emotions using deep learning. Based on the analyzed brainwave and emotion data, it uses generative AI to verbalize and visualize the thoughts. The generated content is displayed on a monitor, making it easier for doctors and caregivers to understand what the patient is thinking by reviewing it. This system is used as a communication support tool for caregivers and medical professionals. For example, the thought analysis system acquires the patient's brainwaves. In this case, it utilizes EEG, a highly real-time and versatile brainwave measurement device. EEG is one of the main technologies for measuring brainwaves and has excellent temporal and spatial resolution. For example, by analyzing the frequency of brainwaves, such as alpha waves when the patient is relaxed and beta waves when the patient is excited, it is possible to measure the patient's emotions and concentration. Next, the acquired brainwave data is analyzed using deep learning. Deep learning learns the relationship between brainwaves and emotions and can estimate emotions from brainwaves. For example, if a patient is experiencing stress, deep learning can be used to analyze their emotions and quantify the degree of stress. Based on the analyzed brainwave and emotional data, generative AI is used to verbalize and visualize the patient's thoughts. The generative AI generates text based on the brainwave data, verbalizing what the patient is thinking. For example, if a patient is thinking "I'm hungry," the generative AI can output that thought as text. The generative AI can also generate images based on the brainwave data, visualizing the patient's thoughts as images. For example, if a patient is thinking "cat," the generative AI can display that image as an image. The generated content is displayed on a monitor, making it easier for doctors and caregivers to understand what the patient is thinking. For example, if a patient is thinking "I can't see the clock," doctors and caregivers can understand the patient's needs and take appropriate action by checking the generated text and images on the monitor. This system is used as a communication support tool for caregivers and medical professionals.For example, it can support communication with patients who have difficulty communicating verbally, such as bedridden patients, patients with aphasia, and patients with dementia. This reduces the burden on caregivers and medical staff and allows for a more accurate understanding of patients' needs. The thought analysis system verbalizes and visualizes patients' thoughts, making it easier for doctors and caregivers to understand what patients are thinking.

[0064] The thought analysis system according to this embodiment comprises an acquisition unit, an analysis unit, a generation unit, and a display unit. The acquisition unit acquires the patient's brainwaves. The acquisition unit acquires brainwaves using, for example, an EEG, a highly real-time and versatile brainwave measurement device. EEG is one of the main technologies for measuring brainwaves and has excellent temporal and spatial resolution. For example, by analyzing the frequency of brainwaves, such as alpha waves when the patient is relaxed and beta waves when the patient is excited, the patient's emotions and concentration can be measured. The analysis unit uses deep learning to analyze the brainwave data acquired by the acquisition unit and analyze the relationship between brainwaves and emotions. Deep learning learns the relationship between brainwaves and emotions and can estimate emotions from brainwaves. For example, if the patient is feeling stressed, deep learning can analyze that emotion and quantify the degree of stress. The generation unit uses generation AI to verbalize and visualize the data analyzed by the analysis unit. The generation AI generates text based on the brainwave data and verbalizes what the patient is thinking. For example, if a patient is thinking "I'm hungry," the generating AI can output that thought as text. The generating AI can also generate images based on brainwave data, visualizing the patient's thoughts as images. For example, if a patient is thinking "cat," the generating AI can display that image as an image. The display unit displays the content generated by the generating unit on a monitor. By displaying the generated text and images on the monitor, the display unit makes it easier for doctors and caregivers to understand what the patient is thinking. For example, if a patient is thinking "I can't see the clock," doctors and caregivers can understand the patient's needs and take appropriate action by checking the generated text and images on the monitor. In this way, the thought analysis system according to this embodiment can verbalize and visualize the patient's thoughts, making it easier for doctors and caregivers to understand what the patient is thinking.

[0065] The acquisition unit acquires the patient's brainwaves (EEG). The acquisition unit uses, for example, an EEG (Electroencephalography) device, which is highly real-time and versatile. EEG is one of the main technologies for measuring brainwaves and boasts excellent temporal and spatial resolution. Specifically, the EEG device places multiple electrodes on the scalp to measure the brain's electrical activity with high precision. This allows for real-time acquisition of the patient's EEG patterns. For example, by analyzing the frequency of brainwaves, such as alpha waves when the patient is relaxed or beta waves when they are excited, the patient's emotions and concentration can be measured. Furthermore, the EEG device is equipped with noise reduction capabilities, minimizing interference from the external environment. This improves the accuracy of the acquired EEG data, leading to more precise processing in the analysis unit. Additionally, the EEG device is highly resistant to patient movement, enabling EEG measurement during daily activities. This allows for the acquisition of EEG data under various conditions, such as when the patient is relaxed or stressed. The acquisition unit collects this EEG data in real time and transmits it to the analysis unit. This ensures real-time performance throughout the entire system, enabling rapid analysis and response.

[0066] The analysis unit uses deep learning to analyze brainwave data acquired by the acquisition unit, and analyzes the relationship between brainwaves and emotions. Deep learning learns the relationship between brainwaves and emotions, and can estimate emotions from brainwaves. Specifically, the deep learning model is trained using a large amount of brainwave data and corresponding emotion data. As a result, the model learns the relationship between brainwave patterns and specific emotional states, and can estimate emotions with high accuracy even for new brainwave data. For example, if a patient is experiencing stress, deep learning can analyze their emotions and quantify the degree of stress. The analysis unit also preprocesses the brainwave data, performing noise reduction and feature extraction. This improves the quality of the data input to the deep learning model and increases the accuracy of the analysis results. Furthermore, the analysis unit can process data in real time and generate emotion analysis results quickly. This allows doctors and caregivers to immediately grasp the patient's current emotional state and take appropriate action. The analysis unit can also utilize historical data and statistical information to analyze long-term emotional fluctuations and trends. This allows for continuous monitoring of changes in the patient's emotional state and adjustment of treatment plans as needed.

[0067] The generation unit uses a generation AI to verbalize and visualize data analyzed by the analysis unit. The generation AI generates text based on electroencephalogram (EEG) data, verbalizing the patient's thoughts. Specifically, the generation AI uses natural language processing technology to convert features extracted from EEG data into text. For example, if a patient is thinking "I'm hungry," the generation AI can output that thought as text. The generation AI can also generate images based on EEG data, visualizing the patient's thoughts as images. For example, if a patient is thinking "cat," the generation AI can display that image as an image. The generation AI uses a deep learning model to learn the correspondence between EEG data and text or images. This allows it to generate text and images with high accuracy even for new EEG data. The generation unit outputs the generated text and images in an appropriate format and sends them to the display unit. This allows doctors and caregivers to visually confirm the patient's thoughts. Furthermore, the generation unit can evaluate the quality of the generated text and images and make corrections or improvements as needed. This improves the accuracy and reliability of the generated content, allowing doctors and caregivers to understand patients' thoughts more precisely.

[0068] The display unit displays the content generated by the generation unit on a monitor. By displaying the generated text and images on the monitor, the display unit makes it easier for doctors and caregivers to understand what the patient is thinking. Specifically, the display unit uses a high-resolution display to clearly show the generated text and images. For example, if a patient thinks, "I can't see the clock," doctors and caregivers can understand the patient's needs and take appropriate action by checking the generated text and images on the monitor. The display unit is designed to allow intuitive operation of the generated content through a user interface. This allows doctors and caregivers to quickly check the necessary information and take appropriate action. Furthermore, the display unit also has a function to save the generated content for later reference. This enables analysis and evaluation based on past data, which can be used to help with patient treatment and care. The display unit offers multiple display modes, allowing users to select the optimal display method depending on the situation. For example, there are modes that display only text, and modes that display images and text simultaneously. This allows doctors and caregivers to display information optimally according to the situation and understand the patient's thoughts more accurately.

[0069] The acquisition unit can acquire brainwaves using an EEG, a highly real-time and versatile electroencephalogram (EEG) measurement device. The acquisition unit can, for example, acquire brainwaves using an EEG, a highly real-time and versatile EEG measurement device. EEG is one of the main technologies for measuring brainwaves and excels in both temporal and spatial resolution. For example, by analyzing the frequency of brainwaves, such as alpha waves when the patient is relaxed and beta waves when the patient is excited, it is possible to measure the patient's emotions and concentration. This allows for real-time acquisition of brainwaves using an EEG. EEG, a highly real-time and versatile EEG measurement device, is one of the main technologies for measuring brainwaves and excels in both temporal and spatial resolution. For example, by analyzing the frequency of brainwaves, such as alpha waves when the patient is relaxed and beta waves when the patient is excited, EEG can measure the patient's emotions and concentration. Some or all of the above-described processing in the acquisition unit may be performed using AI, or without AI. For example, the acquisition unit can input the EEG data acquired by the EEG into a generating AI and have the generating AI perform the analysis of the EEG data.

[0070] The analysis unit can analyze brainwaves and emotions using a deep learning model. For example, the analysis unit can analyze brainwaves and emotions using a deep learning model. Deep learning learns the relationship between brainwaves and emotions and can estimate emotions from brainwaves. For example, if a patient is experiencing stress, deep learning can analyze their emotions and quantify the degree of stress. This improves the accuracy of brainwave and emotion analysis by using a deep learning model. The deep learning model learns the relationship between brainwaves and emotions and can estimate emotions from brainwaves. For example, if a patient is experiencing stress, the deep learning model can analyze their emotions and quantify the degree of stress. Some or all of the above processing in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input brainwave data into a generating AI using a deep learning model and have the generating AI perform the analysis of the brainwave data.

[0071] The generation unit can generate text based on electroencephalogram (EEG) data using a generation AI. For example, the generation unit can generate text based on EEG data using a generation AI. The generation AI generates text based on EEG data, verbalizing the patient's thoughts. For example, if the patient is thinking "I'm hungry," the generation AI can output that thought as text. Thus, by using a generation AI, text can be generated based on EEG data. The generation AI generates text based on EEG data, verbalizing the patient's thoughts. For example, if the patient is thinking "I'm hungry," the generation AI can output that thought as text. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or without using a generation AI. For example, the generation unit can input EEG data into a generation AI and have the generation AI perform text generation.

[0072] The generation unit can generate images based on electroencephalogram (EEG) data using a generation AI. For example, the generation unit can generate images based on EEG data using a generation AI. The generation AI generates images based on EEG data, visualizing the patient's thoughts as images. For example, if the patient is thinking of a "cat," the generation AI can display that image as an image. Thus, by using a generation AI, images can be generated based on EEG data. The generation AI generates images based on EEG data, visualizing the patient's thoughts as images. For example, if the patient is thinking of a "cat," the generation AI can display that image as an image. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or without using a generation AI. For example, the generation unit can input EEG data into a generation AI and have the generation AI perform image generation.

[0073] The display unit can display the generated text and images on a monitor. For example, the display unit can display the generated text and images on a monitor. By displaying the generated text and images on a monitor, the display unit makes it easier for doctors and caregivers to understand what the patient is thinking. For example, if a patient thinks, "I can't see the clock," by checking the generated text and images on the monitor, doctors and caregivers can understand the patient's needs and take appropriate action. Thus, displaying the generated text and images on a monitor makes it easier for doctors and caregivers to understand what the patient is thinking. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the generated text and images into a generating AI and have the generating AI perform the generation of the display content.

[0074] The acquisition unit can estimate the patient's emotions and adjust the timing of brainwave acquisition based on the estimated emotions. For example, the acquisition unit can acquire brainwaves when the patient is relaxed to collect low-stress data. It can also acquire brainwaves when the patient is excited to record emotional changes in detail. Furthermore, the acquisition unit can acquire brainwaves when the patient is asleep and analyze brainwave patterns during sleep. This allows for the collection of more appropriate data by adjusting the timing of brainwave acquisition based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI or not using AI. For example, the acquisition unit can input the patient's emotional data into a generative AI and have the generative AI perform emotion estimation.

[0075] The acquisition unit can analyze the patient's past electroencephalogram (EEG) data and select the optimal acquisition method. For example, the acquisition unit can acquire EEG data at a specific time period based on past EEG data. It can also acquire EEG data during a specific activity based on past EEG data. Furthermore, it can acquire EEG data during a specific emotional state based on past EEG data. This allows for efficient EEG acquisition by selecting the optimal acquisition method based on 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 past EEG data into a generating AI and have the generating AI select the optimal acquisition method.

[0076] The acquisition unit can filter brainwave data based on the patient's current health and activity levels. For example, if the patient is exercising, the acquisition unit can remove noise caused by exercise before acquiring brainwave data. Furthermore, if the patient is eating, the acquisition unit can consider the effects of food when acquiring brainwave data. Additionally, if the patient is resting, the acquisition unit can prioritize acquiring brainwave data from a relaxed state. This allows for the acquisition of less noisy brainwave data by filtering based on health and activity levels. Some or all of the above processing in the acquisition unit may be performed using AI, or without AI. For example, the acquisition unit can input patient health and activity data into a generating AI and have the generating AI perform the filtering.

[0077] The acquisition unit can estimate the patient's emotions and determine the priority of EEG data to acquire based on the estimated emotions. For example, if the patient is stressed, the acquisition unit will prioritize acquiring stress-related EEG data. It can also prioritize acquiring relaxation-related EEG data if the patient is relaxed. Furthermore, if the patient is focused, it can prioritize acquiring concentration-related EEG data. This allows for the priority acquisition of important data by prioritizing EEG data based on emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, or not. For example, the acquisition unit can input the patient's emotion data into a generative AI and have the generative AI perform emotion-based priority determination.

[0078] The acquisition unit can prioritize the acquisition of highly relevant data by considering the patient's geographical location information when acquiring electroencephalogram (EEG) data. For example, if the patient is at home, the acquisition unit will prioritize acquiring EEG data from home. It can also prioritize acquiring EEG data from the hospital if the patient is at the hospital. Furthermore, if the patient is out, the acquisition unit can prioritize acquiring EEG data from their location. This allows for the acquisition of highly relevant EEG data by considering geographical location information. 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 patient's geographical location information into a generating AI and have the generating AI prioritize the acquisition of highly relevant data.

[0079] The acquisition unit can analyze the patient's social media activity and acquire relevant data when acquiring electroencephalogram (EEG) data. For example, if the patient is experiencing stress on social media, the acquisition unit can acquire EEG data at that time. It can also acquire EEG data if the patient is relaxed on social media. Furthermore, it can acquire EEG data if the patient is excited on social media. In this way, relevant EEG data can be acquired by analyzing 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 the patient's social media activity data into a generating AI and have the generating AI acquire the relevant EEG data.

[0080] The analysis unit can estimate the patient's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the patient is relaxed, the analysis unit will display the analysis results in a calm manner. If the patient is stressed, the analysis unit can also display the analysis results concisely. Furthermore, if the patient is agitated, the analysis unit can display the analysis results in detail. This allows for the provision of more appropriate analysis results by adjusting the presentation of the analysis based on 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 processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input patient emotion data into the generative AI and have the generative AI perform adjustments to the presentation based on emotions.

[0081] 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. It can also perform a simplified analysis on less important EEG data. Furthermore, it can perform an analysis with an appropriate level of detail on EEG data of moderate importance. This allows for efficient analysis by adjusting the level of detail based on importance. 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 based on importance.

[0082] 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 relaxation-specific analysis algorithm to EEG data related to relaxation. It can also apply a stress-specific analysis algorithm to EEG data related to stress. Furthermore, it can apply a concentration-specific analysis algorithm to EEG data related to concentration. By applying different analysis algorithms depending on the category, the accuracy of the analysis 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 input the category of the EEG data into a generating AI and have the generating AI execute the application of an analysis algorithm according to the category.

[0083] The analysis unit can estimate the patient's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the patient is relaxed, the analysis unit can perform a longer analysis. If the patient is stressed, the analysis unit can perform a shorter analysis. Furthermore, if the patient is agitated, the analysis unit can perform an analysis of an appropriate length. By adjusting the length of the analysis based on 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 not using AI. For example, the analysis unit can input the patient's emotion data into the generative AI and have the generative AI perform the adjustment of the analysis length based on emotions.

[0084] The analysis unit can determine the priority of analysis based on the acquisition timing of electroencephalogram (EEG) data during analysis. For example, the analysis unit may prioritize the analysis of the most recent EEG data. The analysis unit can also perform analysis while referring to past EEG data. Furthermore, the analysis unit can prioritize the analysis of EEG data from a specific time period. This allows for efficient analysis by determining the priority of analysis based on the acquisition timing. 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 acquisition timing of the EEG data into a generating AI and have the generating AI determine the priority of analysis based on the acquisition timing.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the electroencephalogram (EEG) data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant EEG data. It can also postpone the analysis of less relevant EEG data. Furthermore, the analysis unit can appropriately analyze EEG data with a moderate degree of relevance. This allows for efficient analysis by adjusting the order of analysis based on relevance. 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 relevance of the EEG data into a generating AI and have the generating AI perform the adjustment of the analysis order based on relevance.

[0086] The generation unit can estimate the patient's emotions and adjust the way it expresses the generated text and images based on the estimated emotions. For example, if the patient is relaxed, the generation unit can generate text and images with calm expressions. If the patient is stressed, the generation unit can also generate text and images with concise expressions. Furthermore, if the patient is agitated, the generation unit can generate text and images with detailed expressions. This allows for the generation of more appropriate text and images by adjusting the expression based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input patient emotion data into a generation AI and have the generation AI adjust the expression based on emotions.

[0087] The generation unit can adjust the level of detail of the generated data based on the importance of the electroencephalogram (EEG) data. For example, the generation unit can generate detailed text and images for important EEG data. It can also generate simplified text and images for less important EEG data. Furthermore, it can generate text and images with an appropriate level of detail for EEG data of moderate importance. This allows for efficient generation by adjusting the level of detail based on importance. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without 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 based on importance.

[0088] The generation unit can apply different generation algorithms depending on the category of the electroencephalogram (EEG) data during generation. For example, the generation unit can apply a generation algorithm specifically for relaxation to EEG data related to relaxation. It can also apply a generation algorithm specifically for stress to EEG data related to stress. Furthermore, it can apply a generation algorithm specifically for concentration to EEG data related to concentration. By applying different generation algorithms depending on the category, the generation accuracy is improved. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the category of the EEG data into the generation AI and cause the generation AI to apply a generation algorithm according to the category.

[0089] The generation unit can estimate the patient's emotions and adjust the length of the text and images it generates based on the estimated emotions. For example, if the patient is relaxed, the generation unit can generate longer text and images. If the patient is stressed, it can generate shorter text and images. Furthermore, if the patient is agitated, it can generate text and images of an appropriate length. By adjusting the length of text and images based on emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input patient emotion data into a generation AI and have the generation AI adjust the length of text and images based on emotions.

[0090] The generation unit can determine the generation priority based on the acquisition timing of the electroencephalogram (EEG) data during generation. For example, the generation unit may prioritize generating the most recent EEG data. The generation unit can also generate data while referring to past EEG data. Furthermore, the generation unit can prioritize generating EEG data from a specific time period. This allows for efficient generation by determining the generation priority based on the acquisition timing. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the acquisition timing of the EEG data into the generation AI and have the generation AI determine the generation priority based on the acquisition timing.

[0091] The generation unit can adjust the generation order based on the relevance of the electroencephalogram (EEG) data during generation. For example, the generation unit can prioritize generating highly relevant EEG data. It can also postpone the generation of less relevant EEG data. Furthermore, the generation unit can appropriately generate EEG data with moderate relevance. This allows for efficient generation by adjusting the generation order based on relevance. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without 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 adjust the generation order based on relevance.

[0092] The display unit can estimate the patient's emotions and adjust the display method based on the estimated emotions. For example, if the patient is relaxed, the display unit will display in calm colors. The display unit can also provide a simple and highly visible display method if the patient is stressed. Furthermore, if the patient is agitated, the display unit can provide a display method that includes detailed information. This allows for the provision of more appropriate information by adjusting the display method based on emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, or not. For example, the display unit can input patient emotion data into a generative AI and have the generative AI adjust the display method based on emotions.

[0093] The display unit can select the optimal display method by referring to the patient's past data when displaying information. For example, the display unit can select the optimal display method based on the display method the patient has preferred in the past. The display unit can also select a display method with high visibility based on the patient's past data. Furthermore, the display unit can select a display method that includes detailed information based on the patient's past data. In this way, the optimal display method can be selected by referring to past data. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the patient's past data into a generating AI and have the generating AI perform the selection of the optimal display method.

[0094] The display unit can adjust its display method to take into account the patient's current health condition when displaying information. For example, if the patient is tired, the display unit can provide a simple and highly visible display method. If the patient is relaxed, the display unit can also provide a display method that includes detailed information. Furthermore, if the patient is agitated, the display unit can provide a visually stimulating display method. This allows for the provision of more appropriate information by adjusting the display method based on the patient's health condition. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input patient health condition data into a generating AI and have the generating AI perform adjustments to the display method based on the patient's health condition.

[0095] The display unit can estimate the patient's emotions and determine the display priority based on the estimated emotions. For example, if the patient is stressed, the display unit will prioritize displaying stress-related information. It can also prioritize displaying relaxation-related information if the patient is relaxed. Furthermore, if the patient is agitated, it can prioritize displaying agitation-related information. This allows important information to be displayed preferentially by determining the display priority based on emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, or not. For example, the display unit can input patient emotion data into a generative AI and have the generative AI determine the display priority based on emotions.

[0096] The display unit can select the optimal display method when displaying information, taking into account the patient's device information. For example, if the patient is using a smartphone, the display unit can provide a display method that matches the screen size. Furthermore, if the patient is using a tablet, the display unit can provide a display method optimized for a larger screen. Additionally, if the patient is using a smartwatch, the display unit can provide a concise and highly visible display method. This improves visibility by selecting the optimal display method based on device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the patient's device information into a generating AI and have the generating AI select the optimal display method based on the device information.

[0097] The display unit can provide multilingual displays according to the patient's language settings when displaying information. For example, the display unit can automatically set the display language based on the language settings of the patient's device. The display unit can also provide a language switching function if the patient uses multiple languages. Furthermore, if the patient selects a specific language, the display unit can provide the information in that language. This makes it easier for the patient to understand by providing multilingual displays according to their language settings. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the patient's language setting data into a generating AI and cause the generating AI to execute a multilingual display based on the language settings.

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

[0099] The thought analysis system may also include a voice output unit. The voice output unit can output the generated text and images as speech. For example, if a patient is thinking "I'm hungry," the generated text can be output as speech and communicated to a doctor or caregiver. Also, if a patient is thinking "cat," the generated image can be described aloud. This allows for understanding the patient's thoughts through speech, even when visual confirmation is difficult. The voice output unit can output the generated content using speech synthesis technology. For example, speech synthesis technology can be used to output the generated text in a natural-sounding voice. Furthermore, when describing a generated image aloud, the voice output unit can also describe the features of the image in detail. This can complement visual information. Some or all of the above-described processing in the voice output unit may be performed using AI, for example, or without AI. For example, the voice output unit can input the generated text and image data into a generating AI and have the generating AI perform the generation of speech output.

[0100] The thought analysis system may also include a feedback unit. The feedback unit allows doctors and caregivers to provide feedback on the patient's thoughts. For example, a doctor can reassure the patient by responding to their thoughts with "Understood." Similarly, a caregiver can reduce the patient's anxiety by responding to their requests with "I will address that immediately." This enables two-way communication and improves patient satisfaction. The feedback unit can output the feedback entered by doctors and caregivers as text or audio. For example, feedback entered by a doctor can be displayed as text, and feedback entered by a caregiver can be output as audio. This allows for the provision of both visual and auditory feedback. Some or all of the processing described above in the feedback unit may be performed using AI, or without AI. For example, the feedback unit can input feedback data entered by doctors and caregivers into a generating AI, which can then generate the feedback.

[0101] The thought analysis system may also include a data storage unit. The data storage unit can store acquired electroencephalogram (EEG) data and analysis results. For example, it can periodically store a patient's EEG data to track long-term changes. Furthermore, by storing analysis results, the patient's condition can be evaluated by comparing them with past data. This allows doctors and caregivers to more accurately understand the patient's condition. The data storage unit can search and reference stored data as needed. For example, it can search for EEG data for a specific period and display it along with the analysis results. It can also visualize changes in the patient's condition as graphs or charts based on the stored data. This facilitates data management and utilization. Some or all of the above-described processes in the data storage unit may be performed using AI, for example, or without AI. For example, the data storage unit can input acquired EEG data and analysis results into a generating AI, allowing the generating AI to perform data storage and management.

[0102] The thought analysis system may also include a notification unit. This notification unit can notify doctors and caregivers of important analysis results and changes in the patient's condition. For example, if a patient is experiencing sudden stress, the notification unit can send an alert to the doctor. Also, if the patient's condition is stable, the notification unit can notify caregivers to encourage appropriate action. This enables a quick response and ensures the patient's safety. The notification unit can output notification content as text or audio. For example, important analysis results can be displayed as text. Changes in the patient's condition can also be notified as audio. This allows for both visual and auditory notification. Some or all of the above-described processes in the notification unit may be performed using AI, or not. For example, the notification unit can input important analysis results and patient condition change data into a generating AI, and have the generating AI generate notifications.

[0103] The thought analysis system may further include a user configuration section. This section allows doctors and caregivers to customize the system's settings. For example, they can set the frequency and content of notifications. They can also customize the display method and audio output settings. This allows doctors and caregivers to optimize the system to their needs. The user configuration section can save settings and modify them as needed. For example, different settings can be applied to specific patients. Settings can also be backed up and restored. This enables flexible setting management. Some or all of the above-described processes in the user configuration section may be performed using AI, or not. For example, the user configuration section can input setting data entered by doctors and caregivers into a generating AI, which can then manage and apply the settings.

[0104] The thought analysis system may further include an emotion estimation unit. The emotion estimation unit can estimate emotions based on the patient's electroencephalogram (EEG) data and adjust the analysis results based on the estimated emotions. For example, if the patient is relaxed, the analysis results can be displayed in a calm expression. If the patient is stressed, the analysis results can be displayed concisely. This allows for the provision of more appropriate information by adjusting the analysis results based on emotions. The emotion estimation unit can analyze EEG data and quantify emotions. For example, it can display relaxation levels and stress levels numerically. It can also visualize changes in emotions as graphs or charts. This allows for an intuitive understanding of the emotional state. Some or all of the above-described processes in the emotion estimation unit may be performed using AI, for example, or without AI. For example, the emotion estimation unit can input EEG data into a generating AI and have the generating AI perform emotion estimation and adjustment of the analysis results.

[0105] The thought analysis system may further include an emotional feedback unit. The emotional feedback unit can provide feedback based on the estimated patient's emotions. For example, if the patient is relaxed, it can play calming music. If the patient is stressed, it can display relaxing images. This allows for improved patient condition by providing appropriate feedback based on emotions. The emotional feedback unit can customize the feedback content according to the estimated emotions. For example, if the patient is highly relaxed, it can play relaxing music. If the patient is highly stressed, it can display stress-reducing images. This allows for personalized feedback for each patient. Some or all of the above processing in the emotional feedback unit may be performed using AI, or without AI. For example, the emotional feedback unit can input estimated emotional data into a generating AI and have the generating AI generate the feedback content.

[0106] The thought analysis system may further include an emotion recording unit. The emotion recording unit can record the estimated emotions of the patient and track long-term emotional changes. For example, it can record daily emotional changes and display them as graphs or charts. It can also record emotional changes during specific events or situations. This allows for a detailed understanding of emotional changes. The emotion recording unit can analyze the recorded emotional data and extract patterns and trends. For example, it can find patterns in emotional changes during specific time periods or activities. It can also identify factors that influence emotional changes. This can provide information useful for managing and improving emotions. Some or all of the above processing in the emotion recording unit may be performed using AI, for example, or not using AI. For example, the emotion recording unit can input estimated emotional data into a generating AI and have the generating AI perform emotional recording and analysis.

[0107] The thought analysis system may also include an emotion prediction unit. This unit can predict future emotions based on past emotional data. For example, it can analyze past data to predict emotional changes in specific situations or time periods. It can also predict the impact of specific events or activities on emotions. This allows for appropriate responses to be taken in advance. Based on the predicted emotions, the emotion prediction unit can provide advice to doctors and caregivers. For example, it can recommend relaxing activities in response to a predicted increase in stress. It can also suggest stress reduction measures in response to a predicted decrease in relaxation. This makes emotional management easier. Some or all of the above processing in the emotion prediction unit may be performed using AI, for example, or without AI. For example, the emotion prediction unit can input past emotional data into a generating AI and have the generating AI predict future emotions.

[0108] The thought analysis system may also include an emotion sharing unit. This unit can share the estimated emotions of the patient with family and friends. For example, if the patient is relaxed, this information can be notified to the family. Similarly, if the patient is stressed, this information can be shared with friends. This allows the patient's emotional state to be shared with those around them, making it easier for them to receive support. The emotion sharing unit can customize the emotional information it shares. For example, it can be set to share only specific emotional states. The frequency and timing of sharing can also be set. This enables emotion sharing tailored to individual needs. Some or all of the processing described above in the emotion sharing unit may be performed using AI, or not. For example, the emotion sharing unit can input estimated emotional data into a generating AI, causing the generating AI to perform the sharing of emotional information.

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

[0110] Step 1: The acquisition unit acquires the patient's brainwaves. The acquisition unit acquires brainwaves using, for example, an EEG, a highly real-time and versatile electroencephalogram (EEG) measurement device. EEG is one of the main technologies for measuring brainwaves and has excellent temporal and spatial resolution. For example, by analyzing the frequency of brainwaves, such as alpha waves when the patient is relaxed and beta waves when the patient is excited, it is possible to measure the patient's emotions and concentration level. Step 2: The analysis unit uses deep learning to analyze the electroencephalogram (EEG) data acquired by the acquisition unit, and analyzes the relationship between EEG and emotion. Deep learning learns the relationship between EEG and emotion, and can estimate emotion from EEG. For example, if a patient is experiencing stress, deep learning can be used to analyze that emotion and quantify the degree of stress. Step 3: The generation unit uses a generation AI to verbalize and visualize the data analyzed by the analysis unit. The generation AI generates text based on the electroencephalogram (EEG) data, verbalizing what the patient is thinking. For example, if the patient is thinking "I'm hungry," the generation AI can output that thought as text. The generation AI can also generate images based on the EEG data, visualizing the patient's thoughts as images. For example, if the patient is thinking "cat," the generation AI can display that image as an image. Step 4: The display unit displays the content generated by the generation unit on the monitor. By displaying the generated text and images on the monitor, the display unit makes it easier for doctors and caregivers to understand what the patient is thinking. For example, if a patient thinks, "I can't see the clock," doctors and caregivers can understand the patient's needs and take appropriate action by checking the generated text and images on the monitor.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the acquisition unit, analysis unit, generation unit, and display unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit acquires the patient's brainwaves using the EEG device of the smart device 14. The analysis unit analyzes the brainwave data using deep learning by the specific processing unit 290 of the data processing unit 12 and estimates emotions. The generation unit verbalizes and visualizes the analyzed data using the generation AI generated by the specific processing unit 290 of the data processing unit 12. The display unit displays the generated content on the display 40A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the acquisition unit, analysis unit, generation unit, and display unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit acquires the patient's brainwaves using the EEG device of the smart glasses 214. The analysis unit analyzes the brainwave data using deep learning by the identification processing unit 290 of the data processing unit 12 and estimates emotions. The generation unit verbalizes and visualizes the analyzed data using the generation AI generated by the identification processing unit 290 of the data processing unit 12. The display unit displays the content generated by the display of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the acquisition unit, analysis unit, generation unit, and display unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit acquires the patient's brainwaves using the EEG device of the headset terminal 314. The analysis unit analyzes the brainwave data using deep learning by the specific processing unit 290 of the data processing unit 12 and estimates emotions. The generation unit verbalizes and visualizes the analyzed data using the AI ​​generated by the specific processing unit 290 of the data processing unit 12. The display unit displays the generated content on the display 343 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the acquisition unit, analysis unit, generation unit, and display unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit acquires the patient's brainwaves using the EEG device of the robot 414. The analysis unit analyzes the brainwave data using deep learning by the specific processing unit 290 of the data processing unit 12 and estimates emotions. The generation unit verbalizes and visualizes the analyzed data using the generation AI by the specific processing unit 290 of the data processing unit 12. The display unit displays the content generated by the display of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) An acquisition unit that acquires the patient's electroencephalogram, The brainwave data acquired by the acquisition unit is analyzed by the analysis unit, and the brainwaves and emotions are analyzed by the analysis unit, A generation unit that verbalizes and visualizes the data analyzed by the analysis unit, The system includes a display unit that displays the content generated by the generation unit on a monitor. A system characterized by the following features. (Note 2) The acquisition unit is, EEG (Electroencephalography) is used to acquire brainwaves, a highly real-time and versatile electroencephalography device. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyzing brainwaves and emotions using deep learning models. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate text based on brainwave data using a generative AI. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Generate images based on brainwave data using a generation AI. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned display unit is Display the generated text and images on the monitor. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, The system estimates the patient's emotions and adjusts the timing of EEG acquisition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, Analyze the patient's past electroencephalogram (EEG) data and select the optimal acquisition method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, When acquiring electroencephalograms (EEGs), filtering is performed based on the patient's current health status and activity level. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, The system estimates the patient's emotions and prioritizes the EEG data to be acquired based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, When acquiring electroencephalogram (EEG) data, the system prioritizes the acquisition of highly relevant data, taking into account the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, During electroencephalography (EEG) acquisition, the patient's social media activity is analyzed to obtain relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the patient'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 14) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the EEG data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of EEG data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the patient's emotions and adjusts the length of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of analysis is determined based on when the electroencephalogram (EEG) data was acquired. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the EEG data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the patient's emotions and adjusts the way text and images are represented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the 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 21) 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 22) The generating unit is It estimates the patient's emotions and adjusts the length of the text and images generated based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) 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 24) 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 25) The aforementioned display unit is The system estimates the patient's emotions and adjusts the display method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned display unit is When displaying the data, the system selects the optimal display method by referring to the patient's past data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned display unit is When displaying information, the display method is adjusted to take into account the patient's current health status. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned display unit is The system estimates the patient's emotions and determines the display priority based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned display unit is When displaying information, the optimal display method is selected considering the patient's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned display unit is When displayed, the display will be multilingual according to the patient's language settings. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An acquisition unit that acquires the patient's electroencephalogram, The brainwave data acquired by the acquisition unit is analyzed by the analysis unit, and the brainwaves and emotions are analyzed by the analysis unit, A generation unit that verbalizes and visualizes the data analyzed by the analysis unit, The system includes a display unit that displays the content generated by the generation unit on a monitor. A system characterized by the following features.

2. The acquisition unit is, EEG (Electroencephalography) is used to acquire brainwaves, a highly real-time and versatile electroencephalography device. The system according to feature 1.

3. The aforementioned analysis unit, Analyzing brainwaves and emotions using deep learning models. The system according to feature 1.

4. The generating unit is Generate text based on brainwave data using generative AI. The system according to feature 1.

5. The generating unit is Generate images based on electroencephalogram (EEG) data using a generation AI. The system according to feature 1.

6. The aforementioned display unit is Display the generated text and images on the monitor. The system according to feature 1.

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

8. The acquisition unit is, Analyze the patient's past electroencephalogram (EEG) data and select the optimal acquisition method. The system according to feature 1.

Images